Solving the optimal power generation for the thermal generating unit problem, considering the multiple fuel constraints and the presence of renewable energy-based generators using the Pied kingfisher optimizer

This research presents the application of a recently proposed meta-heuristic algorithm, Pied kingfisher optimization (PKO), to solve the optimal power generation for thermal generating unit (OPG-TGU) problem. The main objective of the entire research is to achieve the optimal value of the entire electricity production cost (EEPC) for the ten-multiple-fuel generator power systems serving a load demand of 2500 MW. Besides, a 200 MW wind-based generator and a 100 MW photovoltaic-based generator are also integrated into the given power system as a direct solution to partly lower EEPC values and reduce the generating burden on all generators in the system. The results obtained by PKO are compared with those of another meta-heuristic algorithm, Frilled Lizard Optimization (FLO), for performance evaluation. The results clearly indicate that PKO completely outperforms FLO in terms of convergence speed, the ability to reach the best solution, and especially the ability to avoid local optima. In particular, PKO is more effective than FLO at 2.95% in terms of the minimum EEPC (Min.EEPC), 7.04% in the mean EEPC (Mean.EEPC), and 12.59% in the maximum EEPC. Besides these competitive results, PKO also offers surprising stability throughout the process of addressing the given problem, achieving an STD value of only 0.0055, while FLO achieves 9.7894. Therefore, PKO is recognized as an effective and robust search method, highly recommended for dealing with such OPG-TGU problems.

Keywords: Optimal power generation for thermal generating unit; entire electricity production cost; multiple fuel option constraint, photovoltaic based generator;  wind based generator;  Pied kingfisher optimization.