demand response – a new option for wind integration ? marian klobasa, dr. mario ragwitz fraunhofer...
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Demand Response –
A New Option for Wind Integration ?
Marian Klobasa, Dr. Mario Ragwitz
Fraunhofer Institute for Systems and Innovation
Research
European Wind Energy Conference 2006
Athens, 2. March 2006
2Demand Response for Wind Integration
Marian Klobasa
Outline
Motivation for Demand Response
Potentials for Demand Response
Simulation of Wind Energy, Electricity System and Demand
Impacts of Wind Fluctuation on Electricity Systems
3Demand Response for Wind Integration
Marian Klobasa
Benefits of Demand Response?
Improving of system reliabilityPeak load and balancing power can be reduced
Efficient electricity use by increased transparency
Reduction of price peaks and lower price volatility Increase of short term price elasticity and improvement of market-clearing Better market functioning Reduced risks for market actors
Use of demand response as an existing resource might need lower investments than new generation capacity
Studies gave evidence of substantial economical and technical potentials
Demand response increases the possibilities for wind integration when balance between supply and demand is tightening
4Demand Response for Wind Integration
Marian Klobasa
Increased Elasticity can reduce Electricity Prices
€/MWh
MWh/h
Supply Curve
Demand Curve
5Demand Response for Wind Integration
Marian Klobasa
Realistic Option?
Experiences from Scandinavia and Germany
• 24 Jan 2000 (Price peaks up to 400 €/MWh)Demand response in Sweden 200-1000 MW, in Norway 800-1100 MW
• 5 Feb 2001 (Price peaks 240 €/MWh, 9 hours over 100 €/MWh)DR in Sweden up to 700 MW, in Norway up to 500 MW
• Winter 2002/03 (December-price level 90 €/MWh)Nordel: DR in Norway 800 MW, in Sweden 200 MWECON: DR in Norway 1000 MW
• DR in Germany (2005): 200 MW contracted by SaarEnergie for minute reserve market
Source: FinGrid, SaarEnergie
6Demand Response for Wind Integration
Marian Klobasa
Outline
Motivation for Demand Response
Potentials for Demand Response
Simulation of Wind Energy, Electricity System and Demand
Impacts of Wind Fluctuation on Electricity Systems
7Demand Response for Wind Integration
Marian Klobasa
Potential for demand response
Sector Appliances Electricity Demand
[TWh]
Demand Response
[%]
Max. power
shift [MW]
Basic Chemical Electrolysis 6,6 67 580
Basic Metal Electric Arc Furnace 6,8 50 400
Non-ferrous Metal Electrolysis 10,5 25 300
Pulp & Paper Pulper, Refiner, Stock Preparation
11,9 16 240
Food Retail Cooling devices 6,3 33 400
Food Industry Cold storage, Process cooling
5 33 325
Residential Cooling and freezing 18,6 33 780
Total 65,7 3025
8Demand Response for Wind Integration
Marian Klobasa
Example steel production: electric arc furnace
• Typical batch process• Tap to tap time: 45 minutes• Power Supply: 100 MW• Capacity: 200 tons• Yearly production 200 t furnace:
1,5 Mio. tons• Steel price: 320 €/t (2003),
> 500 €/t (2005)• Turn over: 500 – 700 Mio. €• Additional turn over in balancing
market: 2,5 Mio. ۥ Price for balancing power:
70 €/MW per day• Price for balancing energy:
180 €/MWhSource: Stahl-Online
9Demand Response for Wind Integration
Marian Klobasa
Realisable Demand Response Potential - Industrial Sector
0
500
1000
1500
2000
2500
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
Load
in M
W
Air Cond.Cooling Apl.Food IndustryNon-ferrous MetalBasic MetalBasic ChemicalPulp &Paper
Technical potential for demand response
hours
Additional potential:• Tertiary sector: 1 GW
• Refrigeration• Air conditioning
• Residential sector: up to 9 GW• Space heating, warm water• other
10Demand Response for Wind Integration
Marian Klobasa
Prerequisites for demand response
Technology: Adoption of existing I&C technology for demand response – innovation of I&C technologies is main driver for system optimisation.
Development of suitable tariffs and business models (including extension of intraday markets).
Consideration of customer behaviour, potential benefits and risk for electricity traders.
Adoption of new demand response business option by energy and general management in industrial companies.
