A Multi-Objective Optimization Scheme for Resilient, Cost-Effective Planning of Microgrids

Natural disasters and cascading events have historically caused severe disruptions of the electric power system. For instance, in 2012, hurricane Sandy left over 8 million people in darkness and caused total damage costing $65 billion. Under emergency conditions, microgrids can help the electric power system recover critical loads, such as hospitals, data centers, and water pumping stations. While the literature has mostly focused on the utilization of existing microgrids, the idea of planning for future microgrids in combination with switching operations to make the grid more resilient against devastating events is investigated in this study. This work aims to simultaneously maximize the resiliency of distribution networks - in terms of service to the critical loads - and minimize the dispatchable generation capacity of microgrids. The considered microgrid model entails dispatchable power generators, renewable energy resources, and electrical energy storage systems (ESS) to serve consumers. Considering the topological and operational limitations, the robust optimization scheme optimizes the objectives by the effective selection of the node for microgrid connection and the minimum change in its generation capacity. While the problem is modeled as a multi-objective, mixed-integer linear programming (MO-MILP) problem, the results show more than 99% accuracy compared to the exact results of an exhaustive search algorithm. Numerical tests are performed on the IEEE 37-node test feeder and the IEEE 123-node test feeder to assess the proposed method's performance. Given the accuracy, computation time efficacy, and the generic formulation of the problem, the optimization scheme can be easily applied to any real network.