Journal
SUSTAINABLE ENERGY GRIDS & NETWORKS
Volume 17, Issue -, Pages -Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.segan.2018.100183
Keywords
Smart microgrid; Renewables; Battery; Identification; Model predictive control; Optimization
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Battery systems are becoming more and more widespread for stationary applications both at power grid level and user level. In this second context, small battery-based storage systems are frequently proposed for the installation in combination with local renewable sources, to increase the self-consumption of the locally generated renewable energy and, in some cases, even to enable the user to disconnect from the main network. Increasing use of storage devices for stationary applications implies a more detailed characterization of the behavior at the terminals of these systems. In the same time, the development of new Energy Management Systems is required in order to take advantage of both local information and data from the service provider, such as radiation forecasts from weather forecast services. In this paper, a new EMS is proposed, characterized by a two-level architecture: the higher level, based on a receding horizon control scheme, optimally schedules the operation of the storage by using information on radiation from a forecast service provider; the lower level implements a heuristic procedure (if-then) on a low-cost local controller, in order to perform corrective actions. The proposed architecture has been tested on a real case study. (C) 2019 Elsevier Ltd. All rights reserved.
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