11Demand Response for Wind Integration
Marian Klobasa
Outline
Motivation for Demand Response
Potentials for Demand Response
Simulation of Wind Energy, Electricity System and Demand
Impacts of Wind Fluctuation on Electricity Systems
12Demand Response for Wind Integration
Marian Klobasa
Electricity System Simulation
Structure of simulation model
Data for conventional power plants Installed capacity, fuel type, combined heat and power
production, availability
Electricity demand (incl. load curves)
Wind generation (based on wind speed data)
Simulation of power plant operation
Determined by: variable costs, minimum operation time
Results of simulation
Fuel use, electricity production, CO2-emissions
Basis for analysis of balancing strategies
13Demand Response for Wind Integration
Marian Klobasa
Simulation of power plant operation
Wind generation Electricity demand
Operation of power plants
Fuel use,electricity production,
emissions, costs
Balancing CapacityBalancing Energy
PrognosisPower plantdatabase
Deviationshift potential
Inpu
t dat
aS
imul
atio
nR
esul
ts
14Demand Response for Wind Integration
Marian Klobasa
Simulation of wind generation
Input data• DWD-Data (3 years) for 180
locations– Wind speed– Pressure und Temperature
• Time interval 10 Minutes• 10 Turbine types and power
curves• Spatial distribution
=> High resolution time series of wind generation
15Demand Response for Wind Integration
Marian Klobasa
Bottom up model for simulation of the load curve
• Output– Simulation of yearly load curves of 60 sectors in hourly time
resolution and total load curve for Germany
• Data basis– UCTE (12 month, 3 typical days,
Base year 2000)– VIK/VDEW Data– ISI-Load profiles (typical days)
• Method– Generation of load curves for 6 typical days– Algorithm for generation of yearly load curves in hourly time
resolution (basis are 6 typical days)
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
1 750 1499 2248 2997 3746 4495 5244 5993 6742 7491 8240
Stunden
MW
16Demand Response for Wind Integration
Marian Klobasa
Outline
Motivation for Demand Response
Potentials for Demand Response
Simulation of Wind Energy, Electricity System and Demand
Impacts of Wind Fluctuation on Electricity Systems
17Demand Response for Wind Integration
Marian Klobasa
Influence of wind power on power plant operation
0
10.000
20.000
30.000
40.000
50.000
60.000
70.000
1 43 85 127 169 211 253 295 337 379 421 463 505 547 589 631 673 715
hours [h]
MW
gas turbine
combined cycle
hard coal
lignite
nuclear
water
Year 2020Without windgeneration
18Demand Response for Wind Integration
Marian Klobasa
Influence of wind power on power plant operation
0
10.000
20.000
30.000
40.000
50.000
60.000
70.000
1 43 85 127 169 211 253 295 337 379 421 463 505 547 589 631 673 715
hours [h]
MW
gas turbine
combined cycle
hard coal
lignite
nuclear
water
Year 2020With 39 GWwind generation
19Demand Response for Wind Integration
Marian Klobasa
Influence of wind power on power plant operation
0
10.000
20.000
30.000
40.000
50.000
60.000
70.000
1 43 85 127 169 211 253 295 337 379 421 463 505 547 589 631 673 715
hours [h]
MW
wind
gas turbine
combined cycle
hard coal
lignite
nuclear
water
Year 2020With 39 GWwind generation
Wind generation
20Demand Response for Wind Integration
Marian Klobasa
Additional balancing power
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Windpenetration P(Wind,inst)/P(Load,max)
Sha
re o
f Bal
anci
ng P
ower
on
Inst
alle
d W
ind
Pow
er
Forecast Error 6,5 % Forecast Error 5,5 % Forecast Error 4,5 %
21Demand Response for Wind Integration
Marian Klobasa
Additional balancing energy
0%
2%
4%
6%
8%
10%
12%
14%
16%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Windpenetration P(Wind, inst)/P(Load,max)
Sh
are
of
ba
lan
cin
g p
owe
r o
n w
ind_
e
ne
rgy
Forecast Error 6,5 % Forecast Error 5,5 % Forecast Error 4,5 %
22Demand Response for Wind Integration
Marian Klobasa
Additional balancing costs
• Calculation of balancing costs
– Costs approach: opportunity and part load costs
Range: 30 – 400 €/MW per day
– Price approach: balancing market prices
Range: 100 – 2000 €/MW per day
– Demand response costs starts at 70 €/MW per day.
• Additional balancing power of 6 GW up to 2020 could lead to
an increase between 200 – 600 Mio. €.
• 1 GW demand response can lower this value by 25 %.
23Demand Response for Wind Integration
Marian Klobasa
Additional balancing costs Total and Specific Costs
0
50
100
150
200
250
300
2000 2005 2010 2015 2020
Jahr
To
tal C
ost
s in
Mio
. €/
a
0,00
0,50
1,00
1,50
2,00
2,50
3,00
Sp
eci
fic c
ost
s p
er
MW
h
win
d e
ner
gy
Base Case [Mio.€/a] Demand Response Case [Mio.€/a]Base Case [€/MWh(Wind)] Demand Response Case [€/MWh(Wind)]
€/MWhMio. €/a
24Demand Response for Wind Integration
Marian Klobasa
Conclusion
Increase of balancing power around 0,1 MW per MW wind energy with improved forecast tools.
Balancing energy around 0,1 MWh per MWh wind energy with improved forecast tools.
Technical potential for demand response is high. Demand response starts to be available at 70 €/MW per day
and could lead to significant cost decreases. Furthermore demand response could compensate local
fluctuations and could help to delay or overcome grid extension measures.
Main challenge will be the development of markets and business models to transfer cost reductions to the customers.
25Demand Response for Wind Integration
Marian Klobasa
Acknowledgement
Project carried out in the framework of the program „Energy Systems of Tomorrow" – an initiative of the Austrian Federal Ministry for Traffic, Innovation
and Technology (BMVIT).
Further Information:
Wind integration supported by Demand Response, Final Reportin Cooperation with
Vienna University of Technology, Energy Economics Groupwww.eeg.tuwien.ac.at
Marian [email protected]/e/departm.htm