Economic Dispatch of Smart Grid with Unknown Cost Functions and Switching Network Topology

Abstract

The capability of economic dispatch (ED) algorithms to address the power dispatch problem with unknown cost functions and switching network topology is an important feature for practical applications of power dispatch algorithms in smart grid. Inspired by distributed fast consensus technique and reinforcement learning (RL) approach, this research presents a kind of ED strategy consisting of a fixed-time consensus tracking (FCT) algorithm and a distributed RL-based power dispatch algorithm to address the economic dispatch problem (EDP) with unknown cost functions and switching network topology. Different with existing results on EDP of smart grid where the feasible power outputs are calculated from centralized algorithm, a distributed FCT algorithm is utilised to balance the power demand and output for each dispatch duration, where the achievement of such a consensus leads to feasible power outputs and secures the system performance against switching interaction topology. Then, a distributed RL-based power dispatch algorithm is developed to train a policy for solving EDP with unknown cost functions through the technique of distributed training with distributed execution (DTDE). Finally, case studies are presented to demonstrate the effectiveness of the proposed ED algorithms.