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

This article focuses on developing an advanced operational framework for managing photovoltaic (PV)-enriched microgrids integrated with solar electric vehicles (SEVs) using a deep double Q-network (DDQN), a form of deep reinforcement learning. The framework dynamically optimizes energy flows by adapting to the stochastic nature of solar generation and SEV charging demands, incorporating detailed mathematical models that include environmental, economic, and technical constraints to ensure grid stability, cost efficiency, and sustainability. Simulation results based on synthesized data demonstrate improvements in operational cost savings, energy efficiency, carbon emission reductions, and insights into optimal SEV charging schedules aligned with peak solar irradiance. The study highlights challenges related to grid stability with increasing SEV penetration and proposes demand response strategies and adaptive control to mitigate these issues, emphasizing the economic and environmental benefits of integrating SEVs into microgrids through AI-driven optimization.