Dynamic Microgrids Formation Considering Energy Storage Sharing for Resilience Enhancement

Energy storage (ES) is a key method for addressing the intermittency of renewable energy generation during the restoration process of the distribution system (DS). As an advanced ES management framework, shared energy storage (SES) enables more efficient resource utilization and economic optimization. This paper proposes a hierarchical decision-making framework to investigate the resilience enhancement and economic benefits of SES under disaster conditions. The problem is modeled as a bi-level optimization model: the upper level aims to maximize the economic returns of the shared energy storage operator (SESO) through SES allocation, while the lower level focuses on the distribution system operator (DSO) forming dynamic microgrids and optimally dispatching SES to maximize load restoration. To solve the bi-level optimization problem where the non-convexity exists in the lower level, a hybrid algorithm combining heuristic and analytical methods is developed. Numerical results on a modified IEEE 33-node test feeder validate that the proposed framework improves DS resilience while ensuring economic profitability for the SESO, providing insights into deployment of SES in DS under disaster scenarios.

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Publication Details

Published
2025-04-11
DOI
https://doi.org/10.1109/pssgt64932.2025.11033860
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21

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Dynamic Microgrids Formation Considering Energy Storage Sharing for Resilience Enhancement

Wentao Shen, Jian Zhong, Jingxuan Zhu, Chen Chen et al.
1 citations
Microgrid Control and Optimization
1.21
article

Dynamic Microgrids Formation Considering Energy Storage Sharing for Resilience Enhancement

Wentao Shen, Jian Zhong, Jingxuan Zhu, Chen Chen, Quan Liu
article en
1 citations

Abstract

Energy storage (ES) is a key method for addressing the intermittency of renewable energy generation during the restoration process of the distribution system (DS). As an advanced ES management framework, shared energy storage (SES) enables more efficient resource utilization and economic optimization. This paper proposes a hierarchical decision-making framework to investigate the resilience enhancement and economic benefits of SES under disaster conditions. The problem is modeled as a bi-level optimization model: the upper level aims to maximize the economic returns of the shared energy storage operator (SESO) through SES allocation, while the lower level focuses on the distribution system operator (DSO) forming dynamic microgrids and optimally dispatching SES to maximize load restoration. To solve the bi-level optimization problem where the non-convexity exists in the lower level, a hybrid algorithm combining heuristic and analytical methods is developed. Numerical results on a modified IEEE 33-node test feeder validate that the proposed framework improves DS resilience while ensuring economic profitability for the SESO, providing insights into deployment of SES in DS under disaster scenarios.

Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, Research and Development
Affordable and clean energy
Openalex Percentile: Top 20%
Microgrid Control and Optimization
1.21
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Dynamic Microgrids Formation Considering Energy Storage Sharing for Resilience Enhancement — Wentao Shen, Jian Zhong, et al. · (2025) | TGRS Research Map | TGRS