A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing

A Techno-Economic Analysis of a Solar PV and DC Battery Storage System for a Community Energy Sharing Eid Gul*1, Giorgio Baldinelli2, Pietro Bartocci2,3, Francesco Bianchi4, Piergiovanni Domenghini1, Franco Cotana1,2 Jinwen Wang5 1Biomass Research Center, University of Perugia, Via G. Duranti n.67, 06125 Perugia Italy 2Department of Engineering, University of Perugia, Via G. Duranti n.67, 06125 Perugia Italy 3Instituto de Carboquímica (ICB-CSIC), Miguel Luesma Castán 4, 50018, Zaragoza, Spain 4Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia PG, 06125 Perugia Italy 5School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Abstract Energy harvesting from renewable sources can play a vital role to decarbonize the environment, limit global warming and mitigate the growing energy demand. The objective of this work consists of decarbonizing a University Campus and neighboring communities by producing electricity from solar photovoltaic systems integrated with an energy storage system and local grid station. A new mathematical model is developed to maximize the system power generation and balance the load demand. The simulation and optimization software System Advisor Model (SAM) is used to develop and test model results. The software is used to analyze and optimize the solar energy generation, the energy demand, and the economic performance: capital cost, overall investment, net present value, and the Levelized Cost of Energy of the project. A novel approach decentralized load centers is adopted to share power with adjacent communities. At the aim of improving the system flexibility, reliability, and climate resilience, the established model is grid-connected. The CO2 emissions reduction is also determined to evaluate the environmental impact of the interventions. Key words: “Techno-Economic analysis”, “Solar PV”, “Battery systems”, “System Advisor Model”, “Energy sharing”, “Environment analysis” Revised Manuscript with No Changes Marked Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Nomenclature Abbreviations AC Alternate Current CEC California Energy Commission CSP Concentrated Solar Panels DC Direct Current DHI Diffused Horizontal Irradiance DNI Direct Normal Irradiance EU European UnionGHI Global Horizontal Irradiance LCOE Levelized Cost Of Energy LIB Lithium-Ion Battery Max Maximum MPPT Maximum Power Point Tracker NPV Net Present Value NREL National Renewable Energy laboratory O&M Operation and Maintenance cost PV PhotoVoltaic RES Renewable Energy Sources SAM System Advisor Model SDS Sustainable Development Scenario SOC State of the Charge STC Standard Test Conditions UNO United Nations Notations a p temperature power coefficient C battery(t), charge stored in the battery C batteryMax(t) maximum charged capacity of DC battery C t future costs at each time period t E pv rated output power E t electricity output of the system in year t MaxP guaranted guaranteed power supply nlifetime of the developed system P AC active power output of DC inverter P Battery power stored in battery P tbattery power supplied by the battery at time t P DC, STC rated DC power at standard test conditions P Demand power demand at time t P Grid power supplied from grid P tLoad power supplied to load at time t P tmax maximum power at time t P PV power produce by PV system P tsolar power produced by PV system at time t R m actual radiation intensity (W/m 2 ) R stc radiation intensity under standard test condition T c real temperature of the solar cell T stc temperature at standard test conditions (25°C) Ƞconversion efficiency (%) 1. Introduction Accelerating towards achieving the United Nation (UN) and European Union (EU) 2050 carbon-neutral goals, energy harvesting from renewable sources can play a critical role to solve the environmental and climate problems [1][2]. The excess utilization of fossil fuels creates unprecedented environmental problems, which are not only affecting humans, but also endangering other living species [3]. In the light of the Paris climate agreement 2015, article 2, to limit the global temperature to 1.5°C [4], it is highly necessary to exploit new energy sources, which have fewer impacts on the environment [5]. Renewable energy sources such as solar, wind, hydro, biomass, geothermal, and marine energy sources have enough capacity to meet the primary energy demand [6][7]. The COVID-19 pandemic heavily influenced the global energy demand which records a 6% decline according to the global energy reviews 2020 report of the International Energy Agency [8]. Despite the COVID-19 pandemic and the decline in energy demand, a significant growth in renewable energy technologies has been recorded, and according to the International Energy Agency report IEA 2021, in 2020 the renewable energies capacity increased by 45% (280 GW) [9]. During the COVID-19 pandemic, a significant reduction in energy demand has been recorded also in European Union countries, where 9% reduction in electricity generation has recorded, while the renewable energy sector grew of 15% [10]. Solar energy has a huge potential to produce clean and low-cost energy [11]. Energy generation from the solar system has been largely adopted and considered as a key source of clean energy, significantly reducing greenhouse gases emission [12]. Solar energy technologies such as solar PV and concentrated solar power collectors (CSP) is the major technology for the conversion of solar energy into electricity and heat [13]. In 2019 the power produced worldwide by the solar PV system is 720 TWh, while in 2019, 22% (131 TWh) growth has been recorded in solar PV power generation: to reach the Sustainable Development Scenario (SDS) 2000-2030, the expected growth is 15% (total capacity of 3,268 TWh) [14]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 The variable and intermittent nature of solar energy creates challenges for grid station to manage the fluctuating power. Su. et al. [15] presented stochastic techniques for micro grid energy scheduling and control strategies to variable energy sources; results show a reduction in operational cost and a significant contribution of renewable energies. McCormick et al. [16] presented a simulation model to investigate the effects on PV system operation and performance of the continuous fluctuation of solar radiation; for this purpose, they used one-minute radiation data in the simulation model and compare the results with onehourly radiations data results. According to Topic et al. [17] a mathematical model is helpful to find the optimal size of PV system regarding the required energy demand, and they also describe a method to accurately size the PV system, number of rows modules angle, and inter-row shading effects. Ma et al. [18] presented a comprehensive and improved mathematical model for solar PV module under real operating conditions, by using basic circuit equation of a solar cell. Authors also considered the real effects of radiations and ambient temperature on the solar cells, and the results showed the accurate characteristic curve of PV cell. Research studies presented in articles [19] [20] [21] show PV systems models, design, operation, performance, parameters, and characteristics of PV modules. Integration of energy storage technologies such as DC battery coupled with PV system can significantly improve the energy utilization and support the smooth operation of PV system [22]. Akeyo et al. [23] presented a detailed design and analysis of a DC battery system configuration with large scale solar PV farm, where he captures the surplus solar energy by using a single DC-DC converter which simultaneously operates a charge controller and MPPT. The author Nottrott et al. [24] presented in their work the dispatch strategies and optimal scheduling of a DC battery configured with a grid-connected solar PV system, using linear programming model for the optimal operation of the DC batter system. Lithium-ion battery (LIB) technology has several advantages over lead acid battery such as its fast-charging capability, high density, long life and less volume and weight, so making it more favorable to use in short-term energy storage for a wide range of applications [25][26][27][28]. Frequent operations of LIB charging/discharging can cause accident and safe operations in the design range result essential. Su et al. [ 29] described the safety warning techniques for a MW-level LIB system, using venting acoustic signal and performed experiments to study the frequent thermal runaway of LIB. Authors also mention the advantages of developed techniques such as high sensitivity, fast implementation, and low cost. González et al. [30] presented automation and monitoring systems dedicated to PV battery energy systems for DC micro grid, where authors present innovative multi-layered architecture for energy management and using Polymer Electrolyte Membrane (PEM) fuel cell produce hydrogen. Torres-Moreno et al. [31] described the energy management strategies for the PV battery based micro-grid and analysed the impact of variable PV generation on DC battery and electric vehicle. Hannan et al. [32], as well as Xiong et al. [33] presented a comprehensive research work and recent advancement on the operation, management, and characteristics of LIB, also mentioning the aging mechanism for LIB. Finally, hydrogen is the prominent vector of energy, especially in the future scenarios, where the energy sources are required to emit zero or lower emissions while the green hydrogen produce zero emission and generates by the renewable energy sources [34][35]. The surplus electricity of renewable energy sources can be used to produce hydrogen energy by using, for instance, the electrolysis process [36] [37]. Zhang et al. [38] developed a hybrid solar-wind-hydrogen energy system model, where hydrogen is produced through the wind surplus energy, storing the hydrogen itself, and using a fuel cell to produce electricity and supply to the load centers. Marino et al. [39] produced hydrogen energy from a stand-alone PV system, and by using a fuel cell to transform the hydrogen energy into electricity. Table 1 adds the results of a Literature review made on PV battery systems. Table 1. Literature reviews of PV battery system with and without grid connection. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Authors /Reference Location /Software Developed system/ operating strategies Findings Heine et al. [40] Arizona USA / SAM PV-battery system for Residential building • 37-44% of the peak electricity purchased • 4-12% of demand loads due to mismatch. • Batteries, 1.5-1.6 times larger than required to meet the annual peak energy requirements. Agyekum et al. [41] Ghana/ SAM 20 MW PPA PV system with and without dc battery • CF ranging from 16% to 18% • annual energy between 36 and 43 GWh • LCOE 8.84 ¢€/kWh, 9.88 ¢€/kWh and 10.05 ¢€/kWh DiOrio et al. [42] California Tennessee USA SAM Economic analysis of Solar PV and DC battery systems •Financial impact of adding PV + Storage •24 hours forecast for weather and load data •NPV; California: $ 31,874, Tennessee $ 60,731 • Battery Bank Size California: 110 kWh/55 kW Tennessee 300 kWh / 150 kW Pomares et al. [43] Doha, Qatar SAM Solar PV, CSP system coupled with DC •Satellite-derived data for 2003-2013 •200 MW solar plant planning •CO 2 emissions in Qatar by 0.51 million tons (Mt) by 2020 and 4.5 Mt by 2030 Georgiou et al. [44] Cyprus SAM/ Matlab Grid connected PV battery system •Annual 24 h battery dispatch strategies •Root Mean Squared Error (nRMSE) 2.10 •Annual net grid energy comparison in two cases Li et al. [45] Northwest region China SAM Grid-connected residential solar photovoltaic •highest monthly electricity generation of 496.53 kWh and lowest electricity generation 55.47 kWh •NPV of the grid-connect PV/battery system increased •loan term has the highest impact on LCOE and NPV of the grid-connected PV/ battery system Xiong et al. [46] Dubai UAE MATLAB/Simulink, SAM Residential PV-Storage Model in a Distribution Network/Generic Hybrid PV Storage Model •Initial cost: 8446, LCOE: 0.043 NPV: 12,417 $, payback years: 9 •generalized approach for optimizing battery size in PV systems Zhang et al. [47] Sweden MATLAB/Genetic algorithms SAM Battery sizing and Gridconnected PV-battery system. Multi-objective optimization •Three rule-based operation strategies •Conventional operation strategy does not bring economic incentives for PV systems to deploy batteries even when the battery price is lowered by 50%. •Dynamic price load shifting strategy aims to benefit from the electricity price difference •Hybrid operation strategy outperforms the conventional operation strategy Shabani et al. [48] Sweden MATLAB Control strategies and design for grid connected PV system and technoeconomic impact of battery system •9.4% reduction in battery life cycle cost •30% of demand supplied by model battery •Self-sufficiency ration in scenario 1, 19.9%, and Self-sufficiency ration in scenario 2 is 25.1%. Garni et al. [49] Saudi Arabia HOMER Pro Tracking system for grid connected PV battery system and techno economic analysis • Tracking system produced 34% more power than fixed axis system • NPC $12,662 million and LCOE was 0.05434 $/kWh Said et al. [50] Dubai UAE / RETscreen Performance comparison of off/on grid PV system • NPV 612–665 $/year and annual-LCA savings 1.7-1.9 years 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Economic, and environmental analysis •during the project life 229–237 t CO2GHG emissions reduction Luerssen et al. [51] Singapore/ TRNSYS Life cycle-cost analysis of off grid PV cooling system and battery and thermal storage system • 9–10%, reduces the LCC • 51–77%, fuel cost savings • 27–50% higher investment • 39–48% battery yield only In this work, a mathematical model is developed to maximize the system power generation and balance the system power generation to the energy demand of the interconnected system. The system is composed of a solar PV system, a DC battery storage, and a local grid station. The surplus/curtailed solar energy is stored in the DC battery to provide energy when the sunshine is not available; moreover, DC battery operation strategies increase the utilization of solar energy. To test and investigate the developed system performance, a simulation and optimization software (SAM) is used [52]. In this work, the load is decentralized and categorized into four load centers. A novel approach is adopted for energy sharing to the load centers. The developed system is connected to the local grid station to purchase and sell electricity, according to the load demand. To assess the impact of the developed model on the environment, the total CO2 emission reduction has been evaluated. An economic analysis has also been performed to determine the capital cost, the NPV, the operation and maintenance cost (O&M), and the Levelized Cost of Energy (LCOE). Section 2 describes the methodology and the model of the developed system. The methodology section is further classified into five categories, which present the objective functions, the model of PV system, the DC battery system, the inverter, the dispatch strategies of DC battery, and the energy sharing model. Section 3 presents the economic and environmental analysis of the developed system. Section 4 provides a brief detail and explanation of the obtained results and the significance of the system. Finally, section 5 presents a comprehensive discussion of the research work in comparison of literature reviews. The significance and contributions of this work are, (a); sustainable, socio-economic, and environmentally feasible energy mix solution for urban and sub-urban area, (b); PV-battery based energy system model for community energy sharing (c); decentralize and divides load centers and improve system reliability, flexibility and climate resilience. (d); increased penetration of renewable energy and reduce the burden from local grid station. 