Intelligent Dispatch and Optimization Technology in Market-Based Electricity Trading

This paper presents a deep reinforcement learning (DRL)-based approach for intelligent dispatch optimization in electricity market environments. By modeling generation units, storage systems, and market signals within a multi-agent framework, the proposed system enables adaptive, real-time decision-making under uncertainty. Key components include state representation design, policy learning through actor-critic networks, and dynamic constraint handling. Simulation results on a modified IEEE 39-bus system show that the DRL model reduces dispatch cost by up to 12.6% during high-renewable scenarios, lowers curtailment, and improves system resilience under faults and forecast errors. Compared to traditional methods, the DRL agent demonstrates faster response, better generalization, and lower dependence on point predictions, indicating strong potential for future grid applications.