Load Frequency Control Under Deregulated Multi-Area Power System Integrating with Renewable Energy Sources and Electric Vehicles Using AZOA Optimization

Abstract In modern power systems, the integration of renewable energy sources (RES) and electric vehicles (EVs) poses significant challenges to maintaining system stability and reliability, particularly in deregulated multi-area power systems. This research focuses on load frequency control (LFC) under such systems, where traditional energy generation is complemented by intermittent RES and EV loads. The deregulated environment, characterized by independent entities managing generation, distribution, and consumption, adds complexity to frequency regulation. This paper introduces the American Zebra Optimization Algorithm (AZOA), a novel optimization method, to enhance the performance of LFC in multi-area power systems. The AZOA is designed to optimize the parameters of a cascaded two-degree of-freedom proportional-integral and proportional-derivative with filter (2DOF(PI)-PDF) controller effectively, minimizing frequency deviations and maintaining a stable power supply despite the fluctuating nature of renewable energy and dynamic EV loads. Simulations were carried out under three distinct scenarios: poolco-based transactions with conventional generation, bilateral-based transactions with parallel AC-HVDC tie-lines, and contract violation transactions with distributed generation and EV integration. Performance comparison with particle swarm optimization (PSO), genetic algorithm (GA), and teaching-learning-based optimization (TLBO) demonstrates that AZOA achieves superior performance with 35% lower integral square error (ISE), 42% faster settling time, and 28% reduced peak overshoot. The results validate AZOA's effectiveness in maintaining frequency stability across multiple areas under diverse operating conditions.