Wind Power Forecasting in Electricity Markets

Transcription

1 Wind Power Forecasting in Electricity Markets Audun Botterud*, Zhi Zhou, Jianhui Wang Argonne National Laboratory, USA Ricardo Bessa, Hrvoje Keko, Jean Sumaili, Vladimiro Miranda INESC Porto, Portugal Project website: University of New South Wales Sydney, Australia, February 3, 2012

2 Outline Background Brief Intro to Argonne National Laboratory Wind energy in the United States New Statistical Approaches to Wind Power Forecasting Point forecasting Uncertainty forecasting Forecasting in Operational Decisions System Operation: Unit Commitment and Dispatch Wind Power Trading under Uncertainty Concluding Remarks 2

3 Argonne is America's First National Laboratory and one of the World's Premier Research Centers Founded in 1943, designated a national laboratory in 1946 Part of the U.S. Department of Energy (DOE) laboratory complex 17 DOE National Laboratories Managed by UChicago Argonne, LLC About 3,200 full-time employees 4,000 facility users About $600M budget Main site: 1500-acre site in Illinois, southwest of Chicago Broad research and development portfolio Numerous sponsors in government and private sector 3

4 Argonne: Science-based Solutions to Global Challenges Energy production, conversion, storage and use Environmental Sustainability National Security Use inspired science and engineering Discovery and transformational science and engineering Major User Facilities S&T Programs 4

5 The Decision Information Sciences Division Develops State-of-the-Art Energy Analysis Models DEVELOPS decision support tools for energy systems analysis, power systems analysis, and environmental analysis that are: Useful, Usable, and Used APPLIES models to Conduct country/region/state/city-specific studies for domestic and foreign clients Consult clients and lending agencies on specific investments or energy project loans TRANSFERS software tools by Conducting training programs Energy demand forecasting, energy and electric system analysis, analysis of environmental impacts (first training course in 1978) International technical cooperation projects to provide technical support funded by World Bank/GEF, regional lending banks USDOE, USAID, IAEA, etc. Software licensing and distribution Work on renewable energy include hydro, wind, and solar 5

6 Argonne Campus Advanced Photon Source Bldg 221 6

7 The Best Land-Based Wind Resources in the United States Are in the Great Plains and Upper Midwest 7

8 Problem is: Not a Lot of People Live Where the Resource is U.S. Population Density by County (July 1, 2009) 8

9 Over 160,000 Miles (450,000 Curcuit-Miles) of Transmission Lines To Move Power; But Will Need to be Upgraded for Large-Scale Renewables 9 9

10 Share of Wind Power in Selected Countries, 2010 LBNL: 2010 Wind Technologies Market Report. 10

11 Growth in U.S. Wind Power Capacity Source: AWEA 11

12 Installed Wind Power Capacity in U.S. States 12

13 Does Wind Power Influence Electricity Markets Today? Midwest ISO Wind Power and Iowa* LMPs, May 11-17, 2009: Wind power ramping events Price [$/MWh] Wind Power [MW] Negative prices (LMPs) Time [hour] 0.0 DA price RT price Wind power *MEC Interface 13

14 U.S. DOE s 20% Wind Energy by 2030 Report Explores a modeled energy scenario in which wind provides 20% of U.S. electricity by 2030 Describes opportunities and challenges in several areas Turbine Technology Manufacturing, materials, and jobs Transmission and integration Siting and environmental effects Markets Wind power forecasting is identified as a key tool to better handle uncertainty and variability from wind power in system operations 14

15 Brief Overview of Argonne s Wind Power Research Environmental Impacts of Wind Power Impact on critical wildlife habitats Visual impact analysis Wind Turbine Reliability Improved coatings and lubricants Better gear box reliability Wind Power Forecasting and Electricity Markets Improved statistical forecasting models Use of forecasting in operational decisions Funded by DOE EERE s Wind and Water Power Program (since 2008) 15