2. Methodology The objective of this work is to maximize the system power generation from the solar PV system and the DC battery, balancing the load demand, also considering the connection to the local grid. To achieve this objective, a new mathematical model is developed for the maximization of system generation and balance load demand. An estimation model developed by NREL is used to evaluate the solar energy production from the solar PV system and the interconnected energy storage DC battery [53]. The system performance optimization is analyzed with the simulation software SAM [52]. The software SAM is an open-source tool, developed by the National Renewable Energy Laboratory, Department of Energy United States of America. This software has been widely used for techno-economic assessments of renewable energies and hybrid energy systems. In this work, the latest version 2020.11.29 is used to estimate the system performance, the power produced by the model system and the cost of the project. The input parameters consist of load demand, sizing of PV system and DC battery, system components, weather data file and solar radiation data of the selected location, and financial parameters such as credits and incentives, discount and interest rate, and power trade tariff. By using this tool, the operational strategies of DC batteries, limits on energy trading with grid, and electricity supplied to the load centers were developed. Fig. 1. illustrates the process flowchart, classified into four different stages, where at first stage the required data are collected, an energy system model is developed, and the operational strategies are defined to achieve the objectives of this research. At the second stage, input data consist of weather data, location, system configuration parameters, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 economic values, and hourly load demand data fed into the simulation software. At the third stage, the software performs the simulation and the optimization for connected PV system, DC battery and grid station; at the final fourth stage the software provides its output results such as power generation, system losses and economic analyses values. Fig. 1. Flowchart illustration of the different stages implemented in the model process The location of the developed PV-battery system is in the Campus of Engineering at the University of Perugia - Central Italy, coordinates: 43.13° latitude and 12.38° longitude. The weather data is obtained by the SAM software which provides the average data of Diffuse Irradiance (DHI) 1.43 kW/m2/day, Beam Irradiance (DNI) 4.44 kW/m2/day and Global Horizontal Irradiance (GHI) 3.95 kW/m2/day. A novel decentralized load centers and an energy sharing approach is implemented to supply energy to the nearby communities; hence, the total system load is divided into four load centers. One is the Engineering campus, while the other three loads are the nearby communities. Fig. 2. shows the hourly diffuse irradiance (DHI), beam irradiance (DNI), and global irradiance (GHI) values at the location of the PV system. Data show that the selected location has a considerable amount of solar radiation/day for the entire year, especially in the summer season, when the sunlight intensity, angle of incidence, and length of the day are high and can provide a suitable environment for the solar energy production. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 2. Hourly diffuse irradiance (DHI), beam irradiance (DNI), and global irradiance (GHI) profile 2.1 Objective function The first objective of the developed model consists of maximizing the total power generation, which sums the power produced by the PV system, curtailed PV power stored in the DC battery and the power purchased by the local grid station. The second objective is to balance the power generation and the system load demand which are decentralized consists of the three community load centers and on the campus load. Objective 1. Maximize Power Generation. max t t t t PV Battery Grid P P P P   (1) Objective 2. Balance the load demand t t t t PV Battery Grid demand P P P P   (2) In eq. (1). Ptmax is the total power produced at time interval t, PPV is the active power produced by the solar system, PBattery is the curtailed PV power stored in the DC battery, and PGrid is the power purchased by the local grid station. In eq. (2). the total power produced must be equal to the connected system load demand (PDemand). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2.1.1 Constraints Balance load demand: during the sunny hours, the active power generation must be equal to the load demand, while the surplus power is supplied to the DC battery and the grid. Eq. (3). shows that the power produced by the PV system will directly supply to load, while the curtailed power is first stored in the DC battery and then shifted to the grid. sup t Load t t solar Battery t grid P P P P        (3) When the sunshine is unavailable, no power is produced by the PV system – eq. (4). - while eq. (5). shows that the load will be transferred to the DC battery. 0 t solar P (4) sup load t t Battery P P (5) The power supplied by the DC battery is limited and when the state of charge is equal or less than 20%, the load will be transferred to grid station, as it is shown in eqs. (6). and (7). 20% t battery P (6) sup Grid t t Demand P P (7) The power produced by the PV system is directly feeding the load centers, and the surpluses/curtailed power is stored into the DC battery. When the PV system does not produce energy, the grid feeds the load centers. At nighttime, the stored power mixes with the grid station and meets the load demand. 2.2 System details and architecture In the developed energy system model the installed capacity of PV system is 2 MW. The solar panels are manufactured by Sunpreme Inc. a global PV manufacturing company. The solar panels are installed on the rooftop of the engineering campus building and the height of PV panels from the surface is 1-meter, while the tilt angle is 20°, and its direction is 180° (South). The orientation of PV panels is presented in Fig. 5. The second important component in the model is lithium-ion DC battery and the energy storage capacity of DC battery is 1.5 MWh. The DC-AC inverters used in this model are manufactured by Satcon technology; and its conversion efficiency is 95.8%, and the maximum output power of inverter is 2000 kWac. The established energy system is interconnected with the local grid station for electricity sharing. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 3. Schematic design of the developed system. Fig. 3. shows the developed system schematic design, where the energy produced by the PV system after conversion into AC directly feeds the load centers, while the curtailed/surplus energy is stored into the DC battery. The developed system is also interconnected with the local grid station to purchase and sell electricity. 2.3 Solar PV system Model The solar PV system is the key part of the designed model and the output power of solar PV system Ppv is determined using National Renewable Energy laboratory developed model [53]. ( )[1 ( ) m pv pv p c stc stc R P E T T R     (8) In eq. (8). the Ppv is the actual output power, while Epv is the rated output power; Rm and Rstc are respectively the actual radiation intensity (W/m2) and the radiation intensity under Standard Test Condition (1000 W/m2): Tc is the real temperature of the solar cell while Tstc is the temperature of the Standard Test Conditions (25°C); apis temperature power coefficient for the solar cell module (-0.35%/°C). The Active output AC power received after the DC/AC conversion is [54]; ,* AC DC stc inverter P P   (9) In eq. (9). the PAC is the active power output of DC inverter in (kW), PDC, STC is the rated DC power under standard test conditions (kW), while ƞ is the conversion efficiency (%) of the inverter. 2.3.1 Characteristics and parameters of the PV panels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3Demand campus center center center P Load Load Load Load    (12) Fig. 11. shows the Engineering campus of Perugia University, which is the selected place of the developed system; the total surface area required for the 2 MW PV system is 10,260 m2, while the total available area at the roofs top is 10,900 m2; the number of PV panels installed are 3960. Fig. 12. shows the nearby communities with the load centers classification. Fig. 11. Engineering Campus University of Perugia Fig.: 12. Power sharing network for community At the aim of better understanding and investigating the load behavior of the four load centers, a 24-hour hourly analysis is performed. Fig. 13. shows the hourly load profile of all the connected load centers: it is a fundamental tool to design the system according to load demand. Fig. 13. Hourly load profile of the four connected load centers 0 100 200 300 400 500 600 700 800 Campus load Load center 1 Load center 2 Load center 3 Hourly load demand Load demand (kWh) Hour of the day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 14. shows the monthly total load demand of the system. The figure shows that a significant increase in energy demand is registered in summer; according to the diagram, the maximum load demand is 998 kWh, while the average load demand is 520 kWh a day. Fig. 14. Monthly system electrical load demand profile 3. Economic and Environmental Analysis In this section, an economic and environmental analysis has been performed to investigate the economic characteristics and the impact on the environment of the developed system. Two economic parameters were analyzed a); NPV and b); LCOE. The NPV of the project is determined by using the eq. 13, presented by [61]. 0(1 ) n t t t R NPV i    (13) Where Rt is the total cash outflow-inflow during the period t, i is the return rate or discount rate gain after the investment, and the t, is number of time periods. The LCOE describes the cost of energy produced by the PV system. In this project, the LCOE “annuitizing” technique is used (eq. 14); the LOEC is determined by the total annual cost of the system divided by the average annual output power of the system [62]. 0 1 ( )( ) (cos ) (1 ) 1 (1 ) ( ) ( ) / n t t n t Annuitizing n t t Cr Annual t r r LCOE Average output E n           (14) Where Ct is the future costs at each time period t, Et is the electricity output of the system in year t, r is the discounted rate and n is the lifetime of the developed system (25 years). The energy produced by the developed system is shared to nearby communities and the surplus energy is also shared with the local grid station. The energy purchased and sold to the grid station with a flat rate tariff, where the price of the electricity purchased from grid is 11.1¢$/kWh and electricity sold to the grid 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 at the price of 5.0 ¢$/kWh [63]. Table 5. shows the total energy charges over one year and it also presents a comparison of the energy bills with and without system; furthermore, it shows the net saving of the system. Table 5. Annual energy charges with and without system. Month Bill Without system ($) Bill with system ($) Saving ($) Jan 32,747 27,201 5,545 Feb 35,418 22,850 12,567 Mar 39,412 23,216 16,195 April 39,384 25,537 13,847 May 44,362 19,824 24,537 Jun 72,494 26,929 45,564 July 73,251 37,511 35,739 Aug 61,760 28,438 33,322 Sep 56,478 27,054 29,423 Oct 36,297 20,149 16,147 Nov 30,012 22,400 7,611 Dec 30,859 22,834 8,024 Annual 552,477 303,948 248,528 Fig. 15. describes the payback cash flow of the developed system, as the lifetime of the system is 25 years, and the simple payback period is 6.4 years. Fig. 15. Payback cash flow of the model system At the aim of investigating the impact of the developed system on the environment, an analysis has been performed to determine the reduction of CO2 emission. According to the Italian Institute for Environment Protection and Research 2020 report, the Emission factor of thermal power plants is 367.3 gCO2/kWh [64][65]. Since the total energy produced by the PV system is 2,838,145 kWh/year, a reduction of around 1150 Tons/year of CO2 emissions is found. