A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.

There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC / DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications.

One of the well-known methods to share active and reactive power in microgrids (MGs) is droop control. A disadvantage of this method is that in steady state the frequency of the MG deviates from the nominal value and has to be restored using a secondary control system (SCS). The signal obtained at the output of the SCS is transmitted using a communication channel to the generation sources in the MG, correcting the frequency. However, communication channels are prone to time delays, which should be considered in the design of the SCS; otherwise, the operation of the MG could be compromised. In this paper, two new SCSs control schemes are discussed to deal with this issue: (1) a model predictive controller (MPC); and (2) a Smith predictor-based controller. The performance of both control methodologies are compared with that obtained using a conventional proportional integral-based SCS using simulation work. Stability analysis based on small signal models and participation factors is also realized. It is concluded that in terms of robustness, the MPC has better performance.

In this paper, finite-set model-predictive control (FS-MPC) methodologies for a grid-connected three-level neutral-point-clamped converter are investigated. The proposed control strategies produce fixed switching frequency, maintaining all the advantages of predictive control such as fast dynamic response, inclusion of nonlinearities and restrictions, and multivariable control using a single control loop. The first of the proposed FS-MPC strategies is based on a multiobjective cost function, designed to regulate both the inverter currents and the balancing of the dc-link capacitor voltages. The second FS-MPC strategy is derived from the first one, and it is based on a cost function that regulates only the grid current, with the balancing of the capacitor voltages being realized by controlling the duty cycles of the redundant vectors. The proposed control systems are experimentally validated using a 5-kW prototype. The experimental results show a good performance for both strategies, in steady-state and transient response, with a total harmonic distortion lower than 2% for the currents supplied to the grid.

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.