An Efficient AI enabled Energy Management System framework for PV-V2G-ESS Integrated with Electric Vehicle Charging Stations.

An integrated AI-enabled Energy Management Strategy (EMS) for a photovoltaic (PV)-driven electric vehicle (EV) charging station featuring energy storage systems (ESS) and V2G has been introduced in this paper. The introduced strategy utilizes the reliability of the conventional rule-based system while adding the flexibility of the explainable artificial intelligence (AI)-enabled supervisory control.   In order to optimize the modes of operation of the PV-EV system, an AI-based controller based on the Decision Tree algorithm was developed. In addition, there is no reliance on forecasting algorithms while keeping the decision-making process clear in the proposed approach, which is useful in real-world implementations.   According to the performance analysis, which included simulations during one day, the AI-assisted EMS outperformed the conventional rule-based EMS in all cases. Renewable energy usage increased from 67% to 80%, whereas the grid electricity use decreased by 23%. Moreover, the peak demand of the grid was minimized by 20%, and the V2G energy share increased considerably.   It has been established that the use of explainable AI in combination with the standard EMS can offer a good tradeoff between flexibility, robustness, and transparency of the approach. The presented methodology is capable of solving the problems associated with static rule-based approaches and sophisticated black-box AI solutions.   Future research will concentrate on hardware implementation in real time, taking into account users' charging constraints, dynamic pricing, application of other AI techniques, and expansion to the multi-station energy management systems in microgrids

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