An Optimal Scheduling Method of Shared Energy Storage System Considering Distribution Network Operation Risk

Jiahao Chen, Bing Sun (), Yuan Zeng, Ruipeng Jing, Shimeng Dong and Jingran Wang
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Jiahao Chen: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Bing Sun: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Yuan Zeng: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Ruipeng Jing: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Shimeng Dong: State Grid Corporation of China, Beijing 100031, China
Jingran Wang: State Grid Jibei Electric Power Company Limited, Beijing 065300, China

Energies, 2023, vol. 16, issue 5, 1-24

Abstract: Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, a major challenge exists in terms of how to consider both the efficiency of the operation and the reliability cost when formulating the SESS scheduling scheme. A SESS optimal scheduling method that considers the DN operation risk is proposed in this paper. First, a multi-objective day-ahead scheduling model for SESS is developed, where the user’s interruption cost is regarded as the reliability cost and it is the product of the occurrence probability of the expected accident and the loss of power outage. Then, an island partition model with SESS was established in order to accurately calculate the reliability cost. Via the maximum island partition and island optimal rectification, the SESS was carefully integrated into the power restoration system. Furthermore, in order to minimize the comprehensive operation cost, an improved genetic algorithm for the island partition was designed to solve the complex SESS optimal scheduling model. Finally, a case study on the improved PG&E 69 bus system was analyzed. Moreover, we found that the DN’s comprehensive operation cost decreased by 6.6% using the proposed method.

Keywords: shared energy storage system; operation risk; optimal scheduling; island partition; reliability cost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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