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4. Results In this section the obtained results are presented. Table 6. shows the financial situation of the developed system, describing the total installation cost including battery maintenance and replaced cost, LCOE, NPV, the project life of the developed system and further financial parameters such as inflation rate, discount and simple payback rate, debt term and fractions are shown. Table 6. also shows also that the electricity bills with and without the system, highlighting the economic saving. Table 6. Financial situation and project analysis parameters Name Value Net capital cost $2,046,993 Project life 25 years LCOE $ 3.9 cents/kWh Inflation rate 1.5% Real discount rate 6% Debt fraction 50% Debt Amount $ 1,023,496 Debt Term 25 years Debt Rate 5% Net present value $ 971,200 Simple Payback period 6.4 years Discounted payback period 8.8 years Electricity bill without system (year 1) $552,477 Electricity bill with system (year 1) $303,949 Net savings with system (year 1) $248,529 Furthermore, Table 7. describes the energy produced by the system as the total annual energy produced, the capacity factor and the energy yield. The results show that the LCOE is 3.91 ¢$/kWh while the performance ratio during a year is 0.88. Table 7. Energy generation by the designed PV system. Metric Value Annual energy (year 1) 2,838,145 kWh Capacity factor (year 1) 16.6% Energy yield (year 1) 1,451 kWh/kW Performance ratio (year 1) 0.88 Battery roundtrip efficiency 87.8% Battery charge energy from system 100.0% Fig. 16. shows the total annual energy production by the solar system for a year. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 16. Monthly energy produced by the system In Fig. 17. a heat map of the power produced by the developed solar system for a whole year is presented. The results indicates that during the summer season the maximum energy is produced by the PV panels, and the performance of system increased while during winter season the power generation decline. The red part of the map represents the maximum power generation at any time throughout the year, while the blue part shows the periods of power production absence. Fig. 17. Monthly and annual energy produced by the system During the operation of electric network there are always some losses, especially in the energy conversion process. Fig. 18. shows the total percentage of the energy losses during the operation of the system. The important and notable loss is the front-side soiling loss, which is 5%, while the other energy loss is due to 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 the AC inverter efficiency, which is accounted for 3.5%. Furthermore, the other loss such as DC mismatch, DC wiring loss and AC power clipping loss is accounted for 1.0%, 1.3%, and 1.2% respectively. Fig. 18. Energy losses in whole system One of the core objectives of this work is to develop a system that efficiently meets the load demand of all interconnected load centers; as shown in Fig. 19. the AC energy produced by the system is satisfying the load demand, while the excess energy is stored into the DC battery. Fig. 19. shows also that the max energy produced by the system is in the month of Jun, while during the winter season the generation decreases. It is noted that in the month of Jan and Nov the produced energy is lower than the load demand and hence the lacking energy is purchased by the connected local grid. During the whole year, the surplus energy is shared with the local grid. Fig. 19. Monthly energy production and system load demand 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 The economic analysis of the modeled system provides details about the overall investment, net capital cost, and cash flow Fig. 20. shows the cash flow after tax, during the 25 years lifetime of the project. Fig. 20. Cash flow and system lifetime (25 years) values Fig. 21. describes the annual energy production by the proposed PV system, with the correspondent annual reduction in the power generation. According to the manufacturer details, the annual degradation rate of the installed PV panels is about 0.8%. The degradation of PV panels is due to the adverse weather conditions, the ageing effect, and the exposure to the UV light. Results shows that in the first year the energy generation was 2.8 GWh, while, because of the PV panel degradation, in the final year its production was 2.4 GWh. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 21. Yearly degradation in PV system energy generation With the purpose of improving the system reliability and flexibility, the energy storage technologies can play a critical role, and they guarantee the smooth operation of the power system, while the integration of energy storage systems with renewables can significantly increase their utilization and management. In Fig. 22. it is shown that a significant amount of surplus solar energy is stored in the DC battery, while the stored energy is supplied to the load during the absence of a solar system. The performance of the connected DC battery is quite high, and its roundtrip efficiency is 88%. The results show that the dispatch strategies of DC battery significantly improve the utilization of stored energy. Fig. 22. Year operation of DC battery 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 To investigate the system performance in more detail, a monthly analysis has performed, including the system power generation, the load demand, and the amount of energy shared between the model system and the local grid station. Fig. 23. shows the results of performance analysis, showing that the modeled system performance is very high, and the power produced by the system efficiently meets the load demand. Fig. 23. Month by month, hourly electricity load demand, system generation, and grid purchase The integration of model system with local grid station significantly helps to increase the system reliability and climate resilience. According to the Fig. 23. data during the dark time energy purchased from local grid and mix with DC battery energy and feed to the load centers, while during the daytime surplus stored in DC battery and extra electricity sold to the grid station. Fig. 24. shows the different scenarios of energy sharing over the horizon of one year. The results show the energy produced by the system, that shared with the load center and the energy supplied to and from the connected grid. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 24. Monthly energy supplied to load centers and energy sharing between system and grid station 5. Discussion The main objective of this work was to investigate a sustainable and socio-economic feasible solution to decarbonize small communities in urban and sub-urban areas, using energy from renewable energy sources. At this purpose, an energy mix model is developed to demonstrate how a small-scale PV-battery system coupled with the grid station can provide enough energy to meet the demand of small communities. The second goal of this work was to perform an economic and environmental analysis of a PV-battery system, and to propose a feasible solution that effectively reduces the greenhouse gas emissions and requires low capital cost, operation and maintenance charges and LCOE. The obtained results describe that both objectives were successfully achieved, as the results of Table 7, indicate. Fig. 16 shows that during the first year, the energy produced by the developed system was 2.838 GWh and the system performance ratio was 0.88, while the capacity factor resulted equal to 16.6%, showing the significance and applicability of the proposed research work. Table 6. presents the economic values of the system: the net capital cost of the project was 2.04 million dollars,. Furthermore, the LCOE $ is 3.9 ¢$/kWh, the net present value is 0.970 million dollars, and the discounted payback period was 8.8 years. The proposed system not only provides low-cost clean energy, but it also significantly reduces greenhouse gas emissions, as it is shown that the system reduces the 1,150 Tons of CO2 emission, only in the first year. Due to the uncertain and variable nature of the solar system, it was highly challenging to maximize the utilization of solar power. Taking advanced steps and implementing effective strategies such as integration of PV system with the DC batteries and the local grid station, as well as the decentralization/division of the load centers, a significant increase of the penetration of generated power and a reduction of the burden from grid station has been achieved. Results also indicate that developed techniques increase the reliability and flexibility of the proposed model, and the proposed joint operation of PV, batteries, and grid station provides a sustainable, reliable, and socio-economic feasible solution for the transition to the net zero CO2 emissions. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 [57] Pietro Iurilli, Claudio Brivio, Vanessa Wood, On the use of electrochemical impedance spectroscopy to characterize and model the aging phenomena of lithium-ion batteries: a critical review, Journal of Power Sources, Volume 505, 2021, 229860, ISSN 0378-7753, https://doi.org/10.1016/j.jpowsour.2021.229860 [58] Rivera-Barrera, J.P.; Muñoz-Galeano, N.; Sarmiento-Maldonado, H.O. SoC Estimation for Lithiumion Batteries: Review and Future Challenges. Electronics 2017, 6, 102. https://doi.org/10.3390/electronics6040102 [59] Jin hao, M., Stroe, D-I., Ricco, M., Guangzhao, L., & Teodorescu, R. (2019). A Simplified Model based State-of Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model. IEEE Transactions on Industrial Electronics, 66(10), 7717 - 7727. [8536907]. https://doi.org/10.1109/TIE.2018.2880668 [60] Ruixin Yang, Rui Xiong, Hongwen He, Hao Mu, Chun Wang, A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles, Applied Energy, Volume 207, 2017, Pages 336-345, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2017.05.183. [61] Suzan Abdelhady, Performance and cost evaluation of solar dish power plant: sensitivity analysis of levelized cost of electricity (LCOE) and net present value (NPV), Renewable Energy, Volume 168, 2021, Pages 332-342, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2020.12.074 [62] Chun Sing Lai, Malcolm D. McCulloch, Levelized cost of electricity for solar photovoltaic and electrical energy storage, Applied Energy, Volume 190, 2017, Pages 191-203, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2016.12.153 [63] Marina Bertolini, Chiara D'Alpaos, Michele Moretto, Do Smart Grids boost investments in domestic PV plants? Evidence from the Italian electricity market, Energy, Volume 149, 2018, Pages 890-902, https://doi.org/10.1016/j.energy.2018.02.038 [64] Rapporto 2020, Fattori di emissione atmosferica di gas a effetto serra nel settore electtrico nazionale e nei principali Paesi Europei, ISPRA – Istituto Superiore per la Protezione e la Ricerca Ambientale, ISPRA, Rapporti 317/2020, ISBN 978-88-448-0992-8 http://www.isprambiente.gov.it/ [65] Noussan M, Roberto R, Nastasi B. Performance Indicators of Electricity Generation at Country Level—The Case of Italy. Energies. 2018; 11(3):650. https://doi.org/10.3390/en11030650 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 A Novel Technique for the A Techno-Economic Analysis of a Solar PV and DC Battery Storage System for a Community Energy Sharing Eid Gul*1, Giorgio Baldinelli2, Pietro Bartocci2,3, Francesco Bianchi4, Piergiovanni Domenghini1, Franco Cotana1,2 Jinwen Wang5 1Biomass Research Center, University of Perugia, Via G. Duranti n.67, 06125 Perugia Italy 2Department of Engineering, University of Perugia, Via G. Duranti n.67, 06125 Perugia Italy 3Instituto de Carboquímica (ICB-CSIC), Miguel Luesma Castán 4, 50018, Zaragoza, Spain 4Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia PG, 06125 Perugia Italy 5School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Abstract Energy harvesting from renewable sources can play a vital role to decarbonize the environment, limit global warming and mitigate the growing energy demand. The objective of this work consists of decarbonizing a University Campus and neighboring communities by producing electricity from solar photovoltaic systems integrated with an energy storage system and local grid station. A new mathematical model is developed to maximize the system power generation and balance the load demand. The simulation and optimization software System Advisor Model (SAM) is used to develop and test model results. The software is used to analyze and optimize the solar energy generation, the energy demand, and the economic performance: capital cost, overall investment, net present value, and the Levelized Cost of Energy of the project. A novel approach decentralized load centers is adopted to share power with adjacent communities. At the aim of improving the system flexibility, reliability, and climate resilience, the established model is grid-connected. The CO2 emissions reduction is also determined to evaluate the environmental impact of the interventions. Key words: “Techno-Economic analysis”, “Solar PV”, “Battery systems”, “System Advisor Model”, “Energy sharing”, “Environment analysis” Revised Manuscript with changes Marked 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Nomenclature Abbreviations AC Alternate Current CEC California Energy Commission CSP Concentrated Solar Panels DC Direct Current DHI Diffused Horizontal Irradiance DNI Direct Normal Irradiance EU European Union GHI Global Horizontal Irradiance LCOE Levelized Cost Of Energy LIB Lithium-Ion Battery Max Maximum MPPT Maximum Power Point Tracker NPV Net Present Value NREL National Renewable Energy laboratory O&M Operation and Maintenance cost PV PhotoVoltaic RES Renewable Energy Sources SAM System Aadvisor mModel SDS Sustainable Development Scenario SOC State of the Charge STC Standard Test Conditions UNO United Nations Notations a p temperature power coefficient C battery(t), charge stored in the battery C batteryMax(t) maximum charged