Real-time scheduling optimization of smart grid based on deep reinforcement learning

In this paper, Deep Reinforcement Learning (DRL) technology, combined with its ability of feature extraction and decision optimization, is used to automatically learn the optimal scheduling strategy. By constructing an economic dispatching model, considering the constraints of power balance and generator sets, the cost of power generation can be minimized, the utilization rate of new energy can be maximized, and user satisfaction can be improved. In the study, the state space includes load demand, output of generator set, predicted output of new energy and state of power equipment, while the action space includes output setting of generator set and control instructions of power equipment. The reward function evaluates the scheduling decision and guides the learning of the optimal strategy. The Deep Deterministic Policy Gradient (DDPG) algorithm is selected, and the action strategy is generated by Actor network. The Critic network evaluates the value function, and the target network stabilizes the training process. Experiments are carried out on the platform of MATLAB/Simulink. The results show that DDPG algorithm is superior to traditional methods in reducing power generation cost, improving new energy utilization rate and user satisfaction, and shows good generalization ability and system stability. This study provides an effective DRL solution for real-time scheduling optimization of smart grid.