Optimizing Load frequency control of multiple areas power system using HGAGOA integrating with renewable energy and SMES effect

Abstract

Load frequency control (LFC) is pivotal in maintaining grid stability, particularly with the growing integration of renewable energy sources (RES) into power systems. This paper presents a novel approach for multi-area based LFC incorporating RES, utilizing a sophisticated metaheuristic optimization technique. By integrating various energy sources, including conventional thermal units and variable RES like wind and solar, and considering multi-area coordination and forecasting models, the proposed methodology dynamically adjusts control parameters to optimize LFC performance. The results indicate that Hybrid Genetic and Grasshopper Optimization Algorithm (HGAGOA) is the most effective algorithm for minimizing the objective function in only 10 iterations, followed by Genetic Algorithm (GA) with 40 iterations and then Grasshopper Optimization Algorithm (GOA) with greater than 50 iterations. By integrating the complementary strengths of GA and GOA, HGAGOA offers enhanced convergence and improved solution quality, making it a preferred choice for optimization tasks demanding rapid and reliable convergence. Hence, the HGAGOA outperforms the GOA and GA in both objective functions, change in frequency deviation and power deviation. This framework offers a promising avenue for sustainable and reliable power system operation, with potential applications in real-time implementation and integration with advanced grid management systems for further improvements in performance and resilience.