capacity of DC battery C t future costs at each time period t E pv rated output power E t electricity output of the system in year t MaxP guaranted guaranteed power supply nlifetime of the developed system P AC active power output of DC inverter P Battery power stored in battery P tbattery power supplied by the battery at time t P DC, STC rated DC power at standard test conditions P Demand power demand at time t P Grid power supplied from grid P tLoad power supplied to load at time t P tmax maximum power at time t P PV power produce by PV system P tsolar power produced by PV system at time t R m actual radiation intensity (W/m 2 ) R stc radiation intensity under standard test condition T c real temperature of the solar cell T stc temperature at standard test conditions (25°C) Ƞconversion efficiency (%) 1. Introduction Accelerating towards achieving the United Nation (UN) and European Union (EU) 2050 carbon-neutral goals, energy harvesting from renewable sources can play a critical role to solve the environmental and climate problems [1][2]. The excess utilization of fossil fuels creates unprecedented environmental problems, which are not only affecting humans, but also endangering other living species [3]. In the light of the Paris climate agreement 2015, article 2, to limit the global temperature to 1.5°C [4], it is highly necessary to exploit new energy sources, which have fewer impacts on the environment [5]. Renewable energy sources such as solar, wind, hydro, biomass, geothermal, and marine energy sources have enough capacity to meet the primary energy demand [6][7]. The COVID-19 pandemic heavily influenced the global energy demand which records a 6% decline according to the global energy reviews 2020 report of the International Energy Agency [8]. Despite the COVID-19 pandemic and the decline in energy demand, a significant growth in renewable energy technologies has been recorded, and according to the International Energy Agency report IEA 2021, in 2020 the renewable energies capacity increased by 45% (280 GW) [9]. During the COVID-19 pandemic, a significant reduction in energy demand has been recorded also in European Union countries, where 9% reduction in electricity generation has recorded, while the renewable energy sector grew of 15% [10]. Solar energy has a huge potential to produce clean and low-cost energy [11]. Energy generation from the solar system has been largely adopted and considered as a key source of clean energy, significantly reducing greenhouse gases emission [12]. Solar energy technologies such as solar PV and concentrated solar power collectors (CSP) is the major technology for the conversion of solar energy into electricity and heat [13]. In 2019 the power produced worldwide by the solar PV system is 720 TWh, while in 2019, 22% (131 TWh) growth has been recorded in solar PV power generation: to reach the Sustainable Development Scenario (SDS) 2000-2030, the expected growth is 15% (total capacity of 3,268 TWh) [14]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 The variable and intermittent nature of solar energy creates challenges for grid station to manage the fluctuating power. Wencong Su. et al. [15] presented stochastic techniques for micro grid energy scheduling and control strategies to variable energy sources; results show a reduction in operational cost and a significant contribution of renewable energies [15]. P.G. McCormick et al. [16] presented a simulation model to investigate the effects on PV system operation and performance of the continuous fluctuation of solar radiation; for this purpose, they used one-minute radiation data in the simulation model and compare the results with one-hourly radiations data results [16]. According to D. Topic et al. [17], a mathematical model is helpful to find the optimal size of PV system regarding the required energy demand, and they also describe a method to accurately size the PV system, number of rows modules angle, and inter-row shading effects [17]. T. Ma, et al. [18] presented a comprehensive and improved mathematical model for solar PV module under real operating conditions, by using basic circuit equation of a solar cell. Authors also considered the real effects of radiations and ambient temperature on the solar cells, and the results showed the accurate characteristic curve of PV cell [18]. Research studies presented in articles [19] [20] [21] show PV systems models, design, operation, performance, parameters, and characteristics of PV modules. K. Heine et al. used the System Advisor Model software for a simulation approach to understand the battery size for residential buildings and parametric investigation by changing the capacity of the battery and defining a size that yields a maximum Net Present Value (NPV) [22]. E.B. Agyeku et al. show the comprehensive techno-economic analysis of Solar PV system with and without DC battery. For this purpose, they used the System Advisor Model (SAM) software, analysing the impacts of PV system, with and without DC battery and implementing the economic analysis of the developed system [23]. At the National Renewable Energy Laboratory several case studies have been conducted by Nicholas Di Orio et al. for the economic analysis of Solar PV and DC battery systems. For this analysis, the SAM software has been used and the results of a techno-economic analysis of the projects and dispatch strategies for DC batteries is presented [24]. By using again, the SAM software, L. Martín-Pomares et al. developed a model to assess the potential of solar energy for the long term, and large scale in Qatar. They performed an analysis over eleven-year hourly satellite-derived solar radiation data considered through an improved version of the Heliosat-3 model [25]. Several other papers show the developed models for the solar PV system coupled with DC battery by using SAM models; these articles also describe the operation and performance of the designed system, economics analysis, and a lifetime of the developed models [26][27][28][29]. Integration of energy storage technologies such as DC battery coupled with PV system can significantly improve the energy utilization and support the smooth operation of PV system [2230]. Akeyo et al. [23] presented a detailed design and analysis of a DC battery system configuration with large scale solar PV farm, where he captures the surplus solar energy by using a single DC-DC converter which simultaneously operates a charge controller and MPPT [31]. The author A. Nottrott et al. [24] presented in their work the dispatch strategies and optimal scheduling of a DC battery configured with a grid-connected solar PV system, using linear programming model for the optimal operation of the DC batter system. Lithium-ion battery (LIB) technology has several advantages over lead acid battery such as its fast-charging capability, high density, long life and less volume and weight, so makinge it more favorable to use in short -term energy storage for a wide range of applications [25][26][27][28]. Frequent operations of LIB charging/discharging can cause accident and safe operations in the design range result essential. Su et al. [ 29] describeds the safety warning techniques for a MW-level LIB system, using venting acoustic signal and performed experiments to study the frequent thermal runaway of LIB. Authors also mention the advantages of developed techniques such as high sensitivity, fast implementation, and low cost. González et al. [30] presented automation and monitoring systems dedicated to PV battery energy systems for DC micro grid, where authors present innovative multi-layered architecture for energy management and using Polymer Electrolyte Membrane (PEM) fuel cell produce hydrogen. Torres-Moreno et al. [31] described the energy management strategies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 for the PV battery based micro-grid and analysed the impact of variable PV generation on DC battery and electric vehicle. Hannan et al. [32], as well as Xiong et al. [33] presented a comprehensive research work and recent advancement on the operation, management, and characteristics of LIB, also mentioning the aging mechanism for LIB. Finally, hydrogen is the prominent vector of energy, especially in the future scenarios, where the energy sources are required to emit zero or lower emissions while the green hydrogen produce zero emission and generates by the renewable energy sources [34][35]. The surplus electricity of renewable energy sources can be used to produce hydrogen energy by using, for instance, the electrolysis process [36] [37]. Zhang et al. [38] developed a hybrid solar-wind-hydrogen energy system model, where hydrogen is produced through the wind surplus energy, storing the hydrogen itself, and using a fuel cell to produce electricity and supply to the load centers. Marino et al. [39] produced hydrogen energy from a stand-alone PV system, and by using a fuel cell to transform the hydrogen energy into electricity. Table 1 adds the results of a Literature review made on PV battery systems. Table 1. Literature reviews of PV battery system with and without grid connection. Authors /Reference Location /Software Developed system/ operating strategies Findings Heine et al. [40] Arizona USA / SAM PV-battery system for Residential building •37-44% of the peak electricity purchased • 4-12% of demand loads due to mismatch. • Batteries, 1.5-1.6 times larger than required to meet the annual peak energy requirements. Agyekumet al. [41] Ghana/ SAM 20 MW PPA PV system with and without dc battery • CF ranging from 16% to 18% • annual energy between 36 and 43 GWh • LCOE 8.84 ¢€/kWh, 9.88 ¢€/kWh and 10.05 ¢€/kWh DiOrio et al. [42] California Tennessee USA SAM Economic analysis of Solar PV and DC battery systems •Financial impact of adding PV + Storage •24 hours forecast for weather and load data •NPV; California: $ 31,874, Tennessee $ 60,731 • Battery Bank Size California: 110 kWh/55 kW Tennessee 300 kWh / 150 kW Pomares et al. [43] Doha, Qatar SAM Solar PV, CSP system coupled with DC •Satellite-derived data for 2003-2013 •200 MW solar plant planning •CO2emissions in Qatar by 0.51 million tons (Mt) by 2020 and 4.5 Mt by 2030 Georgiou et al. [44] Cyprus SAM/ Matlab Grid connected PV battery system •Annual 24 h battery dispatch strategies •Root Mean Squared Error (nRMSE) 2.10 •Annual net grid energy comparison in two cases Li et al. [45] Northwest region China SAM Grid-connected residential solar photovoltaic •highest monthly electricity generation of 496.53 kWh and lowest electricity generation 55.47 kWh •NPV of the grid-connect PV/battery system increased •loan term has the highest impact on LCOE and NPV of the grid-connected PV/ battery system Xiong et al. [46] Dubai UAE MATLAB/Simulink, SAM Residential PV-Storage Model in a Distribution Network/Generic Hybrid PV Storage Model •Initial cost:8446,LCOE:0.043 NPV:12,417 $, payback years: 9 •generalized approach for optimizing battery size in PV systems Zhang et al. [47] Sweden MATLAB/Genetic algorithms Battery sizing and Gridconnected PV-battery •Three rule-based operation strategies •Conventional operation strategy does not bring economic incentivesfor PV systemsto 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 SAM system. Multi-objective optimization deploy batteries even when the battery price is lowered by 50%. •Dynamic price load shifting strategy aims to benefit from the electricity price difference •Hybrid operation strategy outperforms the conventional operation strategy Shabani et al. [48] Sweden MATLAB Control strategies and design for grid connected PV system and technoeconomic impact of battery system •9.4% reduction in battery life cycle cost •30% of demand supplied by model battery •Self-sufficiency ration in scenario 1, 19.9%, and Self-sufficiency ration in scenario 2 is 25.1%. Garni et al. [49] Saudi Arabia HOMER Pro Tracking system for grid connected PV battery system and techno economic analysis • Tracking system produced 34% more power than fixed axis system • NPC $12,662 million and LCOE was 0.05434 $/kWh Said et al. [50] Dubai UAE / RETscreen Performance comparison of off/on grid PV system Economic, and environmental analysis • NPV 612–665 $/year and annual-LCA savings 1.7-1.9 years •during the project life 229–237 t CO2GHG emissions reduction Luerssen et al. [51] Singapore/ TRNSYS Life cycle-cost analysis of off grid PV cooling system and battery and thermal storage system • 9–10%, reduces the LCC • 51–77%, fuel cost savings • 27–50% higher investment • 39–48% battery yield only [32]. Peer-to-peer energy sharing in nearby communities can significantly improve the energy utilization, system flexibility, and reliability, while decentralized load centers can reduce the load demand on local grid stations [33]. Y. Zhou et al. presented a multi agent simulation framework mechanism for peer-to-peer energy sharing, where they developed three agent step length control, learning process involvement, and lastdefense method for energy sharing [34]. An autonomous and decentralization energy sharing techniques have been developed by A. Azizi et al., where authors performed energy management strategies without using communication system for DC micro grid; furthermore, this study showed an effective and efficient approach for cost-effective energy sharing and a reliable operation of DC grid [35]. At the aim of making the energy system reliable, flexible, and climate-resilient, it is highly necessary to decentralize and categorize the power centers. Decentralization of load center supports the decision-making process and reduces the system architecture, increasing the integration and utilization of renewable energy. In this work, a mathematical model is developed to maximize the system power generation and balance the system power generation to the energy demand of the interconnected system. The system is composed of a solar PV system, a DC battery storage, and a local grid station. The surplus/curtailed solar energy is stored in the DC battery to provide energy when the sunshine is not available; moreover, DC battery operation strategies increase the utilization of solar energy. To test and investigate the developed system performance, a simulation and optimization software (SAM) is used [523936]. In this work, the load is decentralized and categorized into four load centers. A novel approach is adopted for energy sharing to the load centers. The developed system is connected to the local grid station to purchase and sell electricity, according to the load demand. To assess the impact of the developed model on the environment, the total CO2 emission reduction has been evaluated. An economic analysis has also been performed to determine the capital cost, the NPV, the operation and maintenance cost (O&M), and the Levelized Cost of Energy (LCOE). Section 2 describes the methodology and the model of the developed system. The methodology section is further classified into five categories, which present the objective functions, the model of PV system, the DC battery system, the inverter, the dispatch strategies of DC battery, and the energy sharing model. Section 3 presents the 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 economic and environmental analysis of the developed system. Section 4 provides a brief detail and explanation of the obtained results and the significance of the system. Finally, section 5 presents a comprehensive discussion of the research work in comparison of literature reviews. The significance and contributions of this work are, (a); sustainable, socio-economic, and environmentally feasible energy mix solution for urban and sub-urban area, (b); PV-battery based energy system model for community energy sharing (c); decentralize and divides load centers and improve system reliability, flexibility and climate resilience. (d); increased penetration of renewable energy and reduce the burden from local grid station. 