16 Project Overview: Development and Testing of Advanced Wind Power Forecasting Techniques Goal: To contribute to efficient large-scale integration of wind power by developing improved wind forecasting methods and better integration of advanced wind power forecasts into system and plant operations. Collaborators: Industry Partners: Sponsor: Institute for Systems and Computer Engineering of Porto (INESC Porto), Portugal Horizon Wind Energy and Midwest ISO (MISO) U.S. Dept. of Energy (Wind and Water Power Program) The project consists of two main parts: Wind power forecasting Review and assess existing methodologies Develop and test new and improved algorithms Integration of forecasts into operations (power system and wind power plants) Review and assess current practices Propose and test new and improved approaches, methods and criteria 16

17 Outline Background Brief Intro to Argonne National Laboratory Wind energy in the United States New Statistical Approaches to Wind Power Forecasting Point forecasting Uncertainty forecasting Forecasting in Operational Decisions System Operation: Unit Commitment and Dispatch Wind Power Trading under Uncertainty Concluding Remarks 17

18 Wind Forecasts are the Result of Combination of a Diverse set of Models and Input Data NWP Output Data Weather Data Off-site Met Data Site Power Gen & Met Data Physical Models Statistical Models Forecast Results 18

19 Statistical Wind to Power Models We can train neural networks or other mappers with any optimization algorithm and define a training criterion to generate the adequate mapper (wind to power) x g(x,w) O T (target) Neural Network Training criterion Training algorithm A classical performance criterion is Minimum Square Error (MSE) We are exploring new criteria based on Information Theoretic Learning Entropy, correntropy, etc 19

20 W2P Models with Information Theoretic Learning (ITL) Non-Gaussian nature of wind power forecast errors Mean Square Error (MSE) is only optimal under Gaussian distribution The ITL idea ideal case is when the error pdf is a Dirac function - all errors equal (of the same value) all errors equal, means perfect matching between output Y and target T, by adding a bias to the output neuron Renyi s Quadratic Entropy combined with Parzen pdf estimation H R2 log f 2 Y (z) dz fˆ N 1 2 Y ( z) G( z y i, I) N i 1 Gaussian Kernel G(z k y ik, 2 ) 1 2 e 1 2 (z 2 k yik ) 2 20

21 Results Comparison: Forecast vs. Realized Values Comparison of day-ahead forecasts and realized output for wind farm in the Midwest Training based on mean square error (MSE) Training based on Information Theoretic Learning (MCC, MEE, MEEF, cmcc) 21

22 Statistical Methods for Uncertainty Estimation Kernel Density Forecast (KDF) Forecasts the full probability density function Based on Kernel Density Estimation (KDE) Quantile-Copula Nadaraya-Watson Choice of kernel function important depends on the type of variable Time adaptive formulations Kernel Density Estimation Quantile Regression (QR) Estimates a set of quantiles (or intervals) Commonly used for wind power forecasting Linear and splines quantile regression Potential problem: Quantiles may cross 22

23 Illustration of Kernel Density Forecast Forecast the wind power pdf at time step t for each look-ahead time step t+k of a given time-horizon knowing a set of explanatory variables (NWP forecasts, wind power measured values, hour of the day) Wind Speed (m/s) Wind Power (p.u.)

24 Time-adaptive Nadaraya-Watson Estimator Recursive KDE Estimator 1 1 Exponential Smoothing 1 for stationary data streams 1 forgetting factor for nonstationary data streams Time-adaptive Nadaraya-Watson Estimator, 1 1 knowledge of the model at time instant t, which is updated using recent values of measured wind power and NWP data Bessa, Sumaili, Miranda, Botterud, Wang, Constantinescu, Time-Adaptive Kernel Density Forecast: A New Method for Wind Power Uncertainty Modeling, 17 th Power System Comp. Conf., Stockholm, Sweden,

25 Probabilistic Forecast Evaluation : U.S. Midwest Wind Farm Quantile/interval forecast: calibration plot sharpness plot Trade-off between calibration and sharpness NW: KDF Forecast with NW estimator SplinesQR: Splines Quantile Regression 25

