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Demand Response – A New Option for Wind Integration ?

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. Outline. Motivation for Demand Response Potentials for Demand Response

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Demand Response – A New Option for Wind Integration ?

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  1. Demand Response – A New Option for Wind Integration ? Marian Klobasa, Dr. Mario RagwitzFraunhofer Institute for Systems and Innovation Research European Wind Energy Conference 2006 Athens, 2. March 2006

  2. Outline Motivation for Demand Response Potentials for Demand Response Simulation of Wind Energy, Electricity System and Demand Impacts of Wind Fluctuation on Electricity Systems

  3. Benefits of Demand Response? • Improving of system reliability Peak 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

  4. €/MWh Demand Curve Supply Curve MWh/h Increased Elasticity can reduce Electricity Prices

  5. 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 MW ECON: DR in Norway 1000 MW • DR in Germany (2005): 200 MW contracted by SaarEnergie for minute reserve market Source: FinGrid, SaarEnergie

  6. Outline Motivation for Demand Response Potentials for Demand Response Simulation of Wind Energy, Electricity System and Demand Impacts of Wind Fluctuation on Electricity Systems

  7. Potential for demand response

  8. 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. € • Price for balancing power:70 €/MW per day • Price for balancing energy:180 €/MWh Source: Stahl-Online

  9. Technical potential for demand response • Additional potential: • Tertiary sector: 1 GW • Refrigeration • Air conditioning • Residential sector: up to 9 GW • Space heating, warm water • other hours

  10. 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.

  11. Outline Motivation for Demand Response Potentials for Demand Response Simulation of Wind Energy, Electricity System and Demand Impacts of Wind Fluctuation on Electricity Systems

  12. 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

  13. Simulation of power plant operation Wind generation Electricity demand shift potential Power plantdatabase Deviation Input data Prognosis Operation of power plants Balancing Capacity Balancing Energy Simulation Fuel use, electricity production, emissions, costs Results

  14. 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

  15. 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)

  16. Outline Motivation for Demand Response Potentials for Demand Response Simulation of Wind Energy, Electricity System and Demand Impacts of Wind Fluctuation on Electricity Systems

  17. Influence of wind power on power plant operation Year 2020 Without wind generation

  18. Influence of wind power on power plant operation Year 2020 With 39 GW wind generation

  19. Influence of wind power on power plant operation Wind generation Year 2020 With 39 GW wind generation

  20. Additional balancing power

  21. Additional balancing energy

  22. Additional balancing costs • Calculation of balancing costs • Costs approach: opportunity and part load costsRange: 30 – 400 €/MW per day • Price approach: balancing market pricesRange: 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 %.

  23. Additional balancing costs

  24. 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.

  25. Acknowledgement Further Information: Wind integration supported by Demand Response, Final Report in Cooperation with Vienna University of Technology, Energy Economics Group www.eeg.tuwien.ac.at 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). Marian Klobasa M.Klobasa@isi.fraunhofer.de www.isi.fhg.de/e/departm.htm

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