Hybrid Energy Storage System in Load Frequency Control Using Slime Mould Algorithm Under Deregulated Market

ABSTRACT In deregulated power systems, maintaining stable load frequency control (LFC) is increasingly challenging due to the integration of diverse energy sources and the unpredictable nature of demand and generation. Traditional control strategies often struggle to provide robust performance under these dynamic conditions, especially when contract violations occur. Additionally, ensuring frequency stability across multi‐area grids with mixed energy profiles especially integration of hybrid energy storage systems, requires advanced and adaptive control mechanisms. This paper proposes a novel application of the Slime Mold Algorithm (SMA) to optimize a novel TIIDN‐P controller under a deregulated environment. This paper also proposes a novel three‐area hybrid power system. Each area comprises a thermal power plant integrated with a renewable energy source and a dedicated energy storage system (ESS): Area 1 includes a wind turbine, Area 2 features geothermal energy, and Area 3 incorporates biogas. The proposed system is chosen since it has not yet been investigated and those energy sources are easily available in India. By leveraging the adaptive search capabilities of SMA, the proposed TIIDN‐P control strategy achieves significantly improved frequency regulation performance compared to other controllers. In comparison to PSO (Particle Swarm Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and MBA (Mine Blast Algorithm), the proposed algorithm reduced settling time significantly up to 40% for different areas' frequency. Simulation results confirm that TIIDN‐P controllers offer superior dynamic response, reduced overshoot, and enhanced robustness under varying system conditions. The integration of hybrid ESS configurations not only stabilizes system frequency but also effectively mitigates the impact of uncontracted power fluctuations. Sensitivity analysis further validates the resilience of the proposed approach. The sensitivity analysis proved that the proposed system as well as the controller, is robust enough in various cases. The stability analysis using eigenvalue analysis revealed that the proposed system is stable in various conditions. Moreover, this paper presents an intensive comparison of various types of Energy Storage Systems and their hybrid systems to find out the best ESS and the best hybrid system which is the first‐ever of its kind. An investigation reveals that RFBESS and FWESS hybrids are the best option from the case study.