Wind Energy and Grid Integration Recent US Wind Integration Experience J. Charles Smith Nexgen Energy LLC Utility Wind Integration Group January 24-25, 2006 Madrid, Spain
Outline of Topics Building and Validating Wind Plant Models Recent Studies Dealing With Cost Impacts of Variability and Uncertainty Summary of Wind Integration Costs and Best Practices
Building and Validating Wind Plant Models Why? Operating characteristics of wind plants are not well known in power system engineering circles Essential for range of electrical-side studies How? Conservative estimates Extrapolations from measurement data + NOAA archive data (wind speed) Meteorological modeling From volumes of historical data and experience (Be Careful!) Better Likely the best for now Have to wait for this
Use meteorological modeling to simulate weather for historical years e.g. MM5 model used for weather forecasting zoom in for both space and time (e.g. 5 min, 2 mi x 2 mi) Use actual weather to guide simulation, nudge back to reality Save important weather variables at points of interest Wind speed and direction @ hub height Temperature Pressure Convert time series of wind speed data to generation using turbine power curves Wind Plant Modeling Approach: Re-creating the Weather
Validation 25000 Delta Sector Tower 24 Selected comparisons with measured data Good pattern match at hourly level High correlation between time series Applications to date Xcel/MN Xcel/PSCo NYISO Power (kw) Power (kw) 20000 15000 10000 5000 0 1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721 Time Step (1 hr) 25000 Delta Sector Tower 24 20000 15000 10000 5000 0 1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 Time Step (1 hr)
Advantages Captures all important aspects of large wind generation scenario for power system studies Common, realistic meteorology for region Effect of geographic separation for points of interest is captured to high degree of accuracy Seasonal and diurnal variability also characterized Historical nature of data simplifies power system analysis Most grid-side quantities for period are known, available from archives (load, line flows, generation, etc.) Any correlations between wind generation and load are embedded in data (if synchronized) Meaningful study scenarios can be readily constructed Downside Computationally intensive Relatively expensive
Dealing with Variability and Uncertainty Variability Load varies by seconds, minutes, hours, by day type, and with weather Supply resources may not be available or limited in capacity due to partial outages Prices for power purchases or sales exhibit fluctuations Uncertainty Operational plans are made on basis of best available forecasts of needs; some error is inherent Supply side resource available with some probability (usually high) Key Questions How does wind generation affect this existing variability and uncertainty? What are the costs associated with the changes?
An Analytical Approach - General Assess operating time frames individually Regulation Load following Short-term planning and hourly operations Determine incremental impacts Assessments both with and without wind generation Must consider wind generation + load, not wind alone Required assumptions Operating practices and procedures Business structure
Time Scales of Interest System Load (MW) Time (hour of day) 0 4 8 12 16 20 24 Days Unit Commitment seconds to minutes Regulation tens of minutes to hours Load Following day Scheduling
Baseline Data for Study Long (many hours to multiple years) of: System load Aggregate wind generation Matching load and wind data by hour improves analysis Measurement data for analysis of very fast fluctuations
Extracting the Regulation Characteristics from Wind Generation Measurements 200 180.912 0.15 Total_Trend i Delta_Trend i Echo_Trend i 150 100 + per-unit Total_Reg j Total_Rate Delta_Reg j Delta_Rate Echo_Reg j Echo_Rate 0.1 0.05 0 Foxtrot_Trend i Foxtrot_Reg j Golf_Trend i Foxtrot_Rate 0.05 50 Golf_Reg j Golf_Rate 0.1 0 0 0 2 4 6 8 10 12 14 16 18 20 0 i 60 60 18.056 0.15 0 5 10 15 20 0 j 20 3600 HOUR
