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

Abstract

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.

Community Microgrid Planning Considering Building Thermal Dynamics

“…The SOC transition is illustrated by constraint (14). Constraint (15) and (16) denote that the output of RES should be bounded by its available capacity. The indoor temperature is constrained by (17).…”

“…The column-and-constraint generation (CCG) is leveraged to solve the problem. A chance constrained information-gap decision model for multi-stage MG planning model is presented in [15]. A customized bilinear Benders decomposition algorithm is developed to solve the model.…”

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

“…However, complex MG optimization is also proposed without considering the possibility of CPP market circumstances. Authors in [31] proposed a chance-constrained information gap decision model for multi-period MG expansion planning (MMEP). The proposed model was used to maximize the level of DERs as well as satisfying operational constraints with high probability.…”

“…The computational steps of the Jaya optimization technique depend upon the complexity at lines . The complexity of other lines (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) are superseded by these lines; the complexity of line 13 is influenced by line 15. Sub-lines 15-23 take O(1); while line 15 can iterate maximally with k iterations, and thus its cost is O(k).…”

“…MMEP [31] Maximizes the level of distributed energy resource (DER), satisfying operational constraints with high probability.…”