Hybrid team game algorithm for dynamic economic-emission electric power generation scheduling

Abstract

The rising greenhouse gases, insufficient electricity production, and growing demand call for optimal thermal power generation scheduling to minimise fuel costs and emissions. Traditional optimisation methods are unable to find solutions due to non-convex objective functions and constrained convergence. Meta-heuristic techniques gain attention to address static and dynamic economic-emission generation scheduling challenges, owing to their adaptable nature and derivative-free structures. This paper proposes a Hybrid Team Game Algorithm (HTGA) that incorporates learning and exchange operators, simplex search, passing, mistake, substitution, and player within the field operators to solve the dynamic economic-emission generation scheduling (DEEPGS) of thermal units while considering multiple fuel options, valve loading effect, prohibited zone avoidance, meeting ramp-rate limits, and spinning reserve constraints. By evaluating generation scheduling at initial intervals, the forward strategy provides a solution to the dynamic EEPGS problem. A non-interactive approach uses the price penalty method to combine the objective functions, and heuristics are employed to explore feasible solutions using a replacement technique and proportional power sharing for unmet load demand. The algorithm's versatility is demonstrated across benchmark problems and electric power systems, as affirmed by Friedman and Wilcoxon's tests. Notably, HTGA requires fewer sensitive parameters, enhancing its practical applicability in addressing the DEEPGS of thermal units.