Support Vector-Based Grey Wolf Optimizer for Optimal Scheduling of Generators in Thermal Power Plants

In this study, an advanced Support Vector-based Grey Wolf Optimizer (SVGWO) is proposed for solving the Economic Load Dispatch (ELD) problem in thermal power systems. The ELD problem is inherently complex due to its non-convex and non-linear characteristics, making it a challenge for traditional optimization methods. The primary objective of this research is to minimize the total cost of power generation while ensuring that the load demands are met under operational constraints. The proposed methodology integrates Support Vector Machine (SVM) with the Grey Wolf Optimizer (GWO) to improve the search process by effectively navigating the solution space and enhancing convergence speed. The performance of the SVGWO is validated using standard test systems, including the IEEE 30-bus and IEEE 14-bus systems, under varying load demands. The results demonstrate the algorithm's ability to achieve significant cost reductions while maintaining a fast convergence rate. Comparative analysis with existing methods, highlights the superior performance of SVGWO in terms of cost minimization and robustness. The findings suggest that the SVGWO is an efficient and reliable tool for addressing complex ELD problems, offering a promising solution for improving economic efficiency in thermal power generation.