Forecasting Tool for Spot Market Trading with Concentrating Solar Power (CSP) Plants

  Penalty per MWh depending on relative forecasting error (upper plot), DNI daily sum (lower left plot), and DNI volatility (lower right plot) (Source: Kraas et al., 2011)

Forecasts of power production are necessary for the electricity market participation of 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. In this research project, we study the mitigation impact on deviation penalties of an electricity production forecasting tool for the 50 MW_el parabolic trough plant Andasol 3 in Spain.

CSP plants’ advantage relative to wind turbines and photovoltaic cells is the possibility to implement thermal energy storage systems and to generate dispatchable energy. Still, the predictability of electricity production from CSP plants is limited by the forecasting accuracy of DNI. Therefore, they cannot operate on day-ahead electricity markets without bearing the risk of paying penalties for deviating from the scheduled generation, diminishing the expected profit of the plant and thus reducing the competitiveness of this renewable energy technology.

In Spain, electricity markets are divided in day-ahead and intraday market sessions, whereas day-ahead market participation requires a power production forecast for the following day. This forecast has to be in an hourly resolution and must be announced to the market operator before 10 a.m. each day. For a CSP plant, this means that a 38-hour site-specific weather forecast is required to calculate the electricity production for market sale by means of a power plant model. Deviations from the production schedule may lead to penalties. Therefore, the accuracy of forecasting DNI, which is a main input factor for the optimization of CSP facilities, is important also from an economic perspective.

Simulating the economic results of forecast utilization for renewable energy producers has been done mostly for wind energy. Our study is part of the development of an electricity production forecasting tool for CSP power plants in the 50 to 200 MW_el range. Therefore, power plant characteristics and the production model of the Andasol 3 plant are assumed. Andasol 3 is a 50 MW_el parabolic trough power plant which is combined with a molten salt thermal storage for 7.5 hours of full-load operation. Based on solar irradiance forecasts this forecasting system is designed for participation in the day-ahead electricity market. We elaborate the estimated economic profitability of such a forecasting tool. In particular, the aim is to reveal whether a DNI forecasting tool would enhance the profitability of operating at the electricity market by avoiding, or at least reducing, penalty charges and thus reduce the costs of the CSP technology.

References

Kraas B., Schroedter-Homscheidt M., Pulvermüller B., Madlener R. (2011). Economic Assessment of a Concentrating Solar Power Forecasting System for Participation in the Spanish Electricity Market, FCN Working Paper No. 12/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.