Enhanced Harris hawk optimizer for hydrothermal generation scheduling with cascaded reservoirs

The paper intends to propose an enhanced Harris hawk optimizer (EHHO) to solve the highly constrained and non-linear multiobjective hydrothermal generation scheduling (MOHGS) optimization problem. Hydrothermal generation scheduling aims to minimize thermal unit fuel costs and environmental consequen...

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Vydané v:Expert systems with applications Ročník 226; s. 120270
Hlavní autori: Kumar, Ashok, Dhillon, J.S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.09.2023
Predmet:
Enhanced Harris hawk optimizer
Heuristic algorithms
Multiobjective hydrothermal electric power system problems
Opposition based learning
Standard benchmark problems
Standard benchmark problems
Heuristic algorithms
Multiobjective hydrothermal electric power system problems
Enhanced Harris hawk optimizer
Opposition based learning
ISSN:0957-4174, 1873-6793
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Popis
Shrnutí:The paper intends to propose an enhanced Harris hawk optimizer (EHHO) to solve the highly constrained and non-linear multiobjective hydrothermal generation scheduling (MOHGS) optimization problem. Hydrothermal generation scheduling aims to minimize thermal unit fuel costs and environmental consequences while utilizing available water during the optimization time horizon. A multi-chain and cascaded hydro system are taken for hydropower generation. A non-linear relationship between water discharge rate and power generation is established for a multichain-linked hydro system. Water movement between linked reservoirs accounts for the time delay. A fuzzy decision-making approach solves the conflicting objectives of the MOHGS problem. The two-step proportional sharing heuristic method handles the complicated constraints imposed on hydrothermal units instead of indirect constraint handling methods. Harris Hawk Optimization (HHO) is a swarm-based optimizer that works on the natural hunting behavior of hawks. Despite having good adaptability, scalability, flexibility, and robustness, it tends to get stuck in the local search region and gives immature convergence for confined engineering optimization problems. The proposed EHHO speeds up the global search phase of the HHO by utilizing opposition-based learning, keeping it out of the stagnated local search region, and avoiding untimely convergence. Compared to the existing heuristic methods, the EHHO gives better results while solving thirteen unconstrained benchmarks, thirteen constrained benchmarks, and minimum cost and emission for three MOHGS problems. The convergence curves, box plots, and Wilcoxon signed-rank test justify the robustness of the EHHO. The results revealed that EHHO supports its efficacy in applications of non-linear, discontinuous, highly constrained optimization problems.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120270