Robust Energy Management System Based on Interval Fuzzy Models

Abstract

Energy management systems (EMSs) are used for operators to optimize, monitor, and control the performance of a power system. In microgrids, the EMS automatically coordinates the energy sources aiming to supply the demand. The coordination is carried out considering the operating costs, the available energy, and the generation and transmission capabilities of the grid. With this purpose, the available energy of the sources is predicted, and the operating costs are minimized. Thereby, an optimal operation of the microgrid is achieved. Often, the optimization procedure is executed throughout a receding horizon (model predictive control approach). Such approach provides some robustness to the microgrid operation. But, the high variability of the nonconventional energy sources makes the prediction task very complex. As a consequence, the reliable operation of the microgrid is compromised. In this paper, a scenario-based robust EMS is proposed. The scenarios are generated by means of fuzzy interval models. These models are used for solar power, wind power, and load forecasting. Since interval fuzzy models provide a range rather than a trajectory, upper and lower boundaries for these variables are obtained. Such boundaries are used to formulate the EMS as a robust optimization problem. In this sense, the solution obtained is robust against any realization of the uncertain variables inside the intervals defined by the fuzzy models. In addition, the original robust optimization problem is transformed into an equivalent second-order cone programming problem. Hence, desired mathematical properties such as the convexity of the optimization problem might be guaranteed. Therefore, efficient algorithms, based, e.g., on interior-point methods, could be applied to compute its solution. The proposed EMS is tested in the microgrid installed in Huatacondo, a settlement located at the north of Chile.