Application of V2G Dynamic Electricity Price Optimization Algorithm Based on Deep Reinforcement Learning in Emergency Scenarios

This article proposes a vehicle-to-grid integration (V2G) dynamic electricity tariff optimization algorithm rooted in deep reinforcement learning (DRL), aiming to solve the issue of power system load balancing and efficient scheduling in emergency scenarios. The algorithm adopts a deep Q-grid (DQN) structure. By continuously studying the behavioral paradigm of electric vehicles in the charging and discharging process, it combines the real-time demands of the power grid and dynamically adjusts the electricity bill policy to improve the overall operating performance of the V2G system. To be precise, the algorithm builds a modality-action-reward mechanism to monitor the evolution of grid load and vehicle charging demand in real time, and adjust electricity charges based on dynamic changes in power supply and demand. In this way, the system cannot only effectively equalize supply and demand, but also maximize the operational stability and efficiency of the power grid. To verify the effectiveness of the algorithm, this article conceives simulation experiments under various emergency situations. The results show that the proposed algorithm can significantly reduce the peak load of the system and increase the power scheduling capacity under various emergency conditions. Compared with the conventional fixed electricity tariff policy, the electricity tariff control accuracy of this formula jumps by 15%, and the power scheduling efficiency increases by 20%.