Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy

被引:25
作者
Li, Jianlin [1 ]
Fang, Zhijin [1 ]
Wang, Qian [1 ]
Zhang, Mengyuan [1 ]
Li, Yaxin [1 ]
Zhang, Weijun [2 ]
机构
[1] North China Univ Technol, Beijing Future Technol Innovat Ctr Electrochem Ene, Beijing 100144, Peoples R China
[2] State Grid Fujian Elect Power Res Inst, Fuzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Shared energy storage (SES); dynamic partitioning strategy; optimal operation; Nash bargaining theory; actual utilization rate of energy storage; OPTIMIZATION; SYSTEMS;
D O I
10.35833/MPCE.2023.000345
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.
引用
收藏
页码:359 / 370
页数:12
相关论文
共 30 条
  • [1] Boyd N., 2011, Ha-nover: New Foundations and Trends in Machine Learning
  • [2] Sharing Storage in a Smart Grid: A Coalitional Game Approach
    Chakraborty, Pratyush
    Baeyens, Enrique
    Poolla, Kameshwar
    Khargonekar, Pramod P.
    Varaiya, Pravin
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (04) : 4379 - 4390
  • [3] Shared community energy storage allocation and optimization
    Chang, Hsiu-Chuan
    Ghaddar, Bissan
    Nathwani, Jatin
    [J]. APPLIED ENERGY, 2022, 318
  • [4] The Utilization of Shared Energy Storage in Energy Systems: A Comprehensive Review
    Dai, Rui
    Esmaeilbeigi, Rasul
    Charkhgard, Hadi
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (04) : 3163 - 3174
  • [5] A New Cooperation Framework With a Fair Clearing Scheme for Energy Storage Sharing
    He, Xuan
    Xiao, Jiang-Wen
    Cui, Shi-Chang
    Liu, Xiao-Kang
    Wang, Yan-Wu
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (09) : 5893 - 5904
  • [6] A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching
    Henni, Sarah
    Staudt, Philipp
    Weinhardt, Christof
    [J]. APPLIED ENERGY, 2021, 301
  • [7] The Sharing Economy for the Electricity Storage
    Kalathil, Dileep
    Wu, Chenye
    Poolla, Kameshwar
    Varaiya, Pravin
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (01) : 556 - 567
  • [8] Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game
    Li, Xiaozhu
    Chen, Laijun
    Sun, Fan
    Hao, Yibo
    Du, Xili
    Mei, Shenwei
    [J]. RENEWABLE ENERGY, 2023, 208 : 679 - 692
  • [9] [李笑竹 Li Xiaozhu], 2022, [太阳能学报, Acta Energiae Solaris Sinica], V43, P499
  • [10] Sharing economy as a new business model for energy storage systems
    Lombardi, P.
    Schwabe, F.
    [J]. APPLIED ENERGY, 2017, 188 : 485 - 496