2. Methodology The objective of this work is to maximize the system power generation from the solar PV system and the DC battery, balancing the load demand, also considering the connection to the local grid. To achieve this objective, a new mathematical model is developed for the maximization of system generation and balance load demand. An estimation model developed by NREL is used to evaluate the solar energy production from the solar PV system and the interconnected energy storage DC battery [537]. The system performance optimization is analyzed with the simulation software SAM [5236]. The software SAM is an opensources tool, developed by the National Renewable Energy Laboratory, Department of Energy United States of America. This software has been widely used for techno-economic assessments of renewable energies, and hybrid energy systems. In this work, the latest version 2020.11.29 is used to estimate the system performance, the power produced by the model system and the cost of the project. The input parameters consist of load demand, sizing of PV system and DC battery, system components, weather data file and solar radiation data of the selected location, and financial parameters such as credits and incentives, discount and interest rate, and power trade tariff. By using this tool, the operational strategies of DC batteries, limits on energy trading with grid, and electricity supplied to the load centers were developed. Fig. 1. illustrates the process flowchart, classified into four different stages, where at first stage the required data are collected, an energy system model is developed, and the operational strategies are defined to achieve the objectives of this research. At the second stage, input data consist of weather data, location, system configuration parameters, economic values, and hourly load demand data fed into the simulation software. At the third stage, the software performs the simulation and the optimization for connected PV system, DC battery and grid station; at the final fourth stage the software provides its output results such as power generation, system losses and economic analyses values. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 1. Flowchart illustration of the different stages implemented in the model process The location of the developed PV-battery system is in the Campus of Engineering at the University of Perugia - Central Italy, coordinates: 43.13° latitude and 12.38° longitude. The weather data is obtained by the SAM software which provides the average data of Diffuse Irradiance (DHI) 1.43 kW/m2/day, Global Horizontal Irradiance (GHI) 3.95 kWh/m2/day, Beam Irradiance (DNI) 4.44 kWh/m2/day and Global Horizontal Irradiance (GHI) 3.95 kW/m2/day Diffuse Irradiance (DHI) 1.43 kWh/m2/day. A novel decentralized load centers and an energy sharing approach is implemented to supply energy to the nearby communities; hence, the total system load is divided into four load centers. One is the Engineering campus, while the other three loads are the nearby communities. Fig. 2.1 shows the hourly diffuse irradiance (DHI), beam irradiance (DNI), and global irradiance (GHI) GHI, DNI, and DHI values at the location of the PV system. Data show that the selected location has a considerable amount of solar radiation/day for the entire year, especially in the summer season, when the sunlight intensity, angle of incidence, and length of the day are high and can provide a suitable environment for the solar energy production. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 21. Hourly diffuse irradiance (DHI), beam irradiance (DNI), and global irradiance (GHI) and DHI solar irradiance profile. 2.1 Objective function The first objective of the developed model consists of maximizing the total power generation, which sums the power produced by the PV system, curtailed PV power stored in the DC battery and the power purchased by the local grid station. The second objective is to balance the power generation and the system load demand which are decentralized consists of the three community load centers and on the campus load. Objective 1. Maximize Power Generation. max t t t t PV Battery Grid P P P P   (1) Objective 2. Balance the load demand t t t t PV Battery Grid demand P P P P   (2) In eq. (1). Ptmax is the total power produced at time interval t, PPV is the active power produced by the solar system, PBattery is the curtailed PV power stored in the DC battery, and PGrid is the power purchased by the local grid station. In eq. (2). the total power produced must be equal to the connected system load demand (PDemand). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 drawn from the Fig. 8.6 and, Fig.79., provided by the manufacturer. A similar simplified model for SOC estimation has been developed and used by Ming et al. [59]. According to the manufacturer data sheet, with the first increase in temperature, the battery capacity and performance increases as showns in fig. 7. According to the given figures, at 0°C the battery capacity is 80.2 % while at 45°C the battery capacity rises to 105.4%. According to the manufacturer details the battery self-discharging rate is 1.5% per month; besides, the continuous operation (charging and discharging) also reduces the battery performance and storage efficiency. The minimum SOC is selected 20% to shield the battery from harmful deep-cycle effects which could accelerate the degradation of battery or influence for replacement. Besides, after 3750 cycles elapsed, the battery efficiency decreases to 77.56% and the battery needs to be replaced: the maintenance and replacement cost of battery is included in the project capital cost. When the dc battery state of the charge (SOC) goes below 20%, the load is shifted to the connected grid. The state of the charge of the DC battery is determined by eq. 10. ( ) ( ) ( ) battery batteryMax C t SOC t C t  (10) Where Cbattery (t) is the charge stored in the battery at time (t) while CbatteryMax(t) is the maximum charged capacity of DC battery. Fig. 7 Lithium-Ion DC battery thermal behavior model 2.4.1 Characteristics and parameters of DC battery The maximum output power is 500 kW, adjusted with connected inverter; Table 34. shows its technical parameters. Table 43. Technical parameters of the DC battery system Name Parameters Battery type Lithium-Ion: Nickel Manganese Cobalt Oxide Nominal bank capacity 1562 kWh (dc) Nominal bank power 543 kW (dc) DC to AC conversion efficiency 96% Nominal bank voltage 500.4 Vdc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Total numbers of cells 90489 Cells in series 139 Cell nominal voltage 3.6 Vdc Cell capacity 3.2 Ah Strings in parallel 651 Max discharge current 1085 A Max charge current 999.9 A Fig. 8.6 shows the cycle degradation and depth of the discharge of connected DC battery, and five different curves at different levels of DOD (10%, 20%, 40%, 80%, and 100%) are presented. Data show that the selected Lithium-Ion DC battery best operating curve is the yellow one, which gives 70% effective capacity at 80% of DOD: it allows the DC battery to supply more stored energy to the system as compared to other curves, while its effective capacity at 5000 cycle remains at 70%. Fig. 9.7 shows the calendar degradation of DC battery, where four different curves show the different levels of operating temperature vs SOC of the battery against its effective capacity. Data in fig. 9.7 show that the selected Lithium-Ion DC battery best operation curve is the green one, which shows that, at T 30°C, its SOC is 100%, while its effective capacity remains at 80%, at the age of 3500 days. The other curves show less effective capacity such as the red (50%), and the yellow (10%), however, the blue curve shows a 90% capacity, but its SOC is only 50%. Furthermore, the rise in operating temperature significantly reduces the effective capacity of battery. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 86 Cycle degradation curves of Battery Fig. 97. Calendar degradation curves of Battery 2.4.2 DC battery dispatch strategies The dispatch strategies are particularly important for the smooth operation of the system. The connected DC battery minimum SOC is 20%, and the maximum SOC is 80%, while the initial SOC is selected as 50%. The SOC range, 20-80% is derived from Fig. 8 and Fig. 9, provided by the manufacturer. The SOC ranges between 20-80% will increase the battery capacity and performance, hence, it stores and supplies more energy to balance the load demand. This assumption is also supported by Yang et al. [60]:the range of SOC for the lithium-ion battery could be between 20% to 90%, and authors also mention the limited influence of temperature over the SOC and OCV values. The initial SOC is selected as 50% since, at this value, the DOD is also 50%, and the DOD is inversely proportional to the SOC;, the phenomenon of partially charging and discharging the battery cells will balance the initial operation of the battery and will reduce the degradation and self-discharging of battery until it remains isolated from system. The dispatch strategies are presented in Fig. 10.8, which shows the 1-year operation of the DC battery with the optimized operation. The operation of the DC battery is divided into 4 periods 1-4, where the DC battery charge and 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 discharge according to the set limits. Fig. 10.8 shows the set limits of charging and discharging of battery that allow to operate the battery in best-optimized way to maximize its utilization. Fig. 108. Dispatch strategies of DC battery over the 1-year horizon The operational strategies of the DC battery are optimized according to the power generation and load demand. The DC battery is charged only with solar energy. The operation time of the DC battery is divided into four strategies over a one-year operation. In the first strategy, the operation time of the battery starts at 3 pm and finishes at 7 pm from 1st May to 30th Oct, and during this time the battery supplies only 20% of its charge. While in 2-strategy battery supplies maximum 80% of the charge during the evening period, which starts from 1st May to 30th Oct. In the 3rd strategy, the operation time of the battery starts at 3 pm to 7 pm during the first four months (1st Jan to 30th April) and from 1st Nov to 31st Dec, while during this period only 30% of the charge is supplied to the load. In the 4th strategy, the battery start supplying energy from 8 pm to the next day 2 pm, during the 1st of Jan to 30th April and 1st Nov to 31st Dec, during this operation time, the battery supplies 70% of its charge to the load centers. 2.5 Energy sharing Model 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 In the designed model a novel approach has been adopted: to share energy through the nearby communities by decentralized load centers, main campus load, Load center 1, Load center 2, and Load center 3. The advantage of the decentralized load is that it does not overburden the system and improves the system flexibility and reliability, supporting the decision making. A grid station is connected to the system, as the intermittent and variable nature of solar energy makes highly complex to maintain the connected load. Eq. 11 guarantees that the total system power must be greater or equal to the connected load. #( ) pv dc bat grid m m m demand guaranted P P P P MaxP M      (11) Where Max Pguaranted is the guaranteed power supply, Pmpv is the power produced by the PV system, Pmdc-bat is the power supplied by the DC battery to the load, the Pmgrid is the power purchased from the grid station, Pdemand is the total system connected load, and M stands for the number of operational times for one year. The eq. 12. shows the total load demand of the system, where PDemand is the sum of four decentralized load centers connected with the developed system. 