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In the forthcoming era of power grids emphasizing clean energy and green transportation, stringent safety and reliability standards are imperative. This study addresses the limitations of traditional reinforcement learning in managing the control performance degradation due to the extensive integration of new energy sources in microgrid by proposing a multi-step self-correcting Q-learning algorithm. This algorithm features a self-correcting estimator for accurate system state estimation and an eligibility trace mechanism to expedite convergence, facilitating rapid controller responses to system fluctuations and minimizing the impact of frequency regulation delays. The simulation section of this paper presents an enhanced two-area load frequency control model, integrating wind power and electric vehicle modules, and subjected to various disturbances to mimic real-world power system load changes. The results demonstrate that the proposed algorithm excels in control performance metrics when com- pared to existing methods. [ABSTRACT FROM AUTHOR]