Evaluating and aggregating the grid-support capability of energy storage clusters by considering the peak regulation requirements

Frontiers in Energy Research

Energy Research

Qipeng Tan¹  Leqing Li¹  Minhui Wan¹  Yongqi Li² 

[1] China Southern Power Grid Power Generation Co., Ltd., Energy Storage Research Institute, Guangzhou, China;null;

关键词: peak regulation requirements;    combination weighting method;    grid-support capability evaluation;    grid-support capability aggregation;    revised Chino polytope;   

DOI  :  10.3389/fenrg.2023.1281267

 received in 2023-08-22, accepted in 2023-10-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

With the rapid progression of Energy Storage Systems (ESSs), the capability of extensively distributed and heterogeneous ESSs to support the power grid remains largely underexplored. To better exploit the potential of these numerous ESSs and enhance their service to the power grid, this paper proposes a model for evaluating and aggregating the grid-support capability of energy storage clusters by considering the peak regulation requirements. To begin with, the proposed model employs subjective and objective combination weighting methods to establish a grid-support capability matrix between ESSs indicators and grid demand scenarios, thereby facilitating the identification of the ESSs with a strong ability to regulate peak power. Next, based on the dual-peak pattern of grid load and diverse characteristics of ESSs, the ESSs in the peak regulation cluster are evaluated by clustering again. In addition, taking into account the operational constraints of the ESSs and the peak regulation requirements, a grid-support capability aggregation model for energy storage clusters based on the revised Chino polytope is proposed. The case study results demonstrate that the proposed model not only balances computational efficiency and aggregation accuracy to a certain extent but also enhances the capability of energy storage clusters to participate in peak regulation of the power grid.

【 授权许可】

Unknown   
Copyright © 2023 Li, Li, Wan and Tan.