Coordinating electric vehicle charging with multiagent deep Q-networks for smart grid load balancing

Integrating EVs (Electric Vehicles) with the electrical system presents essential load distribution difficulties because EV recharging structures are unpredictable and variable. The article presents an innovative technique employing multiple-agent deeper Q-Networking (MADQN) to coordinate electric automobiles and improve the electricity system balance of load. The suggested MADQN simulation rapidly optimizes battery charge plans by utilizing the capabilities of multiple agent networks as well as deeper reinforced learning. The framework adjusts to current network situations utilizing cooperative decision-making between substances, considering variables like a need for power, accessibility to green energy sources, and protection of the arrangement. Beneficial load distribution is made possible when reducing expenses and ecological damage because of the system's capacity to gather data from and modify intricate, changing circumstances. The findings from the modelling indicate how well the suggested MADQN method works to enhance network efficiency, lower peak usage, and use more sustainable power resources. These factors help build a more robust, adaptable, intelligent grid environment. •MADQN proposed to coordinate EVs and improve grid load balancing.•Optimizes charging plans using multi-agent systems and deep Q-learning.•Adapts to grid conditions via cooperative decision-making.•Enables efficient load distribution, reduces costs and environmental impact.•Improves grid efficiency, reduces peak demand, increases renewable energy use.