Automatic Generation Control Using Particle Swarm Optimization for Enhanced Renewable Energy Integration in Hybrid Systems

Abstract

In recent years, there has been a significant increase in the penetration of renewable energy sources in to the power systems around the world, due to the need for sustainable energy sources and the reduction of greenhouse gas emissions. The integration of multiple renewable energy sources can enhance the reliability and efficiency of renewable energy systems. However, the integration of multiple renewable energy sources requires efficient control strategies to optimize their performance and ensure reliable operation because of the uncertainties and intermittent of RES. Automatic generation control plays a vital role in controlling the integration of hybrid systems by ensuring adjustments to power generation, optimizing energy storage and maintaining power system stability between different control areas. Most of the time the interconnected power systems in the AGC relies only on the synchronous generating units because of the challenges due to intermittent and variable nature of renewable energy sources. This thesis proposes AGC for integrating hybrid system incorporating with renewable energy sources (PV and wind) and energy storage system (VRFB and FESS) to overcome those challenges. A two-area AGC system model was developed to regulate frequency and system stability by adjusting generation based on load deviations. PID controller was used as a control strategy for the proposed system. This PID controller was optimized through an effective Particle Swarm Optimization technique. The objective of applying PSO algorithm was to tune PID controller in order to get optimal parameters of the controller gains. Then the power system achieved a significantly improved settling time, maintained grid frequency within the specified limits and mitigated variations in tie line power between the control area. The simulation results confirm the effectiveness of the proposed automatic generation control using particle swarm optimization for enhancing renewable energy integration in hybrid systems. This was achieved through significantly improved settling time, better grid frequency regulation and reduced tie-line power deviations. This study offers a promising solution for addressing the challenges associated with renewable energy integration.