University of New South Wales Sydney, Australia, February 3, 2012
Wind Power Forecasting in Electricity Markets
Audun Botterud*, Zhi Zhou, Jianhui WangArgonne National Laboratory, USA*[email protected]
Ricardo Bessa, Hrvoje Keko, Jean Sumaili, Vladimiro MirandaINESC Porto, Portugal
Project website: http://www.dis.anl.gov/projects/windpowerforecasting.html
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
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 ofEnergy (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 inIllinois, southwest of Chicago
Broad research anddevelopment portfolio
Numerous sponsors in government and private sector
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Argonne: Science-based Solutions to Global Challenges
Energy production, conversion, storage and use
National Security
Environmental Sustainability
Use‐inspired science and engineering…
… Discovery and transformational science and engineering
Major User Facilities S&T Programs4
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-specificstudies for domestic and foreign clients
– Consult clients and lending agencies onspecific 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 solar5
Argonne Campus
Bldg 221
Advanced Photon Source
6
7
The Best Land-Based Wind Resources in the United States Are in the Great Plains and Upper Midwest
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Problem is: Not a Lot of People Live Where the Resource is
U.S. Population Density by County (July 1, 2009)
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Over 160,000 Miles (450,000 Curcuit-Miles) of Transmission Lines To Move Power; But Will Need to be Upgraded for Large-Scale Renewables
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Share of Wind Power in Selected Countries, 2010
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LBNL: 2010 Wind Technologies Market Report.
Growth in U.S. Wind Power Capacity
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Source: AWEA
Installed Wind Power Capacity in U.S. States
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0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
3500.0
4000.0
‐120.0
‐70.0
‐20.0
30.0
80.0
130.0
180.01 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103
109
115
121
127
133
139
145
151
157
163
Wind Po
wer [M
W]
Price [$/M
Wh]
Time [hour]
DA price RT price Wind power
Does Wind Power Influence Electricity Markets Today?
Negative prices (LMPs)
Wind power ramping events
Midwest ISO Wind Power and Iowa* LMPs, May 11-17, 2009:
*MEC Interface13
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
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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)
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Project Overview: Development and Testing of Advanced Wind Power Forecasting Techniques
Collaborators: Institute for Systems and Computer Engineering of Porto (INESC Porto), Portugal
Industry Partners: Horizon Wind Energy and Midwest ISO (MISO)
Sponsor: 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
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.
http://www.dis.anl.gov/projects/windpowerforecasting.html
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
Wind Forecasts are the Result of Combination of a Diverse set of Models and Input Data
18
NWP Output Data Weather Data Off-siteMet Data
Site Power Gen& Met Data
Forecast Results
Physical Models Statistical Models
Statistical Wind to Power Models
19
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)
A classical performance criterion is Minimum Square Error (MSE)We are exploring new criteria based on Information Theoretic LearningEntropy, correntropy, etc
g(x,w)
Training algorithm
Training criterion
x OT (target)
Neural Network
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 neuronRenyi’s Quadratic Entropy combined with Parzen pdf estimation
dz)z(flogH 2Y2R
N
1i
2iY ),(G
N1)(f̂ Iyzz
2ikk2 )yz(
2
12
ikk e2
1),yz(G
Gaussian Kernel
20
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
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
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
Kernel Density Estimation
Illustration of Kernel Density Forecast
23
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)
0
0.20.4
0.60.8
1
0
5
10
15
20
Wind S
peed (m/s)
Wind Power (p.u.)
Time-adaptive Nadaraya-Watson Estimator
1∙
1∙ ∙ ∙
1∙
forgetting factor
Recursive KDE Estimator Exponential Smoothing
for stationary data streams
for nonstationary data streams
|∙ , 1 ∙ ∙
∙ 1 ∙
knowledge of the model at time instant t, which is updated using recent values of measured wind power and NWP data
Time-adaptive Nadaraya-Watson Estimator
1
Bessa, Sumaili, Miranda, Botterud, Wang, Constantinescu, “Time-Adaptive Kernel Density Forecast: A New Method for Wind Power Uncertainty Modeling,” 17th Power System Comp. Conf., Stockholm, Sweden, 2011.
