Hunan Electric Power ›› 2024, Vol. 44 ›› Issue (1): 67-76.doi: 10.3969/j.issn.1008-0198.2024.01.010

• New technology and Application • Previous Articles     Next Articles

Optimal Operation Method of Shared Energy Storage Considering Distribution Network State

YAO Jun1, LU Jun1, LIAO Yipu2, WANG Zhenyu3,4, LIANG Enmin1, GONG Gangjun1   

  1. 1. Beijing Engineering Research Center of Energy and Electric Power Information Security (North China Electric Power University), Beijing 102206, China;
    2. Zhejiang University, Hangzhou 310000, China;
    3. State Grid Electric Power Research Institute Co., Ltd. (Nanrui Group Co., Ltd.), Nanjing 211100, China;
    4. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd., Wuhan 430074, China
  • Received:2023-11-03 Revised:2023-12-08 Online:2024-02-25 Published:2024-03-11

Abstract: Considering the influence of the access of shared energy storage on the distribution network, this paper proposes an optimal operation method of shared energy storage considering the state of the distribution network. Firstly, the operation model of shared energy storage is described, and the operation model of shared energy storage power station, the stability model of distribution network and the cost model of user electricity are constructed by unifying energy storage resources through shared energy storage operators. Then, a two-objective optimization model considering the state of distribution network is constructed. The game between distribution network and shared energy storage power station is completed based on the concept of multi-objective optimization, and the power consumption expectation is met based on Euclidean distance method. Finally, simulation based on 33-node distribution system proves the superiority of this paper. The numerical example shows that the proposed method can effectively reduce the operation cost of shared energy storage power station, make the power grid operation more stable, and reduce the cost of electricity consumption.

Key words: shared energy storage, power flow optimization, multi-objective, particle swarm optimization

CLC Number: