EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS PROCEEDINGS

i 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS ECRES 2024 16-17 May 2024 Mallorca / Spain www.ecres.net PROCEEDINGS Edited by Prof. Dr. Erol Kurt ISBN: 978-605-70842-3-1 i Organizing Institutions University of the Balearic Islands Electrical and Computer Engineering Research Group - ecerg.com ECER TEKNOLOJI VE DANISMANLIK TICARET LIMITED SIRKETI Published by Erol Kurt on 15th May 2024 (Marmaris/Muğla/Türkiye) All rights of the Proceedings of European Conference on Renewable Energy Systems (ECRES) are preserved. No part of this publication may be produced, stored in retrieval system, or transmitted in any form of electronic, mechanical and photocopying or reproduction technique without the prior permission of the publisher. The responsibility for the ingredients covering information and opinion rests exclusively with the authors and independent from the organizers and publisher. Book typesetted and designed by Prof. Dr.Yunus Uzun and Dr. Bekir Dursun ii Cooperative Institutions Supporting Institutions iii Committees CONFERENCE FOUNDER & CHAIRMAN Prof. Dr. Erol KURT, Gazi University, Türkiye CO-CHAIRMAN Prof. Dr. Jose Manuel Lopez Guede, University of Basque Country, Spain LOCAL ORGANIZING COMMITTEE Assoc. Prof. Dr. Ramón Pujol (University of the Balearic Islands) Assoc. Prof. Dr. Andreu Moia Pol (University of the Balearic Islands) INTERNATIONAL ORGANIZING COMMITTEE Prof. Dr. Adnan Sozen, Gazi University, Turkey Prof. Dr. Alfredo Vaccaro, Sannio University, Italy Prof. Dr. Carlos Rubio-Maya, Univ. Michoacana de San Nicolás de Hidalgo, Mexico Assoc. Prof. Dr. Fontina Petrakopoulou-Robinson, Universidad Carlos III de Madrid, Spain Prof. Dr. Ian Hunter, University of Leeds, UK Prof. Dr. Jose Manuel Lopez Guede, University of Basque Country, Spain Prof. Dr. Mehmet Tekerek, Kahramanmaras Sutcu Imam University, Turkey Prof. Dr. Murat Kunalbayev, Inst. of Information and Computational Technologies, Kazakhstan Prof. Dr. Nicu Bizon, Pitesti University, Romania Prof. Dr. Peter Childs, Imperial College London, UK Prof. Dr. Poul Alberg Østergaard, Aalborg University, Denmark Prof. Dr. Raoul Rashid Nigmatullin, Kazan National Research Tech. Univ., Tatarystan, Russia Prof. Dr. Saad Mekhilef, University of Malaya, Malaysia Prof. Dr. Saeed Badshah, Int. Islam. Uni. Islamabad, Pakistan Prof. Dr. Sagdulla L. Lutpullaev, Uzbekistan Academy of Sciences, Uzbekistan Prof. Dr. Serguei Martemianov, University of Poitiers, France Prof. Dr. Shadi Shahedipour-Sandvik, University at Albany, USA Prof. Dr. Shadia J. Ikhmayies, Al Isra University, Jordan Prof. Dr. Waqar Mahmood, University of Engineering and Technology, Pakistan Prof. Dr. Yunus Uzun, Aksaray University, Turkey Assoc. Prof. Dr. Yussupova Gulbakhar, Turan University, Kazakhstan INTERNATIONAL SCIENTIFIC COMMITTEE Prof. Dr. Alessandro Zanarini, University of Bologna, Italy Assoc. Prof. Dr. Ali Jazie, University of Al-Qadisiyah, Iraq Dr. Angeliki Chatzidimitriou, Aristotle University of Thessaloniki, Greece Prof. Dr. Antonio Soria Verdugo, Universidad Carlos III de Madrid, Spain Dr. Athar Waseem, International Islamic University, Pakistan iv Assoc. Prof. Dr. Aybaba Hançerlioğulları, Kastamonu University, Turkey Prof. Dr. Ayman El-Hag, American University of Sharjah, UAE Prof. Dr. Bernabé Marí Soucase, Polytechnical University of Valencia, Spain Assist. Prof. Dr. Burak Akin, Yildiz Technical University, Turkey Assoc. Prof. Dr. Bünyamin Tamyürek, Eskisehir Osmangazi University, Turkey Prof. Dr. Carolina Marugan Cruz, Universidad Carlos III de Madrid, Spain Assoc. Prof. Dr. Christiane Hennig, German Biomass Research Center, Germany Prof. Dr. Corneliu Marinescu, Transilvania University of Brasov, Romania Assoc. Prof. Coşku Kasnakoğlu, TOBB Ekonomy and Technology University, Turkey Assoc. Prof. Dr. Çigdem Yangin Gömeç, Istanbul Technical University, Turkey Dr. Danny Müeller, Technische Universität Wien, Austria Assoc. Prof. Dr. Diana Zalostiba, Riga Technical University, Latvia Dr. Eduar Eduardo Zarza, CIEMAT Solar Platform of Almeria, Spain Prof. Dr. Eleonora Guseinoviene, Klaipeda University, Lithuania Prof. Dr. Farqad Al-Hadeethi, Royal Scientific Society of Jordan, Jordan Assoc. Prof. Dr. Fontina Petrakopoulou-Robinson, Universidad Carlos III de Madrid, Spain Prof. Dr. Francesco Calise, University of Naples Federico II, Italy Assoc. Prof. Dr. Francesco Cottone, University of Perugia, Italy Prof. Dr. Guang-Bin Huang, Nanyang Technological University, Singapore Prof. Dr. Guido Van Oost, University of Gent, Belgium Prof. Dr. Güngör Bal, Gazi University, Turkey Prof. Dr. H. Mehmet Şahin, Karabük University, Turkey Prof. Dr. Haitham Abu-Rub, Texas A&M University at Qatar, Qatar Assoc. Prof Dr. Hasan Köten, Medeniyet University, Turkey Prof. Dr. Herman Vermaak, Central University of Technology, Free State, South Africa Prof. Dr. Ibrahim Dincer, University of Ontario, Canada Prof. Dr. Ibrahim Sefa, Gazi University, Turkey Prof. Dr. Ilya Galkin, Riga Technical University, Latvia Assoc. Prof. Dr. Ilona Sárvári Horváth, University of Borås, Sweden Prof. Dr. Jongho Yoon, Hanbat National University, S. Korea Prof. Dr. Jongsoon Song, Chosun University, S. Korea Prof. Dr. Jorge R. Frade, University of Aveiro, Portugal Prof. Dr. Jose A. Aguado, University of Malaga, Spain Prof. Dr. Jose A. Ramos Hernanz, Universidad del Pais Vasco, Spain Prof. Dr. Jose M. Lopez Guede, Universidad del Pais Vasco, Spain Prof. Dr. Josep Guerrero, Aalborg University, Denmark Assoc. Prof. Dr. K.Premkumar, Rajalakshmi Engineering College, Chennai, India Prof. Dr. Kozo Taguchi, Ritsumeikan University, Japan Prof. Dr. Leijun Xu, Jiangsu University, China Prof. Dr. Jun Yang, Huazhong University of Science and Technology, China Dr. Loreto V. Gutierrez, CIEMAT Solar Platform of Almeria, Spain Prof. Dr. Mahmood Ghoranneviss, Islamic Azad University, Iran Prof. Dr. Maria Venegas, Universidad Carlos III de Madrid, Spain Assoc. Prof. Dr. Mario E. Magana, Oregon state University, USA Prof. Dr. Maris Klavins, University of Latvia, Latvia Prof. Dr. Mehmet Önder Efe, Hacettepe University, Turkey Prof. Dr. Mehmet Tekerek, Kahramanmaras Sutcu Imam University, Turkey Assoc. Prof. Dr. Merih Palandöken, İzmir Katip Çelebi University, Turkey Prof. Dr. Metin Gürü, Gazi University, Turkey Prof. Dr. Milan Stork, University of West Bohemia, Czech Republic Prof. Dr. Mohammad N. A. Hawlader, International Islamic University, Malaysia Prof. Dr. Munir Nayfeh, University of Illinois at Urbana-Champaign, USA Prof. Dr. Muris Torlak, Sarajevo University, Bosnia and Herzegovina Prof. Dr. Mustafa Ilbas, Gazi University, Turkey Prof. Dr. Mykola Radchenko, Admiral Makarov National University of Shipbuilding, Ukraine Prof. Dr. N. Nasimuddin, Institute for Infocomm Research, Singapore Dr. Nam Choon Baek, Korea Institute of Energy Research, S. Korea v Prof. Dr. Namazov Subhan Nadiroglu, Azerbaijan Technical University, Azerbaijan Prof. Dr. Narasimha G. Reddy, Lamar University, USA Assoc. Prof Dr. Natalia Tintaru, Vilnius University, Lithuania Prof. Dr. Nicolae Paraschiv, Petroleum - Gas University of Ploiesti, Romania Prof. Dr. Nicu Bizon, Pitesti University, Romania Prof. Dr. Nikolay Djagarov, Nikola Vaptsarov Naval Academy, Bulgaria Dr. Nilufar R. Avezova, Uzbekistan Academy of Sciences, Uzbekistan Prof. Dr. Pedro Juan Roig, Universidad Miguel Hernández, Spain Prof. Dr. Peter Lund, Aaalto University, Finland Prof. Dr. Poul Alberg Østergaard, Aalborg University, Denmark Prof. Dr. Rafael K. Jardan, Budapest Univ. of Technology and Economics, Hungary Prof. Dr. Rafaela Hillerbrand, RWTH Aachen University, Germany Prof. Dr. Ramazan Bayindir, Gazi University, Turkey Prof. Dr. Raoul Rashid Nigmatullin, Kazan National Research Technical University,Russia Dr. Rosaria Villari, Italian National Agency for New Technologies, Italy Prof. Dr. Saffa Riffat, Nottingham University, UK Assoc. Prof. Dr. Sergej Osipov, University of Daugavpils, Latvia Assist. Prof. Dr. Sertac Bayhan, Texas A&M University at Qatar, Qatar Prof. Dr. Shadia J. Ikhmayies, Al Isra University, Jordan Prof. Dr. Sing Lee, Institute for Plasma Focus Studies, Australia Prof. Dr. Sor Saw Heoh, Nilai University, Malaysia Prof. Dr. Souad A.M. Albathi, Int. Islamic Uni. Malaysia, Malaysia Prof. Dr. Sujit Barhate, Savitribai Phune Pune University, India Assoc. Prof. Dr. T.Thamizhselvan, Rajalakshmi Engineering College, Chennai, India Prof. Dr. Tae Hee Lee, Hanyang University, S. Korea Prof. Dr. V. Jagannathan, Bhabha Atomic Research Center, India Prof. Dr. Wail N. Al-Rifaie, Philadelphia University, Jordan Prof. Dr. Wang Ru-Zhu, Shanghai Jiao Tong University, China Assoc. Prof. Dr. Yong Song, Institute of Nuclear Energy Safety Technology, China Prof. Dr. Zhiqiang Zhu, Institute of Nuclear Energy Safety Technology, China vi FOREWORD Dear Colleagues, We are glad to see you and your contribution for the 12. European Conference on Renewable Energy Systems (ECRES 2024). The event has been organized in Mallorca, Spain on 16-17 May 2024 by the local organizers University of Basque Country, University of the Balearic Islands, ECER Technology and Consultancy and ECERG Electrical and Computer Research Group in the hybrid format. Besides, many institutions world-widely took a part as the cooperating institutions including many international refeered academic journals. The purpose of the ECRES is to bring together researchers, engineers and natural scientists from all over the world, interested in the advances of all branches of renewable energy systems such as wind, solar, hydrogen, hydro-, geothermal, solar concentrating, fuel-cell. It aims to present and disseminate the cutting-edge results to the international community of energy in the form of research, development, applications, design and technology. It is thereby expected that it can assist researchers, scientists, manufacturers, companies, communities, agencies, associations and societies to keep abreast of new developments in their specialist fields and to find innovative solutions in their problems. The conference was initially considered as the meeting point of international projects including EU Erasmus actifities by the conference founder Prof. Dr. Erol KURT. Historically, the previous events were completed very successfully in Alanya/Antalya (2012), Antalya (2013), Kemer/Antalya (2015), Istanbul (2016), Sarajevo / Bosnia and Herzegovina (2017), Istanbul (2018), Madrid (2019), Istanbul-remote (2020), Istanbul-remote (2021), Istanbul (2022), Riga (Latvia), respectively. This serial event has been a continuous one even in the pandemic periode, worldwidely. Many of the extended forms of selected papers were puublished in SCI, E-SCI, SCOPUS and EBSCO indexed reputable journals following the previous events. This year, 221 papers were submitted world-widely. Among them, 73 papers from 32 countries have been accepted and presented. Following the physical and virtual presentations, these abstracts and papers are put into the present proceedings. We state our gratitutes to all authors, keynote speakers, special session organizers, reviewers, session chairmen and scientific board for their precisious contribution and hope to extend these cooperation for the next events, too. This proceeedings have been delivered to the participants via the conference website link under a specific ISBN. In addition, high amount of selected and improved papers will be considered for the publication in reputable journals indexed in Science Citation Index (SCI-indexed), Emerging SCI-indexed, SCOPUS-indexed and EBSCO-indexed journals after the standard peer-review processes of the journals. We would like to send our warmest greetings to all and looking forward to having your future contribution to the future events for a much green, and peaceful world. (10 May 2024, Muğla) Prof. Dr. Erol KURT Chairman of ECRES Series Gazi University, Technology Faculty Department of Electrical and Electronics Engineering 06500 Besevler ANKARA TÜRKİYE E-mail: [email protected] vii viii CONTENTS KEYNOTES Title Presenter Page 3D printed Nonlinear Energy Harvesters Based on Biocompatible Foamed Piezo-Electret Materials Francesco Cottone 3 Eco-Design in Shielded Metal Arc Welding (SMAW) Ruben Lostado 4 Caloric Cooling and Heat Pumping Technologies Based on Sustainable Solid-State Refrigerants Adriana Greco 5 The Transition Toward A Fully Decarbonized Energy System: The Pivotal Role of Power-to-X Technology Francesco Calise 6 REGULAR ABSTRACTS Paper ID Title Authors Page 12 Policy Instruments for Promoting Renewable Energy: Empirical Evidence on Their Diffusion Across EU Roberta Arbolimo, Raffaele Boffardi, Mariangela Bonasia, Luisa De Simone, Antonio Lopes 10 13 Gasification and Pyrolysis of Biomass using A Plasma System Vladimir Messerle, Oleg Lavrichshev, Alexandr Ustimenko 11 17 Hensus – A Tool for Configuring Hybrid Power Systems for Fishing Vessels Nikola Vladimir, Marija Koričan 12 18 Performance Enhancement of The Application Layer for Distribution Planning Platform Panitarn Chongfuangprinya, Anthony Hoang, Bo Yang, Yanzhu Ye, Natsuhiko Futamura, Yoshihisa Okamoto 13 20 Experimental Analysis and Assessment of Local Air Temperature and Heat Transfer in A Serpentine Heat Exchanger Napassawan Wongmongkol, Naoki Maruyama, Chatchawan Chaichana 14 25 Layout Optimization of Airborne Wind Energy Farms Considering Scaling Effects Luís A.C. Roque, Rui Carvalho Da Costa, Luís Tiago Paiva, Manuel C.R.M. Fernandes, Dalila B.M.M. Fontes, Fernando A.C.C. Fontes 15 30 Evaluation of The Techno-Economic Aspects and Sustainability of Integrated Renewable Energy Systems Georg Klepp, Timo Broeker, Niko Schneidewind 16 31 Elevation of Domestic to International Carbon Credit Voluntary Scheme: The Case of Greenhouse Gas Mitigation in Renewable Energy Project Chaichan Ritkrerkkrai, Wongkot Wongsapai, Phitsinee Muangjai, Prattakorn Sittisom 17 39 Innovative Integration of A Mec-Ad System for Enhanced Biogas Production Dolores Hidalgo, Miguel A. Sánchez-Gatón, Rudd Timmers, Jesús M. Martín-Marroquín 18 40 Fostering Sustainable Anaerobic Digestion Processes with Advanced Nutrient Recovery Solutions Dolores Hidalgo, Jesús M. MartínMarroquín, Francisco Corona 19 44 Plasma Processing Of Rubber Powder From End-Of-Life Tires: Numerical Analysis and Experiment Vladimir Messerle, Alexandr Ustimenko, Oleg Lavrichshev 20 46 Advanced Solutions for Residential Photovoltaics (pv) Systems: Models, Concepts, and Demonstrators Hesan Ziar*, Alba Alcañiz Moya, Juan Camilo Ortiz Lizcano, Olindo Isabella 21 59 Integration of Solar Stirling in A Multi-Generation and Storage Power System Georg Klepp 23 60 AI Enhanced Testing Stand for Determining The Thermal Properties of Materials Mihail-Bogdan Carutasiu, Teodor Lupoiu, Horia Necula 24 73 A High-Power Supercapacitor Device Assembled by Rgo Nanosheet Encapsulated mnco2s4 Nanoflowers Yuttana Mona, Chatchawan Chaichana, Pana Suttakul, Napassawan Khammayom, Ramnarong Wanison, Uma Shankar Veerasamy 25 84 Removal of Perfluorinated Compounds (pfcs) According to Raw Water Characteristics and Drinking Water Treatment Systems Seon-Ha Chae, Min Jung Jeon, Hyunook Kim 26 88 Evaluation of The PerAnd Polyfluorinated Substances(pfass) Removal Models in A Water Treatment Pilot Plant Min Jung Jeon, Ingyu Lee, Moonyeong Choi, Heekyung Lee, Hyunook Kim, Seon-Ha Chae 27 95 A Numerical Investigation on Multical: The Ecofriendly Heat Pump Based on Multicaloric Effect Luca Cirillo, Adriana Greco, Claudia Masselli 28 116 Harnessing Rice Paper as A Self-Powered Humidity Sensor for Future Electronics Muhammad Muqeet Rehman, Maryam Khan, Woo Young Kim 29 159 Experimental Wave-Structure Interaction of Membrane-Type Floating Photovoltaics Hanna Pot, Sebastian Schreier 30 161 Process Simulation of Soec-Ft-Reactor Combined System for Synthetic Liquid Fuel Production from Renewable Power and Carbon Dioxide Yohei Tanaka 31 162 Design and simulation in 3d of axial flux permanent magnetic machines for electrical power generation Taib Mustapha, Driss Mohammed, Bekkouche Benaissa 32 171 Assessment of Sewage Sludge as A Component for The Tire Char Co-Gasification Process Katarzyna Śpiewak, Grzegorz Czerski, Przemysław Grzywacz, Dorota Makowska 33 173 The Digitalization Process of The Smart Grids in African Context Marco Bovo, Daniele Torreggiani, Patrizia Tassinari 34 180 Production of Biogas from Unconventional Biomass By Ball-Mill and Thermal Hydrolysis Pretreatments Changgyun Lee, Hyunook Kim 35 188 Realization of an Indirect Solar Cooker Angeline Kpeusseu Kouambla Yeo, Paul Magloire Ekoun Koffi, Bati Ernest Boya Bi 36 4 Eco-Design in Shielded Metal Arc Welding (SMAW) Ruben Lostado University of La Rioja, Spain Cite this paper as: Lostado, R., Eco-Design in Shielded Metal Arc Welding (SMAW). 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The European Union (EU) has been at the forefront of promoting sustainable production processes and addresses sustainability and climate change as part of its broader approach towards a circular carbon economy. Considering sustainability in welding manufacturing processes involves environmental, social and economic aspects in order to promote practices that are respectful of the environment and human communities. Shielded metal arc welding (SMAW) is a completely manual joining process, widely used today due to its versatility and portability to work in various conditions despite being one of the welding techniques that generates the most negative impacts on the environment and human health. Considering the eco-design and manufacturing process of SMAW welded joints implies minimizing the environmental impact throughout their life cycle (LCA), also considering such important aspects as the materials used, the energy consumed and the mechanical load capacity of said joints. The work focuses on double V groove butt welded joint and tries to determine how some of the SMAW welding process parameters influence in the environmental impact produced during its manufacturing process and its residual stresses generated, as well as in the ultimate load strength(ULS) of such as welded joints. Keywords: Arc, Metal, Eco-design, Environment, Welding © 2024 Published by ECRES 5 Caloric Cooling and Heat Pumping Technologies Based on Sustainable Solid-State Refrigerants Adriana Greco University of Naples Federico II, Italy Cite this paper as: Greco, A. Caloric Cooling and Heat Pumping Technologies Based on Sustainable Solid-State Refrigerants. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Caloric cooling is the most famous Not-In-Kind technology alternative to Vapor Compression (VC), basing on the phenomenon called caloric effect manifesting in solid-state materials that can be employed as new-generation refrigerants. The needing to find novel technologies able to replace VC derives from the prescriptions of Kyoto Protocol to progressively phase-out the HFC refrigerants, due of high Global Warming Potential (GWP) and largely employed in VC systems that nowadays are responsible of more than 20% of the world energy consumption. Caloric cooling bases on materials exhibiting caloric effects that are characterized by GWP=0. These features attributed to solid-state caloric cooling and air conditioning the hope it can assume the role of breakthrough ecofriendly. The common denominator of caloric refrigeration is the caloric effect, a physical phenomenon manifesting in some solid-state materials that, because of an adiabatic change in the intensity of an external field applied to them is showed in a change in temperature. Basing on it the Active Caloric Regenerative refrigeration cycle has been developed: A Brayton-based thermodynamic cycle where the caloric material acts both as refrigerant and regenerator with the final purpose of subtracting/adding heat from/to a cold/hot reservoir (cooling/heat pumping mode). Depending both on the nature of the field applied (magnetic, electric or mechanical) and the material to which it is applied a different caloric effect is observable: Magnetocaloric, electrocaloric, machanocaloric. Each one of these effects has a different cooling and heat pumping technique. Magnetocaloric refrigeration is the most consolidated solid-state technology, as it was, about 25-30 years ago, the first to attract the interest of scientific community for the development of cooling applications because of the magnetocaloric effect of Gadolinium, the benchmark material, in room temperature range. Currently, around 100 magnetocaloric prototypes were developed but only few of them are close to commercialization. The major limitations are: the poor experimental results in term of energy performances, the very expensive magnetic materials, the low variation of the magnetic field with permanent magnets. Electrocaloric refrigeration grew in a more recent past when remarkable electrocaloric effect was observed in ferroelectric materials. The easiness in electric field generation, together with the flexibility in producing high electric fields in large volumes, are the strongpoints of electrocaloric refrigeration but huge disadvantages are related to the high electrical expense weight in the coefficient of performance. A growing interest is linked to elastocaloric cooling based on Shape-Memory Alloys (SMA) materials that during uniaxial loading/unloading stress cycles exhibit elastocaloric-effect. The benchmark material is NiTi binary alloy because of its remarkable adiabatic temperature change shown at room temperature. Currently, the related prototypes developed in the world are about a dozen and still far from commercialization in terms of useful cooling power achieved; the bottleneck is the short fatigue life of SMA combined with many cycles of loading/unloading. Anyhow mechanocaloric seems to be the most promising technology of tomorrow and we are confident in a close turning point. In this presentation the development of the first Italian elastocaloric device for air conditioning is also presented. Keywords: Refrigerant, Caloric, Cooling, Shape memory, Alloy, Brayton © 2024 Published by ECRES 6 The Transition Toward a Fully Decarbonized Energy System: The Pivotal Role of Power-to-X technology Francesco Calise University of Naples Federico II, Italy Cite this paper as: Calise, f. The transition toward a fully decarbonized energy system: the pivotal role of Power-toX technology. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: In past few years, the majority of the worldwide Countries realized that it was urgent to modify the current energy paradigm, mainly based on the utilization of fossil fuels. The present trend in terms of consumption of fossil fuels and emissions of greenhouse gases is posing severe issues in terms of environmental sustainability of this paradigm. Therefore, a significant effort has been performed in order to promote the transition from the present scenario to a novel one, based on the utilization of renewable energy sources. Moreover, the recent events - pandemic and Ukrainian war - are more and more pushing policymakers to promote the transition toward a fully renewable energy system. Thus, a twofold goal can be achieved. First, the continuous increase of the world average temperature can be mitigated. Then, Countries energy security and dependency can be enhanced by exploiting locally available renewable energy sources. The goal of the full decarbonization, expected in European Union by 2050, can be achieved by a double strategy: i) improving the efficiency of the existing energy networks and systems; ii) increasing the share of the energy produced by renewable energy sources. In this framework, a huge contribution is expected by the increase of the installed power capacity of wind turbines and photovoltaic collectors. Both wind and solar sources are worldwide abundantly available, and a large unexploited potential exists in several Countries. Unfortunately, these renewable energy sources are remarkably fluctuating and unpredictable. Therefore, their integration in present and future energy networks is a very challenging task, due to the significant phase shift between energy supply and demand. Suitable energy storage systems should be used to mitigate this phenomenon. Thermal storage systems are commercially mature and available. Conversely, electrical storage systems are available only for limited capacities and they are featured by high capital costs and low power densities. Simultaneously, modern and efficient energy networks are becoming more and more mature. Smart grids are nowadays used in a plurality of applications. As for the heating and cooling, the state of the art is based on the use of 4th and 5th generation district heating and cooling networks. Therefore, the integration of renewables in such modern energy networks requires the integration of novel technologies to achieve an optimal matching between energy demand and supply. In this framework the Power-to-X technology is becoming more and more attracting. According to this novel paradigm, all the excess electricity produced by renewables, which cannot be stored in the available storage systems, is converted in another energy vector or fuel (X). The most common configuration is Power-to-Heat (P2H) technology, where the excess electricity is converted into heat by using heat pumps. This heat can be stored in suitable thermal energy storage systems and used for a plurality of purposes (space heating, domestic hot water, industrial processes, etc) In the Power-to-hydrogen (P2H2) configuration, the excess renewable electricity is supplied to an electrolyzer which splits water into oxygen and hydrogen. Oxygen can be used for industrial or medical purposes, whereas hydrogen can be used for a plurality of scopes (energy conversion, transport, chemical industry, food industry, etc). It is worth noting that hydrogen use does not determine any production of greenhouse gases. In the Power-to-Power (P2P) arrangement, the produced hydrogen is first stored and subsequently supplied to a fuel cell, which can produce electricity and heat. Thus, P2P system can be used as an electrical storage system, showing attractive storage capacities and economic performance. Finally, another possibility consists in the power-to-gas (P2G) arrangement. Here, the excess electricity is used to produce hydrogen by water electrolysis. Hydrogen is stored in suitable tanks. Simultaneously, the exhaust gases of a conventional power plant fueled by fossil fuels pass trough a CO2 separation unit. Thus, the produced hydrogen can be combined with this CO2 in a methanator, for the production of methane. A plurality of technologies are available for the implementation of all the above mentioned P2X arrangements. Similarly, dozens of scientific approaches are used to design and dynamically simulate such systems. The lecture will summarize both technologies and methodologies, also analyzing the integration of P2X technology in modern energy networks. Special attention is paid to the developed control strategies and optimization techniques, implemented to improve both design and operating efficiency of the system. Keywords: Decarbonization, Energy security, Energy resource, Storage, Policy © 2024 Published by ECRES 7 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 8 REGULAR ABSTRACTS 9 10 Policy Instruments for Promoting Renewable Energy: Empirical Evidence on Their Diffusion Across EU Roberta Arbolimo University of Naples L’Orientale, Naples, Italy, [email protected], ORCID: 0000-0001-5455-5077 Raffaele Boffardi University of Naples L’Orientale, Naples, Italy, rboffa[email protected], ORCID: 0000-0002-0219-7551 Mariangela Bonasia University of Naples Parthenope, Naples, Italy, mar[email protected], ORCID: 0000-0002-7974-5114 Luisa De Simone University of Naples L’Orientale, Naples, Italy, [email protected], ORCID: 0000-0002-1366-8777 Antonio Lopes University of Naples L’Orientale, Naples, Italy, [email protected], ORCID: 0000-0002-3671-2611 Cite this paper as: Arbolino, R., Boffardi R., Bonasia, M., De Simone, L., Lopes, A., Policy instruments for promoting renewable energy: empirical evidence on their diffusion across EU. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Renewable energy plays a pivotal role in the transition towards low-carbon economies, allowing to reduce environmental impacts from energy supply sector. Based on this acknowledgement, policies for supporting renewable energy deployment have spread globally. In this setting, the EU has become one of the most influential actors for the implementation of renewable energy promotion policy, introducing a wide set of acts and instruments aimed at developing an EU-wide common framework for renewable energy. Literature has recognised an overall harmonisation process of these policies driven by two main channels: international policy transfer mechanisms and national determinants. While the effectiveness of the former is well recognised, the role of domestic factors tends to be overlooked. Moreover, scant attention has been paid to disentangle whether different mechanisms differently affect policy diffusion referring in different sectors of renewable energy. Based on these premises, the present research aims at studying the role played by both policy diffusion mechanisms and adopting countries characteristic for explaining the diffusion of renewable energy policies. The main novelty of the research relies on the implementation of analyses separately for each typology of the main renewable energy sources (i.e., solar, hydroelectric, wind, biomass). Our main results show that coercion and competition homogeneously impact the likelihood of a policy emulation, while learning and emulation differently influence the decision of whether to subsidise a typology of renewable or not. At the same time, the features of the emulating countries are key drivers of policy adoption, while the features of the emulate one are not considered. Keywords: Energy Policy; Policy diffusion; Renewables; Feed-in Tariffs © 2024 Published by ECRES 11 Gasification and Pyrolysis of Biomass using A Plasma System Vladimir Messerle CPI, Al-Farabi Kazakh National University Almaty, Kazakhstan, ORCID: 0000-0003-4281-1429 Oleg Lavrichshev IASIT, Al-Farabi Kazakh National University, Almaty, Kazakhstan, ORCID: 0000-0002-5934-8381 Alexandr Ustimenko NTPC Zhalyn Al-Farabi Kazakh National University, Almaty, Kazakhstan, [email protected], ORCID: 00000002-2629-6167 Cite this paper as: Messerle, VE, Ustimenko, AB, Lavrichshev, OA. Gasification and pyrolysis of biomass using a plasma system. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain. Abstract: In this paper, plasma technology is used to process dried mixed animal manure (dung that contains 30% moisture). Irrational use of manure as well as huge quantities of it can negatively impact the environment. In comparison to biomass fermentation, plasma processing of manure can greatly enhance the production of fuel gas, primarily synthesis gas (CO + H2). The organic part of dung, including moisture, is represented by carbon, hydrogen, and oxygen with a total concentration of 95.21%, while the mineral part is only 4.79%. A numerical analysis of dung plasma gasification and pyrolysis was conducted using the thermodynamic code TERRA. For 300-3,000 K and 0.1 MPa pressure, dung gasification and pyrolysis were calculated with 100% dung + 25% air and 100% dung + 25% nitrogen, respectively. The calculations were performed to determine the gasification products' composition, degree of carbon gasification, and specific energy consumption. The specific energy consumption of gasification and pyrolysis of dung at 1,500 K is respectively 1.28 and 1.33 kWh/kg. An installation containing a DC plasma torch with a rated power of 100 kW and a plasma reactor with a 50 kg/h dung capacity was used to conduct dung processing experiments. The specific energy consumed during pyrolysis and gasification of dung in the plasma reactor was 1.5 and 1.4 kWh/kg, respectively. A maximum temperature of 1,887 K was reached in the reactor. Plasma pyrolysis and plasma-air gasification of dung produce combustible gases that have specific heats of combustion of 10,500 and 10,340 kJ/kg, respectively. Calculations and experiments on dung plasma processing showed satisfactory agreement. Keywords: biomass, plasma processing, synthesis gas, thermodynamic calculation, experiment © 2024 Published by ECRES 12 HENSUS – A Tool for Configuring Hybrid Power Systems for Fishing Vessels Nikola Vladimir University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, Croatia, [email protected], ORCID: 0000-0001-9164-1361 Marija Koričan University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, Croatia, [email protected], ORCID: 0000-0001-7480-7411 Cite this paper as: Vladimir, N, Koričan, M. HENSUS – A tool for configuring hybrid power systems for fishing vessels. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Nowadays, the maritime sector is experiencing a significant shift towards sustainable practices, emphasizing the reduction of environmental impact and enhancement of energy efficiency, which is a consequence of global decarbonization goals. Different technical and operative measures are being implemented on variety of ship designs, while alternative powering options are recognized as the most efficient decarbonization measure. This particularly refers to full electrification, which seems to be excellent decarbonizations step (especially if the electricity is obtained from the renewables), but it is associated with different issues, ranging from the infrastructural to the economic ones (related to high investment costs). Therefore, hybrid power systems are a promising intermediate solution, but their viability should be confirmed by analyzing technical and operative characteristics of the vessel. Fishing sector is also faced to decarbonization goals and at the same time the fishing vessels are mainly powered by diesel engines. Moreover, fishing vessels, especially purse seiners, regularly have almost random operative profile, depending on the weather conditions but also taking into account their success in finding for the fish. The design framework for their power systems is not so simple as in case of some liner vessels. This study introduces a user-friendly in-house tool "HENSUS" (Hybrid Energy System for fishing vessels) for fast preliminary configuring of hybrid power systems of fishing vessels. It is aimed for the fishers as the end users, which regularly have no engineering knowledge, but at the same time they should rely on mathematical models in planning their fleets. The application requires information on ship technical and operative data, and based on the optimization algorithms, it provides environmental and economic parameters of various hybrid power system configurations in a lifetime framework and considering different carbon allowance scenarios. Keywords: HENSUS, hybrid power systems, alternative fuels, decarbonization, fishing vessels © 2024 Published by ECRES 13 Performance Enhancement of the Application Layer for Distribution Planning Platform Panitarn Chongfuangprinya Hitachi America, California, USA, [email protected], ORCID: 0000-0002-3642-6703 Anthony Hoang Hitachi America, California, USA, [email protected], ORCID: 0009-0002-1746-9297 Bo Yang Hitachi America, California, USA, [email protected], ORCID: 0000-0001-5162-8840 Yanzhu Ye Hitachi America, California, USA, [email protected], ORCID: 0000-0001-5772-5115 Natsuhiko Futamura Hitachi America, California, USA, [email protected]hi.com, ORCID: 0000-0003-2847-506X Yoshihisa Okamoto Hitachi America, California, USA, [email protected], ORCID: 0009-0002-1814-3520 Cite this paper as: Chongfuangprinya, P, Hoang, A, Yang, B, Ye, Y, Futamura, N, OkamotoY. Performance Enhancement of the Application Layer for Distribution Planning Platform. