The great amount of support schemes that initially fueled the fast, and often uncontrollable, Renewable Energy Sources (RESs) growth have been strongly reduced or revoked in many countries. Currently, the general trend is to try to equate the RESs to the traditional power plants. From the energy market point of view, this entails exposing the RESs more to the market competition and mechanics. This could be done, for example, requiring the stochastic RESs to submit a production schedule in advance and to be financially responsible for any deviation from this. This could push the Wind Farm (WF) operators to make accurate forecasts, fostering the electric system resiliency and an efficient use of balancing resources. From the forecasting point of view this is not a trivial problem, since the schedule submission is often due 10–12 hours before the actual delivery. Since forecast errors are unavoidable, the submitted schedule could turn out to be infeasible, forcing the WF to recur to correcting actions which are generally costly. Focusing on this, the analysis estimates the revenue reduction which would affect a WF operating in the energy market due to forecast errors. To do this in a realistic way, a case study is selected, and realistic forecast scenarios are generated using a copula approach. Important forecast error features like autocorrelation and dependency on forecasted power level and forecast lead-time are modeled. The revenue reduction due to balancing actions is calculated on an annual basis, using typical days, derived through the production data clustering. Losses ranging from 5% to 35% has been found, depending on the days and on the market prices. A sensitivity analysis to the costs of balancing actions is performed. In this way, the effect of different market architectures and, possibly, of different RESs penetration level is considered in the analysis. Finally, the effectiveness of the curtailment as a technique to reduce the impact of forecast errors in highly penalizing market environments is assessed.

Impact of forecast uncertainty on wind farm profitability

Frate G. F.;Ferrari L.;Desideri U.
2019-01-01

Abstract

The great amount of support schemes that initially fueled the fast, and often uncontrollable, Renewable Energy Sources (RESs) growth have been strongly reduced or revoked in many countries. Currently, the general trend is to try to equate the RESs to the traditional power plants. From the energy market point of view, this entails exposing the RESs more to the market competition and mechanics. This could be done, for example, requiring the stochastic RESs to submit a production schedule in advance and to be financially responsible for any deviation from this. This could push the Wind Farm (WF) operators to make accurate forecasts, fostering the electric system resiliency and an efficient use of balancing resources. From the forecasting point of view this is not a trivial problem, since the schedule submission is often due 10–12 hours before the actual delivery. Since forecast errors are unavoidable, the submitted schedule could turn out to be infeasible, forcing the WF to recur to correcting actions which are generally costly. Focusing on this, the analysis estimates the revenue reduction which would affect a WF operating in the energy market due to forecast errors. To do this in a realistic way, a case study is selected, and realistic forecast scenarios are generated using a copula approach. Important forecast error features like autocorrelation and dependency on forecasted power level and forecast lead-time are modeled. The revenue reduction due to balancing actions is calculated on an annual basis, using typical days, derived through the production data clustering. Losses ranging from 5% to 35% has been found, depending on the days and on the market prices. A sensitivity analysis to the costs of balancing actions is performed. In this way, the effect of different market architectures and, possibly, of different RESs penetration level is considered in the analysis. Finally, the effectiveness of the curtailment as a technique to reduce the impact of forecast errors in highly penalizing market environments is assessed.
2019
978-0-7918-5872-1
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1015443
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact