An energy management system (EMS) determines the dispatching of generation units based on an optimizer that requires the forecasting of both renewable resources and loads. The forecasting system discussed in this paper includes a representation of the uncertainties associated with renewable resources and loads. The proposed modeling generates fuzzy prediction interval models that incorporate an uncertainty representation of future predictions. The model is demonstrated using solar and wind generation and local load data from a real microgrid in Huatacondo, Chile, for one-day ahead forecasts to obtain the expected values together with fuzzy prediction intervals to represent future measurement bounds with a certain coverage probability. The proposed prediction interval models would help to enable the development of robust microgrid EMS.

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.

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.

The existing uncertainties during the operation of processes could strongly affect the performance of forecasting systems, control strategies and fault detection systems when they are not considered in the design. Because of that, the study of uncertainty quantification has gained more attention among the researchers during past decades. From this field of study, the prediction intervals arise as one of the techniques most used in literature to represent the effect of uncertainty over the future process behavior. Thus, researchers have focused on developing prediction intervals based on the use of fuzzy systems and neural networks, thanks to their usefulness for represent a wide range of processes as universal approximators. In this work, a review of the state-of-the-art of methodologies for prediction interval modelling based on fuzzy systems and neural networks is presented. The main characteristics of each method for prediction interval construction are presented and some recommendations are given for selecting the most appropriate method for specific applications. To illustrate the advantages of these methodologies, a comparative analysis of selected methods of prediction intervals is presented, using a benchmark series and real data from solar power generation of a microgrid.