26 Wind Power Forecasting Main Findings Point Forecasting Mapping from wind to power by artificial neural networks Development of training algorithms based on Information Theoretic Learning (ITL) Testing model on wind farms in the U.S. Midwest ITL criteria give substantial reductions in forecasting error Uncertainty Forecasting Development of time-adaptive Kernel Density Forecasting (KDF) algorithms Testing model on wind farms in the Midwest and EWITS data KDF tends to give slightly better calibration than quantile regression, whereas sharpness tends to increase Other advantages of KDF is that it provides a full probability density function Adequate scenario generation and reduction Very important for multi-stage decision problems 26

27 Outline Background Brief Intro to Argonne National Laboratory Wind energy in the United States New Statistical Approaches to Wind Power Forecasting Point forecasting Uncertainty forecasting Forecasting in Operational Decisions System Operation: Unit Commitment and Dispatch Wind Power Trading under Uncertainty Concluding Remarks 27

28 Handling Uncertainties in System/Market Operation [MW] Source of uncertainty Load Generating capacity Wind Power Wind power forecasting Operating Reserve Operating Reserves (spin and non-spin)???? Increase operating reserves? Change commitment strategy? - Stochastic UC What are the impacts on the system? Reliability (curtailment,..) Efficiency (system cost, price..) 28

29 A Stochastic Unit Commitment (UC) Model w/wind Power Uncertainty Formulation using wind power forecast scenarios (s) w/probabilities (prob s ):,,,, Objective function (min daily expected cost),,,,, Energy balance (hourly),,,, α,,,, 1,, Commitment Constraints (i, t) A two-stage stochastic mixed integer linear programming (MILP) problem First-stage: commitment Second-stage: dispatch Spinning Reserve balance (hourly),, Non-spinning Reserve balance (hourly) Unit commitment constraints (ramp, min. up/down) Wang J, Botterud A, Bessa R, Keko H, Carvalho L, Issicaba D, Sumaili J, and Miranda V, Wind power forecasting uncertainty and unit commitment, Applied Energy, Vol. 88, No. 11, pp , Z. Zhou, A. Botterud, J. Wang, R.J. Bessa, H. Keko, J. Sumaili, V. Miranda, Application of Probabilistic Wind Power Forecasting in Electricity Markets, Wind Energy, accepted, Dec

30 Operating Reserves vs. Stochastic UC Forecast quantiles Reduced scenario set Dynamic reserve requirement (spinning + non-spinning) + Deterministic UC Stochastic UC + scenario set Commitment schedule Realized generation Commitment schedule Real-time dispatch Real-time dispatch 30

31 Illinois Case Study: Assumptions 210 thermal units: 41,380 MW Base, intermediate, peak units Wind power: 14,000 MW 2006 wind series from 15 sites in Illinois (EWITS dataset) 20% of load Peak load: 37,419 MW 2006 load series from Illinois No transmission network Load/Wind Power (MW) Generation Capacity 4.78% Combine Cycle Turbine 30.95% 20.60% Gas Turbine Nuclear 19.71% Steam Turbine Wind and Load in July-October 2006 Load Wind 120 days simulation period (July 1 st to October 31 st Hour, 2006) Day-ahead unit commitment w/wind power Case study focus is to analyze: point forecast -Use of probabilistic forecasting methods Real-time reliability assessment commitment -Operating reserves vs. stochastic UC (RAC) w/ probabilistic forecast

32 Overview of total cost (Illinois, 4-months period ) Cost (M$) Unserved load Unserved nonspinning reserve Unserved spinning reserve Start-up Fuel P1 PF-F0 PF-F1 PF-F2 PF-F3 PF-D1 PF-D2 PF-D3 SF-S0 SF-S1 SF-S2 Perfect forecast Fixed reserves Dynamic reserves Stochastic UC Point forecast with no additional reserve too risky Stochastic unit commitment has the lowest total costs Dynamic reserves perform slightly better than fixed reserves Overall, more operating reserves lead to lower costs within the same categories Z. Zhou, A. Botterud, J. Wang, R.J. Bessa, H. Keko, J. Sumaili, V. Miranda, Application of Probabilistic Wind Power Forecasting in Electricity Markets, Wind Energy, accepted, Dec

