As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.

Decarbonizing the transportation sector involves adopting electric vehicles (EVs); a shift that introduces significant challenges in energy distribution management and raises concerns about grid stability. Charge Point Operators (CPOs) are important in this transition as they control the EV charging process by balancing the needs of EV users and the grid. This study presents a smart-charging model from the perspective of CPOs for handling EVs located in a commercial parking lot to minimize the Power Setpoint Tracking (PST) error. To solve this sequential decision-making problem, a Markov Decision Process (MDP) model is designed and solved using Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm. The proposed model can effectively manage the uncertainties associated with EV arrivals and fluctuating charging demands by structuring the action and state space to incorporate power constraints. The experimental evaluation using realistic EV behavior data shows that the proposed approach significantly outperforms uncontrolled charging, reducing PST error while effectively managing multiple EV chargers and EVs with varying battery capacities and power limitations.