Increases in regulation requirements (remember definition) due to even large amounts (15%) of wind generation appear to be quite small Large turbine count and geographic diversity contribute to substantial filtering of these fast variations in wind generation output Lack of correlation to system load also contributes to modest impact Some types of (much smaller) loads can have much greater influence on regulation requirements Right: System with wind generation and arc furnace load Mill load dominates regulation needs Regulation Findings
Impacts Within the Hour QUESTIONS Is control area demand more variable within the hour with wind? What are the economic or technical consequences? ANSWERS Effect is confined mostly to tail events (extremes) Impact on CPS2 Increase of 1-2 MW/min in loadfollowing capability
Impacts on Scheduling Supply resources e.g., generating units, purchases, etc. are committed to minimize cost and ensure reliability Commitment decisions are based on short-term forecasts (next day or days) Constraints of individual units must be honored Rules for reliability and system performance (spinning and operating reserves, ramping) must be observed Objective is to minimize overall cost against these constraints and the forecast conditions Significant wind generation can alter familiar load patterns 1.1000 1.0000 0.9000 0.8000 0.7000 0.6000 0.5000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Summary of Xcel Study Results A/S cost estimate of $4.60/MWH of wind energy delivered Breakdown $4.37/MWH due to delivery timing, variability, and forecast error $1 million/yr increased regulation cost, or about $0.23/MWH of wind energy Negligible costs from intra-hour effects Results are conservative, sensitive to assumptions MISO balancing, day ahead, and hour ahead markets would likely reduce integration cost System specific operational issues will need attention
Summary of NYS/GE Operating Cost Impacts Annual Operating Cost Impacts for 2001 Wind and Load Profiles Unit Commitment Total variable cost reduction (includes fuel cost, variable O&M, start-up costs, and emission payments) Total variable cost reduction per MW-hour of wind generation With Day-Ahead Wind Forecasting $ 95M Without Wind Forecasting $ 430M $ 335M $48 / MWh $38 / MWh Wind revenue $ 315M $ 305M Non-wind generator revenue reductions $ 795M $ 960M Load payment reductions (calculated as product of hourly load and the corresponding locational spot price) $ 515M $ 720M Source: GE/NYSERDA study
Study Relative Wind Penetration (%) Summary of Wind Plant Ancillary Service Costs Ancillary Services Cost Comparison $/MWh Regulation Load Following Unit Commitment Total Xcel/EnerNex 15 0.23 0 4.37 4.60 Pacificorp 20 0 2.50 3.00 5.50 BPA 7 0.19 0.28 1.00-1.80 1.47-2.27 Xcel/UWIG 3.5 0.41 1.44 1.85 We Energies I 4 1.12 0.09 0.69 1.90 We Energies II 29 1.02 0.15 1.75 2.92 Source: UWIG
Forecasting Errors and Issues- Standard Deviation of Hourly Load Variability (by month for 11 months) With Wind 1,200 1,000 σ = 858 MW without wind σ = 910 MW with wind Sigma (MW) 800 600 400 200 Statewide Data Without Wind 0 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Peak Load (MW) State Load SuperZone Load Zone K Load State Load-Wind SuperZone Load-Wind Zone K Load-Wind source:ge/nyserda
Summary: Wind Integration Costs and Best Practices A number of studies conducted over past five years More in progress Findings Total integration costs for modest penetrations of wind generation (to 15 or 20%) range from a couple to $5-6/MWH of delivered wind energy Most recent studies show costs due to planning uncertainty and hourly variability to be most significant Regulation costs are small Much work remains Integration of wind plant forecasting in both hour-ahead and dayahead markets Operation of wind plants in competitive markets Sensitivity of integration costs to resource portfolio, operating practices Enhanced algorithms for planning with higher uncertainty
Selected Wind Capacity Values 100 80 ELCC as % Rated Capacity 60 40 20 0 NYSERDA/GE MN/DOC/Xcel (2) Colorado Green MN/DOC/Xcel (1)
Visit www.uwig.org Email info@uwig.org Phone For More Information Charlie Smith 703-860-5160 Bob Zavadil 865-691-5540 x 149 Fax 703-860-1544 Mail Utility Wind Integration Group PO Box 2787 Reston, VA 20195 USA