Distributed Photovoltaic Systems and Electric Vehicle Clusters in Local Energy Networks: A Review
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Distributed Photovoltaic Systems and Electric Vehicle Clusters in Local Energy Networks: A Review

Yuxuan Ye 1*
1 Xishanyang Campus of Huzhou High School
*Corresponding author: alston1774@gmail.com
Published on 28 October 2025
Journal Cover
ACE Vol.201
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-493-9
ISBN (Online): 978-1-80590-494-6
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Abstract

Distributed PV systems offer decentralized renewable generation, while EVs bring about flexible yet unpredictable demand profiles. By coordinating these technologies, we have the potential to decrease carbon emissions, improve grid reliability, and optimize energy usage on a local level. However, impediments such as the intermittent nature of PV generation, the uncertainty of EV charging behaviors, and the limitations of current grid infrastructures continue to pose significant challenges that must be addressed in order to fully realize the potential benefits of these technologies. In this paper, we undertake an in-depth examination of the existing research landscape pertaining to the integration of photovoltaic (PV) systems and electric vehicles (EVs) within local networks. Our analysis spans across technical, operational, environmental, and economic facets. We delve into the technological attributes of distributed PV systems and the distinctive load dynamics exhibited by EV clusters, emphasizing the potential for enhanced synergies through the implementation of smart grid solutions. Key areas of focus include advanced modeling techniques, coordinated scheduling strategies, and optimization frameworks, all aimed at bolstering system performance. Furthermore, we explore the substantial environmental advantages and cost-effectiveness associated with PV–EV integration, while simultaneously shedding light on the policy and market hurdles that impede widespread adoption. By amalgamating insights gleaned from recent scholarly endeavors, this paper offers a holistic perspective on PV–EV integration and serves as a valuable resource for researchers, policymakers, and stakeholders in the energy industry who aspire to design sustainable local energy systems.

Keywords:

Distributed Photovoltaics, Electric Vehicle Clusters, Local Energy Systems, Smart Grids, Energy Integration

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Ye,Y. (2025). Distributed Photovoltaic Systems and Electric Vehicle Clusters in Local Energy Networks: A Review. Applied and Computational Engineering,201,35-44.

References

[1]. REN21. Renewables 2023 Global Status Report. Paris: REN21 Secretariat, 2023.

[2]. International Energy Agency (IEA). Global EV Outlook 2023: Catching up with Climate Ambitions. Paris: IEA, 2023.

[3]. Sovacool, B. K. , Ryan, S. E. , Stern, P. C. , et al. Integrating social science in energy research. Energy Research & Social Science, 2015, 6: 95–99.

[4]. Richardson, D. B. Electric vehicles and the electric grid: A review of modeling approaches, impacts, and renewable energy integration. Renewable and Sustainable Energy Reviews, 2013, 19: 247–254.

[5]. Lund, H. , Østergaard, P. A. , Connolly, D. , Mathiesen, B. V. Smart energy and smart energy systems. Energy, 2017, 137: 556–565.

[6]. Denholm, P. , Hand, M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy, 2011, 39(3): 1817–1830.

[7]. Wang, J., Zhong, H., Xia, Q., Kang, C. , & He, D. Optimal charging strategies for electric vehicles in smart grids: A review. Science China Technological Sciences, 2016, 59: 619–629.

[8]. Kempton, W. , & Tomić, J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Journal of Power Sources, 2005, 144(1): 280–294.

[9]. Kikusato, H. , Mori, T. , Yoshizawa, S. , et al. Electric vehicle charge–discharge management for utilization of photovoltaic by coordination between home and grid. Energy, 2018, 158: 306–318.

[10]. Acha, S. , Green, T. C. , Shah, N. Optimal charging strategies of electric vehicles in the UK power market. IEEE Transactions on Power Systems, 2011, 27(1): 30–40.

[11]. Ota, Y. , Taniguchi, H. , Nakajima, T. , Liyanage, K. M. , Yokoyama, A. , & Baba, J. Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging. IEEE Transactions on Smart Grid, 2012, 3(1): 559–564.

[12]. Yang, Y. , Jia, H. , & Zhao, J. Review on distributed energy scheduling for PV–EV integrated systems. Applied Energy, 2020, 268: 114965.

[13]. Zhao, H. , Wu, Q. , Hu, S. , Xu, H. , & Rasmussen, C. N. Review of energy storage system for wind power integration support. Applied Energy, 2015, 137: 545–553.

[14]. He, Y. , Pang, Z. , & Li, K. Coordinated control of EV charging and renewable generation in distribution systems. IEEE Transactions on Smart Grid, 2016, 7(2): 1117–1127.

[15]. Zhou, Y. , Wu, J. , Long, C. , & Jenkins, N. Forecasting and scheduling of distributed energy resources for a community microgrid. Applied Energy, 2018, 229: 352–363.

[16]. Colmenar-Santos, A. , Campíñez-Romero, S. , Pérez-Molina, C. , & Castro-Gil, M. Profitability of PV grid parity on a national scale: Review and case study. Renewable and Sustainable Energy Reviews, 2015, 49: 637–649.

[17]. Liu, Y. , Wu, J. , Jenkins, N. , & Meng, K. Co-optimization of electric vehicles and renewable generation in microgrids. Energy, 2019, 178: 167–179.

[18]. Zhang, C. , Ding, Y., Li, F., & Zhang, P. A review on real-time demand response in smart grids: Modeling and applications. Renewable and Sustainable Energy Reviews, 2017, 72: 694–707.

