Networked microgrids planning through chance constrained stochastic conic programming

This paper presents a chance constrained stochastic conic program model for networked microgrids planning. Under a two-stage optimization framework, we integrate a multi-site microgrids investment problem and two sets of operational problems that correspond to the grid-connected and islanding modes, respectively. To handle the uncertain nature of renewable energy generation and load variation, as well as the contingent islanding caused by external disruptions, stochastic scenarios are employed to capture randomness and a joint chance constraint is introduced to control the operational risks. A second-order conic program (SOCP) formulation is also utilized to accurately describe the AC optimal power flow (OPF) in operational problems. As the resulting mixed integer SOCP model is computationally difficult, we customize the bilinear Benders decomposition with non-trivial enhancement techniques to deal with practical instances. Numerical results on 5- and 69-bus networked microgrids demonstrate the effectiveness of the proposed planning model and the superior performance of our solution algorithm.