Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

• Published in: Energies (Basel) Vol. 17; no. 6; p. 1491
• Main Authors: Yang, Wenqiang, Zhang, Yihang, Zhu, Xinxin, Li, Kunyan, Yang, Zhile
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Accuracy; Algorithms; Alternative energy sources; Analysis; chaotic initialization; constraint-handling methods; dynamic economic dispatch; Economic aspects; Electric vehicles; Electricity; Electricity distribution; Emissions (Pollution); Forecasts and trends; Genetic algorithms; improved grey wolf optimization algorithm; Learning strategies; Mathematical optimization; Methods; nonlinear convergence factor; Optimization algorithms; Renewable resources; Scheduling; Valves
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17061491

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.