33 Summary: Wind Power Uncertainty in System Operation Probabilistic wind power forecasts can contribute to efficiently schedule energy and operating reserves under uncertainty in wind power generation Dynamic operating reserves (derived from forecast quantiles) + Well aligned with current operating procedures + Lower computational burden - Does not capture inter-temporal events - Uncertainty not represented in objective function Stochastic unit commitment (with forecast scenarios) + Captures inter-temporal events through scenarios + Explicit representation of uncertainty in objective function - More radical departure from current operating procedures - High computational burden Important factors to consider in evaluating probabilistic approaches Quality of probabilistic forecast Risk preferences of system/market operator Broader market design issues Market timeline, deviation penalties, system flexibility, demand response 33

34 Wind Power Trading under Uncertainty in LMP Markets Profit, π h, from bidding into day-ahead market in hour, h: dev h Deviation penalty? h pˆ DA h q DA h pˆ RT h ( qˆ RT h q DA h ) pen( devh) p q pen dev price quantity penalty deviation from schedule Three stochastic variables: What is the optimal strategy? How much to bid into DA market? What is the impact of risk preferences and market design? Botterud A., Zhou Z., Wang J., Bessa R.J., Keko H., Sumaili J., Miranda, V., Wind Power Trading under Uncertainty in LMP markets, IEEE Transactions on Power Systems, in press (available online). 34

35 A Model for Wind Power Trading: Objective Functions and Risk Preferences 1) Risk Neutral: Expected Profit 2) Risk averse: Conditional Value at Risk (CVaR) where 3) Risk averse or risk prone: Expected Utility 35

36 Conditional Value at Risk (CVaR) Probability density th Profit CVaR is the expected value of the profit below threshold, th Objective function: Max [E(Profit) + w*cvar ] 36

37 Utility Function Decision Maker s Preference (Utility Function) 1.0 Risk Averse 0.5 Risk Neutral 0.0 Lowest Profit Risk Prone Highest Profit Objective function: Max E(Utility) 37

38 Analysis of Horizon Wind Farm Three months of data for forecasted and realized wind power and prices (synchronized) Oct Feb hours Wind power forecasts Kernel Density Forecast NW (default) Price forecast is simple average of moving window Normal distribution No price-wind correlation Summary of realized LMPs in this period at MISO hubs: CINERGY FE ILLINOIS MICHIGAN MINN Avg DA Avg RT StDev DA StDev RT #neg DA #neg RT Corr DA-RT Corr DA-wind Corr RT-wind

39 One Hour: Expected Profit vs. CVAR (no penalty) CVAR [$/MW] 2 C* avg U*(β= 3) 4 6 E* All bids 8 Optimal bids U*(β=3) 10 Expected Profit [$/MW] E- expected value, C- CVaR, U utility, avg average forecast 39

40 One Hour: Optimal Bid Depends on Deviation Penalty DA bid as function of deviation penalty 1.0 DA Bid Quantity, qda E C U(β= 3) U(β=3) avg Penalty [$/MWh] E- expected value, C- CVAR, U utility, avg average forecast 40

41 4 months simulation: Realized Profit vs. Deviation ($0/MWh penalty) 0.70 System Operator Avg Abs Deviation [MW] avg zero E* C*(w=0.1) C*(w=0.3) U*(β= 3) U*(β=3) Total Profit [$/MW] Wind Power Producer Conflict of interest between wind power producer and system operator! 41

