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

Wenqiang Yang, Yihang Zhang, Xinxin Zhu, Kunyan Li and Zhile Yang ()
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Wenqiang Yang: School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
Yihang Zhang: School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
Xinxin Zhu: School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
Kunyan Li: School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
Zhile Yang: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Energies, 2024, vol. 17, issue 6, 1-29

Abstract: 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.

Keywords: dynamic economic dispatch; improved grey wolf optimization algorithm; constraint-handling methods; chaotic initialization; nonlinear convergence factor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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