Robust Optimal Scheduling with a Grid-Connected Microgrid Installed in a Large-Scale Power Consumer

This paper presents a methodology to determine a robust optimal operational schedule of the grid-connected microgrid installed in a large-scale electricity consumer. We propose a novel two-stage robust optimization (TSRO) model to address the worst case under the uncertainty set of consumer’s load and renewable energy resources. The proposed model is formulated as a mixed integer linear programming considering technical constraints of the energy storage system (ESS) and distributed conventional generator (DG). The objective of the TSRO problem is to minimize the customer’s total cost including the operation cost of DGs, the electric bill with demand and energy charges, and the operation cost of ESS. Benders decomposition and outer approximation algorithms are applied as the solution methodologies of master-problem and sub-problem of TSRO, respectively. The proposed TSRO model is validated in a test system by analyzing the sensitivity to uncertainty budget ratio, the effect of the demand charge consideration, the appropriate historical peak load, and the real-time cost efficiency. The optimization modeling is implemented using Matlab with CPLEX 12.6.