42 4 months simulation: Realized Profit vs. Deviation ($5/MWh penalty) System Operator Avg Abs Deviation [MW] pf(median) zero E* C*(w=0.1) C*(w=0.3) U*(β= 3) U*(β=3) Total Profit [$/MW] Wind Power Producer Now the interests are better aligned, but wind power profits significantly reduced - Just and reasonable treatment of wind power 42

43 Wind Power Trading under Uncertainty: Main Findings Trade-off between risk and return important for merchant wind power Model assist in analyzing this trade-off under uncertainty in wind power and prices Optimal day-ahead bid driven by price expectations (without penalty) Day-ahead prices on average higher than real-time prices Risk averse strategies give lower DA bids Importance of market design Potential of conflicting objectives between wind power producer and system operator Deviation penalty brings optimal bids closer to expected forecast and reduces system deviations, but reduces wind power revenue 43

44 Outline Background Brief Intro to Argonne National Laboratory Wind energy in the United States New Statistical Approaches to Wind Power Forecasting Point forecasting Uncertainty forecasting Forecasting in Operational Decisions System Operation: Unit Commitment and Dispatch Wind Power Trading under Uncertainty Concluding Remarks 44

45 Concluding Remarks Rapid growth in renewable energy in the United States the last few years Most investments in wind power so far Increasing interest in solar energy Vulnerable to policies/incentives and gas/electricity prices A large-scale wind power expansion requires new operational approaches How to efficiently handle increasing uncertainty and variability? System operator: Reserve requirements, unit commitment, dispatch Wind power producer: Offering wind power into the electricity market Make efficient use of the information in the wind power forecast Improved forecasting models (probabilistic forecasts, ramp forecasts) Stochastic models to aid decisions under uncertainty More general challenges How to design markets that better accommodate wind power and other renewables? How to make industry move up the technological ladder: adaptive, probabilistic methods What will be the impact of a smarter grid and a more flexible demand side? 45

46 Selected Project References for More Details Zhou Z., Botterud A., Wang J., Bessa R.J., Keko H., Sumaili J., Miranda V., Application of Probabilistic Wind Power Forecasting in Electricity Markets, Wind Energy, accepted, Dec Botterud A., Zhou Z., Wang J., Bessa R.J., Keko H., Sumaili J., Miranda, V., Wind Power Trading under Uncertainty in LMP markets, IEEE Transactions on Power Systems, in press (available online), Sept Bessa R.J., Miranda V., Botterud A., Zhou Z., Wang J., Time-Adaptive Quantile-Copula for Wind Power Probabilistic Forecasting, Renewable Energy, Vol. 40, No. 1, pp , Wang J., Botterud A., Bessa R., Keko H, Carvalho L., Issicaba D., Sumaili J., Miranda V., Representing Wind Power Forecasting Uncertainty in Unit Commitment, Applied Energy, Vol. 88, No. 11, pp , Bessa R.J., Miranda V., Botterud A., Wang J., Good or Bad Wind Power Forecasts: A Relative Concept, Wind Energy, vol. 14, no. 5, pp , July Botterud A., Wang J., Miranda V., Bessa R.J., Wind Power Forecasting in U.S. Electricity Markets, Electricity Journal, Vol. 23, No. 3, pp , Mendes J., Bessa R.J., Keko H., Sumaili J., Miranda V., Ferreira C., Gama J., Botterud A., Zhou Z., Wang J., Development and Testing of Improved Statistical Wind Power Forecasting Methods, Report ANL/DIS-11-7, Argonne National Laboratory, Sep Monteiro C., Bessa R., Miranda V., Botterud A., Wang J., Conzelmann G., Wind Power Forecasting: State-of-the-Art 2009, Report ANL/DIS-10-1, Argonne National Laboratory, Nov More information: 46

47 Wind Power Forecasting in Electricity Markets Audun Botterud*, Zhi Zhou, Jianhui Wang Argonne National Laboratory, USA Ricardo Bessa, Hrvoje Keko, Jean Sumaili, Vladimiro Miranda INESC Porto, Portugal Project website: University of New South Wales Sydney, Australia, February 3, 2012