Integrated workflow for optimizing urban energy communities: A case study in Karviná, Czechia

Journal of Physics: Conference Series PAPER • OPEN ACCESS Integrated workflow for optimizing urban energy communities: A case study in Karviná, Czechia To cite this article: S Kichou and N Skandalos 2025 J. Phys.: Conf. Ser. 3140 062009 View the article online for updates and enhancements. You may also like Recommended key performance indicators for operational management of wind turbines S Pfaffel, S Faulstich and S Sheng - The measurement of maintenance function efficiency through financial KPIs D Galar, A Parida, U Kumar et al. - Using the BIMCO Shipping KPI Database to identify costs and benefits of enavigation solutions D A Nesheim and K E Fjørtoft - This content was downloaded from IP address 147.32.99.76 on 28/11/2025 at 08:49 Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 1 Integrated workflow for optimizing urban energy communities: A case study in Karviná, Czechia S Kichou1*, N Skandalos2 1 Czech Technical University in Prague, University Centre for Energy Efficient Buildings, 1024 Třinecká St. 27343 Buštěhrad, Czech Republic. 2 RES & Cool Urban Environment Group, University of Patras, 30100, Agrinio, Greece * sofiane.k[email protected] Abstract. This study presents a comprehensive, urban-scale workflow designed to facilitate the technical and economic planning of Energy Communities (ECs), with an emphasis on deploying photovoltaic (PV) systems and energy storage solutions within the built environment. The proposed workflow evaluates performance based on various Key Performance Indicators (KPIs) that address the complexity of ECs operations. These include metrics for self-sufficiency, selfconsumption, peak grid power capacity, positive energy district (PED) targets, and the impact of electric vehicles (EVs) on the local energy ecosystem. The workflow is built upon three main pillars: (1) 3D Modelling of the Built Environment, which maps the urban landscape and serves as the basis for renewable energy planning; (2) Solar Potential Analysis, to quantify the solar resources available across different building typologies, enabling efficient PV system placement; and (3) Techno-Economic Assessment, which evaluates the operational performance and financial feasibility of the designed EC using selected KPIs. These analyses incorporate dynamic interactions between storage systems, RES, and EVs under various smart energy management strategies, with a focus on maximizing both environmental and economic benefits. In addition, this methodology considers innovative operational strategies, particularly the Vehicle-to-Grid (V2G) services at public charging infrastructures in response to electricity price signals. 1. Introduction Energy Communities (ECs) are becoming increasingly important in climate-neutral cities thanks to their ability to generate and manage local renewable energy sources (RES) more effectively [1]. By transitioning traditional energy consumers into active "prosumers" who both produce and consume energy, ECs can enhance local resiliency, reduce greenhouse gas emissions, and encourage the adoption of sustainable technologies [2]. Recent policy frameworks, such as the EU Climate-Neutral and Smart Cities Mission, further highlight the value of these local energy efforts by setting clear targets for decarbonization and citizen engagement [3]. However, despite significant policy support, many urban energy projects still operate in isolation without fully integrating the potential of electric vehicles (EVs) [4]. Although the benefits of advanced management strategies, such as Vehicle-to-Grid (V2G) services, are well documented, their practical implementation remains limited by regulatory and economic challenges [5]. Consequently, there is a growing need for integrated workflows that account for the diversity of urban environments, energy CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 2 demands, and infrastructure constraints while ensuring both technical resilience and financial viability [6]. To address these challenges, this paper introduces a comprehensive, urban-scale workflow for planning and evaluating ECs. The methodology is built around three core pillars. First, a 3D Modeling of the Built Environment generates spatially explicit data about building forms and orientations, providing the foundation for renewable energy planning. Second, a Solar Potential Analysis estimates site-specific resource availability, ensuring the optimal allocation of PV systems across diverse building types [7]. Third, a Techno-Economic Assessment evaluates operational and financial performance using indicators such