TY - BOOK
T1 - Wind power forecasting accuracy and uncertainty in Finland
AU - Holttinen, Hannele
AU - Miettinen, Jari J.
AU - Sillanpää, Samuli
PY - 2013
Y1 - 2013
N2 - Wind power cannot be dispatched so the production levels
need to be forecasted for electricity market trading.
Lower prediction errors mean lower regulation balancing
costs, since relatively less energy needs to go through
balance settlement. From the power system operator point
of view, wind power forecast errors will impact the
system net imbalances when the share of wind power
increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
Regulating Power Market.
In this publication short term forecasting of wind power
is studied mainly from a wind power producer point of
view. The forecast errors and imbalance costs from the
day-ahead Nordic electricity markets are calculated based
on real data from distributed wind power plants.
Improvements to forecasting accuracy are presented using
several wind forecast providers, and measures for
uncertainty of the forecast are presented.
Aggregation of sites lowers relative share of prediction
errors considerably, up to 60%. The balancing costs were
also reduced up to 60%, from 3 /MWh for one site to
1-1.4 /MWh to aggregate 24 sites. Pooling wind power
production for balance settlement will be very
beneficial, and larger producers who can have sites from
larger geographical area will benefit in lower imbalance
costs. The aggregation benefits were already significant
for smaller areas, resulting in 30-40% decrease in
forecast errors and 13-36% decrease in unit balancing
costs, depending on the year. The resulting costs are
strongly dependent on Regulating Market prices that
determine the prices for the imbalances. Similar level of
forecast errors resulted in 40% higher imbalance costs
for 2012 compared with 2011.
Combining wind forecasts from different Numerical Weather
Prediction providers was studied with different
combination methods for 6 sites. Averaging different
providers' forecasts will lower the forecast errors by 6%
for day-ahead purposes. When combining forecasts for
short horizons like the following hour, more advanced
combining techniques than simple average, such as Kalmar
filtering or recursive least squares provided better
results.
Two different uncertainty quantification methods, based
on empirical cumulative density function and kernel
densities, were analysed for 3 sites. Aggregation of wind
power production will not only decrease relative
prediction errors, but also decreases the variation and
uncertainty of prediction errors.
AB - Wind power cannot be dispatched so the production levels
need to be forecasted for electricity market trading.
Lower prediction errors mean lower regulation balancing
costs, since relatively less energy needs to go through
balance settlement. From the power system operator point
of view, wind power forecast errors will impact the
system net imbalances when the share of wind power
increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
Regulating Power Market.
In this publication short term forecasting of wind power
is studied mainly from a wind power producer point of
view. The forecast errors and imbalance costs from the
day-ahead Nordic electricity markets are calculated based
on real data from distributed wind power plants.
Improvements to forecasting accuracy are presented using
several wind forecast providers, and measures for
uncertainty of the forecast are presented.
Aggregation of sites lowers relative share of prediction
errors considerably, up to 60%. The balancing costs were
also reduced up to 60%, from 3 /MWh for one site to
1-1.4 /MWh to aggregate 24 sites. Pooling wind power
production for balance settlement will be very
beneficial, and larger producers who can have sites from
larger geographical area will benefit in lower imbalance
costs. The aggregation benefits were already significant
for smaller areas, resulting in 30-40% decrease in
forecast errors and 13-36% decrease in unit balancing
costs, depending on the year. The resulting costs are
strongly dependent on Regulating Market prices that
determine the prices for the imbalances. Similar level of
forecast errors resulted in 40% higher imbalance costs
for 2012 compared with 2011.
Combining wind forecasts from different Numerical Weather
Prediction providers was studied with different
combination methods for 6 sites. Averaging different
providers' forecasts will lower the forecast errors by 6%
for day-ahead purposes. When combining forecasts for
short horizons like the following hour, more advanced
combining techniques than simple average, such as Kalmar
filtering or recursive least squares provided better
results.
Two different uncertainty quantification methods, based
on empirical cumulative density function and kernel
densities, were analysed for 3 sites. Aggregation of wind
power production will not only decrease relative
prediction errors, but also decreases the variation and
uncertainty of prediction errors.
KW - wind power
KW - wind energy
KW - forecasting
KW - uncertainty electricity market
KW - imbalance costs
M3 - Report
T3 - VTT Technology
BT - Wind power forecasting accuracy and uncertainty in Finland
PB - VTT Technical Research Centre of Finland
CY - Espoo
ER -