Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm

Borche Postolov (), Nikolay Hinov (), Atanas Iliev and Dimitar Dimitrov
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Borche Postolov: Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia
Nikolay Hinov: Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 8 Kl. Ohridski Blvd, 1000 Sofia, Bulgaria
Atanas Iliev: Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia
Dimitar Dimitrov: Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia

Energies, 2022, vol. 15, issue 16, 1-25

Abstract: This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem.

Keywords: hydrothermal scheduling; genetic algorithm; self-adaptive penalty; Laplace crossover; MPTM mutation (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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