An Event-Driven Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Islanded Microgrids

This paper considers the economic dispatch problem (EDP) with general convex cost functions in islanded microgrids. First, the EDP is equivalently modeled as a distributed uncon-strained optimization problem. Then, a distributed optimization algorithm with finite-time convergence is proposed. Furthermore, an asynchronous distributed event-triggered sampling scheme is established to reduce the information exchange required. Our convergence analysis shows that the proposed algorithm can solve the EDP in finite time, and events cannot be triggered an infinite number of times in any finite time period, that is, it does not have Zeno behavior. Both algorithms are initialization-free, which means that they do not require the sum of the initial output power of dispatchable distributed energy resources (DDERs) to equal the total demand. Finally, simulation results illustrate the effectiveness and scalability of our algorithms.