Multi-Agent Reinforcement Learning-based Distributed Economic Dispatch Considering Network attacks and Uncertain Costs

Abstract

With the development of large-scale power grids, distributed economic dispatch has received considerable attention. However, existing distributed economic dispatch algorithms ignore potential network attacks, which may have a greater impact on the security of the power grid. In addition, due to the access of renewable power generation equipment and external interference, there are usually uncertain terms in the cost function. A distributed economic dispatch collaborative deep reinforcement learning algorithm considering the safety objective function is proposed. All agents in the algorithm make joint decisions by observing the environment and coordinating with local neighbors. The state action value function is approximated by a neural network. Aiming at the problem caused by the uncertain term of the objective function, the idea of lenient reinforcement learning is adopted, and the reward is also fitted with a neural network. Several case studies have been conducted to prove the advantages of this algorithm.