[19]. Wu, X. , Hu, X. , Yin, X. , & Moura, S. Stochastic optimal energy management of smart home with PV, EV and ESS. Journal of Power Sources, 2017, 333: 203–212.

[20]. Li, Z. , Shahidehpour, M. , Bahramirad, S. , & Alabdulwahab, A. Stochastic modeling of electric vehicle charging demand in microgrids. IEEE Transactions on Smart Grid, 2014, 5(2): 759–768.

[21]. Pan, Z., Wang, J. , & Zhang, L. Multi-objective optimization for PV–EV integrated systems in China: A case study. Energy Policy, 2021, 156: 112417.

[22]. International Renewable Energy Agency (IRENA). Innovation Outlook: Smart Charging for Electric Vehicles. Abu Dhabi: IRENA, 2019.

References

[1]. REN21. Renewables 2023 Global Status Report. Paris: REN21 Secretariat, 2023.

[2]. International Energy Agency (IEA). Global EV Outlook 2023: Catching up with Climate Ambitions. Paris: IEA, 2023.

[3]. Sovacool, B. K. , Ryan, S. E. , Stern, P. C. , et al. Integrating social science in energy research. Energy Research & Social Science, 2015, 6: 95–99.

[4]. Richardson, D. B. Electric vehicles and the electric grid: A review of modeling approaches, impacts, and renewable energy integration. Renewable and Sustainable Energy Reviews, 2013, 19: 247–254.

[5]. Lund, H. , Østergaard, P. A. , Connolly, D. , Mathiesen, B. V. Smart energy and smart energy systems. Energy, 2017, 137: 556–565.

[6]. Denholm, P. , Hand, M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy, 2011, 39(3): 1817–1830.

[7]. Wang, J., Zhong, H., Xia, Q., Kang, C. , & He, D. Optimal charging strategies for electric vehicles in smart grids: A review. Science China Technological Sciences, 2016, 59: 619–629.

[8]. Kempton, W. , & Tomić, J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Journal of Power Sources, 2005, 144(1): 280–294.

[9]. Kikusato, H. , Mori, T. , Yoshizawa, S. , et al. Electric vehicle charge–discharge management for utilization of photovoltaic by coordination between home and grid. Energy, 2018, 158: 306–318.

[10]. Acha, S. , Green, T. C. , Shah, N. Optimal charging strategies of electric vehicles in the UK power market. IEEE Transactions on Power Systems, 2011, 27(1): 30–40.

[11]. Ota, Y. , Taniguchi, H. , Nakajima, T. , Liyanage, K. M. , Yokoyama, A. , & Baba, J. Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging. IEEE Transactions on Smart Grid, 2012, 3(1): 559–564.

[12]. Yang, Y. , Jia, H. , & Zhao, J. Review on distributed energy scheduling for PV–EV integrated systems. Applied Energy, 2020, 268: 114965.

[13]. Zhao, H. , Wu, Q. , Hu, S. , Xu, H. , & Rasmussen, C. N. Review of energy storage system for wind power integration support. Applied Energy, 2015, 137: 545–553.

[14]. He, Y. , Pang, Z. , & Li, K. Coordinated control of EV charging and renewable generation in distribution systems. IEEE Transactions on Smart Grid, 2016, 7(2): 1117–1127.

[15]. Zhou, Y. , Wu, J. , Long, C. , & Jenkins, N. Forecasting and scheduling of distributed energy resources for a community microgrid. Applied Energy, 2018, 229: 352–363.

[16]. Colmenar-Santos, A. , Campíñez-Romero, S. , Pérez-Molina, C. , & Castro-Gil, M. Profitability of PV grid parity on a national scale: Review and case study. Renewable and Sustainable Energy Reviews, 2015, 49: 637–649.

[17]. Liu, Y. , Wu, J. , Jenkins, N. , & Meng, K. Co-optimization of electric vehicles and renewable generation in microgrids. Energy, 2019, 178: 167–179.

[18]. Zhang, C. , Ding, Y., Li, F., & Zhang, P. A review on real-time demand response in smart grids: Modeling and applications. Renewable and Sustainable Energy Reviews, 2017, 72: 694–707.

[19]. Wu, X. , Hu, X. , Yin, X. , & Moura, S. Stochastic optimal energy management of smart home with PV, EV and ESS. Journal of Power Sources, 2017, 333: 203–212.

[20]. Li, Z. , Shahidehpour, M. , Bahramirad, S. , & Alabdulwahab, A. Stochastic modeling of electric vehicle charging demand in microgrids. IEEE Transactions on Smart Grid, 2014, 5(2): 759–768.

[21]. Pan, Z., Wang, J. , & Zhang, L. Multi-objective optimization for PV–EV integrated systems in China: A case study. Energy Policy, 2021, 156: 112417.

[22]. International Renewable Energy Agency (IRENA). Innovation Outlook: Smart Charging for Electric Vehicles. Abu Dhabi: IRENA, 2019.

Cite this article

Ye,Y. (2025). Distributed Photovoltaic Systems and Electric Vehicle Clusters in Local Energy Networks: A Review. Applied and Computational Engineering,201,35-44.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN: 978-1-80590-493-9(Print) / 978-1-80590-494-6(Online)
Editor: Anil Fernando
Conference website: https://2025.conffmce.org/glasgow.html
Conference date: 24 October 2025
Series: Applied and Computational Engineering
Volume number: Vol.201
ISSN: 2755-2721(Print) / 2755-273X(Online)

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