A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning

摘要

This paper presents a chance constrained information gap decision model formulti-period microgrid expansion planning (MMEP) considering two categories ofuncertainties, namely random and non-random uncertainties. The main task ofMMEP is to determine the optimal sizing, type selection, and installation timeof distributed energy resources (DER) in microgrid. In the proposedformulation, information gap decision theory (IGDT) is applied to hedge againstnon-random uncertainties of long-term demand growth. Then, chance constraintsare imposed in the operational stage to address the random uncertainties ofhourly renewable energy generation and load variation. The objective of chanceconstrained information gap decision model is to maximize the robustness levelof DER investment meanwhile satisfying a set of operational constraints with ahigh probability. The integration of IGDT and chance constrained program,however, makes it very challenging to compute. To address this challenge, wepropose and implement a strengthened bilinear Benders decomposition method.Finally, the effectiveness of proposed planning model is verified through thenumerical studies on both the simple and practical complex microgrid. Also, ournew computational method demonstrates a superior solution capacity andscalability. Compared to directly using a professional mixed integerprogramming solver, it could reduce the computational time by orders ofmagnitude.