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.
For off-grid microgrids in remote areas (e.g., sea islands), proper configuring the battery energy storage system (BESS) is of great significance to enhance the power-supply reliability and operational feasibility. This paper presents a life cycle planning methodology for BESS in microgrids, where the dynamic factors such as demand growth, battery capacity fading, and components' contingencies are modelled under a multi-timescale decision framework. Under a yearly timescale, the optimal DER capacity allocation is carried out to meet the demand growth, while the investment decisions of BESS are made periodically to yield the optimal sizing, type selection, and replacement plans of BESS during the entire lifetime of microgrid. Then, under an hourly time-scale, the long-term probabilistic sequential simulation is adopted to comprehensively evaluate the investment decisions and derive detailed operation indicators. Moreover, a decompositioncoordination algorithm is developed to address the presented planning model, which iteratively strengthens the feasible space of investment decision model by substituting the operation indicators until an acceptable sub-optimal solution is obtained. Case studies on a wind-solar-diesel microgrid in Kythnos Island, Greece, illustrate the effectiveness of the proposed method. This study provides a practical and meaningful reference for BESS planning in off-grid microgrids.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.