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

被引:38
作者
Yin, Linfei [1 ]
Sun, Zhixiang [1 ]
机构
[1] Guangxi Univ, Coll Elect Engn, Nanning 530004, Guangxi, Peoples R China
关键词
Multi-layer distributed multi-objective consensus algorithm; Large-scale multi-area interconnected power systems; Parallel; Distributed multi-objective algorithm; Multi-objective economic dispatch; COMBINED HEAT; OPTIMIZATION; FLOW; DECOMPOSITION; ELECTRICITY; MICROGRIDS;
D O I
10.1016/j.apenergy.2021.117391
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:17
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