A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches.

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This work presents the modeling and energy management of a microgrid through models developed based on physical equations for its optimal control. The microgrid’s energy management system was built with one of the most popular control algorithms in microgrid energy management systems: model predictive control. This control strategy aims to satisfy the load demand of an office located in the CIESOL bioclimatic building, which was placed in the University of Almería, using a quadratic cost function. The simulation scenarios took into account real simulation parameters provided by the microgrid of the building. For case studies of one and five days, the optimization was aimed at minimizing the input energy flows of the microgrid and the difference between the energy generated and demanded by the load, subject to a series of physical constraints for both outputs and inputs. The results of this work show how, with the correct tuning of the control strategy, the energy demand of the building is covered through the optimal management of the available energy sources, reducing the energy consumption of the public grid, regarding a wrong tuning of the controller, by 1 kWh per day for the first scenario and 7 kWh for the last.