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Behind-the-meter assets (BTMs) such as distributed solar photovoltaic and battery energy storage system increase number of distribution grid assets tremendously. Planning with BTMs is a challenge from information technology aspect because loading and visualizing a large number of BTMs on a distribution planning platform could make the application layer unresponsive and unusable. This research focused on improving the usability and preventing the application layer of such platform from becoming unresponsive. We developed a planning platform with application layer to visualize distribution feeder assets and BTMs on a tree structure and on a map. Furthermore, we developed a method to optimize performance of the application layer for scalability. The goal is to load more feeder assets and BTMs on the platform while maintaining responsiveness of the application layer. We created scenarios with different feeder size and performed benchmark. Examples of performance metrics include loading time, average time to reload, and response time. We found that the proposed method prevented the application layer from becoming unresponsive. The proposed method also provided the best response time. Our method is capable for utility scale planning application. We will apply this method to real-time operation platform with Artificial Intelligent grid control algorithm in the future. Keywords: Behind-the-meter Assets, Distributed Solar Photovoltaic, Application Layer, Visualization, Distribution Planning © 2024 Published by ECRES 20 Plasma Processing of Rubber Powder from End-Of-Life Tires: Numerical Analysis and Experiment Vladimir Messerle CPI, Al-Farabi Kazakh National University Almaty, Kazakhstan, ORCID: 0000-0003-4281-1429 Alexandr Ustimenko NTPC Zhalyn, Al-Farabi Kazakh National University, Almaty, Kazakhstan, [email protected], ORCID: 00000002-2629-6167 Oleg Lavrichshev IASIT, Al-Farabi Kazakh National University, Almaty, Kazakhstan, ORCID: 0000-0002-5934-8381 Cite this paper as: Messerle, VE, Ustimenko, AB, Lavrichshev, OA. Plasma processing of rubber powder from endof-life tires: numerical analysis and experiment. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain. Abstract: Tire recycling is becoming an increasingly important problem due to the growing number of end-oflife tires (ELTs). World-wide, ELTs account for more than 80 million tons. ELTs contribute to environmental pollution in the long run. They are flammable, toxic and non-biodegradable. At the same time, ELTs contain rubber, metal and textile cord, which are valuable raw materials. ELTs are buried in landfills, burned, crushed and restored. Most of these methods have a negative impact on the environment. From an environmental point of view, the most preferred ways to recycle tires are retreading and shredding. Rubber powder (RP) or crumb is mainly used for rubber pavers production, waterproofing, curbs, road slabs and various surfaces. An alternative method for RP processing, eliminating the disadvantages of the above approaches, is plasma gasification and pyrolysis. The paper presents thermodynamic and kinetic analysis and experiment on plasma processing of RP from worn tires to produce flammable gas. During plasma-air gasification of RP, the yield of synthesis gas was 44.6% (Н2 – 19.1, СО – 25.5), and the degree of carbon gasification reached 95.6% at a massaverage temperature in the plasma reactor of 1,750 K. The experimental and calculated results agreed satisfactorily. It was found that plasma products from RP did not contain harmful impurities, either in calculations or experiments. Plasma gasification allows for recycling ELTs in an environmentally friendly way while also generating flammable gases that are valuable commodities. In this research, plasma technology was demonstrated to be effective for gasifying RP to produce flammable gas. Keywords: Waste tire rubber powder, plasma processing, synthesis gas, thermodynamic calculation, kinetic calculation, experiment © 2024 Published by ECRES 21 Advanced Solutions for Residential Photovoltaics (PV) Systems: Models, Concepts, and Demonstrators Hesan Ziar Delft University of Technology, Photovoltaic Materials and Devices group, Delft, the Netherlands, : [email protected] ORCID No. 0000-0002-9913-2315 Alba Alcañiz Moya Delft University of Technology, Photovoltaic Materials and Devices group, Delft, the Netherlands, [email protected] Juan Camilo Ortiz Lizcano Delft University of Technology, Photovoltaic Materials and Devices group, Delft, the Netherlands, [email protected] Olindo Isabella Delft University of Technology, Photovoltaic Materials and Devices group, Delft, the Netherlands, o[email protected] Cite this paper as: Ziar H., Moya, A.A., Ortiz Lizcano, J.C., Isabella, O. Advanced Solutions for Residential Photovoltaics (PV) Systems: Models, Concepts, and Demonstrators. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The share of residential PV systems in the European electricity market is rapidly increasing and so is the need for reliable solutions for performance improvement and electrical power prediction of such systems. In this contribution, we are presenting 3-year research results within the H2020 Trust-PV project, particularly the following four aspects: (i) approaches to reliably overcome the geometrical complexity of the urban environment and quantifying its effects on PV system performance, (ii) inherent limit to PV yield prediction as a result of lacking on-site meteorological measurement for residential PV, (iii) probabilistic assessment of randomly shaded PV module’s using the concept of Shading Tolerability and (iv) quantifying the benefits of passive cooling solutions for PV modules. A snapshot of the results is shown in Figure 1. (a) (b) Irradia 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 22 (c) (d) Figure 1. (a) LiDAR-based skyline matrix for different modules of a residential PV system for remotely assessing the influence of surrounding objects on the PV system energy production. (b) The map of the meteorological stations in the Netherlands and their data was used to assess weather data interpolation impact on the PV yield prediction. (c) Experimental setup to validate the accuracy of I-V curve modelling under shading which was further incorporated in the Shading Tolerability assessment tool. (d) Theoretical evaluation of perfect optical filter with different central wavelengths 0 with different wavelength bands Δbw= 20 nm, 50 nm, 100 nm (black, red, purple) on the Si PV cell temperature. Keywords Photovoltaics (PV) systems, performance and reliability, yield prediction, electrical optical thermal modelling © 2024 Published by ECRES 23 Integration of Solar Stirling in A Multi-Generation and Storage Power System Georg Klepp Institute for Energyresearch (IFE), Technical University of Applied Science Ostwestfalen-Lippe (TH OWL), Lemgo, Germany, geor[email protected], ORCID: 0000-0003-1435-7468 Cite this paper as: Klepp, G.. Evaluation of the techno-economic aspects and sustainability of integrated renewable energy systems. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: There is a growing focus on developing standalone electricity generation systems for remote regions. Wind turbines generate electricity. Solar energy can be harnessed through photovoltaic (PV) or concentrated solar power (CSP) systems. The produced electricity fluctuates due to the unpredictable nature of wind and solar radiation leading to periods of excess or insufficient electricity production compared to demand. During surplus periods, excess electricity must be stored. A long-term energy storage solution is converting excess electricity into hydrogen gas, which can be reconverted into electricity during low production periods. Multi-generation systems leverage various energy sources to optimize efficiency and better utilize available resources in specific regions. Solar and wind energy resources complement each other in numerous geographical areas. Combining these renewable sources to create a hybrid hydrogen production system could offer a solution by potentially reducing costs. Furthermore, this hybrid system ensures continuous production by leveraging both energy sources to mitigate intermittency issues. PV-hydrogen systems present a promising approach for green hydrogen production due to its competitive economics, commercial viability, and sustainability. Nevertheless CSP-hydrogen systems are advantageous due to a versatile integration into hydrogen production, electricity generation, heating or cooling. For instance, a CSP-hydrogen system could employ a Stirling engine at the focal point of a solar concentrator to directly produce electricity and regulate the water electrolyzer. Comparisons of multi-generation (hydrogen from solar and wind) and multiple-use techniques are conducted for different locations. The feasibility of PV and CSP-Stirling systems is investigated, acknowledging that the optimal solution depends on the location and energy requirements. Keywords: renewable energy systems, energy storage, Stirling solar © 2024 Published by ECRES 24 AI Enhanced Testing Stand for Determining The Thermal Properties of Materials Mihail-Bogdan Carutasiu UPB, Bucharest, Romania, [email protected], ORCID: 0000-0002-4352-4299 Teodor Lupoiu UPB, Bucharest, Romania, [email protected], ORCID: 0009-0002-0162-1717 Horia Necula UPB, Bucharest, Romania, [email protected] Cite this paper as: Carutasiu, MB, Lupoiu, T, Necula H, AI enhanced testing stand for determining the thermal properties of materials. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The importance of accurately estimate the construction materials of existing buildings is emphasized by the current energy efficiency strategies across all developed countries. If considering that most of the built environment consists of old, inefficient buildings, their retrofitting becomes even more important to meet the reduction goals. The proposed study tackles this issue by developing an AI enhanced guarded hotplate used for automatic recognition of the tested construction material using Computer Vision algorithms and tools. Besides the guarded hotplate, the infrastructure comprises a thermal vision camera which will assure the development of a proper customed databased used to train, test, and validate the Computer Vision model. The model will be later used to estimate new materials and will be tested in field conditions, as different thermal vision cameras could be integrated within drones and provide new images for the model, thus analyzing the existing building stock within a desired area. Moreover, the Computer Vision model can further be improved by integrating several open-source databases, consisting of different photos of existing buildings’ envelope components, and even more important, thermal conductivity of the materials. This way, the model could be trained to estimate the thermal energy lost through that specific building element. Keywords: energy efficiency in buildings, computer vision algorithm, thermal conductivity, heat loss © 2024 Published by ECRES ACKNOWLEDGMENT "This work was supported by a grant from the National Program for Research of the National Association of Technical Universities - GNAC ARUT 2023" 25 A High-Power Supercapacitor Device Assembled by rGO Nanosheet Encapsulated MnCo2S4 Nanoflowers Yuttana Mona Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, y[email protected], ORCID: 0000-0002-1102-9242 Chatchawan Chaichana Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-9392-7088 Pana Suttakul Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-2946-8921 Napassawan Khammayom Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0003-4108-9702 Ramnarong Wanison Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-5078-9893 Uma Shankar Veerasamy* Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0003-1989-8263 Cite this paper as: Mona, Y, Chaichana, C, Pana, S, Khammayom, N, Wanison, R, Veerasamy, U.S. A high-power supercapacitor device assembled by rGO nanosheet encapsulated MnCo2S4 nanoflowers. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: In this work, we make high-performance supercapacitors by preparing rGO@MnCo2O4 and rGO@MnCo2S4 nanostructures as sandwiched type capacitor devices. The three-electrode and twoelectrode configurations were utilized to estimate the capacitance behavior of the prepared electrodes. Also, the rGO@MnCo2S4 and rGO@MnCo2O4 electrodes have 930 and 628 F/g, capacitance. Moreover, the rGO@MnCo2O4 and rGO@MnCo2S4 electrodes were fabricated as the sandwiched type of symmetric capacitor device. The rGO@MnCo2S4 electrode delivers a higher capacitance value (475 F/g) than rGO@MnCo2O4 electrode (290 F/g). In addition, rGO@MnCo2S4 electrode also delivers the higher E and P values of 65.97 Wh/kg and 1250 W/kg, which is comparatively higher than rGO@MnCo2O4 electrode (40.27 Wh/kg and 833 W/kg). However, both rGO@MnCo2O4 and rGO@MnCo2S4 electrodes have shown the excellent capacitance retention value of 89 % and 94.5 %. These results may show the prepared rGO@MnCo2S4 electrode is a suitable electrode material for supercapacitor applications. Keywords: Power density, nanostructure, sandwiched, symmetric capacitors, capacitance © 2024 Published by ECRES 26 Removal of Perfluorinated Compounds (PFCs) According to Raw Water Characteristics and Drinking Water Treatment Systems Seon-Ha Chae K-water Research Institute, K-water, Daejeon, Republic of Korea, [email protected], ORCID: 0000-0003-2326-102X Min Jung Jeon K-water Research Institute, K-water, Daejeon Republic of Korea, [email protected], ORCID: 0009-0005-0671-7672 Hyunook Kim Water-Energy Nexus Laboratory, Department of Environmental Engineering, University of Seoul, Seoul, Republic of Korea, [email protected], ORCID: 0000-0003-1256-480X Cite this paper as: Chae, S.H., Jeon, M.J., Kim, H. Removal of Perfluorinated compounds (PFCs) according to raw water characteristics and drinking water treatment system, 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: This study evaluated the removal efficiency of 10 types of perfluorinated compounds (PFCs; 3 types of perfluorinated sulfonate (PFSA) and 7 types of perfluorinated carboxylic acid (PFCA)) according to raw water (lake water, river water) characteristics in drinking water treatment systems (6 water treatment plants) combining ozone (Pre-O3, Post-O3), granular activated carbon (GAC, FilterAdsorber) and membrane (Microfiltration membrane; MF). PFCs were present at the level of several ng/L in the target raw water, but the concentration was about twice higher in river water than in lake water. It was difficult to decompose and remove by biological treatment process and ozone process, and the removal rate was effective through the GAC process and depended on the organic matter concentration (DOC) of the raw water and the number of years the activated carbon was used. A water treatment system with a high concentration (DOC ≥3∼4mg/L) of organic matter in the influent and a GAC process that had been in use for more than 2 years also showed a negative removal rate. Keywords: Perfluorinated compounds (PFCs), drinking water treatment, ozone, granular activated carbon (GAC), membrane © 2024 Published by ECRES ACKNOWLEDGMENT This research was supported by Korea Ministry of Environment as “Project for developing innovative drinking water and wastewater technologies” (2019002710006). 27 Evaluation of the Perand Polyfluorinated Substances(PFASs) Removal Models in A Water Treatment Pilot Plant Min Jung Jeon K-water Research Institute, K-water, Daejeon Republic of Korea, [email protected], ORCID: 0009-0005-0671-7672 Ingyu Lee Water-Energy Nexus Laboratory, Department of Environmental Engineering, University of Seoul, Seoul, Republic of Korea, [email protected], ORCID: 0000-0002-9838-6428 MoonYeong Choi K-water Research Institute, K-water, Daejeon Republic of Korea, cmy[email protected], ORCID: 0009-0006-98768721 HeeKyung Lee Water-Energy Nexus Laboratory, Department of Environmental Engineering, University of Seoul, Seoul, Republic of Korea, [email protected], ORCID: 0009-0000-5162-9309 Hyunook Kim Water-Energy Nexus Laboratory, Department of Environmental Engineering, University of Seoul, Seoul, Republic of Korea, [email protected], ORCID: 0000-0003-1256-480X Seon-Ha Chae† K-water Research Institute, K-water, Daejeon, Republic of Korea, [email protected], ORCID: 0000-0003-2326-102X Cite this paper as: Jeon, M. J., Lee, I., Choi, M.Y., Lee, H.K., Kim, H., Chae, S.H., Evaluation of the Perand Polyfluorinated Substances(PFASs) removal models in a water treatment pilot plant, 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Recently, PFASs (Perand Polyfluorinated Substances), which are forever chemicals, were designated as water quality monitoring items and started to manage in Korea. In this study, an integrated model for PFASs removal prediction was developed and verified in the drinking water treatment system. The integrated model was composed of the case of the post-ozone-GAC process, which is most applied to water treatment plants in Korea. A total of 10 types of PFASs, including 3 perfluorinated sulfonate (PFSA) and 7 types of perfluorinated carboxylic acid (PFCA), were selected for removal model development and evaluation. To verify the removal model, the spiking test was performed in a pilot plant with a daily capacity of 30 m3, which combined the standard water treatment process with post-ozone and GAC (Granular Activated Carbon) as the advanced water treatment. According to the results, it was verified that the integrated model can predict the removal efficiency of the pilot plant. However, the experimental results showed that the removal prediction model was reliable under limited operating conditions. Keywords: Perand Polyfluorinated Substances(PFASs), drinking water treatment, ozone, granular activated carbon (GAC), pilot plant © 2024 Published by ECRES ACKNOWLEDGMENT This research was supported by Korea Ministry of Environment as “Project for developing innovative drinking water and wastewater technologies” (2019002710006). 28 A Numerical Investigation on MULTICAL: The Ecofriendly Heat Pump Based on Multicaloric Effect Luca Cirillo Università degli Studi di Napoli “Federico II”, Naples, Italy, [email protected] Adriana Greco Università degli Studi di Napoli “Federico II”, Naples, Italy, [email protected] Claudia Masselli Università degli Studi di Napoli “Federico II”, Naples, Italy, [email protected] Cite this paper as: Cirillo, l., Greco, A., Masselli, C. A numerical Investigation on MULTICAL: The Ecofriendly Heat Pump Based on Multicaloric Effect. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Heat Pumps (HP) are the best answer to a transition toward a carbon-neutral heating technology that can efficiently utilize low-grade renewable thermal energy. The employment of caloric-effect based technologies for heat pumping is a potential solution to reduce environmental impact of the HP systems, through the fully carbon neutral zero GWP Caloric Materials as refrigerants (because of their solid-state nature). Caloric effect is a physical phenomenon manifesting in some solid-state materials that, because of an adiabatic change in the intensity of an external field applied to them a temperature change occurs. Depending both on the nature of the field applied (magnetic, electric or mechanical, i.e. stress or hydrostatic pressure) and the material to which it is applied a different caloric effect is observable: magnetocaloric, electrocaloric, elastocaloric, barocaloric. Every single-effect caloric technology has shown limits that hinders the commercialization, related to the costs or the durability of the caloric materials needed to work with high forcing fields. Using a multi-caloric heat pump one can reduce the intensity of the required fields, so both saving costs and optimizing the energy performances. In this project the attention is paid to Multi-Caloric materials which exhibit response to more than one external field-type, i.e. multiple caloric effects. The main aim of the project MULTICAL is to design, fully characterize and optimize numerically the first multicaloric heat pump in the world. MULTICAL will exploit the multicaloric effect given by multiple-fields on single phase materials generated by elastoand magneto-caloric effect. In this paper the project and the numerical investigation to realize the multicaloric heat pump are introduced. Keywords: Multicaloric effect, non conventional heat pump, solid-state refrigerants, magnetocaloric, elastocaloric © 2024 Published by ECRES 29 Harnessing Rice Paper as a Self-Powered Humidity Sensor for Future Electronics Muhammad Muqeet Rehman⸸,* Jeju National University, Jeju, South Korea, [email protected].kr, ORCID: 0000-0003-4712-6404 Maryam Khan⸸ Jeju National University, Jeju, South Korea, [email protected], ORCID: 0000-0001-8716-2907 Woo Young Kim* Jeju National University, Jeju, South Korea, [email protected].kr, ORCID: 0000-0003-2747-6618 Cite this paper as: Rehman, MM, Khan, M, Kim, WY. Harnessing Rice Paper as a Self-Powered Humidity Sensor for Future Electronics. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Inappropriate management of electronic waste (EW) poses a fundamental threat to our ecosystem. Scientists are working on bio-compatible/environment friendly materials to solve this problem. We applied cellulose-enriched edible rice paper (CERP), derived from bio-wastes, in modern electronic devices. CERP display hydrophilic functional groups and a permeable surface, making it highly suitable for detecting surrounding moisture content across a wide humidity range. Several characterization techniques were used to understand the characteristics of CERP, including scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), water contact angle, and LCR meter. CERP humidity sensor displayed fast response/recovery times (6/15 s) and high sensitivity (97%) as a humidity sensor. Furthermore, CERP was also used as the electropositive layer of a triboelectric nanogenerator (TENG), enabling it to work as a self-powered humidity sensor. The CERP-TENG showed significant performance, rendering high output voltage (162 V), output current (2 µA), and power density (3 µW/cm2). The electrical energy generated by the CERP-TENG was sufficient to charge commercially available capacitors and power light emitting diodes (LEDs). The obtained results showed constancy, certainty, and a long lifetime (3 months) without showing any impairment. This work signifies the utilization of bio-wastes for multifunctional and sustainable advanced electronics. Keywords: Harnessing Rice Paper; Self-Powered Humidity Sensor; Recyclable Electronics © 2024 Published by ECRES ACKNOWLEDGMENT This research was funded by [National Research Foundation of Korea (NRF)- Korea Government (Ministry of Science and ICT)] grant number (NRF2020H1D3A1A04081545, 2021R1A4A2000934, 2021R1F1A1062800). ⸸ Both authors have equal contribution * Corresponding author: [email protected] , [email protected] 36 Realization of an Indirect Solar Cooker Angeline Kpeusseu Kouambla Yeo Ecole Doctoral Polytechnique, Yamoussoukro, Cote d’Ivoire, [email protected] Paul Magloire Ekoun Koffi Institut national Polytechnique félix Houphouet Boigny, Yamoussoukro, Cote d’Ivoire, [email protected] Bati Ernest Boya Bi Institut national Polytechnique félix Houphouet Boigny, Yamoussoukro, Cote d’Ivoire,[email protected], Cite this paper as: Kouambla Yeo, A.K., Ekoun Koffi, P.M., Boya Bi, B.E. Realization of an Indirect Solar Cooker. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The The undertaken work aimed to design and implement an innovative indirect solar cooker equipped with a thermal energy storage system, focusing on addressing the challenge of cooking food under shelters. This solution relies on the use of a solar cooker equipped with a cylindro-parabolic concentrator. To achieve our goal, we dimensioned the cooking pot by defining the necessary components for its realization, employing various processes for manufacturing parts in the field of mechanics, including casting and welding. We had a cylindro-parabolic concentrator with known geometric characteristics. The determined parameters included the temperatures at the inlet and outlet of the absorber tube, those at the inlet and outlet of the cooker, those inside the cooking pot, the solar radiation intensity, and the ambient temperature. The main results obtained after two consecutive days of measurements are as follows: the maximum values of T InAbs, T OutAbs, T InCui, T OutCui, T IntCui, Is, T Amb were 149.4 degrees Celsius, 186.6 degrees Celsius, 180.3 degrees Celsius, 170.6 degrees Celsius, and 165.8 degrees Celsius around 12:45 PM, with approximately 805 w/m2 of sunlight at an ambient temperature of 35.3 degrees Celsius. The heat transfer fluid used was 40% glycol water with a boiling temperature around 198 degrees Celsius. The use of polypropylene with a melting temperature of 160 degrees Celsius as a Phase Change Material (MCP) provides the possibility of creating a standalone solar cooker. Indeed, the energy stored in the MCP, through appropriate sizing, can ensure food cooking during the day, even in the absence of direct sunlight, and during the night. This thermal energy storage capability significantly enhances the practicality and efficiency of the solar cooker, extending its usability beyond direct sunlight hours. Keywords: Solar cooker, phase change material, cylindro-parabolic concentrator, polypropylene, solar energy. © 2024 Published by ECRES 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 37 PAPERS 38 39 Mason’s and Martin’s Equivalent Circuits for Vibrations’ Energy Harvesting from Automobile Suspension System with Piezoelectric Stack Ayoub Benhiba Mohammed V University in Rabat, Higher School of Technology of Salé, LASTIMI Laboratory, Salé, Morocco, [email protected], ORCID: 0000-0003-3700-2694 Abdelmajid Bybi Mohammed V University in Rabat, Higher School of Technology of Salé, Materials, Energy and Acoustics Team (MEAT), Salé, Morocco, abdelmajid.by[email protected], ORCID: 0000-0003-4698-7528 Adil Salbi Mohammed V University in Rabat, Higher School of Technology of Salé, LASTIMI Laboratory, Salé, Morocco, [email protected], ORCID: 0000-0003-2142-8623 Ouadia Mouhat Mohammed V University in Rabat, Higher School of Technology of Salé, LGCE Laboratory, Salé, Morocco, [email protected], ORCID: 0000-0001-6604-299X Ilyas Lahlouh Mohammed V University in Rabat, Higher School of Technology of Salé, LASTIMI Laboratory, Salé, Morocco, [email protected], ORCID: 0000-0003-3984-3040 Issam Bouganssa Mohammed V University in Rabat, Higher School of Technology of Salé, LASTIMI Laboratory, Salé, Morocco, [email protected], ORCID: 0000-0003-4788-7393 Cite this paper as: Benhiba, A., Bybi, A., Salbi, A., Mouhat, O., Lahlouh, I., Bouganssa, I.. Mason’s and Martin’s equivalent circuits for vibrations’ energy harvesting from automobile suspension system with piezoelectric stack. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: This paper investigates the vibrations’ energy harvesting from car’s suspension system using a piezoelectric stack located in series with the suspension’s shock absorber. A 2-DOF model is utilized to evaluate the harvested voltage and power. The equivalent electrical circuit approach is used to implement the suspension’s model with its harvester in LTspice simulator. The originality of this work consists in utilizing Mason’s and Martin’s circuits to model accurately the electromechanical behavior of the harvester. The investigations done for a stack composed of 40 piezoelectric layers indicated that, voltage and power curves computed using Martin’s circuit and Mason’s accurate model are similar, but Martin’s model is easy to implement in LTspice. It is also found that, the maximum voltage and power are obtained at the first resonant frequency: 25.12 V and 63.09 mW for a harmonic excitation of 9.8 m / s2. Finally, the comparison of the results with those obtained previously using Mason’s simplified model showed differences of about 2.47 % and 4.98 % of voltage and power respectively. The discrepancies increases when the harvester’s losses are not taken into consideration: about 3.18 % and 6.47 %. Keywords: Energy harvesting, piezoelectric stack, car suspensions, Martin’s model, Mason’s model © 2024 Published by ECRES 1. INTRODUCTION Surrounding vibrations resulting from structures’ dynamics: displacements, deformations, and rotations consist an interesting source of renewable energy. In fact, these vibrations can be converted into useful electrical energy using 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 40 suitable harvesters, such as piezoelectric transducers. In the automobile domain, this energy can be utilized to power several low consumption systems, e.g., Tire Condition Monitoring System and wireless communication circuits. Research works published in the literature indicate that, an important quantity of vibrations’ energy can be scavenged from vehicles’ suspensions [1-6]. To study the suspensions’ dynamics and estimate the quantity of harvestable energy, different models are presented in the literature: quarter car [1-6], half car [1], and full car models [1]. In this context, the fundamental relations of dynamics coupled to the piezoelectricity properties are usually used to investigate the suspensions’ characteristic displacements and evaluate the harvested voltage and power. Simulations are performed using displacements’ and voltages’ transfer functions. The latters are then implemented in a suitable software, e.g., Matlab Simulink using Laplace transforms [1-3]. Other interesting works utilize Bond Graph method [5] or equivalent circuits approach based on Mason’s simplified model [4]. The originality of this work consists in utilizing accurate equivalent circuits inspired from medical imaging and underwater transducers, i.e., Mason’s and Martin’s equivalent circuits for piezoelectric stacks. In addition, the whole suspension’s system with its associated harvester is modeled using equivalent circuits taking into account all kind of dissipations (dielectric, piezoelectric, and mechanical losses). In this paper, to study the energy harvesting capabilities, a piezoelectric stack (n layers) is placed in series with the suspension’s shock absorber. Series connection is chosen to avoid changing the suspension’s dynamics: affecting its bouncing displacements. To demonstrate the efficacy of the proposed approaches, simulations’ results, i.e., harvested voltage and power are compared with those published in previous work using Mason’s simplified model [4]. 2. CARS’ SUSPENSION SYSTEM AND ITS ASSOCIATED PIEZOELECTRIC HARVESTER Cars are a non-limited source of vibrations’ energy which is mainly lost by their suspension’s system: shock absorbers and tires. To recover the dissipated energy and convert it into useful electrical energy, piezoelectric harvesters can be located in series with the suspensions’ shock absorbers [4-6]. In this work, the harvested energy is produced by compressive efforts of a piezoelectric stack placed between the shock absorber and the car’s body modeled by a sprung mass mS as in ref. [6]. Similarly, Lafarge et al. investigated the vibrations’ energy harvesting capabilities in case of a piezoelectric harvester introduced between two surfaces at the bottom of the shock absorber [5]. 2.1.Suspension’s System Thanks to its simplicity, the quarter car model (Fig. 1a) and its equivalent electrical circuit (Fig.1b) are utilized in this work to investigate the vibrations’ energy harvesting from car’s suspension. As given in Fig. 1a, the mechanical model consists of a sprung (mS) and unsprung (mUs) masses representing the quarter of the car’s body and the individual tire assembly respectively. The two masses form a 2-DOF coupled spring mass system, i.e., mS is connected to a spring (kS) and a damper (bS) and mUs is attached to a spring (kUs) and a damper (bUs) corresponding respectively to the tire’s stiffness and absorptivity. In this model, the road profile (z) is only responsible of the vertical displacements (bouncing) of the car’s body (zS) and tire (zUs). To facilitate the study, the 2-DOF mechanical model (Fig. 1a) is converted into its equivalent circuit (Fig. 1b) using the analogy between the mechanical and electrical quantities [4, 7]: the masses (mS and mUs), dampers (bS and bUs), and springs (kS and kUs) are modeled by inductors (LS and LUs), resistors (RS and RUs), and capacitors (CS and CUs) respectively. Also, forces and velocities are taken equivalent to voltages and currents. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 41 (a) (b) Figure 1. Quarter car model: 2-DOF mechanical model (a), equivalent electrical circuit (b). The values of the parameters mS, kS, bS, mUs, kUs, and bUs considered for the simulations are taken from ref. [4]. 2.2. Piezoelectric Stack for Vibrations’ Energy Harvesting Piezoelectric stacks are widely utilized for vibrations’ energy harvesting. As shown in Fig. 2a, the harvester is composed of (n) piezoelectric layers polarized alternatively along their thickness T (vertical direction 3). In this work, the dimensions of the piezoelectric harvester (Motorola PZT 3203HD) are given in Table I and its electromechanical properties are taken from ref. [8]. In this reference, experimental values are given and all material’s properties are expressed using both real and imaginary parts. This allowed us to take into consideration all types of dissipations (piezoelectric, dielectric, and mechanical losses) in our equivalent circuits. To avoid changing the suspension’s dynamics (bouncing displacements), the harvester is connected in series with its shock absorber composed of a spring ks and viscous damping bs [6]. (a) (b) Figure 2. Suspension’s system and its energy harvester: piezoelectric stack (a), system’s mechanical representation (b). Table I. Characteristics of the piezoelectric stack Characteristic Value Number of layers (n) 40 Single layer thickness (T) 0.001m Stack length (L = nT) 0.04 m Surface area (A) (0.007)2 m2 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 42 3. ANALYSIS OF VIBRATIONS’ ENERGY HARVESTING USING EQUIVALENT CIRCUITS 3.1. Mason’s Models Before presenting Mason’s model for a stack composed of n layers, it is useful to express the model for a single piezoelectric bar (n = 1) with a length L and vibrating in 33-mode (length extensional mode) as shown in Fig. 3a. (a) (b) Figure 3. Piezoelectric harvester operating in 33-mode: single piezoelectric element (a), Mason’s equivalent circuit (b). Based on the constitutive relations of piezoelectricity and the wave equation, Mason modeled the electromechanical behavior of the structure by the circuit presented in Fig. 3b [9]. The equivalent circuit is composed of an electrical port connected to the center node of two acoustic ports via an electromechanical transformer (ratio N). The acoustic ports are represented by a T-quadrupole circuit composed of the impedances ZT and ZS (Fig. 3b). The acoustic ports represent the harvester’s front and back faces. Notice that in Mason’s model a negative capacitance (-C0) is connected between the shunt capacitance C0 and the transformer. This capacitance can be computed using the following relation: 𝐶0=𝜀33 𝑇(1−𝑘33 2)𝐴 𝐿 (1) The impedances ZT and ZS can be computed using the following relations: 𝑍𝑇=𝑗𝑍0tan⁡(𝜉𝐿 2) (2) 𝑍𝑆=−𝑗𝑍0 sin⁡(𝜉𝐿) (3) The transformation turns ration N can be calculated using the following relation: 𝑁=⁡𝐴⁡𝑑33 𝐿⁡𝑆33 𝐸 (4) 𝜀33 𝑇, k33, Z0, ω, 𝜉, d33, and 𝑆33 𝐸 are: free permittivity, electromechanical coupling, characteristic impedance, angular frequency, propagation constant, piezoelectric charge coefficient, and short circuit elastic compliance respectively. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 43 Figure 4. Equivalent circuit of the suspension’s system equipped with a piezoelectric stack harvester. The previous harvester is now segmented into (n) piezoelectric elements to form a stack. Mason’s model for a stack (dashed rectangle) assembled in series with the suspension’s shock absorber is shown in Fig. 4. As can be observed, the stack’s equivalent circuit is composed of (n) elementary Mason’s circuits connected mechanically in series and electrically in parallel. The complete equivalent circuit of the suspension’s system equipped with its piezoelectric stack (Fig. 4), is utilized to estimate the harvested voltage and power under a resistive load (R) of 10 kΩ. Firstly, to determine the natural frequencies of the masses (mS and mUs) and check the effect of bounding a piezoelectric harvester in series with the suspension’s shock absorber, a harmonic analysis considering a sinusoidal excitation (acceleration) of 1g (9.8 m / s2) amplitude is conducted using LTspice software. Then, the magnitude of the masses’ displacements ratios (zS / z and zUs / z) computed using Mason’s model for two types of piezoelectric harvesters: a single element (n = 1) and a piezoelectric stack with n = 40 elements are compared. The results are also compared with those obtained for the suspension’s system without the harvesters (Fig. 1). As shown in Fig. 5, the displacements’ ratios simulated when the suspension’s system is equipped with both harvesters (n = 1 and 40) are similar, i.e., two natural frequencies 1.45 Hz and 9.68 Hz corresponding respectively to the bouncing motion of (mS) and (mUs) are observed. In addition, a negligeable difference (about 0.003) is observed at the first resonant frequency. This demonstrates that connecting a piezoelectric stack in series with the suspension’s spring doesn’t affect the suspensions’ dynamics. Figure 5. Suspension’s displacement ratios zS / z and zUs / z without and with the piezoelectric harvester (n=1 and n= 40). 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 44 3.2. Martin’s Model Martin demonstrated that a piezoelectric stack composed of (n) identical elements can be modeled as a single piezoelectric element with a total length L = nT (T: individual thickness) and considering modified parameters [10]. The equivalent circuit is almost similar to Mason’s one. The main difference is that, Martin’s circuit does not contain the negative capacitance -C0. Also, the wave velocity in the stack is determined by the short elastic constant 𝑆33 𝐸 instead of the open circuit elastic constant 𝑆33 𝐷. In this case, the stack’s capacitance C0 can be computed as following: 𝐶0=𝑛⁡𝜀33 𝑇(1−𝑘33 2)𝐴 𝐿/𝑛 (5) The impedances ZT and ZS can be computed using the following relations: 𝑍𝑇=𝑗𝑍0𝑒tan⁡(𝜉𝐿 2) (6) 𝑍𝑆=−𝑗𝑍0𝑒 sin⁡(𝜉𝐿) (7) The transformation ratio N can be calculated using the following relation: 𝑁=⁡ 𝐴⁡𝑑33 (𝐿/𝑛)⁡𝑆33 𝐸 (8) To evaluate the collected voltage and power, Martin’s circuit is associated to the suspension’s equivalent circuit and simulations are done in the frequency domain (0.01 Hz – 14 Hz) using LTspice software. As shown in Fig. 6, the results obtained are compared to those computed using Mason’s accurate model given in Fig. 4 (stack Mason’s model) and Mason’s simplified model explained in details in ref. [4]. It is clearly observed that, the voltage and power curves computed using Martin’s circuit and Mason’s accurate model (stack Mason’s model) are similar: maximum harvested voltage and power (about 25.12 V and 63.09 mW) are obtained the first resonance (about 1.45 Hz). Nevertheless, Martin’s model is simple and easy to implement in LTspice software particularly for stacks with numerous piezoelectric layers. Furthermore, the comparison of the results with those obtained in ref. [4] using Mason’s simplified model show some difference. In this case, the maximum voltage and power are 25.74 V and 66.23 mW, i.e., voltage and power difference are about 2.47 % and 4.98 % respectively. The difference increases when the harvester’s losses are not taken into account: maximum voltage and power are 25.92 V and 67.17 mW respectively (difference percentages about 3.18 % and 6.47 %). It is important to notice that, the difference is also dependent on the considered resistive load (R). In this work R = 10 k, which not the optimal value allowing the maximum values of voltage and power. Consequently, the difference between the results of Mason’s accurate model (also Martin’s model) and its simplified model can become more significant for other resistive loads particularly at the optimal resistance (Ropt) giving the maximum voltage and power. (a) (b) Figure 6. Voltage and power computed using Mason’s (simplified and accurate) and Martin’s models: voltage (a), power (b). 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 45 4. CONCLUSION This paper presented two equivalent circuits to evaluate the harvestable vibrations’ energy from cars’ suspensions using a piezoelectric stack located in series with the suspension’s shock absorber. Simulations performed for a stack composed of 40 layers showed similar voltage and power curves for Martin’s circuit and Mason’s accurate model. It is also found that, maximum voltage and power are obtained at the first resonant frequency: 25.12 V and 63.09 mW. Finally, Mason’s simplified model showed differences of about 2.47 % and 4.98 % of voltage and power in comparison with the two models. In future work, to optimize the energy harvesting capabilities, Martin’s circuit will be used to perform a parametric study about the effect of several quantities: number of layers, harvester’s length, and load resistor. REFERENCES [1] Al-Yafeai, D, Darabseh, T, Mourad, A. A state-of-the-art review of car suspension-based piezoelectric energy harvesting systems. Energies 2020, 13, 2336. https://doi.org/10.3390/en13092336 [2] Xiao, H, Wang, X, John, S. A dimensionless analysis of a 2DOF piezoelectric vibration energy harvester. Mechanical Systems and Signal Processing 2015, 58-59, 355–375. https://doi.org/10.1016/j.ymssp.2014.12.008 [3] Benhiba, A, Bybi, A, Alla, R, Drissi, H. Investigation of vibrations energy harvesting from passive car suspension using quarter car model under bump excitation, E3S Web of Conferences 2022, 336, 00053. https://doi.org/10.1051/e3sconf/202233600053 [4] Bybi, A, Benhiba, Lahlouh, I, Ammar, A, Et-Tahir, A, Drissi, H. Investigation of vibrations energy harvesting from vehicle suspension system and modeling using electrical equivalent circuits. In: ICECCME 2023, 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering; 19-21July 2023, Tenerife, Canary Islands, Spain, pp. 1-6 [5] Lafarge, B, Delebarre, C, Grondel, S, Curea, O, Hacala, A. Analysis and Optimization of a Piezoelectric Harvester on a Car Damper. Physics Procedia 2015, 70, 970 - 973. https://doi.org/10.1016/j.phpro.2015.08.202 [6] Carlos de Carvalho Pereira, J. Energy Harvesting Prediction from Piezoelectric Materials with a Dynamic System Model. In: Huang Hu, Jianping Lin Editors, Piezoelectric Actuators, IntechOpen, 2021. [7] Lopez-Martinez, J, Martinez, J. C, Garcia-Vallejo, D, Alcayde, A, Montoya, F.G. A new electromechanical analogy approach based on electrostatic coupling for vertical dynamic analysis of planar vehicle models. IEEE Access 2021, 9, 119492– 119502 [8] Powell, D. J, Wojcik, G. L, Desilets C. S, Gururaja T. R, Guggenberger, K, Sherrit, S, Mukherjee B. K. Incremental "modelbuild-test" validation exercise for a 1-D biomedical ultrasonic imaging array. In: Proc. IEEE Ultrason. Symp.; 05-08 October 1997, Toronto, ON, Canada, pp. 1669-1674 [9] Sherrit, S, Lee, H. J, Bao, X, Badescu, M, Bar-Cohen, Y. Composite piezoelectric resonator 1D modeling with loss. Behavior and Mechanics of Multifunctional Materials IX 2020, 11377, 113770T [10] Thompson, S. C, Meyer, R. J, Markley, D. C. Performance of transducers with segmented piezoelectric stacks using materials with high electromechanical coupling coefficient. In: Proceedings of Meetings on Acoustics 2013; 2-7 June 2013, Montreal, Canada, 19(1), pp. 030021 52 Use of Wind and Solar Systems to Minimise The Environmental Impact of Domestic Energy Users Ewen Constant University of South Wales, Cardiff, Wales, [email protected], ORCID: 0000-0001-5951-6421 Cite this paper as: Constant, Ewen.Use of Wind and Solar Systems to Minimise The Environmental Impact of Domestic Energy Users. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain. Abstract: The premise of this research is to investigate how to reduce the carbon footprint and minimise the cost of energy to domestic users in the UK, we look specifically at a town in South Wales, and compare three methods to determine both the most cost-effective system, and also how a typical domestic energy user can reduce their carbon footprint by embracing sustainable energy systems. We investigated this using a 4kW Solar PV system and a 1kW wind turbine. We show that during a significant period of the year, this hybrid system can satisfy the needs of a typical three-bedroom home. We supplement the investigation with the use of a battery storage system to support the hybrid generation and extend the system's capability in relation to self-sufficiency. However, due to the climate in the UK, the research found that we had to remain grid-tied to ensure an electricity supply during the darker winter months when irradiance is in limited supply. Keywords: Domestic renewable energy systems, wind and solar generation, domestic energy consumption. © 2024 Published by ECRES 1. INTRODUCTION An adequate supply of energy is essential for the functioning and development of any modern society. It is the life force that powers the machinery of the world, enabling economies to flourish, communities to thrive, and enhancing the quality of life. Within the last 200 years, the primary source of energy has been fossil fuel resources that took millions of years to accumulate and have been harnessed for a relatively short period of time. Despite global efforts to curb energy consumption and transition to more sustainable practices, the ever-increasing global population, coupled with rising affluence, continues to place immense pressure on the planet's finite fossil fuel resources. This has led to a critical need to explore and adopt alternative, cleaner sources of energy to meet the growing global energy demands. In response to the mounting concerns regarding climate change, environmental degradation, and the depletion of finite fossil fuel reserves, the global energy landscape is undergoing a remarkable transformation. A major shift is taking place, characterised by a growing emphasis on clean and renewable energy sources. According to the annual report published by the International Energy Agency (IEA) in 2023, investment in clean energy technologies has witnessed a significant and promising growth rate. Notably, it has surpassed investments in traditional fossil fuels, signifying a substantial shift in the energy investment paradigm. This is a significant change compared to five years ago when the ratio of investment in fossil fuels to clean energy was at a near equilibrium of 1:1. Remarkably, in the current landscape, for every 1 USD spent on fossil fuels, there has been a corresponding increase in spending on clean energy, amounting to 1.7 USD. This transformation underscores the global commitment to transitioning away from conventional fossil fuels in favour of more sustainable and environmentally friendly alternatives. This work investigates a bottom-up approach to providing solutions to these issues, with growing concern over climate change and individual carbon footprint, and without any direct government interaction at an end-user level, many individuals are seeking ways to reduce their individual footprints and reduce CO2 emissions, this desire to net zero at a micro-level is also being realised due to the need to transition to EV vehicles in the coming decade. It can be shown that domestic CO2 consumption can be reduced by 55% [1] Amidst this transformative energy landscape, the imperative to explore and implement sustainable energy alternatives has never been greater. Among these alternatives, wind and solar power have risen as prominent 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 53 contributors to the renewable energy mix [2,3]. The abundant wind resources within the UK and the increasing affordability and efficiency of solar photovoltaic (PV) technology have made these sources increasingly attractive for both large-scale energy generation but more importantly, small-scale localised domestic applications. 2. METHODOLOGY To fulfil the energy requirements of a typical three-bedroom house, this research incorporates both solar and wind energy sources. Hourly, Daily, and monthly solar irradiation data and wind speed measurements were obtained specifically for the chosen site in Saint Athan located in South Wales, UK. The geographic coordinates of the site were recorded as 51.40⁰ latitude and 03.44⁰ longitude. In this study, daily wind speed data for the past six years (from 2017 to 2022) was collected from the UK Meteorological Office to assess the wind resource potential at the project location in South Wales Solar irradiation data was acquired from the PVGIS tool, provided by the European Commission's Joint Research Centre (JRC). Daily and monthly mean values of global irradiation data covering the period from 2005 to 2020 were collected to ensure a robust dataset for modelling the solar resource. A tilt angle of 35 degrees was adopted, as this is the optimal angle for locations on this latitude. To formulate the energy consumption profile of a standard three-bedroom house, the dataset furnished by The UK Energy Research Centre has been employed. This dataset serves as the foundation for modelling the household's energy demand patterns which is integral to the comprehensive analysis of the residence's power usage dynamics. A three-bedroom home was chosen, as this is the most typical house in the UK, with approximately 44% of the UK population living in 3 bedroom homes, as shown in figure 1. Figure 1 UK housing stock 2021 [4] The author's previous research [5,6,7] has detailed the amount of solar irradiance energy that can be captured by a typical 4kW system installed on a rooftop in the UK, and the solar irradiance captured ‘v’ demand profile can be seen below in the graph. What the previous research has shown is that for significant periods of the day and the year, is that the irradiance is capable of satisfying the needs of a domestic household for large periods of the year, However, during the winter months even though surplus energy is produced during the daylight hours, there is not enough energy to support the home at peak times in the mornings and evenings. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 54 Figure 2 Demand and generation profiles [6] The previous research also highlights the demand profiles taken from the UK Energy Resource Centre (UKERC), and these are split into five seasons, and shown as an average in figure 2. The outcome of the previous research showed an annual demand of approximately 4,000kW/h needed to be satisfied. This has assumed a fixed plane with an Azimuth of 0° degrees (𝛼)⁡facing south, with a slope incline of 35°(β). The raw global irradiation (Gi) data has a unit of w/𝑚2, therefore the total area of the panel system (Spv*N) can be multiplied by the Gi data to give our values. The data was then adjusted to account for the efficiency losses (ζs) from the solar panels and losses from the inverter and associated systems (K). Thus, the total usable irradiance (G) that can be collected from a specific location is: 𝐺=𝛼.𝛽.𝐺𝑖.𝑆𝑝𝑣.𝑁.𝜁𝑠.𝐾 1000 ⁡ (1) It is clear we cannot support the household during the winter months which has led us to think of alternative strategies for filling the energy gap, the most obvious and cost-effective solution being a small-scale wind turbine. Considering the available systems including factors such as cost and market availability the Airforce 1 HAWT with three blades has been selected for future calculations (see Appendix: 1). The Wind turbine's rated power output is 1kW, the swept area (A) is 2.54m2, cut-in wind speed (VI) is 3.5m/s, rated wind speed (VR) is 12.5m/s, and the cut-off wind speed (VO) is 52m/s. To evaluate the power output for the mean wind speed in each month, equation (2) was utilised. Additionally, the calculation incorporated the combined effect of temperature and pressure on the air density (ρ) at the specified altitude, thereby the following equation has been used to calculate the air density. ρ = ρ0 – 1.194 x 10-4 x Hm (2) Here, ρ0 represents the air density at sea level, equivalent to one atmospheric pressure and 60⁰F, with a value of 1.225 kg/m³. The site elevation (Hm) was recorded as 49m. Hence, the actual air density at hub height can be calculated as 1.2191kg/m3. The power (PW in W) available in the wind passing through a given area / swept area (A in m2) is dependent on the air density (ρ in kg/m3) and the velocity of the wind (v in m/s); which can be described by the following equation (3). Pw = ½ ρAv3 (3) In real-world scenarios, the actual energy generation of a wind turbine is affected by its efficiency and various losses. To achieve a more precise calculation of the wind turbine's practical energy output, those factors have been considered. The Betz limit, representing the theoretical maximum efficiency of the turbine, is set at 59.3%. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 55 Additional considerations include mechanical losses resulting from gearbox and blade inefficiencies, which amount to 0.3%, and electrical losses within the turbine itself estimated at 1.5%. Furthermore, electrical losses during grid connection and transmission are projected to be around 5%. Downtime losses due to maintenance and failures account for 3% of the turbine's operation time. As the wind turbine is a domestic-sized one, wake losses are negligible. 3. RESULTS AND DISCUSSIONS Based on the data collected, a descriptive analysis of the wind speed characteristics at the project location reveals valuable insights. The mean wind speed was calculated to be 5m/s, indicating the daily average wind speed observed over the data collection period. The standard deviation of 2m/s signifies a moderate variability in wind speeds, suggesting slight fluctuations in the wind resource. The range of the dataset has been determined as 13m/s while the maximum wind speed was 14m/s, indicating the occurrence of strong wind events, while the minimum wind speed was 1m/s, representing relatively calm wind conditions. Figure 3. Average wind speeds over a six-year period. Figure (3) shows average wind speeds over a six-year period. What is surprising is that in this region of the UK, we are not seeing a high level of variation in the wind profile, although the data omits the peak wind speeds during these times for the purposes of analysis, a more pronounced profile of the wind gusts were seen during the winter months. The important factor for us is the turbine startup speed, which is approximately 3.5m/s, as we can see in figure (4) below, frequency analysis of wind speed, we have a significant proportion of our profile above the critical 3.5m/s start-up speed. The estimated time the turbine would be spinning is around 75%. In terms of energy production, we can now see an overlay between wind speed and energy production, figure (5). Figure 4. Wind speed distribution curve. The analysis shows, that as expected the windiest month, February, has the capability to [p9]produce the highest energy production, with an output of approximately 110kWh, showcasing the substantial potential for robust energy generation during periods of elevated wind speeds. Conversely, April registers the lowest production, around 47kWh. Interestingly, from April to September, energy production remains relatively stable, ranging between 45 to 55kWh. The data illustrates the feasibility of harnessing wind energy in our region. This maximum energy 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 56 generation during the winter months, is aligned with our hypothesis and would supplement a solar system ideally, as wind has the potential to produce energy 24 hours a day. Based on the profile of demand shown in figure (5) it is clear that whilst the system has the potential to produce all the required energy for large portions of the year, the time of day that the energy is produced does not match the demand profiles, a typical demand profile for a summer day is shown below, figure (5) this is an overlay of demand ‘v’ supply over a 24 hour period. Figure 5. Energy production ‘v’ demand profiles. It can be seen that whilst we have the potential to supply all the energy required, it is not being produced at the time it is required. This is a typical drawback of the stochastic nature of wind and solar energy. The solution of course is to supply a battery pack to the system. 4. BATTERY SELECTION The energy calculations for the five periods are shown below in table (1) Table 1 Energy demands in various periods of the year. According to table (2), during seasons of ample energy production spring, summer, and high summer, there's a notable surplus of energy, ranging from 38.3% to 50.9%. This surplus underscores the importance of a battery energy storage system (BESS), which can efficiently store this excess energy for later use during peak demand periods, cloudy days, or nighttime hours. Conversely, during the winter season, there's a deficit of -15.8%, indicating a shortfall in renewable energy generation compared to demand. Considering significantly low energy production during the autumn and winter seasons, with values of 0.327 kWh and -1.816 kWh, respectively, it is reasonable to disregard these two seasons for the purpose of calculating excess energy storage requirements, as to include them would produce a requirement for a battery of such size, that the costs would be prohibitive. Focusing on the more productive spring, summer, and high summer seasons, the average daily excess energy production 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 57 across these three seasons can be determined as 7.702 kWh per day. Further, assuming that the battery operates with an efficiency of 90% and has a depth of discharge (DoD) of 90%, battery capacity can be calculated as follows: Total Battery Capacity = Average Excess Energy Production per Day (Efficiency ∗ DoD) (4) Thus total battery storage energy required = 7.702 0.90∗0.90 = 9.5 kWh. (5) There has been much research into suitable battery architecture for this type of application, [7,8,9] and it became clear in the early 2020s that Li-Ion had overtaken Pb acid as the preferred choice of BESS. Considering the price and availability in the UK market, we selected a 9.5kWh, (LiFePO4) lithium-ion battery. 5. ENERGY REQUIREMENTS The premise of this research is to minimise the carbon footprint of domestic users, this can be achieved by minimising the energy taken from the grid. The demand (D) of the household minus the solar (G)(1) plus wind energy (Pw)(3) will leave us with the stored (BE) or grid-fed (Gf) energy requirement. D – (G+Pw) = Energy requirement. (6) To determine the energy requirements, we have taken readings at 30-minute intervals and calculated the average requirements per month. Take January as an example: demand per day = 12.49kW, max production 6.08kW leaving a shortfall of 6.42kW per day, for the whole year, shown per calendar month, the output is shown in table [2] Table 2: Surplus ‘v’ deficit energy production capability. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ave daily demand (kW) 12.5 12.8 12.1 9.9 9.2 9.1 9.2 8.9 10.0 10.2 12.1 12.4 Daily Overall energy situation (kW) -6.4 -3.2 1.3 6.5 8.5 9.0 8.2 6.6 3.8 0.2 -5.2 -6.5 Monthly totals (kW) -199 -88 -156 -201 Monthly totals (kW) 41 194 264 269 255 206 115 7 If we sum the shortfall we have 644.48kW of energy that our system cannot support in the winter period, we have no option other than to buy this energy from the grid, this means that our system will not be self-sufficient and we will have to remain grid-tied, for most domestic energy users, this would be the preferred position, as blackout’s due to lack of energy would be unacceptable. However, being grid-tied means that we have to think about the standing charges that we will incur when we look at a cost model. During the remaining eight months of the year, however, we have an excess of energy and as we are grid-tied, we investigated using a Feed-in tariff to offset the cost of the grid tie. Our excess energy is 1,351kW and in terms of our energy supply, we are now a net producer of energy and the objective of the research to minimise our carbon footprint by using renewable energy sources can be fulfilled under these conditions. The actual figures regarding carbon neutrality are detailed here. The current UK electricity carbon factor as published by the Department for Environment, Food & Rural Affairs (DEFRA) is 0.233kgCO2e per kWh of electricity consumed on average from all sources [10]. Based on a consumption of 4,000kWh this results in a CO2e of 932kg. Also, the United Nations (UN) Intergovernmental Panel on Climate Change (IPCC) has provided a median value among peer-reviewed studies of CO2 emissions, [11] This data shows a life cycle analysis of CO2 emissions from a variety of production processes. Where Solar = 41g/kWh and Wind = 11g/kWh which results in a total CO2 figure of 208kgCO2e/annum, and a lifetime saving (15 years) of 10,860kg of CO2e. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 58 The reduction in carbon footprint is therefore in the region of 75% over the lifetime, but as the majority of CO2 is produced during the manufacturing processes it is estimated that after three years solar panels are carbon neutral and five years for micro wind turbines. 6. COST ANALYSIS Table 3: Cost analysis Description Cost £ (GBP) Turbine kit 3295 Turbine Tower kit 800 Turbine Installation costs 600 Solar System, including fitting and battery installation 5595 Battery System 3175 Total 13,465 The final part of this research focusses on the cost analysis. As well as environmental factors, this proposition has to be favourable to domestic energy users, as such a full financial appraisal has been carried out. Table (3) highlights these costs. Payback Period = Capital Cost or Initial Investment / Annual Cost Saving, with a total capital investment of £13,465 We can refer to table (2) for our daily and monthly totals of energy ±, thus we can deduce that for the period November to February, we have a shortfall of 644.48kW of energy, which needs to be purchased. Between March and October, we have excess energy of 1351.26kW which can be sold to the grid. Thus our total annual energy bill (EB) will be the Annual deficit of energy (AD) multiplied by the cost of energy from the grid (EG) plus the standing charge (SC) minus the annual excess energy produced (AE) multiplied by the feed-in tariff (FIT). EB = AD EG + SC – AE FIT EB = 644.46 x 0.3+0.53 x 365 – 1351.26 x 0.15 EB = £184.11p this is the theoretical cost of energy for a year in the UK, however, this is under idealised conditions. EB without any self generation = 4000kWh’s at £0.3/kW + 0.53 x 365 = £1393.45p Thus, our potential savings equate to 1393.45-184.11 = £1,209.34/year without considering inflation this would give a payback period of: Payback period = 13465/1209.34 = 11.13 years. Adjusted for inflation (2% per annum) this would reduce to 10 years. Price details are correct as of Nov 2023 via the UK's governing body which sets energy prices, Ofgem. The electricity cost is £0.30 per kWh and the daily standing charge is £0.53p. In the UK, the average amount of time people live in a home is just over 20 years, and as such this is a reasonable opportunity for most domestic consumers. 7. CONCLUDING REMARKS The premise of this research was to understand if it is feasible to install a small-scale wind and solar system into a domestic environment, to reduce both the cost to the consumer and more importantly to reduce a household's individual footprint. Whilst the payback period is certainly on the high side, it is important to remember that we have a significant saving in CO2 as well 10,860kg. As such this is a viable and attractive proposition to domestic 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 59 users in the UK, who want to reduce their carbon footprint, but also reduce the financial strain on the household over an extended period of time. ACKNOWLEDGMENT I would like to thank the University of South Wales for allowing the time and resources to complete the research activities, and my co-Author Kasun Lakshitha for his tireless work in support of this research. REFERENCES [1] Yatarkalkmaz MM et al, “The calculation of greenhouse gas emissions of a family and projections for emission reduction”. Journan of Energy Systems. 2019: vol 3: Pages 96-110. doi.org/10.30521/jes.566516 [2] Uğurlu A, et al, “A case study of PV-Wind-Diesel-Battery hybrid system”. Journal of Energy Systems 2017; vol 1(4):Pages 138-147 DOI: 10.30521/jes.348335 [3] A. Qazi et al., "Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions," in IEEE Access, vol. 7, Pages 63837-63851, 2019, doi: 10.1109/ACCESS.2019.2906402 [4] UK Housing stock: https://www.gov.uk/government/statistics/council-tax-stock-of-properties-2021/council-tax-stock-of-propertiesstatistical-summary: Council Tax: stock of properties Statistical Summary, September 2021: Accessed November 2023. [5] Constant E, Richardson J, Feasibility Study of a Sustainable Roof Top Domestic Solar Energy System in the UK. Pending publication from ICREC November 2023 [6] Constant, E., Thanapalan, K., Bowkett, M. Development of a solar-powered self-sustainable energy system. Renewable Energy and Power Quality Journal, Vol 16, 299, 2018. [7] Constant, E., Thanapalan, K., Bowkett, M. Capture and storage of PV-Energy for domestic consumption. In the Proceedings of the 5th IEEE International Conference on Control, Decision and Information Technologies (CoDIT’18), Thessaloniki, Greece, April 10 -13, 2018. [8] Energy Storage Data Reporting in Perspective—Guidelines for Interpreting the Performance of Electrochemical Energy Storage Systems Tyler S. Mathis, Narendra Kurra, Xuehang Wang, David Pinto, Patrice Simon, Yury Gogotsi Advanced Energy Materials First published: 04 September 2019 [9] Naciri, M, Aggour, M, Ait Ahmed, W . “Wind energy storage by pumped hydro station”. Journal of Energy Systems 2017; vol 1: Pages 32-42 http://dergipark.gov.tr/jes/issue/30882/329315 [10] DEFRA UK Government GHG Conversion Factors for Company Reporting (2020) – DEFRA accessed 16/11/2023 [11] Steffen Schlömer (ed.), Technology-specific Cost and Performance Parameters, Annex III of Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2014) [12] Kasun Lakshitha: Feasibility Analysis of Wind and Solar PV Hybrid Systems for Meeting Energy Demands in a Typical Three-Bedroom House in South Wales-UK: MSc Student at University of South Wales, Wales 2023. 60 Startup Concept for The Operation of A Regional AI-Based Marketplace for Renewable Energies Holzinger Jonas Aalen University, Aalen, Germany, [email protected], ORCID: 0009-0000-8668-3217 Nagl Anna Aalen University, Aalen, Germany, [email protected], ORCID: 0000-0001-7875-2403 Bozem Karlheinz bozem | consulting associates | munich, Munich, Germany, [email protected], ORCID: 0000-0002-4449-4108 Ensinger Andreas Ueberlandzentrale Woerth/I.-Altheim Netz AG, Landshut, Germany, [email protected] ORCID: 0000-0003-3965-0935 Roessler Jannik Aalen University, Aalen, Germany, [email protected], ORCID: 0009-0006-2541-7584 Neufeld Christina Aalen University, Aalen, Germany, [email protected], ORCD: 0009-0004-9034-7610 Lecon Carsten Aalen University, Aalen, Germany, [email protected], ORCID: 0009-0006-9390-0543 Cite this paper as: Jonas,H, Anna,N, Karlheinz,B, Andreas,E, Jannik, J, Christina, N, Carsten, L. Startup concept for the operation of a regional AI-based marketplace for renewable energies. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The first results of the research project ‘AI-assisted marketplace for regionally generated renewable sources of electricity (RES-E) based on dynamic pricing models (AI-REN marketplace)’ in the framework of the 'AI factory SME' show that a startup has a solid and promising basis. Producers and customers of regionally generated photovoltaic (PV) electricity benefit from the fact that artificial intelligence can be used to predict the production of PV electricity for the next day and to control demand using dynamic price models. In addition to the reliability of AI-based forecasts of PV electricity generation and demand, it is essential for this startup concept that supply and demand for PV electricity come from the region. This paper presents the development of an economically viable business model of a digital platform that uses PV electricity from regional producers and connects them directly to regional consumers offering dynamic pricing. Furthermore, the platform includes reservations for electric vehicle (EV) charging stations. Keywords: regional renewables marketplace, digital platform, business model, AI Factory SME, startup © 2024 Published by ECRES 1. INTRODUCTION The research project ‘AI-assisted marketplace for regionally generated renewable sources of electricity (RES-E) based on dynamic pricing models (AI-REN marketplace)’ is one of seven projects within the EU Lighthouse Project ‘AI Factory SME’. The goal of this research project is to develop innovative digital business models and an integrated AI-based platform for the Ostalb-district together with the companies ‘Autohaus Widmann’ (car dealership) and the ‘Spedition Brucker’, a logistics and freight-forwarding company, as well as ‘OstalbBürgerEnergie’ (a regional citizens’ energy cooperative for electricity from renewable energies). These business models consider trends such as digitisation, AI and renewables, which gain importance within the energy 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 61 industry and lead to the need to develop new business models and adjust current business models [1, 2]. The following research questions were pursued within this research project as the energy industry is changing from a monopolistic-like structure to a more liberal market structure with more small and local energy providers/producers [3]. What possibilities exist to bring together regionally generated renewable electricity and electric vehicle (EV) charging stations by local businesses and regional, environmentally conscious customers? Is this an idea for a startup company: the operation of a regional AI-based marketplace for renewable energies and e-vehicle charging? 2. THEORETICAL BACKGROUND OF DIGITAL PLATFORMS Generally, a digital platform can be categorised into the types of actors that offer and consume the product, which are a company, a private person, or the government [4, 5]. Within this platform, the electricity is to be mostly offered by local businesses and bought by private consumers. However, it is also possible to buy electricity for regional businesses and for private households to sell surplus electricity. EV charging stations will only be offered by local businesses. Platforms can be clustered into different types, which are data-based or transaction-based on their main source of revenue generation [4]: o Advertising platform (data based, e.g. Google, Meta) o Cloud platform (data based, e.g. Dropbox, Microsoft Azure) o Industrial platform (data based, e.g. Mindsphere, Adamos) o Product platform (transaction based, e.g. Amazon, Ebay) o Lean platform (transaction based, e.g. Flixbus, mytaxi) The platform of this business model is a product platform for the marketplace of regional renewable energy and a lean platform for the booking of EV charging stations, which means the revenue should be mainly transaction based. Platform-based business models offer different attributes that can be used to further optimise the business [6]: o High scalability towards an increasing number of users o Data Analytics leading to optimisation of the product and service o Lowering transaction fees with an increase in transaction volume o Strong network effects, with each consumer and provider benefiting from an increase in user numbers 3. METHODOLOGY AND APPROACH In order to create a robust startup concept, it is important to develop the underlying business model in a sound manner. There are different approaches for the development of business models, e.g. from Osterwalder und Pigneur [7], and Gassmann et al. [8] Nonetheless, these approaches only include some kind of checklist, which is predominantly based on theoretical approaches. Checklists and other development approaches do not take into account the solidity of a business idea and do not test the idea for its resistance. More than half of new business creations lead to failure. [9]. A major reason for failure is an unsustainable business idea. [10] That is why the socalled Business Model Builder was developed in order to compensate for precisely these deficits and to have a scientifically and practically sound basis for the development of the startup concept. The elements of the Business Model Builder are presented in Fig. 1. The Business model consists of three modules that build on each other. First, the general idea of the business is developed. Second, the business model itself is qualitatively elaborated. Finally, follows a detailed quantitative business case. All elements and modules of the Business Model Builder are created on top of each other within an iterative process [11]. This paper concentrates on the qualitative modules (module 1 and module 2) of the business model for the startup company. