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A group-based optimization design for PV-BESS in energy-sharing hybrid communities

Author

Listed:
  • Liu, Changlan
  • Liu, Zhongbing
  • Wu, Yaling
  • Li, Benjia
  • Liu, Ruimiao

Abstract

The utilization of a combination of renewable energy and battery energy storage system (BESS) in energy sharing communities can alleviate load pressure on the grid and reduce the overall cost of electricity by improving the efficiency of energy utilization. However, most designs of the BESS in communities neglect the effects of energy-sharing potential, resulting in more energy exchanges between community and the grid and oversized capacity of the BESS. Therefore, this paper proposed a distributed group shared optimization design for grid-connected PV-BESS in a hybrid industrial community, aimed at enhancing energy-sharing potential while minimizing the required storage capacity. Firstly, the buildings in community were divided into four groups to minimize annual operating cost. Then, to reduce the yearly total cost and transmission loss, the capacity of each group was optimized by the genetic algorithm (GA). The results showed that compared to the distributed non-grouping optimization design, the proposed design leveraged the energy-sharing potential of the industrial community, with a 20.7 % reduction of the total battery capacity and a 16.1 % decrease of the total annual cost. The mismatch between PV generation and electricity demand was the main factor affecting grouping and battery capacity allocation. Differences in peak-to-valley tariff ratios have no effect on grouping or battery capacity allocation. When the PV array area increases and the differences in peak-to-valley tariff ratios decrease, the corresponding total optimal battery capacity and annual cost decrease, but transmission loss is increased. This study provides guidance for the shared BESS design and application of community.

Suggested Citation

  • Liu, Changlan & Liu, Zhongbing & Wu, Yaling & Li, Benjia & Liu, Ruimiao, 2025. "A group-based optimization design for PV-BESS in energy-sharing hybrid communities," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006269
    DOI: 10.1016/j.apenergy.2025.125896
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    References listed on IDEAS

    as
    1. Sameti, Mohammad & Haghighat, Fariborz, 2018. "Integration of distributed energy storage into net-zero energy district systems: Optimum design and operation," Energy, Elsevier, vol. 153(C), pages 575-591.
    2. Walker, Awnalisa & Kwon, Soongeol, 2021. "Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage," Applied Energy, Elsevier, vol. 282(PA).
    3. Huang, Pei & Han, Mengjie & Zhang, Xingxing & Hussain, Syed Asad & Jayprakash Bhagat, Rohit & Hogarehalli Kumar, Deepu, 2022. "Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany," Applied Energy, Elsevier, vol. 326(C).
    4. Mulleriyawage, U.G.K. & Shen, W.X., 2020. "Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study," Renewable Energy, Elsevier, vol. 160(C), pages 852-864.
    5. Shaojie Li & Tao Zhang & Xiaochen Liu & Xiaohua Liu, 2023. "A Battery Capacity Configuration Method of a Photovoltaic and Battery System Applied in a Building Complex for Increased Self-Sufficiency and Self-Consumption," Energies, MDPI, vol. 16(5), pages 1-18, February.
    6. Walker, Awnalisa & Kwon, Soongeol, 2021. "Design of structured control policy for shared energy storage in residential community: A stochastic optimization approach," Applied Energy, Elsevier, vol. 298(C).
    7. Huang, Pei & Sun, Yongjun, 2019. "A clustering based grouping method of nearly zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 235(C), pages 43-55.
    8. Sun, Yongjun & Huang, Gongsheng & Xu, Xinhua & Lai, Alvin Chi-Keung, 2018. "Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls," Applied Energy, Elsevier, vol. 212(C), pages 565-576.
    9. Meng, He & Jia, Hongjie & Xu, Tao & Sun, Jianhang & Wang, Rujing & Wang, Jie, 2024. "Trading mechanism of distributed shared energy storage system considering voltage regulation," Applied Energy, Elsevier, vol. 374(C).
    10. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris, 2020. "A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered," Applied Energy, Elsevier, vol. 268(C).
    11. Kan Xie & Weifeng Zhong & Weijun Li & Yinhao Zhu, 2019. "Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization," Energies, MDPI, vol. 12(9), pages 1-15, April.
    12. Sardi, Junainah & Mithulananthan, N. & Hung, Duong Quoc, 2017. "Strategic allocation of community energy storage in a residential system with rooftop PV units," Applied Energy, Elsevier, vol. 206(C), pages 159-171.
    13. Huang, Pei & Sun, Yongjun & Lovati, Marco & Zhang, Xingxing, 2021. "Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements," Energy, Elsevier, vol. 222(C).
    14. Parra, David & Norman, Stuart A. & Walker, Gavin S. & Gillott, Mark, 2016. "Optimum community energy storage system for demand load shifting," Applied Energy, Elsevier, vol. 174(C), pages 130-143.
    15. Li, Sihui & Peng, Jinqing & Wang, Meng & Wang, Kai & Li, Houpei & Lu, Chujie, 2024. "Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM," Renewable Energy, Elsevier, vol. 221(C).
    16. Maheri, Alireza, 2014. "Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties," Renewable Energy, Elsevier, vol. 66(C), pages 650-661.
    17. Huang, Pei & Huang, Gongsheng & Sun, Yongjun, 2018. "A robust design of nearly zero energy building systems considering performance degradation and maintenance," Energy, Elsevier, vol. 163(C), pages 905-919.
    18. Li, Qi & Xiao, Xukang & Pu, Yuchen & Luo, Shuyu & Liu, Hong & Chen, Weirong, 2023. "Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy," Applied Energy, Elsevier, vol. 349(C).
    19. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.
    20. Wu, Yaling & Liu, Zhongbing & Liu, Jiangyang & Xiao, Hui & Liu, Ruimiao & Zhang, Ling, 2022. "Optimal battery capacity of grid-connected PV-battery systems considering battery degradation," Renewable Energy, Elsevier, vol. 181(C), pages 10-23.
    21. Luthander, Rasmus & Widén, Joakim & Munkhammar, Joakim & Lingfors, David, 2016. "Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment," Energy, Elsevier, vol. 112(C), pages 221-231.
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