Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

A Two-stage Stochastic Mixed-integer Programming Model for Resilience Enhancement of Active Distribution Networks

1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China
2.South China University of Technology, Guangzhou 510641, China
3.Electric Power Research Institute, State Grid Chongqing Electric Power Company, Chongqing 401123, China

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This work was supported by National Natural Science Foundation of China (No. U1866603), Innovation Support Program of Chongqing for Preferential Returned Chinese Scholars (No. cx2021036), and Natural Science Foundation of Chongqing, China (No. CSTB2022NSCQ-BHX0729).

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    Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks (ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming (SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems (ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm (PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

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  • Received:July 29,2022
  • Revised:November 17,2022
  • Adopted:
  • Online: January 28,2023
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