Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic Dispatch

With the increasing expansion of the power grid, economic dispatch problems have received considerable attention. A multi-agent coordinated deep reinforcement learning algorithm is proposed to deal with distributed nonconvex economic dispatch problems. In the algorithm, agents run independent reinforcement learning algorithms and update their local Q-functions with a newly defined joint reward. The double network structure is adopted to approximate the Q-function so that the offline trained model can be used online to provide recommended power outputs for time-varying demands in real-time. By introducing the reward network, the competition mechanism between the reward network and the target network is established to determine a progressively stable target value, which achieves coordination among agents and pledges the losses of the Q-networks to converge well. Theoretical analysis is given and case studies are conducted to prove the advantages compared with existing approaches.

Automated summary

A multi-agent coordinated deep reinforcement learning algorithm is proposed to solve distributed nonconvex economic dispatch problems. Agents run independent reinforcement learning algorithms and update their local Q-functions with a newly defined joint reward. The double network structure is adopted to approximate the Q-function, allowing the offline trained model to provide recommended power outputs for time-varying demands in real-time. The algorithm introduces a reward network to establish a competition mechanism and achieve coordination among agents, resulting in well-converged Q-network losses. Theoretical analysis and case studies demonstrate the advantages compared to existing approaches.