Abstract
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.
Highlights
The energy supplied for microgrids is produced mainly from renewable energy sources since they provide energy without significant carbon dioxide emissions
The energy management systems (EMS) performance was measured in terms of (i) efficiency, by saving the computational time, and (ii) effectiveness, by providing the final values of the power cost associated with the minimization of Equation (11), with an error tolerance of 1 × 10−9 for the constrains and 1 × 10−9 for the target function
The Model Predictive Control (MPC) method outperforms the results provided by OPF for the whole simulation, i.e., it obtains a better optimization of the power flows used in the microgrid, which stands to reduce the cost of power for the analyzed time stages
Summary
The energy supplied for microgrids is produced mainly from renewable energy sources since they provide energy without significant carbon dioxide emissions. With the growth of microgrids and their implementation in electrical transport systems, the development of new control strategies is required to manage aspects related to both the intermittence and distribution of the generation as well as new consumption profiles [4]. The algorithm that is implemented for control in energy management systems (EMS) must be developed with novel techniques to improve the efficiency of microgrids [5,6]. The microgrid requires an EMS to efficiently manage the power flows from the microgrid’s different power sources. EMSs are the key to efficiently manage the energy in Renewable Distributed Generation (RDGs) where microgrids are integrated. Several works have been carried out on the related control and optimization techniques
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