Resilience quantification and enhancement of typhoon-affected distribution system with virtual power plant

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Authors: Pratik Harsh, Hongjian Sun, Debapriya Das, Awagan Goyal Rameshrao, Jing Sai Jiang
Cited by: 1
References: 28

Abstract

Rising incidents of extreme weather events and the increasing penetration of stochastic distributed energy resources have led to critical research on enhancing power system resilience in recent years. Strengthening resilience against high-impact, low-frequency events requires quantifying a given system’s resilience and integrating this quantification into the energy management model of the power system. This paper proposes a unified resilience metric that accounts for the impact of extreme weather on various technical and economic parameters of power system operation, enabling a comprehensive assessment of power system resilience. In addition, a novel virtual power plant (VPP) scheduling framework is developed that incorporates two operational resilience enhancement strategies, distribution network reconfiguration and demand response programs, to improve distribution system performance during extreme events while simultaneously optimizing operation under normal conditions. The effectiveness and advantages of the proposed resilience metrics and VPP scheduling framework are validated through simulation and comparative analyses on a 33-bus distribution network integrated with electric vehicle charging stations, battery swapping stations, solar generating units, and flexible loads at multiple bus locations. The results demonstrate a substantial improvement in system resilience, as reflected by a 96% reduction in the value of the proposed unified resilience metric and a 35% improvement in the system’s load-serving capability.

Fields of Study & Subjects

Smart Grid Security and Resilience Power System Optimization and Stability Optimal Power Flow Distribution

Publication details

Journal / source
Applied Energy
Publisher
Elsevier BV
Volume
426
Pages
128603–128603
Publication date
6 August 2026
Language
EN