Powering Europe - European Wind Energy Association

tablE 1: ClassifiCation of winD PowER foRECast MEthoDs aCCoRDinG to tiME sCalEs RElEVant foR PowER systEM
oPERation
Uses Regulation
Real-time dispatch
decisions
Phenomena Large eddies, turbulent
mixing transitions
5-60 min 1-6 hours Day-ahead seasonal long-term
Methods Largely statistical, driven by
recent measurements
power. A study from the US (California) [GE/AWST
2007] has quantified the cost-benefit ratio to be
1:100. Large additional investments are required to
effectively implement centralised forecast systems,
especially investments in observation networks in order
to provide the necessary meteorological and operational
data. Such investments are justified by the significant
reductions they entail to the operational costs
of power generation.
Time horizons for the relevant system operation actions
are listed in Table 1. There are distinct predictable
meteorological phenomena linked to each horizon.
Professional forecast providers adjust prediction
methods to these phenomena.
The nature of the wind power forecast error statistics
leads to the following important observation: the total
amount of balancing energy stems from the average
forecast error; however, the need for reserve power
is dependent mainly on the extreme forecast error.
Therefore, apart from using the best available forecasts,
the method recommended for reducing the required
balancing power (and thus reserve plant capacity)
is to keep the forecast error as low as possible by
intra-day trading in combination with very short-term
forecasting (2-4 hours ahead) [Lange, 2009].
2.4 Additional balancing costs
The overview of studies investigating wind penetrations
of up to 20% of gross demand (energy) in national
chApTEr 3 powersystemoperationswithlargeamountsofwindpower
Load following, unit
commitment for next
operating hour
Fronts, sea breezes,
mountain-valley circulations
Combination of statistical
and NWP models
Unit commitment and
scheduling, market trading
Low and high pressure
areas, storm systems
Mainly NWP with
corrections for systematic
bias
Resource planning
contingency analysis
Climate oscillations, global
warming
Based largely on analysis
of cyclical patterns
or regional power systems [Holttinen, 2009], already
mentioned in Chapter 2 concludes that increases in
system operating costs arising from wind variability
and uncertainty amount to about €1-4/MWh wind energy
produced. This cost is normalised per MWh of
wind energy produced and refers to the wholesale
price of electricity in most markets.
The studies calculate the additional costs of adding
different amounts of wind power as compared to a
situation without any. The costs of variability are also
addressed by comparing simulations assuming constant
(flat) wind energy to simulations with varying
wind energy.
Both the allocation and the use of reserves create
additional costs. As mentioned in Chapter 2, the consensus
from most studies made so far is that for
wind energy penetration levels up to 20%, the extra
reserve requirements needed for larger amounts of
wind power is already available from conventional
power plants in the system. That is, no new reserves
would be required, and thus additional investments in
new plants wouldn’t be necessary. Only the increased
use of dedicated reserves, or increased part-load
plant requirement, will cause extra costs (energy part)
– and there is also an additional investment cost related
to the additional flexibility required from conventional
plants. The costs themselves depend on the
marginal costs for providing regulation or mitigation
methods used in the power system as well as on the
power market rules.
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