Supervisory control design for microgrids energy management optimization based on renewable generation and consumption forecasting

Solar-based electricity production has become an essential part of the general energy production in the recent years with the will to use more renewable sources. The one issue that appears is the uncertainty of the solar irradiation. It is then more complicated to predict the energy generated in the future times. The Energy Management System used on the grid schedules the energy exchanges between the devices based on the prediction of the state of the system in the next time interval. The Model Predictive Control forecasts the power produced as well as that of the energy demand from the load and defines the state of the system. In order to minimize the corresponding cost function, this forecast should be as accurate as possible, with the minimum prediction error. To address these forecasting needs, we will extract some data from a database using an algorithm directly connected to the server. And we will compute the remaining values using an accurate forecasting method, the Simple Average. Then, for this information to be even more precise, we use the Rolling Horizon approach, that enables a regular updating of the forecast. Simulation results and experiments confirm the influence of some parameters on the prediction error and hence on the cost function

Integration of a Pb-acid battery management algorithm into optimization control strategies for microgrid systems

This work suggests an integration procedure for a ESS (Energy Storage System) control algorithm into optimization control strategies minimizing cost functions for a microgrid system. This approach is based on a modification of the optimization strategy for adding absorption and flotation stages after each bulk charge to preserve the battery lifetime. These stages are computed out of the optimization program to reduce both computation complexity and convergence problems. Simulation results have confirmed the feasibility of this procedure at expenses of only a slight cost function increase, which can be assumed to preserve the battery lifetime.Peer ReviewedPostprint (published version

Integration of a Pb-acid battery management algorithm into optimization control strategies for microgrid systems

This work suggests an integration procedure for a ESS (Energy Storage System) control algorithm into optimization control strategies minimizing cost functions for a microgrid system. This approach is based on a modification of the optimization strategy for adding absorption and flotation stages after each bulk charge to preserve the battery lifetime. These stages are computed out of the optimization program to reduce both computation complexity and convergence problems. Simulation results have confirmed the feasibility of this procedure at expenses of only a slight cost function increase, which can be assumed to preserve the battery lifetime.Peer Reviewe