Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market

• Production forecasting for concentrating solar thermal power plants (Andasol 3).
• Discussion of different solar irradiance forecasting methods and typical accuracies.
• Focus on penalty payment reductions when trading in the Spanish electricity spot market.
• Penalty reductions are found to be significant, in the order of 47%.
• Benefit of more accurate forecasts extends to other areas (e.g. O&M scheduling).

Forecasts of power production are necessary for the electricity market participation of Concentrating Solar Power (CSP) plants. Deviations from the production schedule may lead to penalty charges. Therefore, the accuracy of direct normal irradiance (DNI) forecasts is an important issue. This paper elaborates the mitigation impact on deviation penalties of an electricity production forecasting tool for the 50 MWel parabolic trough plant Andasol 3 in Spain. Only few commercial DNI forecast schemes are available nowadays. One of them, based on a model output statistics (MOS) forecast for the period July 2007 to December 2009, is assessed and compared to the zero cost 2-day persistence approach, which assumes yesterday’s weather conditions and electricity generation also for the following day. The quality of the meteorological forecasts is analyzed both with forecast verification methods and from the perspective of a power plant operator. Using MOS, penalties in the study period are reduced by 47.6% compared to the 2-day persistence case. Finally, typical error patterns of existing MOS forecasts and their financial impact are discussed. Overall, the paper aims at quantifying the economic value of nowadays readily available numerical weather prediction in this use case. A special feature of our study is its focus on a real market case and the use of real data, rather than following a purely academic approach, and thus to provide some new insights regarding the economic benefit of using and improving state-of-the-art forecasting techniques.