Modeling and forecasting energy flow between national power grid and a solar-wind-pumped-hydroelectricity (PV-WT-PSH) energy source

The structure of modern energy systems has evolved based on the assumption that it is the demand side which is variable, whilst the supply side must adjust to forecasted (or unforecasted) changes. But the increasing role of variable renewable energy sources (VRES) has led to a situation in which the supply side is also becoming more and more unpredictable. To date, various approaches have been proposed to overcome this impediment. This paper aims to combine mixed integer modeling with an Artificial Neural Networks (ANN) forecasting method in order to predict the volume of energy flow between a local balancing area which is using PV-WT-PSH and the national power system (NPS). Calculations has been performed based on the hourly time series of wind speed, irradiation and energy demand. The results indicate that both probabilistic and ANN models generate comparably accurate forecasts; however, the opportunity for improvement in the former appears to be significantly greater. The mean prediction error (for a one hour ahead forecasts) for the best model was 0.15 MW h, which amounts to less than 0.2% of a mean hourly energy demand of the considered energy consumer. The proposed approach has huge potential to reduce the impact of VRES on the NPS operation as well as can be used to facilitate the process of their integration and increase their share in covering energy demand.