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Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems

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  • Yin, Linfei
  • Sun, Zhixiang

Abstract

Traditional multi-objective economic dispatch problems all apply centralized multi-objective optimization methods. However, with the rapid development of smart grids, issues such as system calculation speed, robustness, and information privacy have been increasingly vitally. Although the distributed multi-objective optimization method can solve the problems of system robustness and privacy when the scale of the interconnected power system expands and the number of agents increases, each agent independently optimizes its sub-problems and then exchanges information to complete the entire system optimization. The pressure of system communication calculation leads to reduce the overall calculation speed greatly. In response to the above problems, this paper proposes a multi-layer distributed multi-objective consensus algorithm. This method first calculates the optimal power generation of each area of each layer through the network topology and then calculates the power of each unit in each area in parallel according to the calculated optimal power generation. The analysis of the three system simulation results of IEEE118-bus, IEEE2154-bus, and Xinjiang Turpan 117-bus shows that the proposed method can solve the problems of multi-objective and information privacy, and realize the rapid solution of economic dispatch in large-scale multi-area interconnected power systems.

Suggested Citation

  • Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007923
    DOI: 10.1016/j.apenergy.2021.117391
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    6. Tang, Xiongmin & Li, Zhengshuo & Xu, Xuancong & Zeng, Zhijun & Jiang, Tianhong & Fang, Wenrui & Meng, Anbo, 2022. "Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm," Energy, Elsevier, vol. 244(PA).
    7. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    8. Zare Oskouei, Morteza & Mehrjerdi, Hasan & Babazadeh, Davood & Teimourzadeh Baboli, Payam & Becker, Christian & Palensky, Peter, 2022. "Resilience-oriented operation of power systems: Hierarchical partitioning-based approach," Applied Energy, Elsevier, vol. 312(C).
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