A cascaded PID-reinforcement learning-based virtual inertia control in microgrid load frequency control system using electric vehicle energy storage

Abstract

The islanded microgrids increasingly depend on renewable energy sources for power generation and introduce significant frequency control challenges due to the renewable sources’ intermittent nature and low system inertia. Traditional energy storage systems, though employed and effective for frequency stabilization, are often limited by high costs and power density requirements. Accounting issues of microgrid frequency performance under stochastic renewable energy source integration and limitations of conventional energy storage systems, this paper proposes a virtual inertia control strategy that leverages electric vehicle battery storage, supported by a cascade proportional integral derivative-reinforcement learning-based auxiliary controller, to enhance frequency regulation of the load frequency control system in an islanded microgrid. The proposed cascade proportional integral derivative-reinforcement learning-based virtual inertia controller is implemented and tested in a MATLAB/Simulink environment and evaluated under diverse operating conditions involving dynamic load and renewable energy source disturbances, while comparing with other control strategies. The comparative results demonstrate that the proposed virtual inertia control strategy outperforms other compared controllers in terms of frequency stability and overall dynamic response.