  This paper presents a chance constrained information gap decision model for
multi-period microgrid expansion planning (MMEP) considering two categories of
uncertainties, namely random and non-random uncertainties. The main task of
MMEP is to determine the optimal sizing, type selection, and installation time
of distributed energy resources (DER) in microgrid. In the proposed
formulation, information gap decision theory (IGDT) is applied to hedge against
non-random uncertainties of long-term demand growth. Then, chance constraints
are imposed in the operational stage to address the random uncertainties of
hourly renewable energy generation and load variation. The objective of chance
constrained information gap decision model is to maximize the robustness level
of DER investment meanwhile satisfying a set of operational constraints with a
high probability. The integration of IGDT and chance constrained program,
however, makes it very challenging to compute. To address this challenge, we
propose and implement a strengthened bilinear Benders decomposition method.
Finally, the effectiveness of proposed planning model is verified through the
numerical studies on both the simple and practical complex microgrid. Also, our
new computational method demonstrates a superior solution capacity and
scalability. Compared to directly using a professional mixed integer
programming solver, it could reduce the computational time by orders of
magnitude.
