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.

A novel energy management system for a renewable based microgrid is proposed. It provides on-line set points for each generation unit, operation modes for a water supply system, and signals for consumers based on a demand side management mechanism. The smart microgrid is composed of photovoltaic panels, a wind turbine, a diesel generator, a battery bank, and a water supply system. The energy management system (EMS) minimizes the operational costs while supplying the water and electric load demands. It considers a two days ahead prediction of the weather conditions. Also, a neural network for a two days ahead electric consumption forecasting is designed. The system is implemented and tested using a real data set from a reference location. Results show the economic sense of the set points and management, for a practical implementation of the system in a specific location in Chile.

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid’s performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC).

Microgrids are the cornerstone for a new model of electrical generation based on renewable resources. Commonly microgrids are controlled with a centralised hierarchical structure, which is inherited from power systems. However, a time-scale separation between traditional fast frequency restoration and slow economic dispatch may be counterproductive in the long run because the slow long-term economic dispatch increases the prediction uncertainty. In an effort to improve the economical operation of microgrids, this work proposes a distributed model predictive control strategy for the operation of isolated microgrids based on a consensus strategy that tackles both the economic dispatch and frequency restoration over the same time-scale. The proposed controller can operate without knowledge of the microgrid's topology: instead, typical local measurements and other information from neighboring generation units are required. Experimental results demonstrate that the controller is robust to load variations and communication issues, but the plug-and-play nature of the system is preserved.