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 68 In [1] a floating double Boost converter is treated which combines two interleaved Boost converters, so the converter consists of four converter stages. [2] shows floating double Boost converters which combine cascaded Boost converters. In [3] triple interleaved Boost converters are combined to a dual Boost converter. [4] treats an improved floating interleaved Boost converter for photovoltaic systems which uses additional resonant circuits. In [5] an experimental validation of high-voltage-ratio low-input-current-ripple converters for hybrid fuel cell supercapacitor systems is treated, which uses the basic topology and additional two and three interleaved stages. [6] studies an extended state observer-based sliding-mode control for floating interleaved Boost converters. [7] uses a bidirectional structure to study an advanced robust noise suppression control for fuel cell electric vehicles. [8] treats an improved floating interleaved Boost converter with low-ripple input current for fuel cell applications. It uses tapped inductors and additional resonance circuits. The controller design and a fault tolerance analysis of a 4phase floating interleaved Boost converter for fuel cell electric vehicles is treated in [9]. [10] shows an experimental evaluation of an interleaved Boost topology, optimized for peak power tracking control in solar application. [11] applies the model predictive control to the floating interleaved Boost converter. [12] uses the floating Boost converter as supply to a DC micro-grid. [13] explains the development of a four phase floating interleaved Boost converter for photovoltaic systems. 2. STEP BY STEP EXPLANATION Graphical Analyses To get a clear understanding of the converter, we look at the voltages across the components (Fig.2), and start by drawing the voltages across the inductors. The signals are drawn for the steady-state in the continuous inductor current mode and for ideal components. The control signals are shifted by 180 degrees and have a duty cycle of one third. When S1 is turned on, the input voltage U1 is across the first coil L1 and when the switch turns off, the current commutates into the diode D1, and the voltage across the coil is now the input voltage minus the voltage across C1. When the switch S2 is on, U1 is across the coil L2 and when it is off, the difference between input voltage and output voltage is across the inductor L2. The mean value of the voltage across the inductors must be zero. Arbitrarily we start with an input voltage of two divisions. From these signals all other signals can be drawn. We can now draw the voltages at the capacitors and also the input voltage. From Fig. 1 and with Kirchhoff’s voltage law (KVL) one an immediately see that the output voltage U2 is equal to the sum of the output voltages of the two Boost converters minus the input voltage. The output voltage must have four divisions. Now we look at the active switches. When the switches are on, no voltage is across them and when the switches are off, the voltage across the respective capacitor stresses the active switches. Next we look at the passive switches (diodes). When the diodes conduct, the voltage across them is zero and when they are blocking, that is during the on-time of the active switch, the negative voltage across the capacitor stresses the diode. Figure 2. Voltages across the coils, the voltage across the capacitors and the input voltage; the voltages across the active switches, the voltages across the diodes. To construct the currents (Fig. 3) through the components we use as starting point the load current and choose arbitrarily two divisions. When the active switch of the respective converter part is on, the capacitor has to supply the load. When the active switch is off, the current through the inductor is fed via the diode to the output and the capacitor has to take the surplus of the current. First one draws the mean values of the currents through the inductors (the current through the capacitor must be zero in the mean) and afterward refines it with the ripple of the inductor 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 69 currents. The current through the coils increases when a positive voltage is across them and decreases, when a negative voltage is across them. Now one can look at the currents through the semiconductors. When the switch or the diode is on, the current through the inductors flows through the semiconductors and when the switch or the diode is blocking, no current flows through them. At last we construct the input current. The frequency of the AC component is double the switching frequency of the converters because of the phase shift between the control signals. The ripple is also reduced. Figure 3. currents through C1 and L1, currents through C2 and L2, currents through first switch and diode, currents through second switch and diode. Fig. 4 shows the input current, the currents through the coils, the load current, the output voltage, the input voltage and the control signals. The parameters for the converter are L1=L2=47 µH, C1=C2=330 µF, R=12.5 Ω. Figure 4. FDBC in steady state up to down: input current (deep violet), current through S1 (dark blue); current through D1 (grey), current through D2 (dark green); current through L2 (violet), current through L1 (red), load current (brown); output voltage (green), input voltage (blue), control signal of S1 (turquoise), control signal of S2 (black). Basic Calculations After the graphical analyses of the converter we make some simple calculations. The voltage-time balance of the coils (index i is 1 or 2, naming the converter part) can be written according to   iCiidUUdU  1 11 (1) which leads to the voltage across the capacitor Ci 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 70 1 1 1U d U i Ci   . (2) The output voltage is 1 21 2112 1 1 1 1 1U dd UUUU CC             . (3) When both duty cycles are equal (this is to be preferred because the components are stressed equally), this leads to the voltage transformation ratio d d U U M   1 1 1 2 . (4) To get the connections between the currents one must again look at the currents through the capacitors. The charge balance of the capacitors must be )1()(_ iLOAD Li iLOAD dIIdI  . (5) Therefore, one gets for the mean value of the inductor current i LOAD Li d I I 1 _ . (6) The mean value of the input current is the mean values of the inductor currents reduced by the load current. For equal duty cycles one gets d d I d d II d I ILOADLOADLOAD LOAD IN        1 1 1 12 1 2 _ leading to d d I I LOAD IN   1 1 _ . (7) One can see that the input power is equal to the output power, as it should be in an ideal converter. 3. MODEL OF THE CONVERTER Both Boost converters can be treated separately. In mode M1, the switch is on and the diode is off, and one gets for the change of the currents through the coils i Li L u dt di 1  . (8) The current through the capacitor is equal to the negative load current. The load current can be calculated with Ohm’s law to R uuu iCC LOAD 121   . (9) The state equation for the capacitors can therefore be written according to 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 71   i CCCi C Ruuu dt du / 121   . (10) The state equations for the second mode M2 (active switch off, diode on) are i CiLi L uu dt di  1   i CCLiCi C Ruuui dt du / 121   . (11) One can write these four equations into one matrix differential equation for mode M1 and M2   1 2 1 2 1 2 1 2 1 22 11 2 1 2 1 1 1 1 1 11 00 11 00 0000 0000 u RC RC L L u u i i RCRC RCRC u u i i dt d C C L L C C L L                                                                         ,   1 2 1 2 1 2 1 2 1 222 111 2 1 2 1 2 1 1 1 1 1 111 0 11 0 1 1 000 0 1 00 u RC RC L L u u i i RCRCC RCRCC L L u u i i dt d C C L L C C L L                                                                               . (12, 13) When the switching period is small compared to the time constants of the converter, one can weight (12) with the duty cycle and (13) with one minus the duty cycle leading to   1 2 1 2 1 2 1 2 1 222 2 111 1 2 2 1 1 2 1 2 1 1 1 1 1 111 0 11 0 1 1 000 0 1 00 u RC RC L L u u i i RCRCC dRCRCC dL d L d u u i i dt d C C L L C C L L                                                                                 . (14) This model is nonlinear. To derive transfer functions and to construct Bode plots one has to linearize the matrix equation. This can be done by the perturbation method. The variables are written as combinations of the operating point value (written by capital letters with a zero in the index) and the perturbation of the operating point (written by small letters and a roof on top). For the operating point one gets the equations   01 10110  UUD C ,   01 10120  UUD C , 0)1( 102010 1010  R U R U R U ID CC L , 0)1( 1020  R I ID LOAD L . (15) It is easy to see that the results for the operating point values are equal to the results derived from inspecting the circuit in paragraph 2. The small signal model can be written according to                                                                                                                2 1 1 2 20 2 1 10 1 2 20 2 1 10 1 2 1 2 1 222 20 111 10 2 20 1 10 2 1 2 1 0 1 0 1 0 1 0 1 111 0 11 0 1 1 000 0 1 00 d d u C I RC C I RC L U L L U L u u i i RCRCC DRCRCC DL D L D u u i i dt d L L C C C C L L C C L L . (16) 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 72 With abbreviations for the elements of the state matrix and the input matrix and using the Laplace transformation one gets the transfer function between the output voltage and the duty cycles     423124134431134233244433311343344224 2 4433 34 1242312412443132422412313244 2 32 3 1 1 )()()( )( )( AAAAAAAAAAsAAAAAAAAsAAss BAAABAABAAsBABAsBs sD sUC    (17)   423124134431134233244433311343344224 2 4433 34 2342344334 2 1 )()()( )( )( AAAAAAAAAAsAAAAAAAAsAAss BAAsBAs sD sUC    (18) This model is useful when the parameters of the converter stages differ. It should not be used when the values of the components are equal in both Boost parts of the converter. When the converter is built symmetrically, it is easier to use a second order model for on capacitor and multiply the result for the output voltage and subtract the input voltage. In this case one gets the simple model from (8-12)                                                               d u C I RC L U L u i RCC DL D u i dt d L C C L C L1 0 0 0 0 1 1 2 1 1 0 78920.228485.4 95465.147212.1 )( )( 2 211222 2122122 eess ese AAsAs BAsB sD sUC       (19) Where the working point parameters are D0=0.33, U10=24 V, R=12.5 Ω, U20=47.6 V, UC0=35.8 V, ILOAD=3.8 A, IL0=5.68 A, calculated with ideal working point equations. Fig. 5 shows the Bode plot and the step response for one converter part. The transfer function describes a non-phase minimum system. The zero is on the right side of the complex plane. The zero can be calculated according to 49849.8 1 1 0 0 0 0 00 22 1221 e L D I U C IL U C D B BA s L C L C Z      , zero frequency at 14.3 kHz. (20) The larger the inductor value, the greater the influence of the zero. To reduce the influence of the zero the frequency should be high, so a small inductor is enough. The position of the poles can be found at 2112 2 22222,1 AAAsAs PP  , poles at -2.4243e2 +-j5.3723e3; pole frequency at 855 Hz (21) The DC-gain is 35 dB 56.2, and changing the duty cycle by 0.01 changes the capacitor voltage by 56.2 mV. The damping is caused by RC A2 22   . A small load resistor (high load) damps the system, also a small capacitor. The Bode plot and the step response are shown in Fig. 5. Figure 5. Bode plot (solid line: gain response, dotted line: phase response), step response. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 73 Fig. 6 shows the start-up of the converter and the transients caused by changes of the duty cycle. The steps are small, six or three percent up and down. To avoid large current amplitudes during the transient, one must use a ramp to change the duty cycle instead of steps. Figure 6. Floating double Boost converter, duty cycle steps, up to down: current through L1 (red); output voltage (turquoise), voltage at the output of the first converter stage (green), input voltage (blue). 4. INRUSH CURRENT The inrush depends on the resonances of the two Boost converters. The peak current depends on the slope of the input voltage, when it is turned on. For a step (and omitting the converter losses and the load) the current is a positive half-wave (when the current reaches zero, the diode turns off). The inrush current can be calculated and the idealized input peak current for the model parameters (for a single stage) and the time for the peak can be found by         t LC U L C i1 sin2 1 , A6.6321 1 U L C I and μs391 1 CLT  , respectively. (22) With an additional input transistor SIN1 which starts with a duty cycle from zero and increases to one by a ramp function, the current into the converter can be reduced and controlled. This additional transistor can also be used as a fuse to turn off the converter very fast in the case of a short-circuit, overcurrent or overheat. Between the drain of the transistor SIN1 and the anode of the additional diode DIN, an input capacitor has to be connected. Fig. 7 shows the circuit diagram of the pre-stage, and the current through one coil, the output voltage, the voltages across the two capacitors, and the input voltage during such a turn on. When SIN1 is turned on, the positive input voltage UIN is across the coils and when SIN1 is turned off, the current free-wheels through DIN. Therefore, SIN2 has to be turned on during the turn-on procedure. When the converter is working, SIN2 must be off and SIN1 must be on all the time. When the pre-stage must work as fuse, SIN2 must be on, and SIN1 must be off. (a) 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 74 (b) Figure 7. Additional inrush current reduction pre-stage, turn-on transient (with load), up to down: current through the first coil (red); output voltage (dark green); voltage across the second capacitor(turquoise); input voltage (blue), voltage across first capacitor (green). 5. CONCLUSION The floating double Boost converter has some interesting features:  voltage transformation ratio (1+d)/(1-d),  the processed power is parted into two converters, so the losses and the produced heat are better distributed,  it can be easily combined with an additional two switch-diode pre-stage to avoid the inrush and which can be used as a fuse,  both converters should be controlled by the same duty cycle, the system to be controlled can so be simplified to a second order system,  the ripple of the input is reduced compared to a single Boost converter. The converter is useful for solar and fuel-cell applications. REFERENCES [1] Simões, M G, Lute, C L, Alsaleem, A N, Brandao, D I, Pomilio, J A. Bidirectional floating interleaved buck-boost DC-DC converter applied to residential PV power systems. in 2015 Clemson University Power Systems Conference (PSC), Clemson, SC, USA, 2015, pp. 1-8. doi: 10.1109/PSC.2015.7101675. [2] Coutellier, D, Agelidis, V G, Choi, S. Experimental verification of floating-output interleaved-input DC-DC high-gain transformer-less, converter topologies." 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, 2008, pp. 562-568. doi: 10.1109/PESC.2008.4591989. [3] Kabalo, M, Blunier, B, Bouquain, D, Miraoui, A. Comparison analysis of high voltage ratio low input current ripple floating interleaving boost converters for fuel cell applications. 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, 2011, pp. 1-6. doi: 10.1109/VPPC.2011.6043101. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 75 [4] Miranda, M, Banakar, P, Gunnal, G., Kiran Kumar, V. Robust Voltage Control of Improved Floating Interleaved Boost Converter for Photovoltaic Systems. 2020 5th International Conference on Computing, Communication and Security (ICCCS), Patna, India, 2020, pp. 1-5. doi: 10.1109/ICCCS49678.2020.9276866. [5] Kabalo, M, Paire, D, Blunier, B,. Bouquain, D M, Simoes, G, Miraoui, A. Experimental Validation of High-Voltage-Ratio Low-Input-Current-Ripple Converters for Hybrid Fuel Cell Supercapacitor Systems. in IEEE Transactions on Vehicular Technology, vol. 61, no. 8, pp. 3430-3440, Oct. 2012. doi: 10.1109/TVT.2012.2208132. [6] L. Xu et al. Extended State Observer-Based Sliding-Mode Control for Floating Interleaved Boost Converters. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 5283-5289. doi: 10.1109/IECON.2018.8591138. [7] Huangfu, Y, Guo, L, Ma, R, Gao, F. An Advanced Robust Noise Suppression Control of Bidirectional DC–DC Converter for Fuel Cell Electric Vehicle. in IEEE Transactions on Transportation Electrification, vol. 5, no. 4, pp. 1268-1278, Dec. 2019. doi: 10.1109/TTE.2019.2943895. [8] Li, Q. et al. An Improved Floating Interleaved Boost Converter with the Zero-Ripple Input Current for Fuel Cell Applications. in IEEE Transactions on Energy Conversion, vol. 34, no. 4, pp. 2168-2179, Dec. 2019. doi: 10.1109/TEC.2019.2936416. [9] Li, Q, Huangfu, Y, Zhao, J, Zhuo, S, Chen, F. Controller design and fault tolerance analysis of 4-phase floating interleaved boost converter for fuel cell electric vehicles. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 2017, pp. 7753-7758. doi: 10.1109/IECON.2017.8217359. [10] Lute, C D, Simões, M G, Brandão, D. I, Durra, A A, Muyeen, S M. Experimental evaluation of an interleaved boost topology optimized for peak power tracking control," IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, USA, 2014, pp. 2096-2102. doi: 10.1109/IECON.2014.7048791. [11] Sartipizadeh, H, Harirchi, F, Babakmehr, M, Dehghanian, P. Robust Model Predictive Control of DC-DC Floating Interleaved Boost Converter with Multiple Uncertainties. in IEEE Transactions on Energy Conversion, vol. 36, no. 2, pp. 1403-1412, June 2021. doi: 10.1109/TEC.2021.3058524. [12] Lin, P, Jiang, W, Wang, J, Shi, D, Zhang, C, Wang, P. Toward Large-Signal Stabilization of Floating Dual Boost Converter-Powered DC Microgrids Feeding Constant Power Loads," in IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 1, pp. 580-589, Feb. 2021. doi: 10.1109/JESTPE.2019.2956097. [13] Lute, M, Simões, G., Brandão, D I, Al Durra, A, Muyeen, S. M. Development of a four phase floating interleaved boost converter for photovoltaic systems. 2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA, 2014, pp. 1895-1902. doi: 10.1109/ECCE.2014.6953650. 76 Gap Analysis and Development of Low Carbon Tourism in The Hotel of Chiang Mai Province Towards Sustainable Tourism Goals Kanokwan Khiaolek Program in Energy Engineering, Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, 50200, Thailand, [email protected]mu.ac.th, ORCID: 0000-0003-3135-3223 Wongkot Wongsapai Multidisciplinary Research Institute, Chiang Mai University, Chiang Mai, 50200, Thailand, [email protected], ORCID: 0000-0002-2273-5177 Korawan Sangkakorn Multidisciplinary Research Institute, Chiang Mai University, Chiang Mai, 50200, Thailand, [email protected], ORCID: 0000-0001-8337-6705 Walinpich Kumpiw Energy Technology for Environment Research Center, Chiang Mai University, Chiang Mai, 50200, Thailand, [email protected], ORCID: 0009-0007-6201-5776 Cite this paper as: Khiaolek, K, Wongsapai, W, Sangkakorn, K, Kumpiw, W. Gap Analysis and Development of Low Carbon Tourism in The Hotel of Chiang Mai Province Towards Sustainable Tourism Goals. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: This paper aims to find gap analysis and develop the potential for greenhouse gas emissions in the tourism sector of Chiang Mai province which leads to sustainable tourism. Chiang Mai province is an important province for tourism in Thailand (Northern Economic Corridor: NEC). Gap analysis of tourism will be conducted in 4 dimensions: (1) management (2) socio-economy (3) cultural and (4) environmental. In 2023, there were 10,678,764 tourists coming to travel in Chiang Mai, with a total revenue of 89,193.79 millionbaht, equivalent to 44.76% of northern tourism revenue and 4.10% of Thailand tourism revenue. Therefore, the development of sustainable tourism should be accelerated to meet the needs of new tourists who care about the environment. This paper assessed the amount of greenhouse gas emissions from 11 designated building of hotels. Visiting the area and surveying the data found that there are 11 hotels with energy consumption of 40.58 GWh and greenhouse gas emissions equal to 18,260.40 tonCO2eq. However, if there is a transition to using alternative energy sources from solar cells, it would require the installation of a solar cell system totaling 38,226.75 kW, can reduce greenhouse gases by 18,260.40 tonCO2eq. Keywords: Sustainable Tourism Goals, Greenhouse Gas Emission, Low Carbon City, Chiang Mai Province © 2024 Published by ECRES 1. INTRODUCTION Thailand has a sustainable tourism development plan. It is an important global tourism destination that a attracts all social class of tourism. Increasing the proportion of high quality tourist and aiming to develop the tourism industry for even more high value. Also, focusing on reducing greenhouse gas emission and low carbon society. Building people's capacity to cope and adapt to reduce loss and damage from natural disasters and impacts related to climate change [1]. In addition, Managing of tourism follow by internation standard which is BCG economy concept. Develop a tourism area management system to be consistent with capabilities. along with supporting environmentally friendly establishments and support the use electric vehicles or other measures to reduce greenhouse gas emissions in the tourism sector [2]. In this regard, Thailand has important tourism in the northern region, which is called “Northern Economic Corridor” (NEC) including Chiang Mai, Chiang Rai, LamPhun and LamPang Province. In 2023, NEC have tourism about 15,926,623 tourists coming to travel in north of Thailand. In the meantime, Chiang Mai province has the highest number of tourists, which equals 10,678.764 people while 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 77 Chiang Rai Province, LamPang Province and LamPhun Province, which equals 6,147,860 people, 1,667,803 people and 1,265,410 people respectively [3]. Therefore, Chiang Mai Province has a potential to reduce greenhouse gas in tourism industry and develop STGs for international. 2. METRODOLOGY 2.1 Gap Analysis Gap analysis can be conducted hierarchically, or known as the Analytic Hierarchy Process, which is a method for solving complex problems to make them appear simpler by relying on a process that mimics human behavior. This method is based on dividing the structure of the problem into 4 levels, namely: setting objectives, establishing criteria for primary decision-making, establishing criteria for secondary decision-making, and setting alternatives in order. Then, the most suitable alternative is analyzed and chosen (see in Fig. 1) [4]. Figure 1. Concept of Analytic Hierarchy Process. 2.2 Renewable Energy The energy consumption of hotel industry comprises lighting systems, air conditioning systems, motors, water pumps, and heating systems. Nowadays, some tourists choose products and services that are environmentally friendly. Therefore, the hotel industry has to turn to using renewable energy from solar cells to reduce the use of fossil fuels, which leads to a reduction in greenhouse gas emissions. Additionally, this can also reduce energy consumption costs. Furthermore, the amount of solar cell installations can be calculated using Eq. (1). The GHG reduction potential is calculated using Eq. (2), refering to ISO 14064-1 [5] and WRI/WBCSD [6]. 𝐸= P (⁡𝑓𝑡𝑒𝑚𝑝)⁡(⁡𝑓𝑑𝑖𝑟𝑡)(𝑓𝑚𝑖𝑠)(𝑓𝑖𝑛𝑣)(𝑡)(𝑑) (1) Where: E is amount of electricity produced by installation of solar rooftops (kWh); P is maximum power (W); 𝑓𝑡𝑒𝑚𝑝 is temperature reduction factor under Standard Test Condition (STC) (%); 𝑓𝑑𝑖𝑟𝑡 is annual dirt and dust reduction factor under STC (%), 𝑓𝑚𝑖𝑠 is reduction factor for mismatch and wiring losses under STC (%); 𝑓𝑖𝑛𝑣 is over all dc-to-ac conversion efficiencies under STC (%); t is power generation hours of the solar rooftop system (hours/day); d is the number of sunny days (days/year). 𝐺𝐻𝐺=(𝐴𝐷)(𝐸𝐹) (2) Where: GHG is amount of GHG emission (kgCO2eq); AD is activity data of GHG emission, which is a quantitative measure of the activities that produce GHG emission (units); EF is emission factor of GHG emission, which is coefficient that shows the relationship between activity data and emission (kgCO2eq/unit). 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 84 generation. The total suitable area for PV in a region is the sum of suitable areas for ground PV and suitable rooftop areas, while the total area for wind generation is the sum of suitable areas of each region. Figure and Figure show the hourly PV and wind potential of Italy respectively, evaluated as the sum of the regional hourly potentials. Figure shows that the PV energy decreases significantly during the winter season, this means that a large-capacity energy storage system is required to ensure the continuous supply of electricity. 3.3. Full potential Scenario optimization: In this scenario, the full potential of PV and wind is used for the optimization calculations. In Figure ‘a’ is the share of PV in the energy system. The remaining electricity is provided by wind. Figure shows that if the entire potential of PV and wind is used for calculations, using only wind energy will require the least amount of storage capacity because seasonal changes in PV generation are quite large and the PV potential is likewise extremely high in comparison to the wind potential. 3.4. Island-mode scenario optimization: The optimization conducted in this study related to the Island scenario, where the annual electricity demand aligns with the annual electricity production, is shown in Figure . The optimal combination for the island scenario is 16.9% PV and 83.1% Wind with a required storage capacity of 7.04 TWh. 3.5. Peak Hour Scenario: For this scenario, at 4936 hours, the hourly demand peaks at 57.37 GWh. Since the wind potential generation at that hour is less than 57.37, the full wind potential was taken into account and equalized the PV production to 57.37 GWh. The optimal combination for the peak-hour scenario is 16.6% PV and 83.4% Wind with a required storage capacity of 12.18 TWh. Figure shows this scenario's required storage capacity for the different PV and wind energy combinations. 4. CONCLUSION Italy boasts abundant renewable energy resources, notably in solar energy with vast potential. This research underscores the capability of both wind and photovoltaic sources to cater to Italy's entire electricity demand independently. However, a singular reliance on either source poses challenges due to production fluctuations, prompting the necessity for an adaptable energy system. This study delves into optimizing storage system capacities across three distinct scenarios. Initially, considering the full potential of both PV and wind energy sources, the analysis reveals that sole reliance on wind power minimizes the required storage to 33 TWh. However, this amount is not practically attainable due to disparities in Italy's PV and wind potential, rendering this scenario unfeasible. To address this challenge and enhance feasibility, two alternative scenarios are explored. The 'Island' scenario, aligning annual PV and wind production with annual demand, suggests an optimal mix of 16.9% PV and 83.1% wind, necessitating a more viable storage capacity of 7.04 TWh. Another scenario matching electricity production with peak hour demand identifies an optimal blend of 16.6% PV and 83.4% wind, requiring 12.18 TWh of storage. Ultimately, the analysis favors a hybrid system comprising 16.9% PV and 83.1% wind for Italy's energy needs. Italy indeed possesses ample suitable land for PV and wind energy generation, providing a robust avenue to fulfill its electricity demand through renewable sources. Recognizing the substantial seasonal variations in PV production, a strategic emphasis on harnessing Italy's wind resources emerges as imperative. Consequently, planners are recommended to prioritize an energy mix consisting of 16.9% PV and 83.1% wind. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 85 This study lays the groundwork for subsequent research endeavors. Future investigations should concentrate on pinpointing cost-effective locations for energy generation and storage and exploring diverse storage technologies for enhanced efficiency and reliability. 5. FIGURES Figure 10. Research flowchart. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 86 Figure 2. Excluded areas for ground PV generation. Figure 3. Suitable areas for ground PV generation. Figure 4. Suitable areas for wind generation. Figure 5. Trnsys PV generation model. Figure 6. Trnsys Wind generation model. Figure 7. Hourly electrical demand for Italy for 2022. Figure 8. Hourly PV Electrical Energy Generation Potential of Italy (TWh). 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 87 Figure 9. Hourly Wind Electrical Energy Generation Potential of Italy (TWh). Figure 10. Maximum storage requirement for different PV and wind energy combustions. Figure 11. Maximum storage requirement for different PV and wind energy combustions. Figure 12. Maximum storage requirement for different PV and wind energy combustions. 6. EQUATIONS H(t) = H(t−1) + RL(t) (1) EH = min (max|H(t)|) (2) 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 88 𝛽= ∑𝑃𝑃𝑉(𝑡) 𝑡=8760 𝑡=1 ∑𝑃𝑤𝑖𝑛𝑑(𝑡)+⁡∑𝑃𝑃𝑉(𝑡) 𝑡=8760 𝑡=1 𝑡=8760 𝑡=1 (3) REFERENCES 1] K. Shivarama Krishna and K. Sathish Kumar, “A review on hybrid renewable energy systems,” Renewable and Sustainable Energy Reviews, vol. 52. Elsevier Ltd, pp. 907–916, Aug. 22, 2015. doi: 10.1016/j.rser.2015.07.187. [2] H. Ikram, A. 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Available: http://shanghaielectric-smec.com/1-3-1-2mw-wind-turbines/ [21] Terna, “RELAZIONE FINANZIARIA ANNUALE,” 2022. Accessed: Dec. 15, 2023. [Online]. Available: https://www.terna.it/it [22] Terna, “Documento di Descrizione degli Scenari 2022,” 2022. Accessed: Dec. 15, 2023. [Online]. Available: https://www.terna.it/it 89 Bibliometric Analysis of Emergent Airborne Wind Energy Systems Alzira Mota ISRC, DMA–ISEP, Politécnico do Porto, Portugal, [email protected], 0000-0002-3871-4215 Luís A.C. Roque SYSTEC–ISR ARISE, DMA/TID–ISEP, Politécnico do Porto, Portugal, [email protected], 0000-0001-5825-1732 Luís Tiago Paiva SYSTEC–ISR ARISE, Universidade do Porto, Portugal, [email protected], 0000-0002-3606-1695 Cite this paper as Mota, A., Roque, Luís A.C., Paiva, Luís Tiago. Bibliometric Analysis of Emergent Airborne Wind Energy Systems. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain. Abstract: Airborne Wind Energy (AWE) systems are an innovative approach to harnessing wind energy at higher altitudes where the wind is generally stronger and more consistent. AWE systems use airborne devices such as kites, drones, or tethered gliders to capture wind energy. Typically, these systems consist of a tether connecting the airborne device to a ground station, a control system for manoeuvring the airborne device, and a means of converting kinetic energy into electrical power. Researchers and engineers are exploring different materials, shapes, and control strategies to optimise the performance and reliability of such systems. In the last decade, there has been an increase in literature dedicated to AWE. This study conducts a comprehensive analysis of 545 documents published on AWE between 2013 and 2023, employing bibliometric techniques on data sourced from the Google Scholar database. Python and VOSviewer software are used for data analysis and graphical presentation. The research encompasses theme mapping, trend topics, bibliometric coupling, and co-occurrence networks to identify potential future research topics. The findings indicate an average annual growth rate of 11% in AWE literature since 2013. Additionally, this study identifies the most influential components of the literature, including highly cited topics, articles, authors, and keywords. Keywords: Airborne Wind Energy; Renewable Energy; Bibliometric Analysis © 2024 Published by ECRES 1. INTRODUCTION As the world addresses the urgent need to transition to cleaner energy solutions, innovative technologies are essential to meet the growing demand for electricity. Airborne Wind Energy (AWE) systems represent a new approach to harnessing wind energy and offer significant advantages over traditional wind turbines. AWE systems have the capability to harness the strong and reliable winds at higher altitudes, offering innovative ways to produce sustainable and efficient energy. There is a wide range of AWE systems, all of which share a common principle: the use of a tethered airborne device, such as a kite or drone, to harness and capture the kinetic energy of the wind. Two concepts have been developed to convert wind energy into power [1]: ground-generation (or ground-gen) systems, which use the traction force of flying devices and the power is produced on a ground-generator, and flygeneration (or fly-gen) systems, which use rotors on the flying devices and the power is produced on-board that is then sent to a ground-station using a conductive tether. Samson et al [2] examine different design types of AWE systems and their control architecture. Their study contributes to the existing literature by focusing specifically on the Altaeros design and discussing its advantages and disadvantages compared against other airborne systems and conventional wind generation. Lozano and et al [3] review the reverse pumping principle, the aerodynamic model constraints, and the flight simulation results. They then describe the experimental setup used to validate the theoretical study. