Title :
Impacts of forecast accuracy on grid integration of renewable energy sources
Author :
Lenzi, Vasco ; Ulbig, Andreas ; Andersson, Goran
Author_Institution :
Power Syst. Lab., ETH Zurich, Zurich, Switzerland
Abstract :
The impact that forecast accuracy of power generation from variable renewable energy sources (RES), i.e., wind turbines and PV, has on a power system´s ability to integrate high RES energy shares is investigated. The goal is to assess the effectiveness of RES integration and the control reserves necessary for accommodating stochastic generation profiles as a function of RES forecast accuracy. A simulation study has been performed on a functionally modeled benchmark power system. High time resolution simulations were accomplished using a predictive power dispatch scheme that directly incorporates forecast information. This study shows, first, that the impact of RES forecast accuracy for RES integration is more important for power systems with high wind energy shares than for those with high PV shares. The integration of the latter is significantly less dependent on forecast accuracy. Second, the impact of forecast accuracy on control reserve needs increases with rising RES shares but stabilizes as soon as the combined installed power capacity of variable RES units reaches the yearly peak-load demand. Third, the prediction horizon length of RES in-feed forecasts was identified as an important factor for effective RES integration.
Keywords :
distributed power generation; power generation dispatch; power grids; stochastic processes; wind power plants; PV system; RES energy shares; RES in-feed forecasts; control reserves; forecast information; functionally modeled benchmark power system; grid integration; high time resolution simulations; high wind energy shares; power generation; prediction horizon length; predictive power dispatch scheme; stochastic generation profiles; variable renewable energy sources; wind turbines; Accuracy; Generators; Load modeling; Power system stability; Production; Wind forecasting; Wind power generation;
Conference_Titel :
PowerTech (POWERTECH), 2013 IEEE Grenoble
Conference_Location :
Grenoble
DOI :
10.1109/PTC.2013.6652486