4.7 Article

Resilience Assessment of Distribution Systems Integrated With Distributed Energy Resources

期刊

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 12, 期 1, 页码 338-348

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2020.2994174

关键词

Resilience; Load modeling; Measurement; Microgrids; Mathematical model; Planning; Meteorology; Distributed energy resources; distribution system; energy storage; extreme weather events; renewable energy; resilience; microgrids

资金

  1. NSERC [NETGP 468468-14, TSTE-00886-2019]

向作者/读者索取更多资源

The resilience of electric systems is gaining attention due to increased vulnerability caused by frequent extreme climactic conditions attributed to global warming effects. This paper presents a method to develop probabilistic extreme event model, impact assessment model, and optimal restoration model for active distribution systems, using a non-sequential Monte Carlo Simulation framework. The proposed approach incorporates time-varying demand, renewable energy output, and energy storage characteristics to enhance system resilience.
The resilience of electric systems is receiving growing attention due to their increased vulnerability to infrastructure damages and widespread outages from frequent extreme climactic conditions attributed to global warming effects. Resilience evaluation methods should recognize the uncertainties and correlations in the performance variations of different types of energy resources, load characteristics, extreme events and their impacts on the grid elements. However, there is a lack of established methods and resilience metrics that are widely accepted. In this context, this paper presents the development of probabilistic extreme event model, impact assessment model, and optimal restoration model for active distribution systems, and integrates the models using a non-sequential Monte Carlo Simulation framework. The inter-dependencies of time-varying demand, renewable energy output, and energy storage characteristics are incorporated in the framework. A set of metrics is proposed to quantify the resilience of the system against extreme events and their outage impacts at the load points. The metrics and their probability distribution thus obtained can be utilized in probabilistic value-based investment planning to select appropriate measures to enhance the system resilience. Selected case studies are conducted on the IEEE 69-bus test system to show the efficacy of the proposed framework.

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