Hybrid Photovoltaic Power Forecasting Algorithm for Managing Virtual Power Plants

This paper proposes a framework, based on the concept of virtual power plant, for promoting the effective participation of remote photovoltaic power generation installations in different energy markets. To this aim, the most significant challenge lies in providing accurate power forecasts for different lead times determined by the markets. To address this challenge, a hybrid forecast strategy based on day-ahead and intra-day prediction models is proposed. For each lead time, a point prediction is provided by choosing, from the outputs of prediction models, the value which most reduces the prediction uncertainty. The framework also requires the cloudiness forecast to improve the accuracy of the prediction, by classifying the days in three categories according to a cloud cover factor, namely, sunny, cloudy and overcast. The predictions are updated every 15 minutes by using real-time information of the day considered. Whenever the algorithm is executed, the generation forecast for the rest of the day is recalculated with a 15-minute resolution. The point prediction is provided with the corresponding confidence interval, which is modelled by a Laplacian distribution function. This interval is of particular importance in the context of energy markets as it allows the risk of penalties for any energy deviation to be modelled. The strategy is evaluated in a VPP working environment demonstrating the potential of the hybrid prediction algorithm.