Joint Scheduling Optimization of a Short-Term Hydrothermal Power System Based on an Elite Collaborative Search Algorithm

Jiefeng Duan and Zhiqiang Jiang
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Jiefeng Duan: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zhiqiang Jiang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 13, 1-18

Abstract: The joint scheduling optimization of hydrothermal power is one of the most important optimization problems in the power system, which is a non-linear, multi-dimensional, non-convex complex optimization problem, and its difficulty in solving is increasing with the expansion of the grid-connected scale of hydropower systems in recent years. In this paper, three effective improvement strategies are proposed given the shortcomings of the standard collaborative search algorithm, which easily falls into local optimization and weakening of global search ability in later stages. Based on this, an elite collaborative search algorithm (ECSA) coupled with three improvement strategies is established. On this basis, taking the classic joint scheduling problem of a hydrothermal power system as an example, the optimization model with the goal of the least pollutant gas emission is constructed, and the system constraint treatment method is proposed. In addition, five algorithms, i.e., ECSA, CSA, PSO, GWO, and WOA are used to solve the model, respectively. Through the comparison of results, taking the median as an example, the emission of polluting gases of ESCA is reduced by about 1.8%, 13.1%, 38.2%, and 11.2%, respectively, and it can be found that ECSA has obvious advantages in the convergence speed and quality compared with the other four algorithms, and it has a strong ability for global search and jumps out of the local optimal.

Keywords: cascade reservoirs; joint scheduling of hydrothermal power; collaborative search algorithm; parameter random; elite reinforcement learning; elite-assisted learning (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: 2022
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