24
Probabilistic Forecast Evaluation : U.S. Midwest Wind Farm
calibration plot sharpness plot
Trade-off between calibration and sharpness
NW: KDF Forecast with NW estimator
SplinesQR: Splines Quantile Regression
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Quantile/interval forecast:
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
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
Handling Uncertainties in System/Market Operation
What are the impacts on the system?– Reliability (curtailment,..)– Efficiency (system cost, price..)
Source of uncertainty
Operating Reserve
∆ Load ∆ Generating capacity
Operating Reserves(spin and non-spin)
∆ WindPower
????
Increase operating reserves?
Change commitment strategy?- Stochastic UC
[MW]
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Wind power forecasting
A Stochastic Unit Commitment (UC) Model w/Wind Power Uncertainty
Formulation using wind power forecast scenarios (s) w/probabilities (probs):
A two-stage stochastic mixed integer linear programming (MILP) problem– First-stage: commitment– Second-stage: dispatch
Objective function (min daily expected cost)
Energy balance (hourly)
Spinning Reserve balance (hourly)
Unit commitment constraints(ramp, min. up/down)
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. 2011. 29
∙ ,,
,,
∑ , , , , ∀ ,
, , α , , , ∀ ,
, , 1 , , , ∀ , Non-spinning Reserve balance (hourly)
Commitment Constraints (i, t)
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. 4014-4023, 2011.
Operating Reserves vs. Stochastic UC
Dynamic reserve requirement (spinning + non-spinning) +
Deterministic UC
30
Forecast quantiles Reduced scenario set
Stochastic UC + scenario set
Commitment schedule
Commitment schedule
Real-time dispatch
Real-time dispatch
Realized generation
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
120 days simulation period (July 1st to October 31st, 2006)– Day-ahead unit commitment w/wind power
point forecast– Real-time reliability assessment commitment
(RAC) w/ probabilistic forecast 31
Case study focus is to analyze:-Use of probabilistic forecasting methods-Operating reserves vs. stochastic UC
0
5000
10000
15000
20000
25000
30000
35000
40000
111
122
133
144
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0112
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2114
3115
4116
5117
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7119
8120
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0123
1124
2125
3126
4127
5128
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Loa
d/W
ind
Pow
er (M
W)
Hour
Wind and Load in July-October 2006
Load
Wind
4.78%
20.60%
19.71%
30.95%
Generation Capacity
Combine Cycle Turbine
Gas Turbine
Nuclear
Steam Turbine
0
200
400
600
800
1000
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1400
1600
P1 PF-F0 PF-F1 PF-F2 PF-F3 PF-D1 PF-D2 PF-D3 SF-S0 SF-S1 SF-S2
Cos
t (M
$)Unserved loadUnserved nonspinning reserveUnserved spinning reserveStart-upFuel
Overview of total cost (Illinois, 4-months period )
32
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
Fixed reserves Dynamic reserves Stochastic UCPerfect forecast
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. 2011.
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
Profit, πh, from bidding into day-ahead market in hour, h:
p – priceq – quantitypen – penaltydev – deviation from schedule
Wind Power Trading under Uncertainty in LMP Markets
What is the optimal strategy?How much to bid into DA market?
Three stochastic variables:
What is the impact of risk preferencesand 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).
)ˆ(ˆˆ DAh
RTh
RTh
DAh
DAhh qqpqp
devhDeviation penalty?
