QU Keqing, QIAO Jingmao, MAO Ling, ZHU Shaojie, ZHAO Jinbin. Optimal Configuration and Site-selection Evaluation Method for Shared Energy Storage Stations[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0313
Citation: QU Keqing, QIAO Jingmao, MAO Ling, ZHU Shaojie, ZHAO Jinbin. Optimal Configuration and Site-selection Evaluation Method for Shared Energy Storage Stations[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0313

Optimal Configuration and Site-selection Evaluation Method for Shared Energy Storage Stations

  • The site-selection and optimization of energy storage units in new power systems are crucial for ensuring system economy and stability. Existing energy storage stations often employ separate distributed frameworks, which fail to fully utilize the peaking and frequency regulation capabilities of energy storage resources. To enhance the utilization efficiency of a large number of controllable and adjustable resources, in this study we investigate the optimization and site-selection strategy for shared energy storage stations, taking into account the "shared" characteristics of energy storage. First, a mixed-integer linear programming configuration model is established for a single shared energy storage station in conjunction with multiple distributed renewable energy sources. Second, the evaluation criteria for the optimal siting of shared energy storage stations are defined, with the output characteristics of wind and solar power taken into account. Then, under the premise of meeting load demand and minimizing daily average economic costs, the Yalmip tool and CPLEX commercial solver are employed to solve the proposed model and obtain the optimal capacity and power configuration for shared energy storage stations. Furthermore, the gray relation analysis method is used to determine the optimal connection location for shared energy storage stations. Finally, simulation analysis and verification are conducted using an IEEE 33-node test case. The results demonstrate that the proposed optimization and site-selection strategy for shared energy storage stations not only achieves optimal planning decisions, but also serves multiple users and meets the requirements of multiple nodes.
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