Open Access

ARTICLE

Optimal Allocation of Distributed Generation and Energy Storage Considering Line Vulnerability under Extreme Weather in Distribution Networks

Yangjun Zhou¹, Chenying Yi¹, Wei Zhang¹, Juntao Pan^2,*, Ke Zhou¹, Weixiang Huang¹, Like Gao¹, Shan Li¹, Yuanchao Zhou³, Ling Li², Liwen Qin¹, Hongwen Wu⁴, Lijuan Yan²
1 Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning, 530000, China
2 Guangxi Power Grid Co., Ltd., Nanning, 530000, China
3 Nanning Power Supply Bureau, Guangxi Power Grid Co., Ltd., Nanning, 530031, China
4 Wuzhou Power Supply Bureau, Guangxi Power Grid Co., Ltd., Wuzhou, 543002, China
* Corresponding Author: Juntao Pan. Email:
(This article belongs to the Special Issue: Next-Generation Distribution System Planning, Operation, and Control)

Energy Engineering https://doi.org/10.32604/ee.2025.073787

Received 25 September 2025; Accepted 20 November 2025; Published online 05 January 2026

Abstract

The increasing integration of distributed generation (DG) and energy storage systems (ESS) has significantly enhanced the flexibility and efficiency of distribution networks. However, the growing frequency of extreme weather events has exposed the vulnerability of distribution lines, posing serious challenges to the reliability and resilience of such systems. Existing DG and ESS planning models often neglect this vulnerability dimension, leading to suboptimal siting decisions and reduced system robustness. To address this issue, this paper proposes a comprehensive multi-objective optimization framework that coordinates the allocation of DG and ESS and explicitly incorporates line vulnerability under extreme weather conditions. The vulnerability index of each distribution line is first evaluated through Monte Carlo simulations that capture the probabilistic influence of micro-climatic and terrain factors. This assessment serves as a pre-processing stage that screens out high-risk lines and thereby constrains the optimization decision space to more reliable nodes for DG and ESS deployment. Building upon this filtered network, a multi-objective optimization model is established to determine the optimal siting and capacities of DG and ESS. The optimization simultaneously minimizes the total annual cost, which includes investment, operation, and maintenance expenses, as well as network power losses, while improving overall system resilience. A case study on a modified IEEE 33-bus distribution system verifies the effectiveness of the proposed method. The results demonstrate that vulnerability-aware planning achieves a better balance between cost and reliability compared with conventional approaches. Specifically, the proposed strategy reduces annual network losses and outage durations while maintaining voltage stability with respect to climate-adjusted line failure rates. Furthermore, the integration of ESS enables effective peak shaving and valley filling, improving system efficiency and operational flexibility. These findings confirm that incorporating line vulnerability into DG and ESS planning provides a practical and scalable pathway for enhancing the resilience and economy of distribution networks.

---

Keywords

Distributed generation; energy storage systems; distribution networks; extreme weather; line vulnerability; multi-objective optimization

---