enfor tryggvi jónsson, enfor a/s & dtu informatics forecasting day-ahead electricity prices and...
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ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Forecasting Day-ahead Electricity Prices and Regulation Costs in Markets With Significant Wind Power Penetration
March 19th 2009
EWEC 2009 – MarseilleTryggvi Jónsson ENFOR A/S & DTU Informatics
Pierre Pinson DTU Informatics
Henrik Madsen DTU Informatics
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Intro The nature of wind power places it inside the equilibrium at
almost all times.
Wind power enters the day-ahead electricity supply function as a stochastic quantity with uncertainty attached to it.
The most rapid changes in the supply function are mostly owed to wind power.
Case study: DK-1 area of Nord Pool’s Elspot
Nord Pool hydro dominated – DK-1 heavily penetrated by wind
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
On the market power of wind energy (forecasts) - I
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
On the market power of wind energy (forecasts) – II
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Model for forecasting day-ahead prices Propose a three layer approach to forecast the day-ahead price
consisting of:
i. A mapping of forecasted wind power penetration and time to the average prices
ii. Dynamical weighting of recently observed prices and prediction error together with the output from the first layer.
iii. Uncertainty estimation conditioned upon the forecasted wind power penetration and the forecasted mean price
New observations considered as soon as they become available
Older observations discounted either exponentially or by a rolling window
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Forecasting properties
Root Mean Square Error Forecasts on January 12th 2007
March 19th 2009 EWEC 2009, Marseille
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Forecasting regulation costs Imbalances on the producers side are priced by a two price
model at Elspot
Implies no regulation costs for producers bringing the system back to balance
Forecasting of regulation costs is done in two steps
i. Prediction of the sign of the system imbalance
ii. Probabilistic forecasting of the penalty
March 19th 2009 EWEC 2009, Marseille
Down regulation hoursUp regulation hours
Cost
Imbalance
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Predicting the system’s imbalance sign Predict the imbalance sign by binary classification for each
“direction”
Do not believe in linearly separable non-overlapping classes
Explanatory variables are
i. Forecasted wind power penetration
ii. Spot price forecast
iii. Time variables
iv. Import/Export forecasts
March 19th 2009 EWEC 2009, Marseille
Down regulation Up regulation
Hit rate 75.5% 76.4%
Bias 1.17% -0.20%
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Predicting the imbalance penalties Predict price intervals with certain probabilities directly
Predictions conditioned upon the forecasted sign
March 19th 2009 EWEC 2009, Marseille
Down regulation Up regulation
ENFOR
Tryggvi Jónsson, ENFOR A/S & DTU Informatics
Summary & Final Remarks The impact of wind power forecasts on the day-ahead prices is
substantial and nonlinear
Same applies for regulation costs
Wind power forecasts therefore play an important role in price forecasting
More intelligent trading with benefits for both producers and the system as a whole
More efficient risk hedging as well
Methods are operationally available and results indicate that they can be tailored to other markets
March 19th 2009 EWEC 2009, Marseille