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

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.

Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

“…Typically, the process of optimization takes place in fields where a high number of variables are found, e.g., system design [152], economics [153], or transport [154]. These algorithms enable the global extremes [155] for a given function to be found quickly within its time domain [156,157].…”