)( hdevpen
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A Model for Wind Power Trading: Objective Functions and Risk Preferences
1) Risk Neutral: Expected Profit
2) Risk averse: Conditional Value at Risk (CVaR)
3) Risk averse or risk prone: Expected Utility
where
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Conditional Value at Risk (CVaR)
Profit
Pro
babi
lity
dens
ity
th
CVaR is the expected value of the profit below threshold, th
Objective function: Max [E(Profit) + w*CVaR ]
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Utility Function
Risk Prone
RiskAverse
RiskNeutral
Decision Maker’sPreference(Utility Function)
0.0
0.5
1.0
LowestProfit
HighestProfit
Objective function: Max E(Utility)37
Analysis of Horizon Wind Farm
Three months of data for forecasted and realized wind power and prices (synchronized)– Oct 22 2009 – Feb 19 2010– 2904 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:
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CINERGY FE ILLINOIS MICHIGAN MINNAvg DA 29.5 30.6 26.0 30.7 24.5Avg RT 29.0 30.2 24.5 30.1 23.7
StDev DA 10.5 10.7 11.3 11.5 13.2StDev RT 17.4 18.2 19.2 20.3 20.5#neg DA 4 4 21 9 29#neg RT 104 106 394 140 443
Corr DA-RT 0.55 0.51 0.55 0.48 0.58Corr DA-wind -0.02 -0.02 -0.05 0.00 -0.05Corr RT-wind -0.05 -0.05 -0.09 -0.01 -0.14
One Hour: Expected Profit vs. CVAR (no penalty)
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avg
E*
C*
U*(β=‐3)
U*(β=3)‐10
‐8
‐6
‐4
‐2
0
2
6.15 6.2 6.25 6.3 6.35 6.4 6.45 6.5 6.55 6.6
CVAR [$/M
W]
Expected Profit [$/MW]
All bids
Optimal bids
E- expected value, C- CVaR, U – utility, avg – average forecast
One Hour: Optimal Bid Depends on Deviation Penalty
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DA bid as function of deviation penalty
0.0
0.2
0.4
0.6
0.8
1.0
0 1 2 3 4 5 6 7 8 9 10
DA Bid Quantity
, qDA
Penalty [$/MWh]
E
C
U(β=‐3)
U(β=3)
avg
E- expected value, C- CVAR, U – utility, avg – average forecast
4 months simulation: Realized Profit vs. Deviation ($0/MWh penalty)
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Wind Power Producer
Sys
tem
Ope
rato
r
Conflict of interest between wind power producer and system operator!
0.00
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0.30
0.40
0.50
0.60
0.70
27000 28000 29000 30000 31000 32000
Avg Ab
s Deviatio
n [M
W]
Total Profit [$/MW]
avg
zero
E*
C*(w=0.1)
C*(w=0.3)
U*(β=‐3)
U*(β=3)
4 months simulation: Realized Profit vs. Deviation ($5/MWh penalty)
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Now the interests are better aligned, but wind power profits significantly reduced- Just and reasonable treatment of wind power
Wind Power Producer
Sys
tem
Ope
rato
r
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
22000.0 24000.0 26000.0 28000.0 30000.0
Avg Ab
s Deviatio
n [M
W]
Total Profit [$/MW]
pf(median)
zero
E*
C*(w=0.1)
C*(w=0.3)
U*(β=‐3)
U*(β=3)
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
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
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
Selected Project References for More Details
46
More information: http://www.dis.anl.gov/projects/windpowerforecasting.html
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. 2011.
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. 2011.
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. 29-39, 2012.
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. 4014-4023, 2011.
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. 625-636, July 2011.
Botterud A., Wang J., Miranda V., Bessa R.J., “Wind Power Forecasting in U.S. Electricity Markets,” Electricity Journal, Vol. 23, No. 3, pp. 71-82, 2010.
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. 2011.
Monteiro C., Bessa R., Miranda V., Botterud A., Wang J., Conzelmann G., “Wind PowerForecasting: State-of-the-Art 2009,” Report ANL/DIS-10-1, Argonne National Laboratory, Nov. 2009.
University of New South Wales Sydney, Australia, February 3, 2012
Wind Power Forecasting in Electricity Markets
Audun Botterud*, Zhi Zhou, Jianhui WangArgonne National Laboratory, USA*[email protected]
Ricardo Bessa, Hrvoje Keko, Jean Sumaili, Vladimiro MirandaINESC Porto, Portugal
Project website: http://www.dis.anl.gov/projects/windpowerforecasting.html