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

Abstract

Large-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.

Weather Modeling and Forecasting of PV Systems Operation

“…This corresponds to an error reduction factor of about 0.7 for the region of a size of 120 9 200 km. A more recent study by the Lorenz's team (Lorenz et al 2011) evaluates enhanced features of the regional PV power prediction system of the University of Oldenburg and Meteocontrol GmbH. As in Lorentz (2009) the study is based on forecasts of global solar irradiance and temperature provided by ECMWF, but this time 77 PV systems from Southern Germany are considered.…”

“…Thus, 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 (Lorenz et al 2011). Meanwhile, the geographic area of interest for forecasting can vary from a large area over which electricity supply and demand must be balanced to a much smaller region where grid congestion must be managed (Pelland 2011).…”

Overview of Irradiance and Photovoltaic Power Prediction

“…An example for statistical post-processing of PV power predictions is given in Lorenz et al (2012b). The proposed empirical approach aims at improving PV power predictions during periods of snow cover, where the original forecasts often show a strong overestimation.…”

Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

“…Nevertheless, despite the fact that future contributions of PV plants to the global electricity consumption will be comparable to that corresponding to wind farms, short-term forecasting models for PV plants are in their early stages. Most of the published works corresponding to short-term forecasting models for PV plants are oriented to solar radiation predictions [6][7][8][9], while only a few works describe models aimed at directly forecasting the hourly power production in PV plants [10][11][12][13][14][15][16][17]. Most of these published models are based on artificial neural networks (ANNs).…”

“…In all these works describing forecasting models with horizons covering 24 h, some forecasted weather variables (such as global solar radiation, temperature, relative humidity or cloudiness, obtained from a NWP tool), are used as inputs in the forecasting model. Even these forecasted weather values are used in [16] to forecast the hourly power production for all PV plants in a local or regional scale. Genetic programming of evolution of fuzzy rules has been proposed in [17] to estimate the output of a PV plant, allowing the selection of the best forecasting model.…”