Work

Optimal planning method for scalable energy storage station in power system considering multiple application scenarios

Year: 2025

Type: article

Abstract: The integration of a high proportion of renewable energy sources presents significant challenges to power system operation. To address this issue, this paper proposes a scalable planning method for energy storage station (ESS) deployment tailored to the requirements of new-type power systems. The method comprehensively considers multiple roles of ESS, including facilitating renewable energy integration, mitigating transmission congestion, performing peak shaving, and enhancing system resilience. In the proposed approach, the system’s vulnerability and weak points are first identified, followed by the construction of region-specific fault scenarios. The planning problem is formulated as a large-scale mixed-integer optimization model. To solve it efficiently, a two-stage optimization framework is adopted. In the first stage, the potential support offered by ESS to renewable sources, load-side demands, and grid-side operations is analyzed using graph theory, thereby identifying candidate nodes for ESS installation. In the second stage, the model is decomposed via Benders decomposition into a master problem (ESS planning) and a subproblem (system operation with ESS), enabling efficient solution computation. The effectiveness of the proposed method is validated using real data from a provincial power system in China and a modified IEEE system. Results demonstrate that the proposed ESS planning strategy effectively reduces operational costs, increases renewable energy utilization, alleviates power flow congestion, and minimizes load loss under severe fault conditions, thus improving the system’s economic performance, flexibility, and resilience. © 2017 Elsevier Inc. All rights reserved.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111140

Authors: Changfeng Liao, Li He, Zhuangxi Tan, Yong Li, Chaoyang Chen, Yijia Cao

Institutions: Hunan University of Science and Technology, Hunan University

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,920