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 ($\text{I}\mu $ 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 $\text{I}\mu \text{G}$ , 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 $\text{I}\mu \text{G}$ . 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 $\text{I}\mu \text{G}$ . 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.

Keywords

• Battery storage
• decomposition-coordination method
• isolated microgrid
• microturbine
• optimal sizing
• renewable energy