A spatiotemporal decomposition algorithm for fully decentralized dynamic economic dispatch in a microgrid

In this paper, we present a spatiotemporal decomposition solution approach to the fully decentralized dynamic economic dispatch (DED) problem in a microgrid. Our approach divides the centralized DED problem into a series of sub-problems in the spatiotemporal dimensions and relies on multiple agents to solve those sub-problems. The proposed method requires no central operator intervention, preserving the decision independence and information privacy of each unit. Approximate value functions are used to describe the interaction among those sub-problems. With the approximate value functions, one agent not only knows the impact of its decision on the decisions of other agents in the same period, but also knows the impact of this decision on the decisions of its subsequent periods. Unlike the existing value function update strategy, which updates the state variables and value functions in one direction, we update the state variables and value functions in two directions based on a forward-push-back strategy. In this manner, the time-delayed problem can be solved, and the iteration speed of the algorithm is greatly improved. Moreover, the proposed algorithm does not require parameter tuning and has good accuracy and adaptability. Numerical simulations for multiple cases demonstrate the effectiveness of the proposed algorithm.