1 2 3Demand campus center center center P Load Load Load Load    (12) Fig. 11.9 shows the Engineering campus of Perugia University, which is the selected place of the developed system; the total surface area required for the 2 MW PV system is 10,260 m2, while the total available area at the roofs top is 10,900 m2; the number of PV panels installed are 3960. Fig. 12.0 shows the nearby communities with the load centers classification. Fig. 911. Engineering Campus University of Perugia Fig.: 120. Power sharing network for community At the aim of better understanding and investigating the load behavior of the four load centers, a 24-hour hourly analysis is performed. Fig. 13.1 shows the hourly load profile of all the connected load centers: it is a fundamental tool to design the system according to load demand. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 113. Hourly load profile of the four connected load centers Fig. 142. shows the monthly total load demand of the system. The figure shows that a significant increase in energy demand is registered in summer; according to the diagram, the maximum load demand is 998 kWh, while the average load demand is 520 kWh a day. 0 100 200 300 400 500 600 700 800 LOAD DEMAND (KWH) HOURS OF THE DAY HOURLY LOAD DEMAND Campus load Load center 1 Load center 2 Load center 3 0 100 200 300 400 500 600 700 800 Campus load Load center 1 Load center 2 Load center 3 Hourly load demand Load demand (kWh) Hour of the day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 142. Annual Monthly system electrical load demand profile 3. Economic and Environmental Analysis In this section, an economic and environmental analysis has been performed to investigate the economic characteristics and the impact on the environment of the developed system. Two economic parameters were analyzed a); NPV and b); LCOE. The NPV of the project is determined by using the eq. 13, presented by [6139]. 0(1 ) n t t t R NPV i    (13) Where Rt is the total cash outflow-inflow during the period t, i is the return rate or discount rate gain after the investment, and the t, is number of time periods. The LCOE describes the cost of energy produced by 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 the PV system. In this project, the LCOE “annuitizing” technique is used (eq. 14); the LOEC is determined by the total annual cost of the system divided by the average annual output power of the system [6240]. 0 1 ( )( ) (cos ) (1 ) 1 (1 ) ( ) ( ) / n t t n t Annuitizing n t t Cr Annual t r r LCOE Average output E n           (14) Where Ct is the future costs at each time period t, Et is the electricity output of the system in year t, r is the discounted rate and n is the lifetime of the developed system (25 years). The energy produced by the developed system is shared to nearby communities and the surplus energy is also shared with the local grid station. The energy purchased and sold to the grid station with a flat rate tariff, where the price of the electricity purchased from grid is 11.1¢$/kWh and electricity sold to the grid at the price of 5.0 ¢$/kWh [6341]. Table 54. shows the total energy charges over one year and it also presents a comparison of the energy bills with and without system; furthermore, it shows the net saving of the system. Table 54. Annual energy charges with and without system. Month Bill Without system ($) Bill with system ($) Saving ($) Jan 32,747 27,201 5,545 Feb 35,418 22,850 12,567 Mar 39,412 23,216 16,195 April 39,384 25,537 13,847 May 44,362 19,824 24,537 Jun 72,494 26,929 45,564 July 73,251 37,511 35,739 Aug 61,760 28,438 33,322 Sep 56,478 27,054 29,423 Oct 36,297 20,149 16,147 Nov 30,012 22,400 7,611 Dec 30,859 22,834 8,024 Annual 552,477 303,948 248,528 Fig. 153. describes the payback cash flow of the developed system, as the lifetime of the system is 25 years, and the simple payback period is 6.4 years. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 135. Payback cash flow of the model system At the aim of investigating the impact of the developed system on the environment, an analysis has been performed to determine the reduction of CO2 emission. According to the Italian Institute for Environment Protection and Research 2020 report, the Emission factor of thermal power plants is 367.3 gCO2/kWh [6442][6543]. Since the total energy produced by the PV system is 2,838,145 kWh/year, a reduction of around 1150 Tons/year of CO2 emissions is found. 4. Results In this section the obtained results are presented. Table 6.5 shows the financial situation of the developed system, describing the total installation cost including battery maintenance and replaced cost, LCOE, NPV, the project life of the developed system and further financial parameters such as inflation rate, discount and simple payback rate, debt term and fractions are shown. Table 6.5 also shows also that the electricity bills with and without the system, highlighting the economic saving. Table 56. Financial situation and project analysis parameters 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Name Value Net capital cost $2,046,993 Project life 25 years LCOE $ 3.9 cents/kWh Inflation rate 1.5% Real discount rate 6% Debt fraction 50% Debt Amount $ 1,023,496 Debt Term 25 years Debt Rate 5% Net present value $ 971,200 Simple Payback period 6.4 years Discounted payback period 8.8 years Electricity bill without system (year 1) $552,477 Electricity bill with system (year 1) $303,949 Net savings with system (year 1) $248,529 Furthermore, Table 67. describes the energy produced by the system as the total annual energy produced, the capacity factor and the energy yield. The results show that the LCOE is 3.91 ¢$/kWh while the performance ratio during a year is 0.88. Table 67. Energy generation by the designed PV system. Metric Value Annual energy (year 1) 2,838,145 kWh Capacity factor (year 1) 16.6% Energy yield (year 1) 1,451 kWh/kW Performance ratio (year 1) 0.88 Battery roundtrip efficiency 87.8% Battery charge energy from system 100.0% Fig. 16.5 shows the total annual energy production by the solar system for a year. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 220. Year operation of DC battery To investigate the system performance in more detail, a monthly analysis has performed, including the system power generation, the load demand, and the amount of energy shared between the model system and the local grid station. Fig. 23.1 shows the results of performance analysis, showing that the modeled system performance is very high, and the power produced by the system efficiently meets the load demand. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fig. 231. Month by month, hourly system electricity load demand ,, ssystem generation, and grid purchase 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 The integration of model system with local grid station significantly helps to increase the system reliability and climate resilience. According to the Fig. 23.1 data during the dark time energy purchased from local grid and mix with DC battery energy and feed to the load centers, while during the daytime surplus stored in DC battery and extra electricity sold to the grid station. Fig. 24.2 shows the different scenarios of energy sharing over the horizon of one year. The results show the energy produced by the system, that shared with the load center and the energy supplied to and from the connected grid. Fig. 242. Monthly energy supplied to load centers and energy sharing between system and grid station Annual energy sharing analysis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 5. Discussion The main objective of this work was to investigate a sustainable and socio-economic feasible solution to decarbonize small communities in urban and sub-urban areas, using energy from renewable energy sources. At this purpose, an energy mix model is developed to demonstrate how a small-scale PV-battery system coupled with the girid station can provide enough energy to meet the demand of small communities. The second goal of this work was to perform an economic and environmental analysis of a PV-battery system, and to propose a feasible solution that effectively reduces the greenhouse gas emissions and requireslow capital cost, operation, and maintenance charges and LCOE. The obtained results describe that both objectives were successfully achieved, as the results of Table 7, indicate,. Fig. 16, shows that during the first year, the energy produced by the developed system was 2.838 GWh, and the system performance ratio was 0.88, while the capacity factor resulted equal to 16.6%, showing the significance and applicability of the proposed research work. Table 6. presents the economic values of the system: the net capital cost of the project was 2.04 million dollars, . Furthermore, the LCOE $ is 3.9 ¢$/kWh, the net present value is 0.970 million dollars, and the discounted payback period was 8.8 years. The proposed system not only provides a low-cost clean energy, but it also significantly reduces greenhouse gas emissions, as it is shown that the system reduces the 1,150 Tons of CO2 emission, only in the first year. Due to the uncertain and variable nature of the solar system, it was highly challenging to maximize the utilization of solar power. Taking advanced steps and implementing effective strategies such as integration of PV system with the DC batteries and the local grid station,as well as the decentralization/division of the load centers, a significant increase of the penetration of generated power and a reduction of the burden from grid station has been achieved. Results also indicate that developed techniques increase the reliability and flexibility of the proposed model, and the proposed joint operation of PV, batteries, and grid station provides a sustainable, reliable, and socio-economic feasible solution for the transition to the net zero CO2 emissions. The comparison of the results with published research papers already mentioned in the introduction section moves from the work of Agyekum [41], where he proposed a standalone PV system with and without battery system for two regions in 3 different climate conditions in Ghana, and he only investigated the economics aspects. The present work, in turn, is focused on energy generation and its maximum penetration, energy sharing, environmental impacts as well as economic aspects,giving also a better LCOE (3.9 ¢$ /kWh against 8-10 ¢$ /kWh). DiOrio et al. [42] presented two case studies for the PV battery system model in California and Tennessee USA, proposing a customer side behind the meter energy storage system (lithium-ion battery system) connected with PV system. By using the same software SAM, he showed different dispatch strategies for manual scheduling and automatic peak shaving to mitigate the demand charges at the domestic level. This work, on the contrary, adopted automatic dispatch strategies with a frontof-meter energy storage system for commercial scale and distributed load centers. Georgiou et al. [44] developed a building integrated PV battery system model by using a linear programming optimization scheme for battery operation and energy trading with grid station, using the software SAM, and Matlab. The scope of their work was limited to the building energy management and energy import, export, and storage. We present a different technique for the load management at a community scale, and we acted as commercial power producer with automatic load distribution and battery operational strategies. Li et al. [45] proposed similar PV battery grid connected models for five different cities in China, by using the SAM software. Authors investigateds the economic and performance analysis of the proposed 5 kW residential PV battery system under different climate conditions; the scope of their research was only focused on limited consumers and the LCOE ranges from $ 10.36 ¢$ to 22.13 ¢$/kWh. On the opposite side this work covers a wide scope, it considersa large number of consumers and the LCOE obtained is $ 3.9 ¢$/kWh. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 In the view of previous literature review, the developed model can be applied for different load sectors such as industrial, agricultural, and commercial load centers. The cost of energy and net capital cost of the proposed model is also less in comparison to the ones presented literature, while the discounted payback period of this work is also high, which gives more relief and easy economic management. The highly significant advantage of this model is that it decentralized/divided load centers, not only increased the utilization of renewable energy but also reduces the burden from the local grid station and help to improve the system flexibility and reliability. While due to the technological limitation of new storage technologies which could be used in this work instead of DC battery were not considered. For future analyses, the proposed work would provide a solid foundation to initiate the potential discussion to accelerate toward energy transition and net zero CO2 emissions. The developed model has a wide range of applicability and can be used for long-term, large-scale renewable energy modeling to integrate more renewable sources such as wind, biomass, and hydropower. It could be possible to introduce new storage technologies such as hydrogen energy, flywheel storage, and compressed air storage system, which were out of the scope of this particular research because of the practical limitations of these technologies. The proposed model would be highly beneficial to meet the large-scale industrial, agricultural, and future electric vehicle loads. Hence, the developed model is also applicable for other regions and urban areas where the energy demand, economic growth, and pollution level is increasing. Conclusions The research work carried out in this paper is aimed to answer the global questions on how to mitigate the growing environmental concerns and tackle the increasing energy demand. The proposed model provides a sustainable, socio-economic, and environmentally feasible solution to solve the stated questions. The developed energy system model includes a solar PV system, integrated with DC batteries and local grid station, and it presents an optimal method to meet the energy demand of an engineering campus and the nearby communities. The obtained results indicate that during the first year of operation a