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 90 Academic and research analysis can benefit from the modularity of the simulators and their input and output interfaces, which adhere to a common and user-friendly architecture [4]. AWE systems aim to operate at altitudes above conventional wind turbines, where reliable high-resolution wind data is limited. Sommerfeld et al [5] investigate whether the assimilation of measurements into the mesoscale Weather Research and Forecasting (WRF) model via observation nudging produces a more accurate and comprehensive data set. A significant advantage of AWE systems is their reduced environmental impact. These systems have a smaller physical footprint and use less material than conventional wind turbines, which can help alleviate concerns about land use and habitat disturbance. In addition, their mobility allows them to be deployed in regions with complex terrain or limited access to land. Faggiani et al. [6] discuss the clustering of units into a large kite wind farm, specifically the spatial arrangement and collective operation. Roque et al. [7] consider a farm of ground-generation AWE systems. A heuristic optimisation technique for designing an AWE farm layout that maximises wind power generation is developed and implemented. Over the last decade, there has been a significant increase in the amount of literature focused on AWE. This research aims to analyse 545 published documents on AWE from 2013 to 2023 using, bibliometric analysis to clarify and identify current lines of research within the AWE research field. The analysis follows a given methodology to identify the current research and to link it to the corresponding researchers and research groups. Python and VOSviewer software were used for data analysis and visual representations. This work covers various aspects such as mapping themes, identifying trending topics, examining bibliometric coupling and co-occurrence networks to identify potential areas for future research. The results reveal an average yearly growth rate of 11% in AWE literature since 2013. Furthermore, the study identifies the most influential components of the literature, such as highly cited articles, authors, and keywords. This document is structured as follows: the methodology is described in section 2, followed by the presentation of the descriptive analysis and main results in section 3. The conclusions are drawn in section 4. 2. METHODOLOGY This analysis focuses on the AWE research field. The bibliometric analysis performed [8] covers the last decade, as there has been an increase in AWE publications since 2013. As in Figure 1, the proposed methodology consists of three phases: (1) planning and preparation, (2) data collection, and (3) analysis and findings. The first step is to determine the main parameters necessary to conduct the analysis, including keywords and the selection of digital repositories such as Web of Science and Google Scholar. The articles and citations collected from both databases show a high correlation. Comprehensive searches are conducted using terms related to airborne wind energy, synonyms and variations of keywords to ensure a thorough search. The terms consist of keywords linked by the connector "and" and are considered "airborne wind energy", "airborne wind farms", "kite generation", "kite wind farms", and "kite energy". After an initial search of the scientific documents, we export and filter data from digital repositories to a data sheet, capturing key details such as document title, authorship, citations, source, publication date and country. In the filtering of data based on relevance to our objectives, we may exclude publications that are irrelevant or out of date. We consider conference proceedings, journal articles and relevant reports. We then conduct a quantitative analysis and produce a scientifically sound map using cluster and network analysis visualization [9]. Python and VOSviewer software are used to create visual representations of data, such as graphs, charts, and maps. These tools enable us to illustrate trends, to identify key authors, and to map collaboration networks. Finally, we report our results. The clusters resulting from the analysis reveal interesting relationships between subtopics and indicate the emergence of new research areas. This analysis examines four components to determine the knowledge gap in research on the performance analysis of AWE systems: Publication of articles, influential authors, institutions, and sources, conceptual framework of scientific topics, and intellectual framework of related research topics. For each component, the number of publications, and keywords, as well as the most productive authors, sources, and organisations are identified. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 91 Figure 1. Proposed methodology consisting of three phases. 3. RESULTS FROM BIBLIOMETRIC ANALYSIS The analysis of the dataset involved 545 documents collected from Google Scholar and Web of Science, sorted by relevance. This sample size constitutes 14% of the total number of documents available on Google Scholar. Figure 2 reports an increase in the number of publications when considering the annual distribution and corresponding cumulative results. It is important to note that, with publications of 2023, we cannot discern a trend contrary to the one from the last decade because the year has not concluded, and not all publications are available in digital repositories. The high number of book chapters published in 2013 [10] and 2018 [11] corresponds to the publication years of the Springer books 'Airborne Wind Energy' and 'Airborne Wind Energy: Advances in Technology Development and Research,' respectively. It should also be noted that the 2013 book has 278 citations, and the 2018 book has 95 citations. Figure 2. Annual distribution of AWES publications from 2013 to 2023. The distribution by document type is as follows: 218 Articles, 180 Conference papers, 73 Theses, 69 Book sections, and 5 books. The most representative document types are articles and conference proceedings account for more than 72% of all published documents in sample dataset. The 545 literature publications were written by 1648 authors, underscoring the significant interest in AWES is a topic of great interest to researchers, institutions, and countries worldwide. For our search in publications, we set a maximum limit of 10 authors per document. On average, each article, conference article, book, and book chapter have approximately 5 authors. Roland Schmehl 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 92 (63), Chris Vermillion (51), Moritz Diehl (39) and Lorenzo Fagiano (30) were the most prominent authors (see Figure 3). Figure 3. Authors with largest number of documents in the sample dataset. The distribution of the most significant publishers of articles and books is depicted in Figure 4. According to the percentage of publications, Elsevier with 30.9% is ranked as the top, followed by MDPI with 14.8% and IEEE with 11.2 %. Figure 4. Major publishers of AWES. Concerning theses, 71.2% are MSc dissertations, 24.7% PhD theses, and 4.1% Bachelor dissertations. During the last decade, the institutions providing theses and dissertations in scientific literature are shown in Figure 5. The four most representative institutions are Delft University of Technology, Politecnico di Milano, ETH Zurich, and Universidade do Porto. To understand the structure and subtopics underlying the bibliometric data, the 610 keywords defined by the authors of articles and conference papers were used for mapping. The network construction involved counting the occurrences of keywords using the Full Counting method. Only keywords with a minimum of four occurrences were considered. Forty-nine occurrences met the threshold. To construct the graph, we utilized the top 80% of the most relevant occurrences, which equates to forty keywords. Figure 6 shows the network visualization. The size of the nodes represents how often a keyword occurs, i.e. a larger node represents more frequent occurrences. The proximity between keywords is an indication of the relevance between them. The network consists of seven clusters. Cluster 1 (light green) is made up of Computation Fluid Dynamics (CFD) Analysis, high altitude wind power, fluid structure interaction, induction factor and energy related to the topic of resource assessment and wind source modelling. Cluster 2 (green) consists of adaptive algorithm, continuous time system, energy system, kite power system, nonlinear system, optimal control, predictive control, and real time 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 93 optimization keywords connected to the optimisation and control area. Cluster 3 (purple) is composed of adaptive control, flight control, parameter estimation, renewable energy system, voltage source converter, and wind related to system design, testing and performance evaluation. Figure 5. Institutions providing AWE theses/dissertations. Cluster 4 (red) is made up of high altitude, kite control, power, and wind turbine terms commonly associated with aerodynamics, high altitude wind energy domain. Cluster 5 (cyan) consists of data, kite system, machine learning, model predictive control, nmpc, path, and tether force terms usually connoted with automatic control and tether safety, safety and reliability. Cluster 6 (blue) is composed of aerodynamic, airborne wind energy systems, airborne wind turbine, high altitude wind, high altitude wind energy, renewable energy, and wind power linked with power conversion. Cluster 7 (yellow) consists of unmanned aerial vehicle keyword corresponding to airborne devices and airborne systems. Figure 6. Keywords clusters maps. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 100 [10] T. Kreetachat et al., Dataset on the optimization by response surface methodology for dried banana products using greenhouse solar drying inThailand, Data in Brief 49 (2023) 109370. https://doi.org/10.1016/j.dib.2023.109370). [11] H.M. Denise et al., Solar drying modes of saladette tomatoes slices on phytochemicals and functional properties, Solar Energy 262 (2023) 111903,doi.org/10.1016/j.solener.2023.111903. [12] M. Usama et al., The energy, emissions, and drying kinetics of three-stage solar, microwave and desiccant absorption drying of potato slices, Renewable Energy 219 (2023) 119509,doi.org/10.1016/j.renene.2023.119509.). [13] C. Prajapati and T. Sheorey, Exploring the efficacy of natural convection in a cabinet type solar dryer for drying gooseberries: An experimental analysis, Journal of Agriculture and Food Research 14 (2023) 100684,doi.org/10.1016/j.jafr.2023.100684. 101 Production of Biochar and Hydrochar from Straw for Sustainable Agriculture Rositsa Velichkova Technical University of Sofia, Sofia, Bulgaria, [email protected], ORCID: 0000-0003-3757-8685 Iskra Simova Technical University of Sofia, Sofia, Bulgaria, [email protected], ORCID: 0000-0002-8426-9255 Martin Pushkarov Technical University of Sofia, Sofia, Bulgaria, [email protected], ORCID: 0000-0002-5522-0815 Cite this paper as: Velichkova R., Simova I., Pushkarov M., Production of Biochar and Hydrochar from Straw for sustainable agriculture. 10. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The aim of the research is to examine biochar and hydrochar derived from straw collected from four distinct samples. All samples were investigated, obtained at three pyrolysis temperatures (400, 600, and 800). The chemical content was measured and analysed. The obtained results indicated that the generated straw biochar and hydrochar have the potential for utilization as fertilizers and soil supplements. A comparison was also conducted between the straw before the pyrolysis process and the resulting biochar and hydrochar. This gives the conclusion that the components which are valuable for the soil are increased. Keywords: straw, biochar, hydrochar, soil © 2024 Published by ECRES 1. INTRODUCTION It is well known that agriculture has played a role in the global carbon crisis that has led us to this point in history. Recent studies indicate that until the beginning of this century, agriculture has been accountable for one-third of the global greenhouse gas emissions. The production of food constitutes the majority of all greenhouse gas emissions related to food—amounting to 86%.[1] Industrial agricultural practices, particularly the application of chemical fertilizers, pesticides, and extensive tillage, contribute to a reduction in humic matter levels in soils. As a consequence, a substantial number of agricultural soils are currently experiencing severe degradation. The diminishing fertility of soils is leading to a decline in agricultural productivity, and the rising costs and diminishing effectiveness of industrial agricultural inputs further compound the challenge of maintaining yields. [2-3]. As a component of the broader movement toward regenerative carbon-based practices, which includes reduced and no-till systems, cover cropping, green manures, rotational grazing, composting, etc., charcoal, commonly referred to as "biochar," is demonstrating its capacity to reduce carbon mineralization. This results in making carbon more stable in the soil, while simultaneously enhancing biological activity. The work presents an experimental study and analysis of obtained biochar and hydrоchar from four different straw samples. The pyrolysis process was used to obtain the biochar and the experiments were done in a muffle furnace. Fig. 1 shows the scheme of the setup for obtaining biochar.[4-6] 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 102 2. METHODOLOGY OF THE EXPERIMENT Four soil samples were used - Straw from Sandanski –Sample 1, Straw from Kavarna – Sample 2, Lucerne-Sample 3 Canola –Sample 4 at three different temperatures process – 4000C, 6000C and 8000C. The pyrolysis process was used to obtain the biochar and muffle furnace is used to perform the experiments. In fig. 1 shows the scheme of the setup for obtaining biochar. Figure 1. Scheme of the test rig for biochar production Where 1-muffle furnace, 2-sample, 3 – Thermal regulator type Lmt -1-1200 OC. The sample is placed dry in an oven and after reaching the desired temperature, which is regulated by 3, the sample stands for 1 hour at this temperature. It is then taken out and allowed to cool and stored in a container for further examination. In Fig. 2, the scheme of the test rig for hydrochar from biochar production is presented Figure 2. Diagram of the experimental setup for producing hydrochar. Where 1. Vessel, 2. Heaters for specialized fluid; 3. Specialized fluid; 4. Manometer for measuring the pressure in the reactor; 5. Reactor; 6. Stand for the reactor in the tank; 7. Water under pressure; 8. Sample; 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 103 9. Pipeline for filling water in the reactor;10. Safety - overflow valve (with setting at 100 bar) – Pressure Relief valve 100 bar;11. Piping for the safety overflow valve;12. Manual water pump (up to 60 bar); The duration of the experiment is again 2 hours, allowing beforehand Vessel position 1 to heat up to the desired temperature (in the present case the temperature is selected to be 180 0C), the vessel has the ability to heat its working fluid up to 300 0C. While the working fluid (position 3) is heated by means of the heaters position 2, the sample is loaded into the reactor position 5. The reactor is disassembled by means of a threaded connection and thus loaded or sample taken out of it. After we load the sample into the reactor position 5, it is screwed and installed on the special stand for the reactor position 6. While the specialized fluid is heated to the desired temperature, we inject a certain amount of distilled water, and this action is performed with a manual water pump. which can realize up to 60 bar pressure. In our case, we pressurize the distilled water to 25 bar (2.5 MPa), and let the sample in the reactor stand for 2 hours, the pressure being monitored by the safety valve position 10. If the pressure exceeds 25 bar (2.5 MPa), it opens and releases the necessary pressure above our desired value. During this 2 hour stay of the sample, the specialized fluid position 3 constantly maintains its temperature of 180 degrees. After the experiment is over, the reactor is allowed to cool down and the sample is taken out of it. When the sample is taken out, it is drained from the distilled water and then its moisture content is measured by leaving the sample in a Desiccator for one day and then its moisture content is measured again. All test samples are stored in a separate container with all their parameters written on them. 3. ANALYSIS OF THE EXPERIMENTAL RESULTS The content of the following chemical elements is investigated: Si, K, Ca, Mn, Fe, Zn, Sr and Bi. The results are visualized graphically and comparison between the samples before treating is presented. Figs 3-8 show the content of K and Mn of the investigated clean samples and as well as the produced biochar and hydrochar under the three temperature regimes. Figure 3. K content in the samples at 400oC 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 104 Figure 4. Ca content in the samples at 400oC Figure 5. K content in the samples at 600oC Figure 6. Ca content in the samples at 600oC 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 105 Figure 7. K content in the samples at 800oC Figure 8. Ca content in the samples at 800oC From the results presented, it can be seen that when obtaining biochar and hydrochar, in most of the examined samples the amount of the given element increases. Each of the chemical elements supports soil and plant growth in a different way. All this showed that the obtained biochar and hydrochar can be used to enrich the soil for the purpose of sustainable agriculture. 4. CONCLUSION A study of wheat straw production through the pyrolysis process at three different temperatures for two experimental formulations was carried out. Proximate analysis and examination of the elemental composition of the resulting samples were conducted. The presence of SiO2 (silica) in high concentrations in the wheat straw biochar is noted. Additionally, there is a notable content of alkali (K2O) and alkaline earth metals (CaO). The findings suggest that biochar and hydrochar from straw could be utilized as environmentally friendly fertilizers in agriculture. ACKNOWLEDGMENT This study is part of the project „ Recycling of carbon fertilizer from straw for sustainable agriculture”, КП-06-ИПКитай/2 от 24.11.2020г, funded by the Bulgarian Science Fund of the Ministry of Education and Science 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 106 REFERENCES [1] Council Regulation (EC) No. 73/2009 establishing common rules for direct support schemes for farmers under the common agricultural policy and establishing certain support schemes for farmers, amending Regulations (EC) No. 1290/2005, (EC) No. 247/2006, (EC) No. 378/2007 and repealing Regulation (EC) No. 1782/2003 [2] Matustík J., Pohorelý M., Kocí V., Is application of biochar to soil really carbon negative? The effect of methodological decisions in Life Cycle Assessment. Sci. Total Environ. 151058. , 2021 https://doi.org/10.1016/J.SCITOTENV.2021.151058. [3] Abas K., Brisson J.,Amyot M., Brodeur J., Storck V., Montiel-Leon J.M, Duy S., Sauve S., Koiv M., Effects of plants and biochar on the performance of treatment wetlands for removal of the pesticide chlorantraniliprole from agricultural runoff. Ecol. Eng. 175, 106477, 2022 https://doi.org/10.1016/J.ECOLENG.2021.106477. [4] Biswas B., Pandey N., Bisht Y., Singh R., Kumar J., Bhaskar T., Pyrolysis of agricultural biomass residues: comparative study of corn cob, wheat straw, rice straw and rice husk. Bioresour. Technol. 237, 57–63, 2017 https://doi.org/10.1016/J. BIORTECH.2017.02.046. [5] Lin J., Sun S., Xu D., Cui C., Ma R., Luo J., Fang L. ,Li H., Microwave directional pyrolysis and heat transfer mechanisms based on multiphysics field stimulation: design porous biochar structure via controlling hotspots formation. Chem. Eng. J. 429, 2022 132195. https://doi.org/10.1016/j.cej.2021.132195. [6] Velichkova R., Simova I., Pushkarov M., Denev I., Markov D., Ivanov I., Angelova R., Production of Biochar From Wheat Straw With Muffle Furnace and Flow Reactor, 2022 8th International Conference on Energy Efficiency and Agricultural Engineering, EE and AE 2022 - Proceedings2022 8th International Conference on Energy Efficiency and Agricultural Engineering, EE and AE 2022, doi: 10.1109/EEAE53789.2022.9831340. 107 BESS Reserves Optimization in Energy Communities Wolfram Rozas-Rodriguez Universidad Nacional De Educación A Distancia, ETS Ingeniería Informática, Madrid, Spain, [email protected], ORCID: 0000−0003−3036−1803 Rafael Pastor-Vargas Universidad Nacional De Educación A Distancia, ETS Ingeniería Informática, Madrid, Spain, rp[email protected], ORCID: 0000−0002−4089−9538 Dave Kane Trilemma Consulting Limited, London, UK, [email protected], ORCID: 0000-0001-6999-0346 Andrew D. Peacock School of Energy, Geoscience, Infrastructure and Society (EGIS), Heriot-Watt University, Edinburg, Scotland, [email protected], ORCID: 0000-0001-8876-3042 José Carpio-Ibañez Universidad Nacional De Educación A Distancia, ETS Ingenieros Industriales, Madrid, Spain, jcarpi[email protected], ORCID: 0000−0001−6397−1734 Cite this paper as: Rozas-Rodriguez, W, Pastor-Vargas, Rl, Kane, D, Peacock, A,D, Carpio-Ibañez J. BESS Reserves Optimization in Energy Communities. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The BESS (Battery Energy Storage System) can be optimized to improve business models for LEM (Local Energy Market). Optimization tools are crucial for attaining the best operating model and boosting productivity and efficiency. While traditional optimization strategies exist, they have limitations. This paper demonstrates how to use advanced artificial intelligence algorithms such as Perceptron and Boosting Regressor to optimize the battery system and share our evaluation methods for the solar renewable energy production model. These algorithms produce highly accurate predictive models. Keywords: Battery optimization, Solar energy, Local Energy Market, Neural Networks, Machine Learning © 2024 Published by ECRES Nomenclature BESS DNO – DSO Battery Energy Storage System. Distribution Network Operator/Distribution System Operator LEM Local Energy Market 1. INTRODUCTION The optimization of the use of batteries in renewable energy systems is a challenge that is being analyzed and researched. In the case of wind renewable energy, [1] presents an optimal capacity of battery energy storage system (BESS) for wind farm integration model, and its effects of equivalent cycle life and reserve degree on BESS capacity. In the field of solar renewable energy, the efforts are very focused on the optimal operating cost and profitability of battery energy storage system portfolios in different electricity market conditions [2]. Other research works focuses on the optimal size of battery energy storage system (BESS) and optimal scheduling of BESS power, as presented in [3]. The outcomes of these strategies are to maximized profit and minimized penalty cost. In these previous works, the solutions focus only on optimization strategies but do not define predictive strategies, using artificial intelligence (AI) algorithms. These algorithms can be used for knowing how frequent the “excursions” (forecast deviations from minute actual Consumption kW) from minutely actual Consumption kW are 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 108 from half-hourly Consumption kWh Forecast in a certain period to improve the BESS Reserve Capacity. Knowing the excursion frequency according to different seasons and periods may help to manage BESS reserves optimally to meet DNO contract conditions. Not meeting real-time load constraints may generate an Energy Community burden in contractual penalties from DNOs or System Operators, and/or operational impacts due to local power system outages. Figure 1 illustrates an actual minute vs. half-hourly forecast load kW comparison. Estimate deviations are named “excursions” and show evidence of higher BESS Capacity than foreseen. They are represented in yellow color. The reserve capacity depends on the number and magnitude of excursions [4], so focusing on this magnitude, it is possible to optimize the BESS reserve capacity by providing IA inference models which can be used to predict the incursions. It is also needed to define evaluation metrics that allow the comparison of actual and new models in terms of performance and quality. There are several key performance indicators, but almost all fall into the economic and technical benefits category for different prosumer types [5]. However, this paper has scaled these two factors into two key performance indicators (KPI): KPI1 is named Excursion Size Categories, and KPI2 is named Actual vs. Forecast Energies Comparison. This comparison may be seen in Figure 1. KPI2 is defined as the ratio of the minute actual energy over the half-hourly forecast energy, so it measures the behaviour of the consumption energy deviations from the model estimated. This paper presents the BESS reserve capacity prediction AI algorithms and the key performance indicators used to evaluate these algorithms. Figure 1. BESS Reserve Capacity Optimization. 2. MATERIALS AND METHODS As it mentioned before, AI algorithms will be used for creating predictive models for the excursions. AI Algorithms needs data (datasets) and this research will use a particular one corresponding to the Cornwall Local Energy Market, which is focused on Local Energy Markets, or LEM [6]. The Cornwall Local Energy Market trial published a dataset [7] about the sites, including energy and Battery Energy Storage System State of Charge measurements from the equipment at a minute granularity level. All this information is standard in all LEM case studies analyzed, but the site metadata is very detailed in this case, but collected from survey partially responded. The dataset also includes consumption and production forecasts, weather forecast measurements, the BESS specifications, and partially filled metadata, which included information about the site comprising household information, Energy Performance Certificate (EPC), appliances, DER, electric vehicle, and electricity bills. All these attributes will make up the analytical dimensions of the final crossed dataset. The Cornwall LEM dataset has an adequately documented dictionary [8]. Figure 2 depicts all energy flows related to the data dictionary. Trilemma Consulting produced several reports analyzing the Sites Metadata [9], the Fleet 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 109 Self-Consumption [10], and the BESS Utilization [11]. All that information offers a clear insight into the Energy Community composition and how it consumes, produces, and stores energy. Figure 2. Cornwall LEM power flows and state of charge stored in the dataset. There are many AI algorithms to apply to these types of data sets, such as the support vector machine [12] or classic forecasting [13] models. Some of them are involved in the modelling and prediction of behavioural and production profiles [14]. However, given their limitations, these models cannot detect complex patterns as in this particular case of “excursions”. These patterns can be detected with neural networks (multilayer perceptron) [15], and gradient boosting regressor algorithms [16]. The main features of these algorithms are: - Multilayer Perceptron Neural Network. The multilayer perceptron (MLP) is a type of neural network that follows a feedforward architecture and is used for supervised learning. This network can have up to two hidden layers. The main function of the MLP network is to minimize the prediction error of one or more targets using one or more predictors. These predictors and targets can be a mix of categorical and continuous fields. The activation function is hyperbolic tangent (tanh) for regression problems. - Gradient Boosting Regressor. XGBoost, Extreme Gradient Boosting, is an algorithm that iteratively trains weak classifiers and then adds them to a final strong classifier. Part of an objective function that measures how well a model trains. This function breaks down into: o Objective function = loss function + regularization term. o The loss function measures the predictability of the model. o The regularization term measures the complexity of the model and helps determine an accurate and stable model. XGBoost is a C&RT decision tree ensemble model, similar to Random Forest, but different in how it is trained. XGBoost optimizes the objective function shown in Equation (1). 𝑂𝑏𝑗=−∑ 𝐺𝑗2 𝐻𝑗+λ 𝑗+3 (1) The ensemble model sums the prediction of several trees together. Finally, the efficiency of the tree structure is measured using the regularization term. This score is like the measure of impurities in a decision tree, except that it also considers the complexity of the model. Figure 3 depicts the Gradient Boosting Regressor. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 116 2. RESEARCH METHOD This initial phase uses a grounded approach collecting data from multiple sources including government and industry reports, media releases, scientific and engineering journals, and interviews with practitioners. In this first stage we consider the key components that make up solar and wind systems, energy storage systems, and transmission systems. By investigating the distinct components, we can understand what and how much material will need to be recycled at the end-of-life of these projects. 2.1. Objectives The objective of this research is to support Engineers Australia’s vision of creating sustainable materials and products from waste [5] through understanding the feasibility of recycling. The work focuses initially on solar and wind farms, as the major sources of energy that are commercially viable now, by exploring components of both the generation and transmission side of these projects. The research will aim to break down the components and determine opportunities for improvement in the ecological value. 2.2. Assumptions Technology currently used in renewable energy systems may be redundant by the end-of-life due to the rapidly changing nature of technology. The materials are likely to be recycled back into manufacturing or construction projects that do not require complex technological equipment. Also, emerging manufacturing techniques proposed for these large systems can be considered for the life-cycle benefits based on this new work. Our scope will be largely restricted to utility-scale assets generally connected to the transmission grid. Significant capacity already exists through customer rooftop solar, and distribution networks are evolving to improve load balancing in such a system. Our assumption will be that the recycling industry that meets the needs of utility-scale assets will also accommodate assets connected at customer sites. 3. ANALYSIS FOR RE-USE Solar and wind farms and battery energy storage systems are being constructed rapidly, and the development of such systems alters the geological properties of the land used, as well as affecting the communities in those regions. The lack of research and development into these issues means that planning to reduce the impacts is limited. One of the most significant issues in the sector is currently the recycling of construction waste, with over 30% of the waste produced ending up in landfill [6], for example up to 145,000t of solar panels being decommissioned in Australia by 2030 [7]. 3.1. General Components Much of the civil and electrical material used to build high voltage electrical generation systems and transmission networks are common across all types of facilities. For example, foundations are made of concrete and reinforced with steel bars; steelwork to produce rigid structures; backfill, gravel and sand to dissipate electrical currents during faults; underground cables to transport power locally; and overhead wire to transmit power to the grid. Bou Melhem & Tam [8] estimate that concrete accounts for up to 80% of construction and demolition waste, equating to approximately 22.5 million tonnes per year in Australia alone. However, waste is a possible resource, and a sustainable solution could eliminate the amount of concrete being disposed of, and instead recycle this into future projects. Wind farms, specifically, use a large amount of concrete per farm, with most projects requiring batching plants in their construction. Gravel and sand are a significant portion of the foundations of these systems, with Mehsas et al. [9] explaining the need to recycle both these materials. To minimise earth potential, these materials are laid as the top layer of transmission systems and are therefore the easiest layers to remove. To filter the combination of fill that has been added for these systems, the simple process of sifting the mixture through different sized mesh screens is the most 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 117 efficient method. A concerning issue is the limited supply of gravel and sand that can be mined for future use [10]. Alternative sources of supply are essential to avoid completely diminishing the natural deposits of aggregates and a solution is to recycle these aggregates into future foundations [9]. Steel is a common component of all renewable energy systems including control panels and equipment racks, structures supporting solar panels, wind turbine towers, battery racks in generation systems; along with a range of components on the transmission side, such as transmission line towers, control panels, and structures supporting high voltage switchgear. Reinforcing bars used in foundations, and steelwork used on structures, are vital in developing strong foundations and equipment to support heavy structures over a long period of time. Foundations are strengthened by reinforcing bars, which are un-processed raw metal and although this raw metal can easily be reformed into other products, this may require a significant amount of effort to retrieve the concrete covered steel. Once the material has been retrieved, steel can be processed using an Electric Arc Furnace (EAF) [11]. Theoretically, the processes to recycle steel are among the simplest methods in developing a circular economy. The fact the extensive steelwork in use has not yet been decommissioned due to failure is a testament to the strength and durability of steel. It is important that the recycled version retains this durability. 3.2. Wind Farm Generation Systems Turbines, both on-shore and off-shore, connect in aggregates to make large-scale electricity generators [12]. Dependent on the climate and operational use of these turbines, the lifespan of these assets range between 20-30 years [13]. One of Vestas™ smaller wind turbines, the V47, details the various components in a typical wind turbine used in Australia [14]. The structure can be classified into 2 main material categories, metals (base frames, main shaft, and mechanical components) and fiberglass (blade and blade hub). The Clean Energy Council developed a plan to avoid most wind turbines ending up in landfill [15]. Desktop studies have shown that between 85-94% of a wind turbine, including steel, aluminium, copper and cast iron, can be dismantled, and recycled in Australia. The remaining 6-15% is the material of blade which is made up of fibreglass composite pieces injected with resin [16]. The carbon fibre and fibreglass are yet to be reused and by 2034, an estimated 15,000t will be created from the decommissioning of wind farms in Australia. The approximate composition of the blade is typically 93% polymer composite reinforced with glass or carbon fibres, 2% PVC, 2% balsa wood, and the remaining 3% metal paint and putty [17]. Due to the multiple chemical changes made to Fibre Reinforced Polymer (FRP) and the specificity of the designs for constructions to suit applications, these materials are not designed for recovery [18]. The material requires chemical manipulation to return it to the basic raw materials. There are still a range of issues relating mechanical, thermal, and chemical recycling including risks to surface defects, fibre length, equipment costs, and process suitability according to the composition of the blade composites [12]. Reuse may be more viable. Mechanical methods shred the material to produce alternatives to standard cement [18] and replace raw materials as fillers, however there is an increase the cure time due to boron in the composites [17]. They have also been successfully constructed into prototypes such as toys, bridges, and new wind turbine blades [12]. Thermal solutions to extract material through pyrolysis lose 50% of the material properties, however, can be recycled into organic liquid fuel, pyrolytic gas or oil or composites reinforced by short, recycled fibre [20]. Another more laborious method is to chemically extract the resin from the fibreglass, as well as solvolysis to create fuel gas [20], which is also being explored to make reinforced industrial products. However, both thermal and chemical methods are so far uneconomical, and chemical methods prove to be more dangerous in comparison to mechanical and thermal recycling. 