as self-sufficiency, self-consumption, and grid interaction. This pillar also integrates dynamic analyses of storage systems, including stationary batteries and EV fleets, under different smart energy management strategies. A particular focus is placed on public EV charging stations with Vehicle-to-Grid (V2G) capabilities, where EV batteries act as flexible storage assets that respond to electricity market price signals. By scheduling charging and discharging to align with energy demand and price fluctuations, EVs can help balance local loads, reduce grid dependency, and enhance economic returns. The approach is validated through a case study in Karvina, Czechia, demonstrating how ECs can combine spatial planning, solar energy potential, storage technologies, and smart control strategies to deliver both environmental and financial benefits. By evaluating key performance indicators across multiple scenarios, the approach shows how optimized system configurations and active EV participation can support the transition toward more resilient, climate-neutral urban energy systems. The methodology is intended to support urban planners, policymakers, and energy professionals in designing practical and scalable EC solutions that align with long-term sustainability goals. 2. Case study description The proposed workflow is applied to a small urban district in Karviná, Czechia, a post-industrial city in the Moravian-Silesian Region. The selected district comprises a mix of residential and public buildings representative of Central European urban typologies. The area under study spans approximately 0.6 km2 and includes 40 buildings, predominantly multi-family apartment blocks with flat rooftops suitable for photovoltaic system deployment (figure 1). Measured energy consumption data retrieved from electricity bills are available for nearly 50% of the buildings. For the remaining buildings, hourly consumption data is generated using pre-defined, normalized load profiles provided by the Czech electricity and gas market operator (OTE). These profiles are categorized based on building typologies and energy supply configurations. The final total energy consumption of the aggregated buildings (excluding the thermal part, which is covered by the district heating system) is 12.48 GWh/year. Figure 1. Satellite image of the selected district (left), highlighted buildings with available measured data (right). KARVINÁ -1:7500 INTENTIONS/ DEVELOPMENT AREAS GARAGE AREAS RESIDENTIAL BUILDINGS SPORTS AREAS SCHOOLS TRADE HEALTHCARE T1 T2 T3 T4 T5 T6 T7 R10 R010 R110 R6 R06 R3 R9 R19 R29 R210 R16 R26 R13 R23 R33 R43 R26-5 R310 R36-7 R46-3 R310-3 R410-3 1 km CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 3 3. Methodology The proposed urban-scale workflow for planning, implementing, and evaluating Energy Communities (EC) consists of three main pillars (figure 2). Additionally, the selected district's design is assessed across multiple scenarios, considering various energy management strategies, storage configurations, EVs, and market price signals to evaluate environmental and economic impacts. Figure 2. Return of investment ratio evolution for the different scenarios. 3.1. Geometry The first pillar involves generating spatially explicit 3D models of the built environment, capturing building forms, orientations, and potential areas suitable for solar installations. The 3D model was developed using OpenStreetMap (OSM) for base geometry and refined via photogrammetric analysis of high-resolution aerial imagery. The resulting geometry corresponds to LOD2 in the CityGML/BIM taxonomy, including building footprints, roof geometry, and estimated height, but without internal building components. Manual review supported the identification of rooftop obstacles and Window-towall ratios (WWR) estimation per façade orientation. Surrounding features, such as trees and adjacent structures, were also considered to capture shading effects. 3.2. Solar potential The second pillar quantifies site-specific renewable energy resources, enabling optimal placement of PV systems across various building types. Solar irradiation was simulated using ClimateStudio, a simulation platform based on Radiance, capable of modeling direct, diffuse, and reflected radiation across all hours of the year. Simulations were based on Typical Meteorological Year (TMY) data with hourly resolution obtained from Meteonorm. The analysis considered the effects of shading from surrounding buildings, rooftop obstacles, and vegetation. Rooftop surfaces receiving more than 1000 kWh/m²/year and façades above 600 kWh/m²/year were identified as suitable for PV integration. WWR were applied to façades to isolate usable opaque areas. The resulting solar maps informed system sizing while accounting for panel spacing, accessibility, and self-shading effects. 