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Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis

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  • Li, Rui
  • Wang, Wei
  • Wu, Xuezhi
  • Tang, Fen
  • Chen, Zhe

Abstract

This paper proposes a multi-objective, bi-level optimization problem for cooperative planning between renewable energy sources and energy storage units in active distribution systems. The multi-objective upper level serves as the planning issues to determine the sizes, sites, and types of renewable energy sources and energy storage units. The fuzzy multi-objective lower level serves as the operation issues to formulate operation strategy and determine the schedules of energy storage units. By means of bi-level programming, the optimal operation strategy of energy storage units is incorporated into the upper level and optimized with planning issues cooperatively. Meanwhile, to address high-level uncertainties and simultaneously capture the temporal correlation related to renewable energy sources, electric vehicles, and load demands, the validity index of Davies Bouldin is adopted to develop sets of probabilistic scenarios with high quality and diversity. A hierarchical solving strategy based on modified particle swarm optimization is applied to solve the bi-level nonlinear, mixed integer optimization problem. Results and further analyses demonstrate that the proposed planning model and optimization methods have the ability to allocate renewable energy sources and energy storage units effectively for reducing costs, enhancing reliability, and promoting clean energy.

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  • Li, Rui & Wang, Wei & Wu, Xuezhi & Tang, Fen & Chen, Zhe, 2019. "Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis," Energy, Elsevier, vol. 168(C), pages 30-42.
    • Handle: RePEc:eee:energy:v:168:y:2019:i:c:p:30-42
    DOI: 10.1016/j.energy.2018.11.069
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    7. Xuehan Zhang & Yongju Son & Sungyun Choi, 2022. "Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources," Energies, MDPI, vol. 15(6), pages 1-18, March.
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    9. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    10. Zhang, Jingrui & Zhou, Yulu & Li, Zhuoyun & Cai, Junfeng, 2021. "Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach," Energy, Elsevier, vol. 219(C).
    11. Zhang, Jingrui & Zhu, Xiaoqing & Chen, Tengpeng & Yu, Yanlin & Xue, Wendong, 2020. "Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks," Energy, Elsevier, vol. 210(C).
    12. Hu, Xiao & Zhang, Heng & Chen, Dongwen & Li, Yong & Wang, Li & Zhang, Feng & Cheng, Haozhong, 2020. "Multi-objective planning for integrated energy systems considering both exergy efficiency and economy," Energy, Elsevier, vol. 197(C).
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    14. Han, Xiaojuan & Li, Jiarong & Zhang, Zhewen, 2023. "Dynamic game optimization control for shared energy storage in multiple application scenarios considering energy storage economy," Applied Energy, Elsevier, vol. 350(C).
    15. Zhou, Siyu & Han, Yang & Chen, Shuheng & Yang, Ping & Mahmoud, Karar & Darwish, Mohamed M.F. & Matti, Lehtonen & Zalhaf, Amr S., 2023. "A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities," Energy, Elsevier, vol. 275(C).
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    17. Lazo, Joaquín & Watts, David, 2024. "Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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