Optimal Scheduling Model of Power System Based on Multi-Objective Evolutionary Algorithm

Authors

  • Zhenyuan Zhang School of Electronic and Electrical Engineering, Zibo Vocational Institute, Zibo 255300, Shandong, China

Abstract

In today’s increasingly prosperous society, almost every industry requires electricity to support its operations, and whether the power system can operate stably and safely has been closely related to the steady development of the economy. Optimal scheduling research plays a decisive role in power system operation and control. In order to ensure reliable power supply and power quality, it is important to optimize the operational efficiency of the power system, so that the system can provide greater economic benefits. Traditional multi-objective optimization methods have major shortcomings, and multi-objective optimization methods based on traditional mathematical planning principles usually have certain vulnerabilities in terms of practical engineering optimization problems, so there is a need for in-depth research on efficient and practical multi-objective optimization
algorithms and theories. In this paper, the modeling technology of the power information-physical system is studied and the multi-objective scheduling optimization of power system is explored using a multi-objective evolutionary algorithm. The algorithm analysis results showed that compared with the traditional dispatching model, the proposed multi-objective evolutionary algorithm not only improved the optimization effect of the power system by about 8.95%, but also offered decision makers guidance on dispatching, with good convergence speed and accuracy compared with previous optimization methods and solutions.

Keywords: Power system, multi-objective evolutionary computation, physical communication, scheduling model

Cite As

Z. Zhang, "Optimal Scheduling Model of Power System Based on Multi-Objective Evolutionary Algorithm",
Engineering Intelligent Systems, vol. 32 no. 5, pp. 543-552, 2024.






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Published

2024-09-01

Issue

Vol. 32 No. 5 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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