Journal of Modern Power Systems and Clean Energy

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Home > Archive>Volume 8, Issue 4, 2020 >737-749. DOI:10.35833/MPCE.2018.000801
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Multi-agent-based Rolling Optimization Method for Restoration Scheduling of Distribution Systems with Distributed Generation
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Affiliation:

1.Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;2.Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai, China

Fund Project:

This work was supported in part by National Key R&D Program of China (No. 2018YFB0905000), in part by the Science and Technology Project of State Grid Corporation of China (No. SGTJDK00DWJS1800232), in part by the National Natural Science Foundation of China (No. 51907122), and in part by the Shanghai Sailing Program (No. 19YF1423800).

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    Abstract:

    Resilience against major disasters is the most essential characteristic of future electrical distribution systems (EDSs). A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper. When a blackout occurs, considering the risk of losing the centralized authority due to the failure of the common core communication network, the available agents after disasters or cyber-attacks identify the communication-connected parts (CCPs) in the EDS with distributed communication. A multi-time interval optimization model is formulated and solved by the agents for the restoration scheduling of a CCP. A rolling optimization process for the entire EDS restoration is proposed. During the scheduling/rescheduling in the rolling process, CCPs in EDS are re-identified and the restoration schedules for CCPs are updated. Through decentralized decision-making and rolling optimization, EDS restoration scheduling can automatically start and periodically update itself, providing an effective solution for EDS restoration scheduling in a blackout event. A modified IEEE 123-bus EDS is utilized to demonstrate the effectiveness of the proposed method.

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History
  • Received:November 16,2018
  • Online: July 22,2020

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