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An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Control; EMS; Microgrid; MPC; Simulation Acoustic generators; Electric load flow; Energy management; Forecasting; Microgrids; Model predictive control; Photovoltaic cells; Predictive control systems; Time series; Wind; Case-studies; Double exponential; Optimal management; Optimal power flows; Photovoltaic systems; Sampling data; Time series predictors; Wind speed forecasting; Energy management systems
Optimal management of a microgrid with radiation and wind-speed forecasting: A case study applied to a bioclimatic building
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P18-FR-2378, P18-RT-1193; UAL18-TIC-A020-B; Consejo Nacional de Ciencia y Tecnología, CONACYT: 2015-01-786, PCI2019-103378; Ministerio de Economía y Competitividad, MINECO: RTI2018-095993-B-I00; CYTED Ciencia y Tecnología para el Desarrollo, CYTED; Junta de Andalucía