Robust Optimization and Affine Arithmetic for Microgrid Scheduling under Uncertainty

Optimization analyses are commonly used in microgrids to identify the most efficient and reliable operation of the available energy resources. Unfortunately, most of the times these programming problems rely on input parameters which are not accurately known. In this context, advanced computing paradigms for solving uncertainty optimization problems represent the most promising enabling methodology. These techniques may show their effectiveness during both the dispatch and the pre-dispatch phase, when operators need to solve the unit-commitment and the economic dispatch problems. To this aim, this paper discusses and compares experimentally some promising existing alternatives to deterministic methods to deal with the solution of optimization problems in the presence of data uncertainty.