Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

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.

Sliding Mode Control with Minimization of the Regulation Time in the Presence of Control Signal and Velocity Constraints

“…In this subsection, our goal is to derive an admissible set of the switching curve parameters, for which conditions (24) and (35) are fulfilled. Therefore, inequality (28) has to be true and ι has to satisfy both restrictions (33) and (40).…”

“…Now, we will derive the minimal regulation time, for which inequality (24) is fulfilled for any t ≥ 0. Moreover, we will decide which strategy is the most beneficial: the one in which the switching curve stops during the control process or the one in which the sliding line moves for the whole control process.…”

“…On the other hand, it is well known that varying the switching frequency in this manner can have unpleasant auditory effects. When dealing with control systems in which maintaining constraints is one of the main factors, model predictive control (MPC) [23,24] is one of the main solutions. However, it has some drawbacks, as it requires online optimization, which makes it difficult to apply to complex systems with short time constants (as the computational power required to perform the optimization can become prohibitively expensive in these cases).…”