impact of frequency control supply by wind turbines on balancing costs
DESCRIPTION
Impact of Frequency Control Supply by Wind Turbines on Balancing Costs. EWEA Conference Copenhagen , 18th April 2012 Malte Jansen (M.Eng) Fraunhofer IWES, Kassel [email protected]. Outline. Security Level. 12. Introduction Data & Model Offering Control Reserve - PowerPoint PPT PresentationTRANSCRIPT
Impact of Frequency Control Supply by Wind Turbines on Balancing Costs
EWEA Conference
Copenhagen, 18th April 2012
Malte Jansen (M.Eng)
Fraunhofer IWES, Kassel
2
Intrayday Forecast
Offer Regulating Power (EP)
Offer Regulating Power (CP)
Economic Impact
Lost Energy
Simulations
Regulating Power Potential
Day-Ahead Forecast
Regulating Power Data
Power Exchange Data
Scenario / Inputparameter
How would costs for the provision of control reserve then change?
How big is the control reserve potential of wind turbines in Germany?
Variation of Input Parameters Product Length Security Level Bidding Strategy
Outline
Introduction
Data & Model
Offering Control Reserve
Economic Impact
Results 0
2
4
6
8
10
12
99%99.9%99.99%
95%Security Level
3
Wind Turbines Participation on Control Reserve Markets
Control Reserve Markets: Secondary Markets (positive and negative)
Daily Tendering Tertiary Markets (positive and negative) Primary Markets are not considered
Time: 07/2010 – 12/2010
Offering within the German EEG (Renewable Energy Sources Act)
4
Balance Control Available Active Power
Modelling possible proof methods
10 MW
20 MW
30 MW
40 MW
14:00 16:00 18:00 20:0012:00
Activation Delivery Deactivation
Proof:
Control reserve = schedule– actual feed-in
10 MW
20 MW
30 MW
40 MW
14:00 16:00 18:00 20:0012:00
Activation Delivery Deactivation
Proof:
Control reserve= available active power– actual feed-in
OFFERING REGULATING POWER
6
Day-Ahead Forecast vs. Actual Feed-In
Security levels:95 %99 %99.9 % 99.99 %
actual feed-in
7
Intraday Forecast (1h) vs. Actual Feed-In
Security levels:95 %99 %99.9 % 99.99 %
actual feed-in
High losses in low wind scenarios
Low losses in high wind scenarios
Increased forecast quality Decreased leadtime Consideration of pre-errors
Offering RP more efficient for high wind scenarios In addition: Low energy prices
during high wind feed-in
8
Control Reserve Offer
Variation of input parameters
Security level Block length Lead time Bidding strategy
Result will lead to a trade-off between system security and economic efficiency
Actual feed-in
Secure day-ahead forecast
Secure intraday forecast
Offer control reserve
Po
we
r (n
orm
aliz
ed
)
00:00
0.05
0.1
0.15
0.2
0.25
0.3
Time
04:00 08:00 12:00 16:00 20:00
ECONOMIC IMPACT
10
Capacity in MW
Energ
y P
rice
in €
/MW
h
Altering Merit-Order-Lists
Merit-Order-List without Wind Merit-Order-List with Wind
Max
New MO-Positions due to Market-Entry of Wind TurbinesParticipation
WT on FC Market Min
Energ
y P
rice
in €
/MW
h
Capacity in MW
RESULTS
12
Off
era
ble
Regu
lati
ng P
ow
er
Rese
rve [
TW
h]
Results (I) – Control Reserve Potential
1h 4h 24hProduct length
0
2
4
6
8
10
12
99%99.9%99.99%
95%Security Level
13
Results (II) – Product Length & Security Level
Results for negative secondary reserve bids
95 % 99 % 99.9 % 99.99 %
x 106
0
1
2
3
Security Level
Cost
Redu
ctio
n [
€]
1 HOUR
4 HOURS
24 HOURS
14
Results (III) – Method to proof the delivery of Regulating Power
Cost-saving potential: Available active power
Increased cost efficiency compared to balance control
Improvement in efficiency +12.1 @ 95% +20.2 @ 99.99%
Higher market share of wind farms
Results for negative secondary reserve bids
Available Active PowerBalance Control
*106
0
1
2
3
95% 99% 99.9% 99.99%
Cost
Reduct
ion [
€]
Security Level
15
Conclusions
Wind Farm participation in control reserve markets … is economically feasible can substitute fossil fuel fired power plants will increase competition in the market can generate additional revenue for the windfarm operators
Problems to overcome are: Proof delivery of services needs to be adjusted