3.3. Solar Farm Generation Systems The most common renewable energy generator is solar photovoltaic technology, and solar farms are expected to be retired after 25 years [21]. The high volume of material required to produce these systems leads to a large-scale recycling requirement. Manufacturing high efficiency solar modules incorporates cleaning and combining quartz 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 118 with other components at very high heat, followed by adding multiple materials [22]. The separate layers of a solar panel have over 25 years of life when subjected to extreme temperature, humidity, wind and ultraviolet radiation variations, and include the solar photovoltaic cells, toughened glass, extruded aluminium frames, Ethylene Vinyl Acetate (EVA) film layers, polymer rear-back-sheets and junction boxes containing diodes and connectors [23]. According to the Institute for Sustainable Futures, the most common photovoltaic (PV) panel uses the crystallinesilicon (c-SI) structure, and is typically made up of approximately 76% glass, 10% plastic polymer, 8% aluminium, 5% silicon, 1% copper, and < 0.1% silver and other metals which are hard to extract [24]. Companies are starting to see the potential returns of recycling solar panels. Although there are a range of materials that form a PV panel, some layers, such as the aluminium frames and back sheets, are made of pure material that can be recycled efficiently. Through manual, thermal, and chemical processes, nearly 100% of the panels can be theoretically re-used in the manufacturing of new products [25]. France has invested in the development of a first solar panel recycling plant [26]. The plant currently only recycles the glass and aluminium frames; however, these materials make up a significant portion of the panels, making it a notable advancement in sustainability in the industry. Australia has also developed a method to recycle 100% of end-of-life PV modules and all associated materials, breaking down the inverters, cables, optimisers, and mounting structures into the raw materials. 3.4. Battery Energy Storage Systems (BESS) Battery Energy Storage Systems (BESS) are being increasingly integrated to help manage the network accommodate power from intermittent renewable generators, to provide high-value grid support services more effectively than fossil-fuel generators, and to help match generation to load throughout each day. The expected lifetime of these storage systems reflects their technology and operation [26], with batteries estimated to last 10-20 years [28]. BESS are quite similar to substations, where most of the equipment connecting, controlling and protecting the batteries are the high voltage switchgear, relay devices, and cables. The control equipment such as the Battery Management Systems (BMS) and the Power Conversion Systems (PCS) are made up of equipment racks with power electronics, cabling, and cooling systems. The equipment unique to a BESS are the battery cells which use different cell chemistries based on their energy density, cycle life, and other performance characteristics [29]. The different cell chemistries include the ubiquitous but dated lead-acid, the increasingly common lithium-ion, and the less commonly used sodium-sulphur, vanadium flow batteries, and zinc-bromine flow batteries, and cerium-zinc [30]. These chemical compounds present challenges to recycling. Currently, much like household and vehicle batteries, the batteries used by BESS use chemicals and plastics. Lithium-ion batteries are the most common cell types, used not only for industrial energy storage systems, but also electric vehicles, with a demand of nearly 250GWh in 2020 and a predicted increase of a multiple of 10 by 2030 [31]. Only 10% of lithium-ion batteries in Australia are recycled in comparison to the 99% of lead-acid batteries because of the expensive nature of the extraction process and the early stage of the recycling industry for this battery chemistry. To combat the issue of harmful chemicals going to landfill and negatively affecting the environment, Sweden’s Northvolt announced their production of the first lithium-ion battery cell featuring a nickel manganesecobalt cathode using metals recovered through the recycling of battery waste [32]. 3.4. Transmission Line and Substation Systems Significant quantities of material are used in overhead conductor to transport high voltages over varying distances – long distances for the transmission of electricity on a national scale, and shorter distances within renewable energy farms. A typical Aluminium Cable Steel Reinforced (ACSR) conductor – aluminium strands wrapped around a composite steel core – can be decommissioned and re-drummed on site in bulk. A mobile recycling prototype developed by Germany’s ZECK GmbH has been adapted and recreated by Transgrid in Australia to pilot the decommissioning of overhead conductors. Underground cables use different conductor material and require more protection from the local environment and submarine cables even more so. A wide range of underground cable sizes and compositions, arising from the rated operating loads and specified conditions, makes it difficult to explore the varying options to determine their design 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 119 lifetimes and recycle them [32]. However, the general composition of a cable is copper cores surrounded by insulating polyvinyl chloride (PVC) and paper layers. Recycling these materials will alleviate the mining of copper from copper ore, and production of virgin PVC. High-voltage equipment responsible for monitoring and transmitting electricity from generators to the grid require a variety of material across the protection, metering, control, and operation of a switchyard. Equipment such as insulators, surge arrestors, voltage and current transformers, circuit breakers, disconnectors, transformers, busbars, and post insulators are made of individual recyclable components. There are no effective recycling schemes developed due to the intricacy of the design of the equipment and the cost savings being outweighed by the labour cost to complete the decomposition [34]. Common high-voltage equipment typically found in substations encompass a variety of apparatus including transformers, disconnectors, circuit breakers, and surge arrestors. The majority of this old but still current technology, is made up of mineral-oil insulating fluids – some containing Polychlorinated Biphenyls (PCBs), or gas (SF6) insulated materials, and currently all go to landfill through qualified disposal companies during the decommissioning of the switchyards. High toxicity levels seen in PCBs negatively affect the ecosystems and living organisms and have demonstrated major issues when determining the method of disposal at the decommissioning stages [35]. Due to the depletion of metal by environmental conditions, and aged oil affecting the durability of the material it joins, it is difficult to return material to its original state without diminishing physical properties, specifically its strength. Transmission line structures supporting overhead conductor, and connect substations to other substations, come in three main forms: steel, concrete, and treated and untreated wood structures. Steel fittings and glass or polymer insulators form part of this system and are required to connect the adjacent structures to form the lines. Timber wood poles are becoming less common due to its durability and inability to withstand the harsh Australian climate. Polymer insulators in substations and on transmission lines have been introduced to alleviate the potential damage caused by porcelain/ceramic or glass insulators at failure, however, have a relatively shorter life estimated at 20 years [36]. Therefore, polymer, porcelain and glass must all be considered for recycling and re-use. Zimmermann & Zattera [34] produced a method to recycle ceramic insulators into thermoplastic composites and hybrid composites. 4. CONCLUSION To provide a more sustainable and credible industry, it is critical to approach the renewable energy sector as a circular economy [32]. Renewable energy generators being introduced at an exponential rate has seen a decline in sustainable practices in the use of resources. Hence re-use and recycling of material at the end-of-life for these systems is crucial. There is a multitude of possibilities to rectifying the mining of a limiting supply of natural resources and move towards a cyclic economy. Understanding the current availability of material in the renewable energy market and length of time before these assets reach the end-of-life will provide a timeline for opportunities to be developed. 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ACS omega 2018, 3(9), 11317-11330. <https://doi.org/ 10.1021/acsomega.8b01560> 121 Carbon Benefits of Transitioning Diesel Public Transport to Electric Drives Thiradet Wiriyayuttaphan Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand, [email protected], ORCID: 0009-0008-5550-6734 Pantip Kayee Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand, [email protected], ORCID: 0009-0003-8789-7577 Napassawan Khammayom Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0003-4108-9702 Ramnarong Wanison Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-5078-9893 Yuttana Mona Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, y[email protected], ORCID: 0000-0002-1102-9242 Witsarut Achariyaviriya Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-1832-0761 Wongkot Wongsapai Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-2273-5177 Pana Suttakul Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand, [email protected], ORCID: 0000-0002-2946-8921 Cite this paper as: Wiriyayuttaphan, T, Kayee, P, Khammayom, N, Mona, Y, Achariyaviriya, W, Wongsapai, W, Suttakul, P. Carbon benefits of transitioning diesel public transport to electric drives. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: This study presents a detailed analysis of the environmental impacts and economic implications of replacing diesel public transportation with electric alternatives. Employing a multifaceted approach, the research integrates life-cycle assessments, real-world energy consumption metrics, and evaluations of the total cost of ownership. Key aspects include the analysis of the entire life cycle emissions of diesel and electric vehicles, encompassing production, operational energy use, and endof-life decommissioning. Notably, the energy mix in electricity production is scrutinized for its significant impact on the overall environmental footprint of electric vehicles. Economic factors, such as transitioning costs, maintenance expenses, energy consumption, and potential subsidies, are also examined. The study incorporates the social cost of carbon to quantify the broader impacts of emissions on public health, environmental integrity, and climate change. Results indicate a substantial reduction in carbon emissions by adopting electric drives in public transportation, particularly in regions with a renewable energy-dominated electricity grid. The results offer critical insights for policymakers, underlining the need for integrated strategies that synergize technological and energy policy initiatives for transitioning diesel public transport to electric drives. This research contributes 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 122 significantly to the discourse on sustainable urban mobility, underscoring the pivotal role of electrification in achieving low-carbon transportation solutions. Keywords: Clean energy; Life-cycle assessment; Public transit; Transportation electrification, Total cost of ownership © 2024 Published by ECRES Nomenclature Detail GHG EV ICEV TCO BEV Greenhouse gas Electric vehicle Internal combustion engine vehicle Total cost of ownership Battery electric vehicle 1. INTRODUCTION Climate change and global warming pose monumental challenges in the 21st century. Mitigation strategies encompass diverse approaches, including adopting renewable energy sources and stricter regulations on greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2). Transportation is a significant contributor to carbon emissions, and its electrification is a key strategy for decarbonization [1]. Electric vehicles (EVs) rapidly replace traditional internal combustion engine vehicles (ICEVs). This shift is evident in ambitious national targets, with Norway aiming for 100% EV sales by 2025 and the European Union targeting a 40% reduction in carbon emissions by 2030 [2, 3]. The global automotive industry anticipates EVs becoming the dominant technology within the next decade. This has spurred extensive research into various aspects of EVs, including their energy consumption, carbon emissions, and total cost of ownership (TCO). Existing research reflects the growing use of EVs in personal vehicles and taxis. Studies have analyzed different EV types, including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs), and their impact on energy consumption and carbon emissions. Notably, research in Japan, China, and New York has investigated the decarbonization potential, realworld environmental impact, and operational feasibility of EVs in diverse settings, mainly urban areas with pro-EV policies [4-6]. Despite significant research on EVs’ environmental benefits and energy efficiency, a crucial financial aspect like TCO has been studied less. TCO significantly influences consumer purchases and government incentives. Detailed TCO information throughout an EV’s lifespan is valuable for consumers, automakers, and policymakers. In recent years, there has been a rise in TCO studies, particularly in regions heavily adopting EVs [7, 8]. These studies aim to understand the financial implications of EV ownership, including purchase price, energy costs, taxes, insurance, and maintenance. The focus has been on regions with high EV penetration, like the EU, the US, Japan, and China [8-11]. However, due to varying EV costs and government support across countries, TCO analyses need customization for each region's specific context. Additionally, TCO models have diverse assumptions based on individual studies. This highlights the complexity of assessing EV economics and the need for tailored analyses to effectively guide policy and consumer choices. In Thailand, where there is a concerted effort to EVs, particularly BEVs, within the automotive sector, conducting a TCO analysis tailored to the country’s specific context is essential for stakeholders to comprehensively grasp the overall expenses associated with EVs. Moreover, there is a notable scarcity of TCO models that cater to public transportation systems operating on local tourist routes. To our knowledge, a comparative analysis of TCO models for BEVs and ICEVs powered by diesel engines, specifically within local public transport in Thailand, and incorporating data from real-world driving tests, has yet to be documented. This study focuses on the TCO for BEVs compared to traditional diesel-powered ICEVs in Thailand’s public transportation sector. We introduce a novel TCO model encompassing a comprehensive range of costs, including initial purchase price, financing, taxes, insurance, maintenance, salvage value, recurring expenses, and energy consumption based on real-world driving data collected in Chiang Mai. This data-driven approach ensures the analysis reflects local conditions accurately. The research utilizes the TCO model findings to generate various scenarios and formulate policy recommendations for facilitating the transition from diesel vehicles to BEVs in Thai public transportation. These insights offer valuable strategic guidance for policymakers and investors aiming to promote BEV adoption within the sector. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 123 2. METHODOLOGY 2.1. TCO Model: Variables and Assumption The TCO model offers a comprehensive framework for evaluating the lifetime financial commitment associated with a product. Based on well-defined assumptions, it aggregates all present and future expenses. For consumers considering new vehicle technologies like BEVs, TCO analysis is a critical tool for informed decision-making. This stems from the diverse range of variable costs incurred throughout a vehicle's lifespan, extending beyond the initial purchase price. By enabling a holistic understanding of the actual cost of ownership, the TCO model empowers consumers to make sound purchasing choices. Furthermore, a comprehensive TCO analysis benefits manufacturers and government entities by informing the development of effective strategies and policies to promote EV adoption. The model's accuracy hinges on the precision of its underlying assumptions and the quality of empirical data employed. It encompasses a broad spectrum of costs, including acquisition price, operational expenses, maintenance fees, and residual (salvage) values. The resulting TCO formula provides a holistic view of the financial commitments over a vehicle's lifespan, aiding not only consumer choice but also policy and manufacturer decisions in fostering a transition towards sustainable transportation solutions. The TCO concept provides a holistic perspective on a vehicle's financial burden throughout its lifespan. It encompasses all present and future expenditures associated with acquisition, operation, and maintenance, culminating in a single, comprehensive value. This metric empowers both consumers and businesses to make informed investment decisions regarding vehicle choices. The general equation for calculating the TCO of a vehicle typically includes the following components: TCO⁡=⁡𝐶purchase+𝐶operation−⁡𝑆value (1) where, 𝐶purchase denotes the initial purchase price of the vehicle. 𝐶operation represents all operational costs paid annually. 𝑆value denotes the salvage value or the estimated resale value of the vehicle at the end of its useful life. The equation for the 𝐶purchase, considering various relevant costs, can be represented as follows: 𝐶purchase=⁡𝑀𝑆𝑅𝑃+𝑇sales+𝐹additional−𝑆government−𝐷dealership (2) where, 𝑀𝑆𝑅𝑃 is the manufacturer’s suggested retail price. 𝑇sales represents all applicable sales taxes, which are calculated as a percentage of the 𝑀𝑆𝑅𝑃 and any taxable fees. 𝐹additional encompass a variety of costs such as destination charges, documentation fees, registration fees, and any optional add-ons or services purchased with the vehicle. 𝑆government denotes financial incentives provided by government entities to encourage the purchase of environmentally friendly vehicles, such as EVs. 𝐷dealership is a discount in price offered by the dealership, which may include promotional offers, negotiation outcomes, or loyalty discounts. The equation for the 𝐶operation, considering various relevant annual costs, can be represented as follows: 𝐶operation=𝐶maintenance+𝐶finance+𝐹tax&renewal+𝐶insurance+𝐶energy (3) where, 𝐶maintenance includes regular maintenance, repairs, and replacement of parts over the life of the vehicle. 𝐶finance accounts for the cost of financing the vehicle, including interest on loans or lost interest that could have been earned on the money if it had not been spent. 𝐹tax&renewal includes annual taxes and fees paid to a government authority for the renewal of a vehicle. 𝐶insurance refers to the premium paid by the vehicle owner to an insurance company for coverage against various risks associated with owning and operating the vehicle. 𝐶energy is the cost of fuel or energy consumption depending on the type of vehicle. To calculate the comprehensive ownership cost over a specified timeframe, the discounted cash flow approach is employed. This approach incorporates time value for money by applying a discount rate to the projected costs of each subsequent year. By determining the present value of these annual expenditures, they are aggregated to 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 124 ascertain the cumulative discounted cost for the delineated period spanning 𝑛 years. The equation for the total discounted cost of the 𝐶operation over a period of 𝑛 years can be expressed as: 𝐶operation 𝑛=∑𝐶operation 𝑖 (1+𝑟)𝑖 𝑛 𝑖=1 (4) where, 𝐶operation 𝑛 is the total cost over a period of 𝑛 years. 𝐶operation 𝑖 represents the estimated annual cost for year 𝑖, which represents each year within the 𝑛-year period (𝑖 = 1, 2, 3, ..., 𝑛). The parameter 𝑟 is the annual discount rate, expressed as a decimal. 2.2. Real-world Energy Consumptions The energy consumption data provided by manufacturers is often imprecise, influenced by the myriad complex factors encountered in real-world driving conditions. Accordingly, this research adopts an empirical approach, utilizing data derived from actual driving tests conducted on public transport routes within the urban context of Chiang Mai. This methodology is employed to enhance the TCO model, aiming to mirror real-life scenarios as closely as possible. The study focuses on current BEVs and conventional diesel-powered ICEVs used as pick-up truck taxis in the urban environment to ensure a comprehensive understanding of energy consumption patterns. The examination concentrates on traffic conditions emblematic of urban driving realities, encompassing light, medium, and heavy congestion. This assessment incorporates varied driving patterns and spontaneous routes to accurately represent the actual driving behaviors encountered in urban traffic within the Chiang Mai metropolitan area. To mitigate the influence of individual driving styles on the collected data, the test utilizes ten drivers. This approach ensures that the assessment's findings are not skewed by the peculiarities of single-driver behaviors, thereby providing a more reliable and representative dataset for analysis. The gathered data, precisely fuel or electricity consumption (in liters or kilowatt-hours) and driving distance (in kilometers), serve as the basis for calculating the energy consumption of each powertrain type. For diesel-powered ICEVs, the energy consumption (measured in liters per kilometer, L/km) can be conceptually determined as: 𝐶energy=⁡Total⁡fuel⁡consumption Total⁡driving⁡distance (5) This equation facilitates the quantification of fuel efficiency by relating the volume of diesel consumed to the distance traveled, thereby offering a metric for assessing the energy efficiency of diesel-powered ICEVs. It should be noted that fuel consumption can be directly calculated by employing mass airflow and the actual air-fuel ratio, provided these metrics are available from the On-Board Diagnostics II (OBD-II) system. This method allows for the accurate calculation of fuel consumption by correlating the mass of air consumed by the engine per hour with the actual ratio of air to fuel used during combustion. This also offers a precise means of assessing the fuel efficiency of diesel-powered ICEVs based on real-time engine performance data. The electrical energy consumption of BEVs is typically quantified by simultaneously measuring the battery’s current and voltage. The consumption of electrical energy by BEVs throughout a journey, expressed in watt-hours (Wh), can be derived from sensor data collected via the OBD-II system, focusing on battery-related metrics. This approach facilitates an accurate determination of the energy utilized by BEVs during travel, providing insights into their efficiency and operational characteristics. Energy consumption for electric motors is customarily quantified in kilowatt-hours per kilometer (kWh/km). To enable a direct comparison with diesel-powered ICEVs, the energy consumption of BEVs is converted into liters equivalent per 100 kilometers (Le/100 km). This conversion utilizes a factor corresponding to the energy content 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 125 of diesel fuel, thereby standardizing the measurement units across different powertrain technologies and facilitating a straightforward comparison of their energy efficiencies. 3. RESULTS AND CONCLUSION Considering an average annual driving distance of 60,000 kilometers over a decade within the urban public transport framework of Thailand, the TCO model for the vehicles under study is presented in Figure 1. The costs considered in the model encompass depreciation, energy consumption, loan interest, insurance, maintenance, taxation, and battery-related expenses. The findings indicate that the TCO for ICEVs and BEVs amounts to 2.15 and 1.94 million Baht, respectively. As anticipated, ICEVs exhibit the higher TCO, with fuel costs contributing 54.21% and depreciation 33.34%. Depreciation emerges as the foremost expense for both ICEVs and BEVs, significantly impacting the cost structure of BEVs. Given the volatility and evolving reliability of new automotive technologies, BEVs experience relatively elevated depreciation rates. Moreover, expenditures tied to the battery, including replacement and home charging infrastructure, escalate the BEV’s overall costs. From an environmental perspective, BEVs represent the preferable option, leveraging electrical power to achieve lower energy consumption and carbon emission than ICEVs. Nonetheless, in the automotive market, BEVs face challenges related to consumer range anxiety and a steep depreciation curve. (a) (b) Figure 1. The TCO of the (a) ICEVs and (b) BEVs over a 10-year ownership period. Given the assumed mileage and ownership duration, the total costs associated with ICEVs and BEVs exhibit minimal differences. Thus, the role of government subsidies and dealership discounts is paramount in advancing the adoption of BEVs and expediting the phase-out of ICEVs. A sensitivity analysis is instrumental in evaluating the adequacy of these supports. The insights derived from this analysis will demonstrate the year in which the TCO for BEVs becomes competitive with that of ICEVs. 33.34% 54.21% 3.10% 4.08% 4.68% 0.67%  Depreciation Energy consumption Loan interest Insurance Maintenance Tax Battery ICEVs 49.94% 18.96% 4.09% 4.59% 3.78% 0.45% 18.19% Depreciation Energy consumption Loan interest Insurance Maintenance Tax Battery BEVs 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 132 5.2. Conventional GVM-DPC and ANN-GVM-DPC technique performance Figure 6. (A) DC-Link Voltage of DPC-GVM, (B) DC-Link Voltage of DPC-ANN method Figure 7. (A) Active Power tracking of Conventional DPC-GVM, (B) Active Power tracking of DPC-ANN Figure 8. (A) Reactive power tracking of Conventional DPC-GVM, (B) Reactive power tracking of DPC-ANN Figure 9. (A) grid’s currents of conventional DPC-GVM method, (B) grid’s currents of DPC-ANN method 0 0.1 0.2 0.3 0.4 0.5 0.6 0 50 100 150 200 250 300 350 Time (s) Voltage (volt) 0 0.1 0.2 0.3 0.4 0.5 0.6 0 50 100 150 200 250 300 350 Time (s) Voltage (volt) Vdc ref Vdc mes via DPC-GVM Vdc ref Vdc mes via DPC-ANN 0.1 0.11 0.12 0.13 0.14 0.15 198 199 200 201 202 203 0.1 0.11 0.12 0.13 0.14 0.15 198 199 200 201 202 203 AB 0 0.1 0.2 0.3 0.4 0.5 0.6 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5x 104 Time (s) Active Power (W) 0 0.1 0.2 0.3 0.4 0.5 0.6 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5x 104 Time (s) Active Power (W) P ref via DPC-GVM P est via DPC-GVM P ref via DPC-ANN P est via DPC-ANN AB 0 0.1 0.2 0.3 0.4 0.5 0.6 -2000 -1500 -1000 -500 0 500 Time (s) Reactive Power (VA) 0 0.1 0.2 0.3 0.4 0.5 0.6 -2000 -1500 -1000 -500 0 500 Time (s) Reactive Power (VA) Q est via DPC-GVM Q ref Q est via DPC-ANN Q ref 0.1 0.11 0.12 0.13 0.14 0.15 -300 -200 -100 0 100 0.1 0.11 0.12 0.13 0.14 0.15 -300 -200 -100 0 100 AB 0 0.1 0.2 0.3 0.4 0.5 0.6 -20 -10 0 10 20 Time (s) Current (A) Ia Ib Ic 0.2 0.22 0.24 0.26 0.28 0.3 -10 -5 0 5 10 0 0.1 0.2 0.3 0.4 0.5 0.6 -20 -10 0 10 20 Time (s) Current (A) 0.2 0.22 0.24 0.26 0.28 0.3 -10 -5 0 5 10 Ia Ib Ic Zoom Zoom DPC-GVM DPC-ANN 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 133 Figure 10. FFT analysis Table I. Specification parameters used in the simulation Parameter Value Unit Phase-to-phase RMS voltage 208 volt DC-Link Voltage, Vdc 200 volt Filter inductor, L 11×10−3 H Filter inductor, R 10 ohm DC-link capacitor, C 1100×10−6 F Table II. Technical Specifications of Photovoltaic System Parameters Value Unit Maximum Power (Pm) 200 W Open Circuit Voltage (Voc) 57.6 V Short Circuit Current (Isc) 4.6 A Maximum Power Voltage (Vmp) 47.06 V Maximum Power Current (Imp) 4.26 A Working temperature -45 to +85 oC Tolerance ± 5 % 6. CONCLUSION In this paper, a robust algorithm is presented to regulate the grid current coming from the solar PV system via a DC\DC converter and three-phase VSI inverter. The conventional GVM-DPC technique is modified using ANN controllers instead of the three classical PI controllers, which are used in the traditional DPC-GVM method. The proposed method DPC-ANN achieved significant performance in the transient and steady-state behavior and showed a strong ability and high efficiency in dealing with external perturbations and sudden fluctuations compared to the conventional DPC-GVM strategy. Three-phase smooth currents according to the control requirements are injected into the grid. Finally, simulation results illustrate that the proposed method is working well requiring the grid conditions. ACKNOWLEDGMENT This work has been achieved under the Electrical and Automatic Research Laboratory (LREA), university of Yahia Fares of Medea, Algeria. REFERENCES [1] M. E. T. Souza Junior and L. C. G. Freitas, “Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review,” Sustainability, vol. 14, no. 6, p. 3597, Mar. 2022, doi: 10.3390/su14063597. [2] A. Banik, A. Shrivastava, R. Manohar Potdar, S. Kumar Jain, S. Gopal Nagpure, and M. Soni, “Design, Modelling, and Analysis of Novel Solar PV System using MATLAB,” Materials Today: Proceedings, vol. 51, pp. 756–763, 2022, doi: 10.1016/j.matpr.2021.06.226. [3] “A Critical Appraisal of PV-Systems’ Performance.pdf.” [4] F. Katiraei and J. Aguero, “Solar PV Integration Challenges,” IEEE Power and Energy Mag., vol. 9, no. 3, pp. 62–71, May 2011, doi: 10.1109/MPE.2011.940579. [5] Erika Twining and D. G. Holmes, “Grid current regulation of a three-phase voltage source inverter with an LCL input 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 134 filter,” IEEE Trans. Power Electron., vol. 18, no. 3, pp. 888–895, May 2003, doi: 10.1109/TPEL.2003.810838. [6] A. I. M. Ali et al., “An Enhanced P&O MPPT Algorithm With Concise Search Area for Grid-Tied PV Systems,” IEEE Access, vol. 11, pp. 79408–79421, 2023, doi: 10.1109/ACCESS.2023.3298106. [7] A. Timbus, M. Liserre, R. Teodorescu, P. Rodriguez, and F. Blaabjerg, “Evaluation of Current Controllers for Distributed Power Generation Systems,” IEEE Trans. Power Electron., vol. 24, no. 3, pp. 654–664, Mar. 2009, doi: 10.1109/TPEL.2009.2012527. [8] Y. Gui, X. Wang, and F. Blaabjerg, “Vector Current Control Derived from Direct Power Control for Grid-Connected Inverters,” IEEE Trans. Power Electron., vol. 34, no. 9, pp. 9224–9235, Sep. 2019, doi: 10.1109/TPEL.2018.2883507. [9] L. A. Serpa and P. M. Barbosa, “A Modified Direct Power Control Strategy Allowing the Connection of Three-Phase Inverters to the Grid Through LCL Filters,” IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, vol. 43, no. 5, p. 13, 2007. [10] M. Debouza, A. Al-Durra, R. Errouissi, and S. M. Muyeen, “Direct power control for grid-connected doubly fed induction generator using disturbance observer based control,” Renewable Energy, vol. 125, pp. 365–372, Sep. 2018, doi: 10.1016/j.renene.2018.02.121. [11] K. Colin, X. Bombois, L. Bako, and F. Morelli, “Closed-loop identification of MIMO systems in the Prediction Error framework: Data informativity analysis,” Automatica, vol. 121, p. 109171, Nov. 2020, doi: 10.1016/j.automatica.2020.109171. [12] Y. Gui, C. Kim, and C. C. Chung, “Grid voltage modulated direct power control for grid connected voltage source inverters,” in 2017 American Control Conference (ACC), Seattle, WA, USA: IEEE, May 2017, pp. 2078–2084. doi: 10.23919/ACC.2017.7963259. [13] A. Borni et al., “Optimized MPPT Controllers Using GA for Grid Connected Photovoltaic Systems, Comparative study,” Energy Procedia, vol. 119, pp. 278–296, Jul. 2017, doi: 10.1016/j.egypro.2017.07.084. [14] Y. Gui, C. Kim, C. C. Chung, J. M. Guerrero, Y. Guan, and J. C. Vasquez, “Improved Direct Power Control for GridConnected Voltage Source Converters, Oct. 2018, doi: 10.1109/TIE.2018.2801835. 135 A Long-Term Strategy Suggestion for Renewable Energy Success Katherine Johnson Johnson Consulting Group, Frederick, MD USA Country [email protected]., ORCID: 0009-0006-05775472 Cite this paper as: Johnson, Katherine. A Long-Term Strategy Suggestion for Renewable Energy Success \12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Energy efficiency (EE) strategies are integral to the transition to a net-zero economy, as homes and businesses powered by solar, and wind must be as efficient as possible to maximize their energy potential. But before customers invest in new solar or renewable energy technologies (i.e., renewable energy desserts), they need to ensure that their homes and buildings are fortified with “energy efficiency vegetables” through proper insulation, air sealing, weatherization and caulking. While these energy efficiency improvements are not as “appealing” as a new solar PV system or solar water heater, they are critical to maximizing the cost-effectiveness of these renewable technologies. This paper will share the latest findings from a literature review exploring the long-term benefits of energy efficiency investments. It will also highlight how non-energy benefits act as a multiplier to these energy investments, leading to job creation and improved health for program participants. Drawing on the results from several recently completed regional and national studies, this paper will demonstrate the importance of energy efficiency investments. It will also quantify the magnitude of these energy and non-energy benefits and share strategies that program administrators should consider in future program initiatives. Keywords: Energy efficiency, renewable energy, non-energy impacts © 2024 Published by ECRES 1. INTRODUCTION The transition to clean, renewable energies will require a massive investment in the technology and infrastructure of the electric grids in both Europe and North America. However, this investment in wind and solar power needs to be combined with a more basic investment that, while not as appealing as renewable energy, has more long-term potential energy savings. Therefore, before we embark on a trillion-euro investment in massive new technologies, we should first buttress our investments in energy efficiency (EE) programs and activities. Whether supported by utilities, government programs, or individuals, these energy efficiency activities will provide a solid foundation to transition to this new clean energy future. This paper summarizes a literature review highlighting the importance of energy efficiency investments and how energy efficiency should be the cornerstone for all future investments in renewable technologies. This paper draws on reports and analyses from multiple energy organizations and regional and municipal programs that showcase how consumers and small businesses can prepare cost-effectively for the transition to a renewable energy economy. As a point of comparison, energy efficient investments should be viewed as “EE Vegetables,” which are the essential elements one needs in a healthy diet before moving on to the more desirable “renewable energy desserts.” This paper highlights how innovative energy organizations encourage customers to focus first on “EE Vegetables” before moving to “renewable energy desserts”. In some cases, solar PV or wind power may never be cost-effective for consumers, which makes it all the more important to invest in energy efficiency first. 