BatteryRL: Deep Q-Network for Intelligent Battery Management in Smart Grid Environment

Battery storage plays a vital role in the emerging smart grid, offering the unique capability to rapidly respond to renewable generation fluctuations and grid disturbances. However, research on profitably leveraging this capability remains limited. Effective solutions must account for the intermittent nature of real-time energy prices, electricity markets, and adaptation to price fluctuations. Reinforcement learning has demonstrated the potential to optimize complicated decision-making under probabilistic scenarios. This study proposes a deep Q-network-based battery trading system to optimize charging and discharging decisions based on real-time electricity prices. The environment was first designed, defining the states, actions, reward function, and DQN agent. The agent was then trained through exploration and exploitation within the environment. Additionally, hyperparameters were analyzed against the cumulative reward or performance of BTS. The proposed approach was benchmarked against a baseline mixed integer non-linear programming model and random agent. The results demonstrate that the DQN-based agent generates the same revenue as the baseline model, which is assumed to have prior knowledge of future states and is not possible in reality. Furthermore, the feasibility of the proposed approach is validated using economic, computational, and adaptability metrics, highlighting its effectiveness for real-world applications of battery trading systems.