A Bi-Level Optimization Scheme for Energy Storage Configuration in High PV-Penetrated Distribution Networks Based on Improved Voltage Sensitivity Strategy

Bo Gao (), Qing Du, Gonghao Zhan and Jian Han
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Bo Gao: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Qing Du: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Gonghao Zhan: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Jian Han: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2025, vol. 18, issue 8, 1-23

Abstract: This study proposes a bi-level optimization strategy to determine the optimal location and capacity for energy storage systems. Before applying the proposed bi-level optimization scheme, the PV output data are clustered by the K-means algorithm, and an active power–voltage sensitivity strategy is presented to identify nodes with high sensitivity in the clustered PV output data. Based on the data of the clustered nodes, a bi-level optimization model is established to determine the optimal location, rated power, and capacity of energy storage using the particle swarm optimization (PSO) algorithm. Finally, compared with traditional voltage sensitivity methods, some case studies with the proposed scheme are executed in the IEEE-33 bus system, which reveals better performance in reducing voltage deviation, network losses, and economic costs.

Keywords: photovoltaic (PV); voltage deviation; voltage sensitivity; particle swarm optimization (PSO); bi-level optimization (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: 2025
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