2. WHY ENERGY EFFICIENCY MATTERS For the past two decades, numerous studies have documented the importance of putting energy efficiency investments first ahead of other energy sources. Now, energy efficiency is a resource often viewed as the first starting point in designing and preparing plans for new power plants. It is a mandated requirement in many jurisdictions to consider energy efficiency options first before committing to a new power plant. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 136 The rationale for this is quite simple: it is shown that energy efficiency provides a “relatively inexpensive response to the challenge and other environmental effects of energy use while continuing to meet demand.”[1] This 2006 study found that every dollar invested in energy efficiency programs saves $0.10 and mitigates environmental damage. Most benefits are from reductions in CO₂ from decreased energy use.[2] The American Council for an Energy-Efficient Economy (ACEEE) documented the energy savings that have occurred worldwide due to investments in energy efficiency. This international scorecard determined that in 2018, France, the United Kingdom, Germany, the Netherlands, and Italy were the top countries investing in energy efficiency.[3] However, their investments and energy efficiency still need to catch up to what is required to fully transition to a renewable economy. Energy Expenditures and Cost Savings: Energy efficiency investments lead to documented substantial energy savings. Based on simple assumptions, one analysis found that the annual savings from energy efficiency investments are approximately around $800 billion. Without these investments, energy costs would have been 77%, or $774 billion[4]. Benefits of Energy Efficiency as a Resource: Energy efficiency has become recognized as an integral element of utility investments and operations. Utility energy efficiency programs have yielded significant energy and economic benefits to utility systems and utility customers. Energy efficiency is considered a vital utility system resource, typically the lowest-cost system resource compared to supply-side investments. Saving energy via customer energy efficiency programs generally can be achieved at one-third to one-fourth the cost of fossil-fuel-based supply-side alternatives.[3] Figure 1. Source: Levelized cost of energy efficiency compared with unsubsidized supply-side resources (Data: ACEEE 2020, Lazard 2020) Although renewable energy resources have dropped significantly for wind and solar projects, energy efficiency remains the most attractive and lowest-cost choice.[6] Figure 1 illustrates why customers should be encouraged to invest in EE vegetables as that will provide a long-term, cost-effective solution for both customers and utilities. [7] 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 137 3. DEFINING “EE VEGETABLES” Encouraging customers to make simple energy-efficient investments is easy as it relies on commonsense activities that align well with current customer behaviors. Utilities and energy organizations offer tips to help homeowners and renters identify easy, low-cost, and no-cost solutions to lower their energy bills. Still, these tiny actions add up and lead to noticeable savings in electricity usage and energy demand declines in the overall grid. The most effective approach has been to focus on easy activities first, such as: Table 2. Examples of “EE Vegetables” “Low cost/no cost” EE Vegetables “Cost-effective” EE Vegetables Turn down the temperature setting of heaters to 49 degrees Celsius. Change out the five most used lightbulbs to LEDs Use energy-saving settings on refrigerators, dishwashers, washing machines, clothes dryers, home computers and monitors. Conduct an energy audit to identify all energy savings opportunities Clean or replace furnace, air-conditioner, and heat-pump filters. Purchase and program smart thermostats. Unplug appliances when not in use Purchase energy-efficient appliances, furnaces, air-conditioning units, heat pumps and water heaters. Wash clothes in cold water Seal leaks through caulking; add attic and wall insulation Use a clothesline to dry clothes Europe Needs Energy Efficiency Vegetables After the Russian invasion of Ukraine led to global oil and gas shortages, governments struggled to find alternative energy sources. They asked consumers to curb energy use. Shortly after the Russian invasion of Ukraine, the International Energy Agency (IEA) released a report called “Accelerating energy efficiency: What governments can do now to deliver energy savings.”[8] The IEA report emphasized the need to reduce energy usage by making some “EE Vegetable” improvements such as upgrading insulation, accelerating heat pump installation, and promoting the use of “smart” thermostats. The following recommendation illustrates how even small conservation actions could lead to significant energy savings, such as lowering the temperature in homes in the winter by just one degree. Despite these efforts, the U.K.’s Environmental Audit Committee criticized the government’s lack of progress in fixing poorly insulated homes, which can be easily addressed by installing energy insulation, another EE Vegetable. “Bold action is needed now. We must fix our leaky housing stock, which is a major contributor to greenhouse gas emissions and wastes our constituents’ hard-earned cash. The government could have gone further and faster.” U.K.’s Environmental Audit Committee (EAC)[9] The Local Government Association in the U.K. determined that poorly insulated houses will cost more than £12.7 billion in higher energy costs over the next two years based on current prices. This association, which represents more than 350 councils in England and Wales, estimated that these leaky homes are more likely to be occupied by the most vulnerable citizens, senior citizens and those on fixed incomes.[10] Some local governments have decided to focus on improving insulation by taking advantage of a special Social Housing Decarbonisation Fund (SHDF) which takes a "fabric first" view that prioritizes loft and wall insulation before replacing energy systems—another way to focus on “EE Vegetables.”[11] 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 138 4. ADDING RENEWABLE ENERGY SOLUTIONS (DESSERTS) Once these residences have been properly insulated, then installing solar PV panels may be a cost-effective solution. Several solar installation companies recommend that homeowners first increase energy efficiency to reduce energy consumption, as this approach can decrease the size of the solar panel configuration and thus lower its cost. As one installer explained, “Each dollar spent on energy efficiency measures can save consumers between $3 and $5 on a solar array.[12] Other renewable energy experts agree that rooftop photovoltaic systems combined with energy efficiency is an effective strategy. As a recent report acknowledged, “overall, there is a consensus that a comprehensive approach integrating energy efficiency with renewable energy production is the most effective way to reduce non-renewable energy consumption and cut carbon emissions.”[13] Many state governments also advocate that solar PV owners should invest in energy efficiency first and recommend a host of “EE vegetables,” including weatherstripping, sealing, adding insulation, and replacing or repairing home heating and cooling systems.[14] 5. ADDITIONAL NON-ENERGY IMPACTS FROM EFFICIENCY Energy efficiency investments also have a multiplier effect on the wider economy. For example, investments in energy efficiency creates more jobs in the energy efficiency field. Moreover, energy efficiency benefits also lead to additional non-energy benefits such as reduced operating expenses and maintenance (OEM) and increased health and comfort of building occupants, paving the way for a clean energy transition. Health Benefits The U.S.’s Environmental Protection Agency (EPA) quantified the health benefits associated with energy efficiency investments and determined that, ”improving energy efficiency and increasing the use of renewable energy can reduce fossil fuel-based generation and its associated adverse health and environmental consequences. The EPA analysis also pointed out that energy efficiency investments reduce carbon emissions, thus limiting the damage associated with greenhouse gas emissions and improving overall air quality long-term.[15] A comprehensive evaluation of a long-running U.S. Department of Energy’s Weatherization Assistance Program, which upgrades low-income homes by installing a variety of energy-efficient equipment, quantified these benefits even further. As the 2023 study found, the household health and related non-energy benefits were equivalent to $14,148 per single-family or mobile home.[16] This study further quantified the value of these non-energy energy benefits as follows:  $538 savings in pay due to fewer missed workdays  $514 savings per household in reduced medical expenses  $300 annual savings in energy costs The study also revealed this compelling statistic: for every $1.00 invested in weatherization (EE Vegetables), $1.72 is generated in energy benefits and $2.78 in non-energy benefits.[17] Energy Efficiency Fuels the Economy More than 2.1 million Americans now work in energy efficiency , representing the biggest part of the entire energy sector. EE includes jobs across a wide range of the U.S. economy, including the:  manufacturing sector, making products ranging from insulation to heat pumps to smart controls  professional services sector, including architects and engineers. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 139  construction sector, ranging from small residential contractors to construction firms that install mechanical systems. The 2023 U.S. Energy and Employment Jobs Report (USEER), published by the U.S. Department of Energy, determined that the energy workforce grew nearly 4% from 2021 to 2022 by creating 300,000 new jobs. Of note, clean energy jobs increased in every U.S. state and rose 3.9% overall. Jobs in related industries, such as electric vehicle batteries, solar and wind technologies also show marked increases year over year.[18] 6. KEY CONCLUSIONS AND RECOMMENDATIONS As this paper demonstrates, “EE Vegetables” such as air sealing and insulation, while not as appealing as solar panels or wind turbines, are the necessary ingredients for a smooth and successful transition to a net zero energy economy. Moreover, these investments provide a myriad of benefits beyond cost-savings for home owners, and create a cleaner and greener planet. As the International Energy Agency summarized on its webpage, “energy efficiency is called the “first fuel” in clean energy transitions, as it provides some of the quickest and most cost-effective CO2 mitigation options while lowering energy bills and strengthening energy security” [18]. REFERENCES [1] Gillingham, K., Newell, R.G., Palmer, K. “The effectiveness and cost of energy efficiency programs,” Resources, 2004, October 21. <https://www.resources.org/archives/the-effectiveness-and-cost-of-energyefficiency-programs/> Accessed 21/2/2024 [2] Ibid [3] American Council for an Energy-Efficient Economy (ACEEE), “Energy efficiency as a resource” webpage, no date. <https://www.aceee.org/topic/energy-efficiency-as-a-resource> Accessed 21/2/2024 [4] Ibid [5] Lazard, 2020. “Levelized cost of energy analysis” Version 14.0. No date. Cited in American Council for an Energy-Efficient Economy (ACEEE), “Energy efficiency as a resource” webpage, no date. < https://www.aceee.org/topic/energy-efficiency-as-a-resource> Accessed 21/2/2024 [6] Subramanian, S., H. Bastian, A. Hoffmeister, B. Jennings, C. Tolentino, S. Vaidyanathan, and S. Nadel. 2022. 2022 International Energy Efficiency Scorecard. Washington, DC: American Council for an Energy-Efficient Economy. www.aceee.org/research-report/i2201. p. 10. [7] Ibid [8] Bradstock, F. 2023. “Energy efficiency is now critical for Europe,” OilPrice.com, 12 January. <https://oilprice.com/Energy/Energy-General/Energy-Efficiency-Is-Now-Critical-For-Europe.html> Accessed 1 March, 2024. [9] Ambrose, J., 2023. “Little progress made on energy efficiency in UK homes,” The Guardian, 26 March. < https://www.theguardian.com/environment/2023/mar/27/little-progress-made-on-energy-efficiency-in-ukhomes-report-finds - :~:text=The UK remains too reliant, and jeopardised security of supply.> Accessed 1 March, 2024. [10] Local Government Association, 2022. “Lack of action on leaky homes will cost taxpayers billions,” Webpage, 23 September. <https://www.local.gov.uk/about/news/lack-action-leaky-homes-will-cost-taxpayers-billions-newlga-analysis - :~:text=New analysis from the local, recently announced Energy Price Guarantee.> Accessed 1 March, 2024. [11] Ibid [12] Green Energy of SA.com, 2024. “Improve your home’s energy efficiency before switching to solar,” Webpage, no date.< https://www.greenenergyofsanantonio.com/post/improve-home-energy-efficiency-before-switchingsolar> Accessed 1 March, 2024. [13] Clean Energy Resource Teams, 2024. “Before installing renewables, do basic energy-efficiency improvements,” Webpage, no date. < https://www.cleanenergyresourceteams.org/installing-renewables-dobasic-energy-efficiency-improvements> Accessed 1 March, 2024. [14] Minnesota Commerce Department Energy & Utilities, 2024. “Minnesotans can tap into solar energy,” Webpage, No date. <https://mn.gov/commerce/energy/solar-wind/solar-for-homes/> Accessed 1 March, 2024. [15] Environmental Protection Agency, 2024. “Part one: the multiple benefits of energy efficiency and renewable energy, 2018 Report. <https://www.epa.gov/sites/default/files/2018-07/documents/mbg_1_multiplebenefits.pdf> Accessed 22 February, 2024. p. 1-7. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 140 [16] Tonn, B. Carrol, D., Pigg, S. Blasnik, M. Dalhoff, G., Berger, J. Rose, E., Hawkins, B. Eisenberg, J., Ucar, F., Bensch, I. Cowan, C. 2014, “Weatherization works: summary of the findings from the retrospective evaluation of the U.S. Department of Energy’s Weatherization Assistance Program,” Oak Ridge National Laboratory, September. <https://www.energy.gov/sites/prod/files/2015/09/f26/weatherization-works-retrospective-evaluation.pdf> Accessed 1 March, 2024. [17] U.S. Department of Energy’s State & Community Energy Programs, 2024. “Weatherization assistance program” Fact Sheet. No date. <https://nascsp.org/wp-content/uploads/2023/08/weatherization-assistance-programfact-sheet.pdf> Accessed 1 March, 2024. [18] U.S. Department of Energy, Office of Policy 2023. “U.S. energy & employment report,” energy.gov, June. < https://www.energy.gov/policy/us-energy-employment-jobs-report-useer - :~:text=As the private sector continues the overall U.S. workforce,> Accessed 1 March, 2024. p. 4. [19]International Energy Organization, 2024. “Energy efficiency,” webpage. No date. <https://www.iea.org/energysystem/energy-efficiency-and-demand/energy-efficiency> Accessed 1 March, 2024 141 Analyzing Stabilization Time for Bypass Diode Thermal Test of c-Si PV Modules Prashob Sisiram Central Power Research Institute, Bangalore, India, [email protected] Rajoli Sudhir Kumar Central Power Research Institute, Bangalore, India, [email protected] Cite this paper as: Prashob S, Rajoli Sudhir Kumar. Analyzing Stabilization Time for Bypass Diode Thermal Test of c-Si PV Modules. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: Bypass diode is used in solar photovoltaic module to prevent damage to the cells when it is shade. This is achieved by bypassing the current through the diode to protect inactive cells due to shading phenomenon. Bypass diode shall be able to withstand the rise in its junction temperature when current is bypassed through it. Failure of the bypass diode causes hotspot creation in the module resulting in to fire hazard. Suitability of the diode is assessed by test procedure mentioned in IEC 61215-2. This procedure is for finding out maximum junction temperature of the diode when in operation and comparing it with the allowed maximum temperature mentioned by the manufacturer. Junction temperature is calculated by measuring the voltage drop across the diode assuming that the diode is in thermal equilibrium with its ambient. This study focuses on validating this assumption and establishing a qualitative relation of junction temperature of the diode and its ambient temperature. Measurement of voltage drop is carried out at different thermal stabilization time to understand the variation in measured voltage drop. It is observed that calculated junction temperature varies depending on the thermal stabilization of the diode with its ambient. Measurement taken after thermal stabilization is found more accurate and stable which will lead to proper decision making while testing bypass diode. Keywords: Bypass Diode, Photovoltaic (PV) Module, Thermal Stabilization, PV Module Testing © 2024 Published by ECRES Nomenclature PV ISC IEC MQT SiC IF Photovoltaic Short circuit current International Electro-technical Commission Module Qualification Test Silicone Carbide Forward operating current VR TJ VD TC Reverse voltage Junction Temperature Voltage drop Critical temperature 1. INTRODUCTION Bypass diode is a critical component in the solar photovoltaic (PV) module, which protects the cell being reversed biased in shaded condition by bypassing the current generated by other cells. Usually a PV module consists 1 to 3 bypass diode depending on the construction, dimensions, etc. During the normal operation of PV module in the field, all the cells in the module are exposed to solar radiation which enables the cells to generate current. This current flows through all the cells which are connected in series, thus adding up the voltages of each cell to produce PV module output voltage. If any of the cell is shaded during the operation, the cell become inactive and subjected to reverse biasing. Thus flow of the current which is generated by other cells leads to creation of hotspot in the shaded cell and eventually leads to cell damage. This phenomenon is prevented by incorporating a diode across the 244 Energy Source Analysis in Microgrid Applications on Rural Dairy Plants Evan A. M. Creeden Eaton EESS Division, Glendale Heights, IL, USA, [email protected], ORCID: 0009-0004-3898-660X Gholamreza Dehnavi University of Wisconsin-Platteville, Platteville, WI, USA, [email protected], ORCID: 0000-0002-3278-0713 Cite this paper as: Creeden, Evan, Dehnavi, Gholamreza. Microgrid Applications on Rural Dairy Plants. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: With microgrids becoming more prevalent across the world, so too is their application to varying industries and customers. This study presents the source optimization and feasibility process for a microgrid design for the University of Wisconsin - Platteville’s future Dairy Plant. In this analysis, multiple renewable energy sources are considered along with energy from the local utility. A software optimization tool known as XENDEE was used to determine the optimal sizes and types of sources to meet three different optimization cases or scenarios. These being cost only, emissions only, and a combination of both (CO2 and cost). For all three cases, reliability was also considered. To perform a reliable optimization, several factors are required to be considered for each renewable source. Such factors are discussed in this study. Moreover, to account for a more accurate monetary loss during a power outage, an improved baseline or reference case was created for use with the XENDEE platform. The process and nuances required for this baseline form another feature of this study. Using the improved reference case, varying scenarios were run and studied to help improve each of the three key scenarios. In the end, the scenarios were met, finalized and are presented here in this report. Keywords: Dairy Plant, Microgrid Design, Renewable Energy Sources, Source Optimization, and XENDEE © 2024 Published by ECRES Nomenclature DER(s) UWP MED SAIFI Distributed Energy Resource(s) University of Wisconsin-Platteville Major Event Days System Average Interruption Frequency Index SAIDI System Average Interruption Duration Index 1. INTRODUCTION Aside from electric vehicles, another fast-growing electric solution to the increasing issues of the modern day are Microgrids. An energy management solution that changes the traditional centralized structure of energy generation to a more decentralized or local form. This allows for a wide range of benefits. It enables the flow of energy back towards the grid during grid-connected mode of operation. Where electricity can both be sold back to the utility, which can greatly increase cost savings, reliability, and self-sufficiency. The Microgrid can also be operated entirely on its own in a mode known as “Islanded” or “autonomous” mode. While the larger utility grid may be facing issues or full-blown outages, the microgrid can be disconnected from the faulty grid and continue normal operation. Thus, greatly increasing the reliability or resiliency of the power system. Based on the emerging benefits, the Microgrid has seen a wide variety of applications to various industries and customers. Such as: the U.S. Army [1], universities and institutions worldwide [2], and even remote or indigenous communities [3]. There are even major programs supporting the application of microgrids like the United States Department of Energy’s Office of Electricity (OE) publishing vision statements exclaiming [4]: “By 2035, microgrids are envisioned to be essential building blocks of the future electricity delivery system to support resilience, decarbonization, and affordability. Microgrids will be increasingly important for integration and 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 245 aggregation of high penetration distributed energy resources. Microgrids will accelerate the transformation toward a more distributed and flexible architecture in a socially equitable and secure manner.” With this same spirit in mind, this study aims to investigate the application of microgrids to the dairy manufacturing industry. More specifically, to analyze the feasibility and optimal selection of distributed energy resources (DERs) in a microgrid design for a future dairy plant at the University of Wisconsin – Platteville. To assist in this study the web-based microgrid optimization and decision support platform XENDEE was used [5]. Before this tool could be utilized however, necessary data needs to be gathered. Data such as load profiles, DER specifications (both existing and new), utility rates and tariffs, and government incentives. Throughout this study, these details and specifics will be gathered and discussed; along with the simulations which relied on this data. Finally, these simulations, will be used to support three commonly desirable scenarios based on cost minimizations, CO2 emission reduction, and a combination of both. For each scenario, resiliency was also desired and so too was optimized. This paper is organized as follows: Section 2 describes Dairy plant equipment and load data needed for the reference case, Section 3 illustrates the assumptions and specifications for the renewable resources, Section 4 summarizes the design cases for the simulations, and Section 5 concludes this study with recommendations as well. 2. EQUIPMENT, LOAD DATA, AND REFERENCE CASE This section is a necessity as it enables the analysis in the sections to follow. The necessary information consists of the load profile of the dairy plant, the required machinery or loads, the possible power outage data for the local area, and the dairy products themselves. The latter three of the four will be used in the reference case to approximate the monetary loss should a disruption in power occur. A loss that could be avoided should a microgrid be present. 2.1. Load Profile: To assist in modeling the future dairy plant, a site visit to a local dairy plant that is estimated to be a similar size to the proposed UW-Platteville dairy plant was arranged. Through this facility, a load profile was constructed as shown in Figure 1. Without a microgrid, XENDEE estimates 209 metric tons of CO2 emission will be generated by the power utility company: Alliant Energy. This value will be used in the reference case to compare the emission reduction of the design cases. Figure 16. Approximate daily load profile of proposed dairy plant. 2.2. Required Machinery: Required machinery should be known to calculate the cost of product lost during an outage, without a microgrid, which is called here the reference case. With each dairy product requiring its own process for production, many dairy plants specialize in a select few products. The proposed plant for the University of Wisconsin – Platteville (UWP) will produce cheese and ice cream to be sold on the campus. The complex process for each dairy product is riddled with 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 246 nuanced specificities and will not be the focus of this study. Rather, the machines themselves and the volume of product that each house is what is important to this analysis. Through several consultations with the School of Agriculture at UW-Platteville, the intent is “to bring in modular pieces of equipment as needed for testing, etc. to be flexible for the needs of the industry and training”. Although, approximate sizes of machines, that are required, were gathered. The “Dairy Products” subsection below will discuss the financial implication of these sizes. 2.3. Outage Data for Local Area: Another crucial item to assist in building the reference case for XENDEE will be how often an outage occurs. This data is commonly used when discussing the reliability of a utility company and is referred to as the System Average Interruption Frequency Index (SAIFI) according to IEEE. A 2021 report by the Citizens Utility Board [6] contains state-wide averages of SAIFI from 2019. The source data was gathered and averaged from hundreds of nation-wide utility companies surveyed by the U.S. Energy Information Administration (EIA) [7]. Using data from 2019, figures 11 and 12, included in [7], detailed the SAIFI values for each state and both included and excluded Major Event Days (MED). MED are often the result of natural weather events and can materially affect annual reliability statistics. Considering the potential microgrid for UW-Platteville, this study will only be looking at the state of Wisconsin. For Wisconsin, these figures were 0.8 (without MED) and 1.1 (with MED). These values correspond to the average number of annual outages. For the most conservative result, a SAIFI value of 1.1 was chosen for the reference case. The average length of an outage must also be determined. This value is also calculated by the EIA and defined by the IEEE. It is referred to as the System Average Interruption Duration Index (SAIDI) and is measured in minutes. Again, using the averages from [6], 356 minutes was found to be the average, which equates to about 5.9 hours. Consulting with The School of Agriculture at UWP, 5.9 hours was found to be enough time for any product left in machines, silos, refrigerators, and freezers to be considered a financial waste. 2.4. Dairy Products: The final consideration in the reference case is the financial loss in dairy products, should an outage occur. With the equipment and volumes obtained by The School of Agriculture, these approximations were calculated. Assuming full silos of milk and cream, the loss would be $6k; milk in both cheese vats would cost $1k; finished product in the cooler would be $10k on the high end; finally, finished product in the freezer would cost another $10k. Product in the remaining machines could then round out to about $3k. Making the total loss in inventory about $30k. While these are rough estimates, they are also heavily based on the inventory at the time of the outage. There are many factors that can influence inventory size at any given moment. Therefore, a decision of 70% of full cooler and freezer capacity was decided by the authors. This would then lower the previous estimated cost of $30 thousand to $21 thousand. However, this new total should also be multiplied by the frequency of power outages for the area, or the SAIFI value found previously, which in this case is 1.1. This makes the total loss of product: $23,100. To obtain the total cost for the reference case in XENDEE, this loss of product should be added to the total energy costs incurred from the local power utility. Given the load profile in Figure 1 annual energy costs were found to be approximately $33,433.59 on Alliant Energy’s GS-3 rate schedule [8] which brings the total cost of the reference case to $56,534. This is the annual result of energy costs and losses from 1.1 power outages without a microgrid. 3. RENEWABLE RESOURCE REQUIREMENTS Each distributed energy resource (DER) involves its own complicated power system to convert nature’s work into electrical energy. XENDEE’s source analysis can be either very detailed or straight-forward, depending on the desires of the user. If the user has specific devices in mind, the device library can be used to pull from specific manufacturers in certain regions of the world. If one is unsure of a specific device or it is not in the library, one can also be made and 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 247 added to the library. This process can be done with either assumptions or entering every detail, but will vary depending on the DER. This section will discuss the various DERs that are optimal given the location of the proposed microgrid, according to the analysis using the software, and the various information needed by each to model. 3.1.Wind: Given the space and budget, wind can be a viable option; however, this DER required the largest initial cost. Therefore, in design cases where the goal was strictly to minimize costs, wind was not used. Although due to the surrounding geographic area, this energy source had the most consistent output of all tested. As with all other technologies tested, there were no specific devices to be tested, so generic baseline data was used from the device library. With this lack of specificities, the software only required site specific data such as the geographic area and the physical limitation of surface area that could be used for the construction of the turbines. The last information required is the rated size of a turbine, which was based on standardized sizes and the peak demand of the dairy plant which led to 60 kW. From this information, the optimal number of turbines can be determined based on the optimization conditions and cases featured in Section 4. 3.2. Solar: Solar Arrays are the most used DER in all design cases as it has a lower initial cost with a high energy yield during times of peak load. As with other technologies, the device library was used to determine cell type, efficiency, per unit install cost, and inverter type. Specific information the software requires is the geographic location of the building, the tax credit for the State/ Provence or Country, and the user defined area of the building traced over a satellite map. XENDEE then minimizes the user-defined area by 44% due to solar setbacks and GPS inaccuracies. For the proposed location of the Dairy Plant building an estimated 13,504.5ft2 was traced out which reduced to 5,942ft2. Using this area and the insolation data for the area, the rated size on the DC bus would be 83kWdc. For each design case this is the maximum size of solar as 100% of the area is used. While there are no tax credits for the state of Wisconsin, the U.S. federal government provides a 26-30% tax credit for the cost of installation [9]. 30% was entered and used in the financial analysis of each design case. 3.3. Battery Storage: With wind and solar present, batter storage was added as a way to ride through a potential outage. However, when added with the diesel generator, the generator was determined to be more optimal for two of the cases. The batteries were however effective at capturing the curtailment during midday and compensating for the lack of solar and wind early in the day. Due to its varying applications, this technology saw the greatest change in size throughout each of the design cases. If the option “auto-size” is selected, the software will optimally size the battery based on the DERs present. This option was used for all analyses. For all cases, an average lithium-ion battery bank was added from the data library; however, specifics can be entered, and a predetermined size can be chosen. 3.4. Diesel Generator: When this resource was most notably used was during a possible outage. Without this resource, a full ride-through would not be possible for some of the cases. The rated size of 80 kW was chosen to ensure it could meet full load and provide additional power if needed to charge the battery storage. Other than the size, an average or more specific generator could be chosen from the library. A CAT D80 GC was chosen to match an existing unit on campus. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 248 4. MIRCOGRID DESIGN CASES Three crucial goals of the University of Wisconsin – Platteville’s (UWP) future microgrid design project are to: “enhance the quality of dairy products by providing reliable energy sources, reduce the energy cost at the dairy plant by utilizing an AI-based energy management system, and utilize renewable energy sources to minimize environmental impacts”. To meet these goals, this section will look at the optimal portfolio of distributed energy resources. To help organize the simulations and meet the goals set forth by UWP, the results are refined into three design cases; each with a focus on different criterion. These three cases are: Cost Savings, CO2 Emission Reduction, and Minimize both Cost and CO2. Having a continuous supply of power is a critical factor in a dairy plant to maintain the quality of the product. This is why all three cases will also prioritize resiliency and ensure that power is not lost. The results of these cases are discussed in more detail in the reminder of this section. 4.1. Case One – Cost Savings: The first set of simulations focus on cost savings and reliability, with no attempt to minimize CO2 emissions. However, due to the nature of renewables, some emission savings will result. To ensure reliability, an outage ride-through is simulated during January when wind and solar power are at minimums. Looking first at the financial results, this simulation shows an upfront cost of $167 thousand, a yearly investment of $33.3 thousand, and a simple payback period of 6 years. The yearly savings amounts to 41.1% from the proposed reference case. Most costs incurred belong to the energy supplied by the utility. Despite emission reduction not being an objective, 137 metric tons were saved which corresponds to a 34.3% decrease in emissions. Three DERs were used in this case: solar, diesel generator, and battery storage. Solar remined fully utilized at 83 kWdc, the diesel generator is 80 kW, and the optimal battery size is 7 kWh. Wind is not used in this case. By comparing the capacities of all sources, it is clear that most energy is still being provided by the utility; on account of solar being limited by the proposed size of the building. Finally, with the help of the diesel generator, a full outage ride-through is achieved. 