Economic Dispatch of Multi Regional Power Systems Based on CMOPSO Algorithm

Abstract

With the progress of society and the aggravation of environmental pollution, the economic dispatch of the power system is developing towards multiple environmental and economic goals. To improve energy utilization efficiency, this study innovatively proposes a multi-objective particle swarm optimization algorithm based on competitive learning, and uses this algorithm to solve multi regional environmental and economic scheduling problems. In addition, the study solves static and dynamic economic (S-DE) scheduling problems in multiple regions through improved competitive group optimization algorithms. The research results show that under different testing systems, the average distribution uniformity indicators of the research algorithm built on competitive learning are 0.8058 and 0.8457, and the average anti generation distance is 67.6316 and 1664.0978. The improved competitive group optimization algorithm solves the maximum, minimum, and average fuel costs for static economic scheduling in multiple regions, which are 656.2243 $/h, 655.8592 $/h, and 655.9866 $/h, respectively. Thus, the designed algorithm can effectively solve economic scheduling problems, which is of great significance for resource integration, saving power generation costs, and reducing pollution emissions.