Local and regional photovoltaic power prediction for large scale grid integration: Assessment of a new algorithm for snow detection

Abstract

ABSTRACTLarge‐scale grid integration of photovoltaic (PV) power requires forecast information on the expected PV power for all levels of electricity supply systems. Regional PV power forecasts provide the basis for grid management and trading of PV power on the energy market. On the local scale, smart grid applications define a sector with increasing need for PV power forecasting. Here, we present and evaluate new and enhanced features of the regional PV power prediction system of the University of Oldenburg and Meteocontrol GmbH. The basic approach to predict regional PV power with an hourly resolution of up to 2 days ahead is based on global model forecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and includes explicit physical modelling steps to convert the predicted solar irradiance into PV power. The proposed new, empirical approach aims at improving PV power predictions during periods of snow cover, where the original forecasts usually show a strong overestimation of the power production. The approach integrates measured PV power production data and additional meteorological forecast parameters. The new approach for power forecasting has been evaluated against the operational forecasts for the control area of the German transmission system operator ‘50Hertz Transmission GmbH’ for a 1‐year period. The root mean square error (rmse) of the forecasts could be reduced from 4.9% to 3.9% for intra‐day forecasts, and from 5.7% to 4.6% for day‐ahead forecasts. The largest improvement was found during January, where the rmse could be reduced by more than half by applying the proposed algorithm for snow detection. Copyright © 2011 John Wiley & Sons, Ltd.