A Decomposition-Coordination Planning Method for Flexible Generation Resources in Isolated Microgrids

Abstract

Low computational efficiency is a general drawback of the existing mixed integer programming (MIP)-based algorithms used for determining the size of flexible generation resources (FGRs), e.g., microturbines (MTs) and battery storage systems (BSs), for isolated microgrids (Iμ Gs). The simulation of these algorithms can consume dozens of hours, with large quantities of stochastic scenarios considered. In this paper, a decomposition-coordination optimization method is proposed to determine the optimal capacities of the FGRs accurately and efficiently when more than hundreds of stochastic scenarios exist. For energy balancing of the I μG, a worst-case scenario is selected from the stochastic scenarios to determine the feasible capacity range of the MT. Based on the idea to divide the stochastic scenarios into the power-deficiency and power-surplus scenario set, the two scenario sets are separately considered in the decomposition step to realizing power balancing for the I μG. The coordination step adopts the pattern search (PS) technique to obtain the optimal capacities of the FGRs with the intent of minimizing the total capital cost of the I μG. The simulations are performed to validate the accuracy of the proposed method. Relative to the general MIP model, the proposed method has nearly identical accuracy and better computational performance.