A distributed algorithm for economic dispatch in a large-scale power system

Abstract

In this paper, we present a distributed economic dispatch strategy for a large-scale power system. At first, we treat each generator and load in the grid as an agent. By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner. Due to the large number of the agents, the agents are sorted into several clusters and each cluster has a leader to communicate with the leaders of its neighboring clusters. The agents in the same cluster can conduct local optimization and communicate with its neighboring agents in parallel. After that, the leader agents of each cluster exchange their information simultaneously. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Compared to our previous work in [14], where the leader agent in each cluster conducts the optimization in a sequential way, the proposed scheme in this paper allows them communicate and conduct optimization simultaneously, which greatly improves the algorithm efficiency. A case study implemented on IEEE 30-bus power system are discussed and tested to validate the proposed method.