Look-ahead Dispatch Method via Deep Reinforcement Learning Embedded With Domain Knowledge
Reinforcement learning has a strong ability for self-learning and self-optimization,which has gradually emerged in the field of look-ahead power dispatch.However,the existing look-ahead power dispatch methods based on reinforcement learning tend to reduce the learning efficiency and convergence.To adapt to the large-scale power grid,this paper incorporates domain knowledge into the regularization terms,such as historical generation data,power balance,renewable energy utilization rate,and line loading rate.These terms are embedded in the reinforcement policy network to guide the training of dispatch agents.The method learns from expert-corrected historical power output trajectories to acquire expert experience in the early stages of training,which makes the parameters of the policy network quickly converge to an effective initial solution.During the later stages of training,introducing loss function regularization terms,such as power balance,guides the agent to adhere to prior dispatch knowledge.It also prevents the blind actions of the agent effectively without compromising the dispatch decision.Finally,the effectiveness of the proposed algorithm is verified in the IEEE118-bus system.
万方数据
维普