3.3. Techno-Economic Assessment The third pillar focuses on converting assessed solar radiation into electricity through techno-economic analyses tailored to the specific characteristics of EC energy systems. The modeling of PV systems and electrical energy storage (EES) systems is based on previously published methodologies [8]. Two distinct energy management strategies are included in the framework for the operational evaluation of ECs with stationary batteries: a) Load-Following Control Strategy (LFCS), representing a conventional approach emphasizing maximum utilization of locally harvested PV energy by storing surplus energy during daylight for nighttime consumption. b) Smart Energy Management Strategy (SEMS), an advanced strategy integrating Day Ahead Market (DAM) electricity prices and forecasts for both load and PV generation. This strategy optimizes the usage of EES to strategically minimize electricity costs. Additionally, this pillar integrates dynamic analyses of public EV charging stations operated in Gridto-Vehicle (G2V) mode as pure consumers and considers a smart EV charging approach utilizing DAM 1. Geometry 3D Modelling 2. Solar Potential Irradiance & shading analysis 3. Techno-economic assessment Electrical & Economics Business model CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 4 electricity prices, including V2G functionality, where users discharge their EV batteries during high tariff periods, enhancing grid stability and gaining economic incentives (figure 3). Finally, the techno-economic assessment uses various KPIs—such as self-sufficiency (Ss), selfconsumption (Sc), Net Present Value (NPV), return on investment (ROI), and electricity bill savings— to evaluate the feasibility and effectiveness of various technology and operational strategies. Figure 3. EV charging stations operation based on LFCS and SEMS. 3.4. Simulated cases Multiple scenarios have been developed to thoroughly analyze the selected EC's performance: • Scenario 1: Full utilization of solar PV potential (roof and façade installations), representing a Positive Energy District (PED). • Scenario 2: Full PV potential with 10 public EV charging stations without smart control. • Scenario 3: Full PV potential combined with 10 smart-controlled EV charging stations, incorporating DAM electricity prices and V2G operations. • Scenario 4: PV potential limited to roof installations only, no longer achieving PED status. • Scenario 5: Roof PV installations with 10 public EV charging stations without smart control. • Scenario 6: Roof PV installations combined with smart-controlled EV charging stations, including DAM pricing and V2G capabilities. Each scenario is evaluated using a range of EES capacities and two energy management strategies: LFCS with fixed electricity prices and SEMS based on DAM prices to minimize EC operation costs. 4. Results and discussions The application of the first two pillars of the proposed workflow revealed that the district's building roofs can accommodate a PV capacity of 6.95 MWp, while façades can support 9.48 MWp, resulting in a total annual energy generation of 12.96 GWh. With these installations, the district meets the Positive Energy District (PED) standard, achieving a renewable energy ratio of 104%. It should be noted that the PED standard in this case excludes the heating component, as the selected case study is connected to a district heating system, which is expected to become green in the coming years. The analysis of PV installations showed that the energy yield per installed PV capacity was 781 hours when considering both rooftop and façade PV, 1028 hours for rooftop PV alone, and 616 hours for façade PV. To further evaluate the EC's performance, various scenarios were analyzed, varying battery energy storage (EES) capacity from 0 to 30 MWh while assessing key performance indicators, including selfsufficiency (Ss), self-consumption (Sc), economic feasibility (NPV, ROI), and electricity costs. The results indicated that Ss could reach 90% with 30 MWh EES when considering both rooftop and façade PV, while Sc attained a maximum of 74% when EVs were included (figure 4). However, limiting PV installations to rooftops increased Sc to over 80% with 10 MWh EES, although Ss dropped to around 50%. CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 5 Economic feasibility was assessed by analyzing the evolution of NPV, payback period (SPP), and ROI over 20 years under two control strategies (LFCS and SEMS). Under LFCS with fixed