A scenario-based economic-stochastic model predictive control for the management of microgrids

The world's electricity generation is heavily dependent on the consumption of fossil fuels. Electric generation from renewable resources is necessary due to the imperative need to reduce greenhouse gases to avoid a climate crisis. These resources exhibit random and intermittent behaviour. Therefore, there is a need to develop new management and control tools for these insertions into the current electricity system. Microgrids have become an effective tool to solve this problem, where these control systems play a principal role. For this reason, an optimal control structure consisting of two Model Predictive Control strategies is proposed for a microgrid Energy Management System. The first controller aims to optimise the microgrid's economic performance under an established criterion, using nominal forecasts of the disturbances on the system, such as the energy generated by renewable resources. The second is a stochastic approach using scenario-based methods to consider forecast errors in the nominal predictions used for the disturbances. The simulations were carried out on a microgrid model corresponding to the National Technological University, Reconquista Regional Faculty, highlighting that actual samples of energy consumption are available. It is worth noting that with the proposed structure, optimal solutions are obtained considering the random behaviour of the disturbances, without making assumptions about the distribution functions of the random variables. Moreover, it applies to different scales of microgrids.