significant amount of clean energy (2.838 GWh/year) has been produced by the developed system at a LCOE of 3.9 ¢$/kWh. The decentralization/division of load centers, the optimal operational strategies of DC batteries and the energy sharing with the local grid station, increased the penetration and the utilization of the power produced by the system, as in the first year the system performance ratio resulted equal to 0.88 and the capacity factor of the PV system reached the 16.6%, with a roundtrip efficiency of the DC battery of 87.8%. Results indicated that the significant amount of clean energy shared with the local grid station and the grid integration improves the system flexibility, and reliability. An economic comparison is also performed to determine the electricity charges with and without the model system, and the results showed that, the electricity charges without the system amounted to $552,477 $/year, while the proposed system reduced the charges to 303,949 $/year, (-45%). The net capital cost of the developed system was 2,046,993 $, while the NPV is 971,200 $, and the discounted payback period is 8.8 years. The environmental analysis of the model indicates that 1150 tons/year of CO2 emissions has been reduced. For the future cases, this work will be used to propose a large-scale energy system model for the municipality of Perugia, Central Italy. Besides,, authors will investigate and integrate other renewable energy sources such as solar, biomass, and hydropower, as well as new energy storage technologies like, for instance, hydrogen energy and pumped hydro energy storage systems, enlarging the scope to industrial, agricultural, and commercial load centers for long term energy distribution in the Municipality of Perugia. This article presents multiple mathematical models, estimations techniques and a novel energy sharing methodology to decarbonize a University Campus and neighboring communities by producing energy from 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Solar PV integrated with an energy storage system. The obtained results show the significant amount of clean energy 2.838GWh/year has produced by the developed system at the $ 3.9 cents/kWh LCOE. To achieve this objective, an energy maximization and decentralize load centers approach is adopted; furthermore, to improve the system flexibility, reliability and efficient utilization of solar energy, the entire system is interconnected with the local grid station. The accomplished results show that a significant amount of green energy can be produced, stored, and shared by the designed PV system, while the performance of the DC battery is satisfactory, and a high roundtrip efficiency is achieved. The paper also presents an economic analysis which illustrates the capital cost, the total investment values, NPV, payback period, cash flow, LCOE, and net saving from the system. Results demonstrate that energy sharing to the neighboring communities by decentralized load center approach, significantly increases the solar energy utilization, with another advantage of not overburdening the system. Finally, the assessment of the environmental impact of the proposed system demonstrated that a significant amount of CO2 emission is reduced. Acknowledgement Sincere gratitude and special thanks to Biomass Research Center, CIRIAF and University of Perugia for providing the facilities and support to conduct this research work. References [1] United Nation Secretary-General annual report on the Work of the Organization 2020, https://www.un.org/sg/en/content/sg/articles/2020-12-11/carbon-neutrality-2050-theworld%E2%80%99smost-urgent-mission [2] European Commission Climate strategies and targets, 2050 long-term strategy, https://ec.europa.eu/clima/policies/strategies/2050_en [3] Environmental Protection Agency USA report on environment 2020, https://www.epa.gov/reportenvironment [4] Conference of the Parties Twenty-first session Paris, 30 November to 11 December 2015 https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement [5] Agustin Alvarez-Herranz, Daniel Balsalobre-Lorente, Muhammad Shahbaz, José María Cantos, Energy innovation and renewable energy consumption in the correction of air pollution levels, Energy Policy, Volume 105, 2017, Pages 386-397, ISSN 0301-4215, https://doi.org/10.1016/j.enpol.2017.03.009 [6] A. Qazi et al., "Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions," in IEEE Access, vol. 7, pp. 63837-63851, 2019, https://doi: 10.1109/ACCESS.2019.290640 [7] B. Muruganantham, R. Gnanadass, N.P. Padhy, Challenges with renewable energy sources and storage in practical distribution systems, Renewable and Sustainable Energy Reviews, Volume 73, 2017, Pages 125-134, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.01.089 [8] IEA (2020), Global Energy Review 2020, IEA, Paris https://www.iea.org/reports/global-energy-review2020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 [9] IEA (2021), Renewable Energy Market Update 2021, IEA, Paris https://www.iea.org/reports/renewable-energy-market-update-2021 [10] Annette Werth, Pietro Gravino, Giulio Prevedello, Impact analysis of COVID-19 responses on energy grid dynamics in Europe, Applied Energy, Volume 281, 2021, 116045, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2020.116045 [11] Ehsanul Kabir, Pawan Kumar, Sandeep Kumar, Adedeji A. Adelodun, Ki-Hyun Kim, Solar energy: Potential and future prospects, Renewable and Sustainable Energy Reviews, Volume 82, Part 1, 2018, Pages 894-900, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.09.094 [12] Priscila Gonçalves Vasconcelos Sampaio, Mario Orestes Aguirre González, Photovoltaic solar energy: Conceptual framework, Renewable and Sustainable Energy Reviews, Volume 74, 2017, Pages 590-601, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.02.081 [13] Alex Fabianne de Paulo, Geciane Silveira Porto, Solar energy technologies and open innovation: A study based on bibliometric and social network analysis, Energy Policy, Volume 108, 2017, Pages 228238, ISSN 0301-4215, https://doi.org/10.1016/j.enpol.2017.06.007 [14] IEA (2020), Solar PV, IEA, Paris https://www.iea.org/reports/solar-pv [15] W. Su, J. Wang and J. Roh, "Stochastic Energy Scheduling in Microgrids with Intermittent Renewable Energy Resources," in IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1876-1883, July 2014, doi: 10.1109/TSG.2013.2280645 [16] P.G. McCormick, H. Suehrcke, The effect of intermittent solar radiation on the performance of PV systems, Solar Energy, Volume 171, 2018, Pages 667-674, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2018.06.043 [17] Danijel Topić, Goran Knežević, Krešimir Fekete, The mathematical model for finding an optimal PV system configuration for the given installation area providing a maximal lifetime profit, Solar Energy, Volume 144, 2017, Pages 750-757, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2017.02.011 [18] Tao Ma, Wenbo Gu, Lu Shen, Meng Li, An improved and comprehensive mathematical model for solar photovoltaic modules under real operating conditions, Solar Energy, Volume 184, 2019, Pages 292304, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2019.03.089 [19] Luis Hernández-Callejo, Sara Gallardo-Saavedra, Víctor Alonso-Gómez, A review of photovoltaic systems: Design, operation and maintenance, Solar Energy, Volume 188, 2019, Pages 426-440, ISSN 0038092X, https://doi.org/10.1016/j.solener.2019.06.017 [20] Hegazy Rezk, Igor Tyukhov, Mujahed Al-Dhaifallah, Anton Tikhonov, Performance of data acquisition system for monitoring PV system parameters, Measurement, Volume 104, 2017, Pages 204211, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2017.02.050 [21] Wael Charfi, Monia Chaabane, Hatem Mhiri, Philippe Bournot, Performance evaluation of a solar photovoltaic system, Energy Reports, Volume 4, 2018, Pages 400-406, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2018.06.004 [22] A. Zahedi, Maximizing solar PV energy penetration using energy storage technology, Renewable and Sustainable Energy Reviews, Volume 15, Issue 1, 2011, Pages 866-870, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2010.09.011 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 [23] O. M. Akeyo, V. Rallabandi, N. Jewell and D. M. Ionel, "The Design and Analysis of Large Solar PV Farm Configurations With DC-Connected Battery Systems," in IEEE Transactions on Industry Applications, vol. 56, no. 3, pp. 2903-2912, May-June 2020, doi: 10.1109/TIA.2020.2969102 [24] A. Nottrott, J. Kleissl, B. Washom, Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems, Renewable Energy, Volume 55, 2013, Pages 230240, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2012.12.036 [25]. Ghassan Zubi, Rodolfo Dufo-López, Monica Carvalho, Guzay Pasaoglu, The lithium-ion battery: State of the art and future perspectives, Renewable and Sustainable Energy Reviews, Volume 89, 2018, Pages 292-308, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2018.03.002. [26]. Changfu Zou, Lei Zhang, Xiaosong Hu, Zhenpo Wang, Torsten Wik, Michael Pecht, A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors, Journal of Power Sources, Volume 390, 2018, Pages 286-296, ISSN 0378-7753, https://doi.org/10.1016/j.jpowsour.2018.04.033. [27]. Yanxu Yu, Jiansu Mao, Xinxi Chen, Comparative analysis of internal and external characteristics of lead-acid battery and lithium-ion battery systems based on composite flow analysis, Science of The Total Environment, Volume 746, 2020, 140763, https://doi.org/10.1016/j.scitotenv.2020.140763. [28]. Chen, T., Jin, Y., Lv, H. et al. Applications of Lithium-Ion Batteries in Grid-Scale Energy Storage Systems. Trans. Tianjin Univ. 26, 208–217 (2020). https://doi.org/10.1007/s12209-020-00236-w [29]. Tonglun Su, Nawei Lyu, Zhixing Zhao, Huairu Wang, Yang Jin, Safety warning of lithium-ion battery energy storage station via venting acoustic signal detection for grid application, Journal of Energy Storage, Volume 38, 2021, 102498, ISSN 2352-152X, https://doi.org/10.1016/j.est.2021.102498. [30]. González, I.; Calderón, A.J.; Portalo, J.M. Innovative Multi-Layered Architecture for Heterogeneous Automation and Monitoring Systems: Application Case of a Photovoltaic Smart Microgrid. Sustainability 2021, 13, 2234. https://doi.org/10.3390/su13042234 [31]. Torres-Moreno, J.L.; Gimenez-Fernandez, A.; Perez-Garcia, M.; Rodriguez, F. Energy Management Strategy for Micro-Grids with PV-Battery Systems and Electric Vehicles. Energies 2018, 11, 522. https://doi.org/10.3390/en11030522 [32]. M.A. Hannan, M.S.H. Lipu, A. Hussain, A. Mohamed, A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations, Renewable and Sustainable Energy Reviews, Volume 78, 2017, Pages 834-854, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.05.001 [33]. Rui Xiong, Yue Pan, Weixiang Shen, Hailong Li, Fengchun Sun, Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives, Renewable and Sustainable Energy Reviews, Volume 131, 2020, 110048, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2020.110048 [34]. Furat Dawood, Martin Anda, G.M. Shafiullah, Hydrogen production for energy: An overview, International Journal of Hydrogen Energy, Volume 45, Issue 7, 2020, Pages 3847-3869, ISSN 0360-3199, https://doi.org/10.1016/j.ijhydene.2019.12.059 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 [35]. Mustafa Balat, Potential importance of hydrogen as a future solution to environmental and transportation problems, International Journal of Hydrogen Energy, Volume 33, Issue 15, 2008, Pages 4013-4029, ISSN 0360-3199, https://doi.org/10.1016/j.ijhydene.2008.05.047 [36]. Tanay Sıdkı Uyar, Doğancan Beşikci, Integration of hydrogen energy systems into renewable energy systems for better design of 100% renewable energy communities, International Journal of Hydrogen Energy, Volume 42, Issue 4, 2017, Pages 2453-2456, ISSN 0360-3199, https://doi.org/10.1016/j.ijhydene.2016.09.086 [37]. Shams Anwar, Faisal Khan, Yahui Zhang, Abdoulaye Djire, Recent development in electrocatalysts for hydrogen production through water electrolysis, International Journal of Hydrogen Energy, Volume 46, Issue 63, 2021, Pages 32284-32317, ISSN 0360-3199, https://doi.org/10.1016/j.ijhydene.2021.06.191 [38]. Weiping Zhang, Akbar Maleki, Marc A. Rosen, Jingqing Liu, Sizing a stand-alone solar-windhydrogen energy system using weather forecasting and a hybrid search optimization algorithm, Energy Conversion and Management, Volume 180, 2019, Pages 609-621, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2018.08.102 [39]. C. Marino, A. Nucara, M.F. Panzera, M. Pietrafesa, V. Varano, Energetic and economic analysis of a standalone photovoltaic system with hydrogen storage, Renewable Energy, Volume 142, 2019, Pages 316329, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2019.04.079 [40] Karl Heine, Amogh Thatte, Paulo Cesar Tabares-Velasco, A simulation approach to sizing batteries for integration with net-zero energy residential buildings, Renewable Energy, Volume 139, 2019, Pages 176-185, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2019.02.033 [41] Ephraim Bonah Agyekum, Techno-economic comparative analysis of solar photovoltaic power systems with and without storage systems in three different climatic regions, Ghana, Sustainable Energy Technologies and Assessments, Volume 43, 2021, 100906, ISSN 2213-1388, https://doi.org/10.1016/j.seta.2020.100906 [42] DiOrio, Nicholas, Dobos, Aron, and Janzou, Steven. Economic Analysis Case Studies of Battery Energy Storage with SAM. United States: N. p., 2015. Web. doi:10.2172/1226239 [43] Luis Martín-Pomares, Diego Martínez, Jesús Polo, Daniel Perez-Astudillo, Dunia Bachour, Antonio Sanfilippo, Analysis of the long-term solar potential for electricity generation in Qatar, Renewable and Sustainable Energy Reviews, Volume 73, 2017, Pages 1231-1246, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.01.125 [44] Giorgos S. Georgiou, Paul Christodoulides, Soteris A. Kalogirou, Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming, Energy, Volume 208, 2020, 118177, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2020.118177 [45] Chong Li (2019) Economic and performance evaluation of grid-connected residential solar photovoltaic systems in Northwest China, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, DOI: 10.1080/15567036.2019.1668882 [46] Xiong, L.; Nour, M. Techno-Economic Analysis of a Residential PV-Storage Model in a Distribution Network. Energies 2019, 12, 3062. https://doi.org/10.3390/en12163062 [47] Yang Zhang, Anders Lundblad, Pietro Elia Campana, F. Benavente, Jinyue Yan, Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden, Energy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Conversion and Management, Volume 133, 2017, Pages 249-263, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2016.11.060 [48] Masoume Shabani, Erik Dahlquist, Fredrik Wallin, Jinyue Yan, Techno-economic impacts of battery performance models and control strategies on optimal design of a grid-connected PV system, Energy Conversion and Management, Volume 245, 2021, 114617, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2021.114617. [49] Hassan Z. Al Garni, Anjali Awasthi, Makbul A.M. Ramli, Optimal design and analysis of gridconnected photovoltaic under different tracking systems using HOMER, Energy Conversion and Management, Volume 155, 2018, Pages 42-57, https://doi.org/10.1016/j.enconman.2017.10.090. [50] Zafar Said, Aamir Mehmood, Adeel Waqas, Ahmed Amine Hachicha, Reyhaneh Loni, Central versus off-grid photovoltaic system, the optimum option for the domestic sector based on techno-economicenvironmental assessment for United Arab Emirates, Sustainable Energy Technologies and Assessments, Volume 43, 2021, 100944, https://doi.org/10.1016/j.seta.2020.100944. [51] Christoph Luerssen, Oktoviano Gandhi, Thomas Reindl, Chandra Sekhar, David Cheong, Life cycle cost analysis (LCCA) of PV-powered cooling systems with thermal energy and battery storage for off-grid applications, Applied Energy, Volume 273, 2020, 115145, https://doi.org/10.1016/j.apenergy.2020.115145 [52] SAM Intro: Blair, Nate, Dobos, Aron P., Freeman, Janine, Neises, Ty, Wagner, Michael, Ferguson, Tom, Gilman, Paul, and Janzou, Steven. System Advisor Model, SAM 2014.1.14: General Description. United States: N. p., 2014. Web. doi:10.2172/1126294 [53] Bo Ming, Pan Liu, Shenglian Guo, Xiaoqi Zhang, Maoyuan Feng, Xianxun Wang, Optimizing utilityscale photovoltaic power generation for integration into a hydropower reservoir by incorporating longand short-term operational decisions, Applied Energy, Volume 204, 2017, Pages 432-445, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2017.07.046 [54] Chong Li, Dequn Zhou, Yuan Zheng, Techno-economic comparative study of grid-connected PV power systems in five climate zones, China, Energy, Volume 165, Part B, 2018, Pages 1352-1369, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2018.10.062 [55] Espedal, I.B.; Jinasena, A.; Burheim, O.S.; Lamb, J.J. Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles. Energies 2021, 14, 3284. https://doi.org/10.3390/en14113284 [56] Li Sun, Guanru Li, Fengqi You, Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer, Renewable and Sustainable Energy Reviews, Volume 131, 2020, 109994, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2020.109994 [57] Pietro Iurilli, Claudio Brivio, Vanessa Wood, On the use of electrochemical impedance spectroscopy to characterize and model the aging phenomena of lithium-ion batteries: a critical review, Journal of Power Sources, Volume 505, 2021, 229860, ISSN 0378-7753, https://doi.org/10.1016/j.jpowsour.2021.229860 [58] Rivera-Barrera, J.P.; Muñoz-Galeano, N.; Sarmiento-Maldonado, H.O. SoC Estimation for Lithiumion Batteries: Review and Future Challenges. Electronics 2017, 6, 102. https://doi.org/10.3390/electronics6040102 [59] Jin hao, M., Stroe, D-I., Ricco, M., Guangzhao, L., & Teodorescu, R. (2019). A Simplified Model based State-of Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model. IEEE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 renewable energy technologies. The more details about applicability and limitation are written in the Section 5. Thanks 20. The Conclusions section should include some further guidelines to continue with the presented research. Answer: Done. Dear reviewer, we modified the conclusion section and we presented details of our future work in the conclusions section. For the future case, this work will be used to proposed large scale energy system model for the municipality of Perugia, Central Italy. Furthermore, the future work will investigate and integrate other renewable energy sources such as solar, biomass and hydropower and new energy storage technologies such as hydrogen energy and pumped hydro energy storage system, enlarging the scope to industrial, agricultural, and commercial load centers for long term energy distribution in the Municipality of Perugia. Thanks Response to Reviewer #3: Dear Reviewer, we are highly thankful to you for the time you have spent on this paper, providing us a great response to improve the quality of this paper. We have addressed all your comments, and the response to every comment is given below for your kind consideration. 1) As can be seen from the many studies cited in the article, the economic analysis of Solar PV systems is a widely discussed topic, therefore, the use of the words 'NEW' or 'NOVEL' in the title and text may be unnecessary. Answer: Done. Dear reviewer, thank you very much for this suggestion. We have changed the title of this work, and the word Novel is removed, and the new title of this work is “A Techno-Economic Analysis of a Solar PV and DC Battery Storage System for a Community Energy Sharing”. Thanks 2) A mathematical model is proposed to maximize the system power generation and balance the load demand in this manuscript. What is the main difference between the benefits of the model proposed and grid-connected (with two-way communication), storage-included inverters so-called "smart inverters"? Answer: Done. Dear reviewer, in our work we developed a mathematical model to maximize the power generation and meet the load demand, while the power produced by the PV system plus the power stored in DC battery and power purchased from local grid at any time interval t, must balance the load demand. The main difference between our work and the grid connected PV system is that we sum-up all the available power (Power from PV + Power from DC battery + Power purchased from grid) and distribute the available power to different load centers. In our work we decentralized/divided the load centers into four different load centers, which are our Engineering campus load, and three nearby communities. By supplying the power to nearby communities helps to reduce the burden from local grid station, and effectively increases the utilization of renewable energy. 3) The DNI, GHI, and DHI words in the figure 1 caption and the Y-axis titles of the charts (DHI, DNI, GHI) are not in the same order. They may be sorted in the same order in terms of increasing the readability. Answer: Done. Dear reviewer, we corrected the caption of figure 1 (new figure number is Fig. 2) and corrected the order. The new caption is, Hourly diffuse irradiance (DHI), beam irradiance (DNI), and global irradiance (GHI) profile. Thanks 4) In section "2.4 DC battery system model", the storage capacity of the DC battery system is given 1.05 MWh. It is different from the value given in section "2.2 System details and architecture". Answer: Done. Dear reviewer, the storage capacity of DC battery is 1.5 MWh which is written in section 2.2, while in section 2.4, by mistake, is was written 1.05 MWh, which is now corrected. Thanks 5) Information can be given about the alternatives of the System Advisor Model (SAM) software, in which the mathematical model, which is the output of the study, is developed and tested. Answer: Done. Dear reviewer, we highly thank you for this comment. In the introduction section Table 1, and in the discussion section, we present the alternative software used in published papers such as Homer, Retscreen, TRNSYS, and Matlab/Simulink for the development of energy system models, especially for PV-battery system. In the view of the literature review, we performed an analysis and comparison of the proposed model respect to others; the complete details can be viewed in introduction section and discussion section. Thanks Response to Reviewer #4: Dear Reviewer, we are highly thankful to you for the time you have spent on this paper, providing us a great response to improve the quality of this paper. We have addressed all your comments, and the response to every comment is given below for your kind consideration. 1. This study is still lack of depth analysis, discussion and comparison from the published literature reviews - the results of research by other authors have not been presented, only the scope of the research performed has been presented. Answer: Done. Dear reviewer, we highly thank you for this comment. In the introduction section we have added more details and a wider analysis of published papers. We also added a Table (1). In the introduction section is now present a deeper view on scope, techniques, and results of published papers. We also added a discussion section (5) in this work, and we presented a brief discussion and comparison of our obtained results with the published papers, for your kind review please check these sections. Thanks 2. Discussion of the obtained results is insufficient - comparison to the results by others is necessary. Answer: Done. Dear reviewer, we have added a new discussion section 5 in this paper, and we presented a brief discussion on the obtained results of this work and we also did a comparison of our obtained results with the published papers. Thanks 3. The Conclusions do not present the obtained results but are only a summary of the purpose of the work, which has already been described in Abstract. Answer: Done. Dear reviewer, we highly thank you for this comment. We modified the conclusion section and now we presented the obtained results and significance of the developed model. We also presented the future work related to this proposed model and its applicability. Thanks Specific comments: AIn Fig. 1 results are presented in kW/m2, in the description before the figure: kWh/m2 Answer: Done. Dear reviewer, thank you very much for this comment, we corrected the description of the figure 1(new figure number is Fig. 2). The results are in kW/m2, which are same as in the description. Thanks BEq. (1) balances power [W] with energy [Wh] Answer: Done. Dear reviewer, we corrected, the Eq.1 and now both side the balance power is in [W], Thanks. CIn 2.2 System details architecture there are many repetitions - it is necessary to rewrite the text Answer: Done. Dear reviewer, thank you very much for this comment. We rewrite the complete system details and architecture in section 2.2 and removed all the repetitions in this section. For your kind review please check the section 2.2 system details and architecture. Thanks D2.3: Standard Test Conditions - capital letters Answer: Done. Dear reviewer, thank you very much, we corrected the Standard Test Conditions word in both places in section 2.3. EIn 2.4 DC battery system model: "a storage capacity of 1.05 MWh", while in 2.2 and Table 3 - 1.5 MWh. Answer: Done. Dear reviewer, the storage capacity of DC battery is 1.5 MWh which is written in section 2.2, and in the Table 3, while in section 2.4, by mistake, is was written 1.05 MWh, which is now corrected. Thanks FIn Fig. 12 load power [kW] is presented, while in the text above: "the maximum load demand is 998 kWh". Answer: Done. Dear reviewer, we corrected the mistake in figure 12 (new figure number is Fig. 14). Now the Load Power (kWh) is mentioned, keeping in the description the maximum load demand of 998 KWh, while the average load demand is 520 kWh. Thanks Highlights • PV-battery based energy system model for community energy sharing • Sustainable and socio-economic feasible energy mix solution for urban and sub-urban areas • Economic and environmental analysis of Grid connected PV-battery system Highlights Cover Letter Perugia: 13/12/2021 Dear Editor We are highly thankful to you and anonymous Reviewers for their great support, valuable comments, and suggestions to improve the quality of this paper. After receiving the reviewers’ response from you, we highly focused on these comments and suggestions and precisely worked on each and every comment. We thoroughly answer all the questions asked by the reviewers, and a Major Revision has been made in this paper by following the reviewers’ guidelines and suggestions. We are pleased to submit a revised version of manuscript entitled “A Techno-Economic Analysis of a Solar PV and DC Battery Storage System for a Community Energy Sharing” for consideration to be published as an original article in ENERGY. Dear editor, with this Cover letter we are uploading the following files; 1. Source file of revised manuscript including figures/tables. (File name Revised Manuscript) 2. Response to the reviewers with detailed description (File name Response to Reviewers) 3. Revised manuscript with marked revisions made (File name Marked Revised Manuscript) 4. Highlights No conflicts of interest exist in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously, and it is not under consideration for publication elsewhere, in whole or in part. I am available for any further information you may need. Thank you and best regards. Sincerely yours, Eid Gul Doctoral Researcher: Energy and Sustainable Development Biomass Research Center/CIRIAF University of Perugia, Italy Cover Letter Credit Author Statement Authors Contributions: Eid Gul: writing, conceptualization, and formulation of methodology. Giorgio Baldinelli: formulation of methodology, review, proofreading and supervision, Pietro Bartocci: conceptualization review, Francesco Bianchi: review and discussion, Piergiovanni Domenghini: profreading and discussion, Franco Cotana: review and profreading, Jinwen Wang: review and profreading. Credit Author Statement