4.2. Case Two – CO2 Reduction: This design case will instead focus on CO2 minimization only with little care as to cost. To achieve this, the energy supplied by the utility company must be minimized. Regardless of financials not being an objective, they are also reported here. A yearly investment of $72.2 thousand is incurred, which is a 21.7% increase in cost compared to the reference case. The majority of this $1.1 million upfront cost are the three wind turbines. Which are needed to offset the solar array’s limitation. The simple payback period is over 20 years. Emission reduction goals are achieved with an annual 0 metric tons of CO2 emissions, and 0 kWh of utility energy purchased. To again ensure reliability, an outage is simulated during January when wind and solar power are at minimums. For this case, the diesel generator was not needed to ride-through the outage, the energy produced by the 3 wind turbines and the increased battery storage was more than enough to ensure reliability. Solar remained at 83 kWdc, wind totaled 180 kW, and the battery is sized at 112 kWh. 4.3. Case Three – Minimize Both Cost and Emissions: The previous two cases utilized XENDEE’s simple cost or emissions optimization presets. In this case a different method needed to be used so that cost and emissions could be optimized together. This required a tool called “premium cost factor” (PCF) in XENDEE, which is a user-entered index ranging from 0 to 2 which gives the user freedom to choose optimization. For this case, a PCF of 0.8 was chosen, which forced the cost savings to a minimum of 20%, while allowing CO2 emissions to be optimized. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 249 This case has a $534 thousand upfront cost with a yearly investment of $54.8 thousand, which amounts to cost savings of 20%. The simple payback period is 9 years. Emissions amount to an annual 40 metric tons of CO2 which is a 80.6% reduction from the reference case. Figure 17. Costs and savings projection (non-discounted) for design case three. (This is a non-discounted projection of project costs that assumes no changes in operation over time.) Figure 18. Microgrid cost breakdown for design case three. Figure 19. Annual electricity balance for design case three. Figure 20. Electricity dispatch for an emergency in January for design case three. (Axes are not scaled by data across all months/day types.) This case utilized all DERs discussed in Section 3, with solar again sized at 83 kWdc, wind totaled 60 kW with the one turbine, the diesel generator is again 80 kW and the battery is optimally sized at 152 kWh. By comparing the capacities of all sources, Figure 4 shows that most energy is produced by wind despite it having a smaller rated size. This is due to Solar’s rooftop limitation and the capacity factor of wind being higher than that of solar. To again ensure reliability, an outage is simulated during January when wind and solar power are at minimums. Figure 5 breaks down the electricity dispatch during this outage. 5. RECOMMENDATIONS AND CONCLUSION While each design case showed valuable data, it is clear based on the goals of the UW – Platteville that the optimal case for their dairy plant microgrid is design case three. This case allows for both cost and CO2 reduction, in addition, showed reliability during an outage. Although it was not part of the initial scope of the microgrid, there is an existing cattle barn onsite that features a much larger roof than the one on the proposed building, which allows for more solar arrays. Serving as a recommendation, each design case was also run with an increase in the solar square footage available. After the solar setbacks, the new area of both buildings would become 11,925 ft2. No additional parameters were changed from the official results. 11. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Riga/Latvia 18-20 May 2023 250 Case One saw no change with the additional solar area. The additional cost of adding more panels was not deemed optimal, if focusing solely on cost savings. Case Two also saw no change in price, technologies, and emission reduction as the goal of 100% CO2 reduction had already been met in the official results. Case Three, however, did see changes in technology, emission, and cost results. The new upfront cost would be slightly higher at $565.9 thousand. However, the new annual cost decreased to $48.3 thousand, which amounts to 19.6% in cost savings. Emission reduction increased from 80% to 85.3% or 31 tons per year. These changes are due to the new solar and battery size of 98.9 kWdc which used approximately 59.5% of the new size. The battery size also increased to 161 kWh to capture additional curtailment. Based on these results, it is recommended that the additional solar area be utilized, if available. While this report was originally meant as a source analysis and feasibility study solely for UW – Platteville, the application of this study to other farmers or processors was apparent. It is the hope of this study that as the interest in microgrids rises, the vast benefits and application to this industry is seen. ACKNOWLEDGMENT Both authors thank Dr. Tera Montgomery of The School of Agriculture and Dr. Tomas Zolper of the mechanical engineering department for their assistance in explaining dairy production processes and calculating the estimated loss of dairy products during an outage. REFERENCES [1] J. Surash and R. Hughes, "Developing Microgrids to Deliver Energy Resilience," 12 April 2022. [Online]. Available: https://www.army.mil/article/255597/developing_microgrids_to_deliver_energy_resilience. [Accessed 26 March 2023]. [2] K. T. Akindeji, R. Tiako and I. E. Davids, "Use of Renewable Energy Sources in University Campus Microgrid – A Review," in 2019 International Conference on the Domestic Use of Energy (DUE), Wellington, South Africa, 2019. [3] A. Schatz and P. Musilek, "Implications of microgrids, economic autonomy and renewable energy systems for remote Indigenous communities," in 2020 IEEE Electric Power and Energy Conference (EPEC), Edmonton, Canada, 2020. [4] R. Bent, W. Du, M. Heleno, R. Jeffers, M. Korkali, G. Liu, D. Olis, P. Pradhan and R. Singh, "Integrated Models and Tools for Microgrid Planning and Designs with Operations," U.S. Department of Energy: Office of Electricity, Washington, DC. [5] XENDEE, "Design," XENDEE, San Diego, California, 2023. [6] Citizens Utility Board, "Eletric Utility Performance: A State-By-State Data Review," Citizens Utility Board, Chicago, IL, 2021. [7] U.S. Energy Information Administration, "Annual Electric Power Industry Report, Form EIA-861 detailed data files," 6 October 2022. [Online]. Available: https://www.eia.gov/electricity/data/eia861/. [Accessed 5 December 2022]. [8] Alliant Energy, "Rates Information," 22 December 2021. [Online]. Available: https://www.alliantenergy.com/accountandbilling/billmeterrates/ratesandtariffs. [Accessed 21 July 2023]. [9] Office of Energy Efficiency & Renewable Energy, "Homeowner’s Guide to the Federal Tax Credit for Solar Photovoltaics," US Department of Energy, March 2023. [Online]. Available: https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-creditsolar-photovoltaics. [Accessed 10 January 2024]. 251 A Novel Approach for Development of Drive Cycle and Integration of Vehicle Parameters as Energy Consumption Indicator Vishal Soni Indian Institute of Information Technology Bhagalpur, Bihar, India, vishal.[email protected], ORCID: 0009-0008-4893-3260 Gaurav Kumar Indian Institute of Information Technology Bhagalpur, Bihar, India, [email protected],ORCID: 0000-00021766-0393 Kari Tammi Aalto University, Espoo, Finland, [email protected],ORCID: 0000-0001-9376-2386 Cite this paper as: Soni, V., Kumar, G.,Tammi,K. Driving Towards Efficiency: A Study on Realistic Drive Cycle Creation and Energy Optimization for Bhagalpur City. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: The present work focuses on development of a realistic drive cycle for Bhagalpur city, which can be used to simulate actual driving conditions. This endeavor addresses two primary gaps in existing research (i) the limited use of drive cycles for energy efficient drive and (ii) the generalization of driving patterns that fail to represent realworld variability. The work commenced with the acquisition of real-time driving data via accelerometer-equipped devices. The subsequent meticulous cleaning process involves smoothing and interpolation which makes the data noise-free and continuous. Principal Component Analysis (PCA) was employed for dimensionality reduction, which effectively retains maximum data while extracting components of kinematic fragment feature parameters. Clustering process groups similar kinematic fragments and component parameters together, laying the groundwork for modeling the drive cycle. Transition analysis, facilitated by a Markov Chains-derived transition matrix, enables to sequence the clustered patterns, culminating in a drive cycle model that accurately emulates real-world driving conditions. The Drive Cycle Impact Factor (DCIF), developed through Variational Autoencoders (VAEs) with PReLU activation function, quantitatively assesses the impact of driving behaviors on energy consumption. By integrating the DCIF with compiled drive cycle data, we introduced a novel methodology to indicate the pattern of vehicle energy use, incorporating parameters specifically tailored to the drive cycle of a particular road trip and an electric vehicle (EV). This advancement marks a significant leap in sustainable vehicle design, enabling precision in energy saving efforts. The integration of DCIF with vehicle navigation systems proposes a real-time method for guiding drivers towards energy-efficient drive. This system not only save energy use in real-time but also aligns with broader goals of sustainable mobility. Keywords: Electric Vehicle Efficiency, Drive Cycle Modeling, Driving Pattern Recognition, Data Analysis © 2024 Published by ECRES 1. INTRODUCTION A drive cycle is traditionally defined as a series of data points that graphically represent vehicle speed versus time, often incorporating speed and gear selection as a function of time [1]. Application of drive cycle is pivotal in advancing research in sustainable transportation and energy efficiency. Drive cycles can be instrumental in assessing vehicle performance, particularly concerning fuel economy and emissions testing. The initial studies of 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 252 drive cycles marked a significant step towards understanding and replicating real-world driving conditions for more accurate vehicle testing. The traditional methodology involves equipping a vehicle with a speed recorder to traverse selected routes and gather data to analyze and characterize speed and acceleration variations. Few initial studies on drive cycle development and their reflection of fuel consumption includes studies by Kent et al. [2], Gandhi et al.'s [3] and Lyons T et al. [4] etc. Prevailing studies rely on static patterns called as modal driving cycle, inadequately representing the variability of real-world transient driving. Figure 1 represents a flowchart highlighting the difference between modal and transient drive. Traditional methodologies in constructing drive cycles encounter significant limitations, particularly when synthesizing a practical duration driving cycle with precise speed-time trace specifications. The challenge arises from the low data density in matrices due to high data resolution, making it difficult to normalize these matrices or to produce a representative speed-time trace that fully captures the statistical characteristics of multiple runs [3]. Studies such as those conducted by Gandhi et al. [2] shows that the relative time spent under different driving modes (e.g., cruising, acceleration) does not significantly vary across different routes or traffic conditions, suggesting a need for further research in diverse urban settings to better understand the impact of trip length and average speed on fuel consumption [4]. These limitations of traditional methodologies, highlightes the necessity for a broader and more nuanced approach to accurately simulate real-world driving conditions. The advent of AI in the construction of drive cycles has revolutionized traditional methodologies, addressing their limitations with innovative approaches. AI techniques, particularly k-means clustering and Markov chain modeling, have been pivotal in creating more accurate and representative drive cycles. These methods involve kinematic fragmentation followed by clustering to model the drive cycle using a one-step Markov chain for transition analysis, effectively capturing the dynamic nature of driving behaviors [5-8]. Furthermore, an advanced approach is proposed by Fotouhi A et al. which utilizes k-means clustering to group micro-trips into four distinct driving feature spaces[9]. Instead of relying on Markov chains, this method selects the nearest micro-trips to cluster centers as representative, simplifying the construction process. AI significantly enhances the drive cycle construction process by enabling the recognition of patterns more efficiently and automating the analysis, making the process quicker and more accurate [10-13][1]. Kivekäs K et al. introduce a novel method for synthesizing diverse driving cycles and passenger numbers for specific bus routes from a limited set of measured cycles [14]. The proposed method focuses on the extraction of microtrips for specific driving states (acceleration, deceleration, idling, and cruising), ensuring a more accurate depiction of driving patterns. This strategy allows each microtrip to represent a single state, highlighting individual driving features without allowing one parameter to overshadow others. The existing methodologies often merge various driving states into a single microtrip, which can obscure the distinct characteristics of each state. Our approach differentiates by creating microtrips that represent singular states, such as acceleration or idling, focusing on individual patterns to enhance the drive cycle's accuracy and relevance. By integrating this methodology, including the introduction of a Drive Cycle Impact Factor, our work aims to revolutionize the construction of drive cycles, making them more reflective of real-world conditions and usable as energy consumption indicator for the automobile industry. Our approach Builds upon the foundation laid by Zhang et al. [7], which categorized speed into 'low', 'middle', and 'high' based on specific thresholds, our methodology innovates by implementing k-means clustering within these predefined speed ranges. Unlike study of Zhang et al. [7], which introduced the speed categorization concept, we further refined this approach by addressing data completeness more rigorously. We meticulously filled missing values with either the mean or zeros and clustered within each speed category, a step not taken in previous studies. This meticulous data processing facilitated the formation of 9 distinct clusters, enabling us to observe and analyze real patterns in driving behavior with unprecedented detail. Traditional approaches often segregate the development of drive cycles from energy consumption factors, failing to acknowledge the interconnectedness of driving behaviors and energy consumption. The present work addresses the limitations of existing methodologies in sustainable transportation by innovatively combining drive cycle construction with energy consumption indicator called as DCIF. Few Factors such as curb weight, drag coefficient, battery capacity, battery voltage, charging time, motor power etc. termed as drive cycle impact factor (DCIF) has been integrated to obtain a single parameter which indicates the state of energy consumption. This strategy not only enhances the granularity of the drive cycle analysis but also significantly improves the realism and applicability of the resulting drive cycle model. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 253 Figure 1. Flow charthighlighting the differences between modal and transient drive 2. METHODOLOGY Figure 2 represents the general methodology adopted in this study. The work starts with data preprocessing where thorough data cleaning, smoothing, and interpolation to refine the dataset has been carried out. Thresholding and segmentation techniques has been adopted for State Identification and Fragment Analysis. Distinct driving states such as acceleration and deceleration is precisely identified. This step is vital for isolating and examining specific driving behaviors within the entire trip, providing clear insights into the patterns of each state. Principal Component Analysis (PCA) is applied to distill the most significant features from the dataset. Through K-Means clustering, similar parameters and kinematic fragments have been aggregated within each speed category, forming 9 distinct clusters. It highlights the unique patterns in driving behavior, enhancing the granularity of this analysis. Transition analysis, leveraging a Markov Chains-derived transition matrix, sequences these patterns, leading to a synthesized drive cycle that authentically represents real-world driving scenarios. The synthesis phase combines the insights from clustering analysis to construct a drive cycle that reflects actual driving conditions. Further, the DCIF metric, which correlates with both EV specifications and drive cycle speed data to indicate the vehicle's energy consumption is introduced. This correlation provides a nuanced understanding of how driving behaviors impact EV energy consumption. Conditional Variational Autoencoder (CVAE) with Parametric Rectified Linear Units (PReLU) layers is employed. This approach captures the subtle variations in driving patterns that affect DCIF. This deep learning technique allows for a sophisticated analysis of driving behaviors, contributing to more effective EV energy management strategies. Figure 2. Research Methodology 3. ANALYSIS Dataset Description and Data Preprocessing Data was gathered citywide, including outskirts of Bhagalpur city using a system comprised of a GPS module, accelerometers, an Arduino board, and an SD card, mounted on a vehicle's dashboard. Dataset captured by a custom 260 Measurements of Separation Bubble and Secondary Vortex on A BACKWARD-Facing Step (BFS) using PIV Xabier Uralde Guinea* Univ. Basque Country UPV/EHU, Nuclear Eng & Fluid Mechanics Dept, 01006 Vitoria-Gasteiz, Spain; [email protected], ORCID: 0009-0001-9811-860X Irati Uriarte Univ. Basque Country UPV/EHU, Nuclear Eng & Fluid Mechanics Dept, 01006 Vitoria-Gasteiz, Spain, [email protected], ORCID: 0000-0002-5109-257X André Brunn iLA 5150 GmbH, Aachen, Germany, [email protected], ORCID: 0009-0003-0718-4498 Unai Fernandez-Gamiz Univ. Basque Country UPV/EHU, Nuclear Eng & Fluid Mechanics Dept, 01006 Vitoria-Gasteiz, Spain, [email protected], ORCID: 0000-0001-9194-2009 Ekaitz Zulueta Univ. Basque Country UPV/EHU, Syst Eng & Automat Control Dept, 01006 Vitoria-Gasteiz, Spain, [email protected], ORCID: 0000-0001-6062-9343 Jose Manuel Lopez-Guede Univ. Basque Country UPV/EHU, Syst Eng & Automat Control Dept, 01006 Vitoria-Gasteiz, Spain, jm.lopez@ehu, ORCID: 0000-0002-5310-1601 Cite this paper as: Uralde-Guinea, X, Uriarte, I, Brunn, A, Fernandez-Gamiz, U, Zulueta, E, Lopez-Guede, J.M. Measurements of separation bubble and secondary vortex on a Backward-Facing Step (BFS) using PIV. 12. Eur. Conf. Ren. Energy Sys. 16-17 May 2024, Mallorca, Spain Abstract: This study employs Particle Image Velocimetry (PIV) to investigate flow structures, focusing on the separation bubble and secondary vortices, in a backward-facing step (BFS) configuration. The BFS geometry generates coherent vortex structures, including the free shear region, corner region, redeveloping region, and mean reattachment point, each exhibiting distinct flow properties. Main attention has been paid to the formation of the so-known separation bubble and secondary vortices. The used setup comprises a vertical retention tank, a conical transition section facilitating flow transition, and a rectangular channel. Channel dimensions of 60 mm height, 60 mm width, and a step height (S) of 22 mm were utilized for three-dimensional predictions, with an aspect ratio (W/h) of 7.5. The Reynolds number (ReH) was around 4062 based on the step height. The major findings reveal the primary separation bubble forming at x/h = 2.5, while the secondary vortex forms at x/h = 0.5. These insights underscore the complex flow dynamics inherent in BFS configurations, with implications for optimizing design and control strategies in various engineering applications. Keywords: PIV, BFS, Separation, Vortex © 2024 Published by ECRES Nomenclature PIV PTV BFS ReH Particle Image Velocimetry Particle Tracking Velocimetry Backward-Facing Step Reynolds Number RMS Root Mean Square 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 261 1. INTRODUCTION The complex nature of turbulent flows has been a significant focus in engineering research. Over the years, researcher in fluid dynamics have worked hard to thoroughly measure and describe the intricate three-dimensional structures present in almost every turbulent flow field. These three-dimensional structures can be comprehensively characterized by quantifying the velocity of the flow in its whole domain. Furthermore, measuring the distance and time of an object is mandatory to determine its velocity. The introduction of photography and the availability of commercial cameras played a crucial role in visualizing flows, allowing the capture of observed patterns and complex, dynamic flow structures [1]. Together with the advancements in photography, the progress in computing, laser, and data acquisition technologies have helped to improve the capacity of optical fluid techniques in measuring diverse flow fields. Among others, the most important optical non-intrusive measurement techniques are Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) [2]. Both methods rely on the measurement of the displacement of tracer particles between two instants of time; however, they differ in the method used to characterize the fluid flow. PIV provides instantaneous velocity mapping by analyzing particle displacement of the flow field at specific locations using an Eulerian analysis, measuring the velocity field in a small region or complete interrogation window. In contrast, PTV tracks individual particles as they move through the fluid over time, this is, a Lagrangian analysis. Both planar and volumetric PIV as well as PTV techniques have demonstrated their effectiveness in acquiring velocity fields and examining the spatial arrangement and temporal development of turbulent structures. Agarwal [3] showcased the integrated application of these methods in the examination of separated flows. They employed high-resolution time-resolved tomographic particle tracking techniques to analyze the development of intermittent quasi-streamwise vortices situated between Kelvin-Helmholtz vortices within a turbulent shear layer. One of the most common turbulent flow problems is the separation of flows and their reattachment, as it is a phenomenon widely seen in various industrial applications; such as diffusers, combustion chambers, channels with sudden expansion, heat transfer systems, and aerodynamic flows around profiles and buildings, among others. The most important features of a separated flow are the free shear flow separation, vortex evolution and reattachment. These turbulent structures with the separation and reattachment of turbulent flows inherently involve the presence of dynamic recirculations. The understanding of the separation of flows and their reattachment is crucial for different engineering applications such as the control of separation in turbulent flows as seen in [4], or for the active control of turbulent separated flows as seen in [5]. One of the most significant both theoretically and for engineering development is the Backward-Facing Step (BFS), which is one representative separation flow model. It is also known as “sudden expansion flows” or “circular expanding low” and it involves three-dimensional flow properties, but it is also suitable for two-dimensional discussions if the flow conditions are suitable. Moreover, it is an important way to comprehend and model flow separation through a wide range of different Reynolds numbers [6]. The basic geometry of a BFS depends on an incoming uniform flow (laminar or turbulent), and a sudden channel step decrease (height h), which increases the total height of the flow. The flow behaviour is divided normally into four regions: The separated shear layer, the recirculation region under the shear layer, the reattachment region, and the attached/recovery region. Furthermore, the recirculation region under the shear layer consists of a separation bubble and secondary vortices that appear close to the step corner. The study focuses on analysing the flow dynamics in a Backward-Facing Step (BFS) environment using a Particle Image Velocimetry (PIV) system in a specifically designed experimental setup. The installation consists of a vertical retention tank, a conical section for flow transition, and a rectangular channel presented in [7]. Key flow structures such as the main recirculation bubble and secondary vortices in the corner are investigated. Additionally, the mean flow velocity, turbulence intensity, and overall turbulence structures in the flow field have been calculated using standard data processing techniques in PIV. These calculations have provided a deeper understanding of the underlying mechanisms in turbulent flows in this specific configuration. By capturing both the flow velocity field 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 262 and the corresponding RMS values, the experiment has enabled the visualization and quantification of key flow structures. 2. METHODOLOGY Theory A two-dimensional plane in a Backward-Facing Step (BFS) flow, the sudden channel step decrease generates the formation of some coherent vortex structures, which exhibit distinct flow properties in different regions of the flow. As shown in Figure 21. Coherent vortex structures (CVS) in an instantaneous Backward-Facing Step flow in a simplified two-dimensional representation: FSR = free shear region; CR = corner region; RR = redeveloping region; R = mean reattachment point. Adapted from Ref. [8], under the terms of the Creative Commons CC By 4.0 license. there are some main coherent vortex structures such as: the free shear region, the corner region, the redeveloping region and the mean reattachment point. Although all of these coherent vortex structures are suitable for a deep analysis of their properties, main attention is going to be paid to the region close to the step: the free shear region and the secondary vortex. The main free shear region arises due to the interaction between the high-speed flow approaching the step and the low-speed recirculation region downstream of it, which generates a separation or recirculation bubble in the flow. This vortex structure is characterized by elongated streamwise vortices and shear layers. Furthermore, the secondary vortex forms in the corner region, where the flow undergoes a sudden change in direction, manifesting the vortex structures as secondary vortices. These secondary vortices are induced by the adverse pressure gradient and the complex flow structures near the step corner; the interaction between the primary recirculation bubble and the step geometry amplifies the formation of these coherent structures, contributing to flow instability and mixing. As it can be observed, these distinctive flow characteristics are exhibited in a rather simple geometry, which explains the relevance of understanding the formation, evolution, and dynamics of these coherent structures; as they are essential to comprehend the underlying mechanisms governing turbulence and mixing phenomena in complex flow configurations. Figure 21. Coherent vortex structures (CVS) in an instantaneous Backward-Facing Step flow in a simplified two-dimensional representation: FSR = free shear region; CR = corner region; RR = redeveloping region; R = mean reattachment point. Adapted from Ref. [8], under the terms of the Creative Commons CC By 4.0 license. Experimental setup The experimental study employed an internally developed BFS test rig, as illustrated in Figure 2. This setup involved a vertical retention tank to collect pump-discharged flow. A conical section at the exit facilitated a smooth transition between upstream flow and the subsequent optical fluid domain. Control mechanisms, including a controller and needle valve, allowed for pump working regime control, and flow regulation was achieved by tuning a shut-off ball valve downstream of the relevant fluid domain. Polyamide tracer particles were manually injected into the vertical retention tank to introduce a final two-phase flow of water and suspended particles. This mixture 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 263 passed through a rectangular channel and spilled into an accumulation tank. The channel geometry (Figure 2) was based on the fluid domain studied by [7] and [9] and transparent methacrylate was selected as the manufacturing material for optical access. The channel dimensions, including a height of 60 mm, width of 60 mm, and step height (S) of 22 mm, were configured for three-dimensional predictions with an aspect ratio (W/h) of 7.5. The water mean flow rate through the inlet section was 0.033 L/s, resulting in an averaged mass flow velocity (Um1) of 0.1646 m/s and a Reynolds number (ReH) of 4061.6 based on the step height. Figure 2. Experimental setup structure A 2D PIV/PTV system was used for visualizing instantaneous velocity fields and magnetic particle trajectories at the measurement plane (Figure 2). The light source is an ultrabright LED Pulsing System (LPS). Proven effective for particle-based velocimetry, the LED light was shaped into a light sheet via a fiber optic system. The LED system, mounted on a translation positioning system, controlled the projection of the light sheet onto the region of interest. The measurement plane for particle tracking coincided with the geometric half of the investigated channel, illuminating approximately 20 mm longitudinally and providing valuable insights. Particle images, illuminated by an LED source, were captured using an IDS UI-3370SE industrial camera equipped with a high-sensitivity 2048 x 2048 pixels CMOS-Global Shutter square sensor. Additionally, a Computar F 2.8 CMount Lens with a 35 mm focal length, providing up to 6 MPx resolution, was utilized. The camera was positioned perpendicular to the channel on a dual translational guide system, enabling precise placement in both longitudinal and transversal directions. Synchronization of laser emissions and camera exposures was meticulously controlled through an ILA 5150 GmbH v2 Synchronizer. The overall repetition rate for capturing velocity field snapshots was set at 60 Hz, resulting in the acquisition of approximately 1000 time-resolved snapshots for each case. Lastly, the different softwares that were used included “SigmaX” used to coordinate the LED pulses and the camera in the synchronizer, “CamILA” used to manage the camera settings and record precisely the images and “Matlab” to postprocess all the data gathered. Camer a Valve Channe l LED light Water tank Valve Water pump controller 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 264 3. RESULTS The results obtained from the experimental measurements can be seen in both Figure 3 and Figure 4; where in Figure 3 the velocity field of the analyzed plane is shown for the time-averaged flow velocity, and in Figure 4 the root mean square (RMS) field for the same plane is shown. As shown in Figure 3, the flow has an average value of 0.1162 m/s at the expansion area, which is smaller than the flow velocity (0.1646 m/s) due to the sudden expansion at the step, as expected. Moreover, the flow velocity is reduced away from the main flow, as some major turbulent areas appear. Below the main flow, there are two major turbulent structures: at x/h = 2.5, the separation bubble or recirculation bubble which occurs due to the flow separation from the downstream edge of the step, forming a region of reversed flow characterized by low velocity and high pressure. Furthermore, at x/h = 0.5, in response to the recirculation bubble formed at the downstream edge of the backward facing step a secondary vortex develops within the corner region, as the flow direction changes abruptly. This secondary vortex is influenced by the main separation bubble, increasing the turbulence and the mixing phenomena. Figure 3. Time-averaged flow velocity field for z=0 plane. In order to provide a deeper understanding of the flow behavior and especially the turbulence intensity and the overall turbulence structures of the flow field across the measurement domain, the root mean square (RMS) is plotted in Figure 4. RMS plotting helps to understand regions of increased turbulence intensity, areas prone to instabilities and evaluate the overall turbulence structure, quantifying the level of turbulence. The region with the highest RMS values is the region of the main flow, as the flow velocity is its highest and therefore the flow is more likely to be turbulent. Furthermore, increased RMS values can be seen in the areas of turbulent structures due to the intense velocity fluctuations and vortex-motion inherent in these flow features. 12. EUROPEAN CONFERENCE ON RENEWABLE ENERGY SYSTEMS Mallorca/Spain 16-17 May 2024 265 Figure 4. Time-averaged Root Mean Square (RMS) values of the velocity field for z=0 plane. 4. CONCLUSIONS The PIV experiment conducted in the Backward-Facing Step geometry has provided relevant information about the complex flow dynamics inherent in this well-known flow configuration. By capturing both the flow velocity field and the corresponding RMS values, the experiment has enabled the visualization and quantification of key flow structures, including the main recirculation bubble and the secondary vortex formation in the corner region. It has been seen how the main recirculation bubble is formed at a distance of x/h = 2.5, while the secondary vortex is formed at a x/h = 0.5. Both of these non-dimensional distances agree with the values of the RMS obtained. The evaluation of these data not only increases our understanding of the fundamental mechanisms governing turbulent flows but also holds significant implications for various scientific and engineering applications. Insights obtained from the analysis of flow structures and turbulence characteristics in Backward-Facing Step flows can inform the design and optimization of flow control strategies, improve the efficiency of heat and mass transfer processes, and advance our ability to predict and mitigate flow-induced phenomena in diverse settings. Thus, the comprehensive evaluation of PIV data in this study will be able to improve the way for further advancements in fluid mechanics research and engineering practice, contributing to the development of innovative solutions for complex flow problems. ACKNOWLEDGEMENT This work has been partially supported by the Government of the Basque Country, program: Elkartek CICe2022; Grant N.: KK-2022/00043. U. F.-G. was supported by the Mobility Lab Foundation, a governmental organization of the Provincial Council of Araba and the local council of Vitoria-Gasteiz. Sincere gratitude is expressed to all participants who contributed to the success of this research investigation. Special thanks are extended to André Brunn from ILA 51510 for their invaluable assistance in the calibration and postprocessing of the experimental data. Their expertise and dedication significantly enhanced the quality and accuracy of our results. Additionally, we extend our appreciation to the whole team of energy engineering department of UPV-EHU for their collaboration and support throughout the experimental process. Their contributions, whether in data collection, analysis, or logistical assistance, were instrumental in achieving our research objectives. This work would not have been possible without the collective efforts of all involved, and we are truly grateful for their contributions. REFERENCES [1] Dabiri, D. Cross-Correlation Digital Particle Image Velocimetry–A review. Instituto Militar de Engenharia, Rio de Janeiro: 2006. [Document text truncated for crawler view.]