Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance

Abstract

Energy Management Systems (EMS) strategies aim at matching energy production with the request, as they are off-phased and highly variable whenever LV networks are considered. This work demonstrates how an EMS based on a Model Predictive Control (MPC) strategy can perform better improving the accuracy of the load forecasting algorithm. To that aim a novel approach is presented, that is characterized by the correlation between real time and historical consumption data. The technique has been tested for over a year of operation. Three test cases have been compared (low error load forecasting, higher error load forecasting and correlation-corrected load forecasting) and techno-economic advantages have been obtained with the new approach. Indeed, a reduction of 14,1% in energy unbalance with the grid and of 8,7% in annual operational costs have been obtained when the load forecast correction is performed. Moreover, the critical components of the system (Electrochemical Energy Storage and Fuel Cell) result to work in less stressful operating conditions, another positive effective of the technique.