Strategic management of solar generation for solar electric vehicle charging in microgrids using deep reinforcement learning

The integration of solar electric vehicles (SEVs) into microgrids, particularly those enriched with photovoltaic (PV) systems, presents unique challenges due to the inherent variability in solar energy and the dynamic consumption patterns of SEVs. This study aims to address these complexities by developing an advanced operational framework that enhances the management of energy flows within microgrids, leveraging the capabilities of modern artificial intelligence. Utilizing a deep double Q-network (DDQN), this research introduces a sophisticated method to dynamically adapt to fluctuations in solar generation and SEV energy demands, ensuring operational efficiency, sustainability, and grid stability. The methodology encompasses detailed mathematical modeling of energy generation, consumption, and storage dynamics, integrated with environmental and economic constraints to simulate realistic microgrid scenarios. The DDQN framework is employed to optimize energy distribution strategies in real-time, based on predictive analytics and responsive control mechanisms. This approach not only copes with the stochastic nature of renewable energy sources and SEV usage patterns but also capitalizes on these aspects to improve overall microgrid performance. This paper contributes a novel integration of DDQN in microgrid management, particularly for systems incorporating SEVs and PV generation. By optimizing the interplay between solar power availability and SEV charging requirements, the study provides strategic insights that can guide infrastructure investments and operational tactics, promoting more efficient and economically viable microgrid systems. The proposed models and strategies are expected to significantly advance the field of energy management, paving the way for the development of smarter, more resilient energy systems in urban environments.