electricity prices, NPV improved with increasing EES capacity up to 8 MWh but declined thereafter, and the lowest SPP value of 9.2 years was achieved with 6 MWh EES (figure 5). In contrast, limiting PV to rooftops resulted in better NPV values when combined with a maximum EES capacity of 1 MWh, whereas increasing EES size beyond this point led to diminishing returns. Under SEMS with dynamic electricity prices, NPV exhibited a linear correlation with EES capacity, with higher investments in storage leading to greater revenues. The difference in NPV between full PV potential and rooftop-only PV was minimal in this case, while SPP exhibited a slower reduction for higher EES sizes, particularly beyond 10 MWh. Electricity costs and ROI trends followed similar patterns. In the LFCS case, maximizing PV capacity helped reduce operational costs, but cost savings slowed once EES capacity exceeded 15 MWh (figure 6). The ROI reached a peak value of 10.8% for EES capacities between 5 and 10 MWh with full PV installations, whereas for rooftop PV, the maximum ROI was achieved without any EES. On the other hand, SEMS operation demonstrated a steady decline in electricity bills with increasing EES capacity, even leading to negative electricity costs when combining full PV potential with 10 MWh EES or rooftop PV with 20 MWh EES. ROI also increased with a larger EES capacity in this case, with a minor difference of 2–3% between full PV and rooftop PV. Finally, the impact of electric vehicles on the EC was examined by considering ten EV charging stations with charging powers of 22 kW AC and 50 kW DC, resulting in an annual EV consumption (G2V) of 0.95 GWh and the potential to deliver 105 MWh back to the EC through V2G services. The results indicate that EV integration can further enhance self-consumption and economic viability by increasing NPV and reducing the payback period. Figure 4. Self-sufficiency (Ss) and Self-consumption (Sc) indicators' evolution for each scenario. Figure 5. Cumulative 20-year NPV values and the SPP evolution for the different scenarios. 0 5 10 15 20 25 30 Electrical Energy Storage size (MWh) 35 40 45 50 55 60 65 70 75 Self-sufficiency, Ss (%) 30 40 50 60 70 80 90 100 Self-consumption, Sc (%) LFCS (Fix electricity price) 0 5 10 15 20 25 30 Electrical Energy Storage size (MWh) 0 5 10 15 20 25 30 35 40 45 Self-sufficiency, Ss (%) 0 10 20 30 40 50 60 70 Self-consumption, Sc (%) SEMS (DAM electricity price) Ss (Roof + Facade PV) Ss (Roof + Facade PV + G2V) Ss (Roof + Facade PV + V2G) Ss (Roof PV) Ss (Roof PV + G2V) Ss (Roof PV + V2G) Sc (Roof + Facade PV) Sc (Roof + Facade PV + G2V) Sc (Roof + Facade PV + V2G) Sc (Roof PV) Sc (Roof PV + G2V) Sc (Roof PV + V2G) CISBAT 2025 Journal of Physics: Conference Series 3140 (2025) 062009 IOP Publishing doi:10.1088/1742-6596/3140/6/062009 6 Figure 6. Electricity bill and annual ROI ratio evolution for the different scenarios. 5. Conclusions This study introduced an integrated, urban-scale workflow for designing and evaluating Energy Communities (ECs), demonstrated through a case study in Karviná, Czechia. By combining 3D spatial modeling, Radiance-based solar potential analysis, and techno-economic assessment, the proposed approach revealed that the district could achieve a renewable energy ratio of 104% and meet Positive Energy District (PED) criteria through optimal rooftop and façade PV deployment. Key performance indicators showed that self-sufficiency reached 90% and self-consumption up to 80%, depending on energy storage and electric vehicle integration. The use of SEMS, including day-ahead electricity pricing and V2G services, significantly enhanced financial performance, improving NPV, ROI, and reducing electricity costs. The results confirm that well-planned ECs can deliver both environmental and economic benefits, offering a replicable planning tool for cities aiming to meet climate-neutral goals. Acknowledgments Authors gratefully acknowledge the support from the H2020 EU-funded project “Climate Positive Circular Communities, ARV” (agreement ID: 101036723), and the project "The Energy Conversion and Storage", project No. CZ.02.01.01/00/22_008/0004617 by Programme Johannes Amos Comenius, call Excellent Research. References [1] López I, Goitia-Zabaleta N, Milo A, Gómez-Cornejo J, Aranzabal I, Gaztañaga H, et al. European energy communities: Characteristics, trends, business models and legal framework. Renewable and Sustainable Energy Reviews. 2024;197:114403. [2] Nucci CA. Climate neutral and smart cities: Towards the energy transition. EPJ Web of Conferences: EDP Sciences; 2024. p. 00016. [3] Mehta P, Tiefenbeck V. 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