Robust Energy Management System for a Microgrid Based on a Fuzzy Prediction Interval Model

Abstract

Microgrids have emerged as an alternative to alleviate increasing energy demands. However, because microgrids are primarily based on nonconventional energy sources (NCES), there is high uncertainty involved in their operation. The aim of this paper is to formulate a robust energy management system (EMS) for a microgrid that uses model predictive control theory as the mathematical framework. The robust EMS (REMS) is formulated using a fuzzy prediction interval model as the prediction model. This model allows us to represent both nonlinear dynamic behavior and uncertainty in the available energy from NCES. In particular, the uncertainty in wind-based energy sources can be represented. In this way, upper and lower boundaries for the trajectories of the available energy are obtained. These boundaries are used to derive a robust formulation of the EMS. The microgrid installed in Huatacondo was used as a test bench. The results indicated that, in comparison with a nonrobust approach, the proposed formulation adequately integrated the uncertainty into the EMS, increasing the robustness of the microgrid by using the diesel generator as spinning reserve. However, the operating costs were also slightly increased due to the additional reserves. This achievement indicates that the proposed REMS is an appropriate alternative for improving the robustness, against the wind power variations, in the operation of microgrids.