Subcontract Report

NREL/SR-550-47853

April 2010

Status of Centralized Wind Power 

Forecasting in North America 

May 2009 – May 2010

K. Porter and J. Rogers

Exeter Associates, Inc.

Columbia, Maryland
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Subcontract Report

NREL/SR-550-47853

April 2010

Status of Centralized Wind Power 

Forecasting in North America 

May 2009 – May 2010

K. Porter and J. Rogers

Exeter Associates, Inc.

Columbia, Maryland

NREL Technical Monitor: Erik Ela

Prepared under Subcontract No. LAM-9-99431-01
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iii

Acknowledgments

The authors wish to thank the National Renewable Energy Laboratory (NREL) and the U.S. 

Department of Energy (DOE), who funded the research that went into this report. 

We also thank Erik Ela and Jeff Hein of NREL, Christina Mudd of Exeter Associates, Inc., Mark 

Ahlstrom of Windlogics Inc., Charlie Smith of the Utility Wind Integration Group, and Mark 

Bolinger of the Ernest Orlando Lawrence Berkeley National Laboratory for reviewing early 

drafts. In addition, we thank Sanjay Patil of PJM; David Edelson of the New York Independent 

System Operator (NYISO); David Maggio of the Electricity Reliability Council of Texas 

(ERCOT); Bart McManus of the Bonneville Power Administration (BPA); Martin Hastings and 

Michael Falvo of the Independent Electric System Operator of Ontario; Keith Parks of Xcel 

Energy; Ulrich Focken of Energy & Meteo Systems GmbH; Alain Forcione and Jacques Bourret 

of Hydro Québec; Jim Blatchford of the California Independent System Operator (CAISO); 

James Shen of the Alberta Electric System Operator; Kenneth Pennock, Dan Meade and John 

Zack of AWS Truewind; Corinna Möhrlen of WEPROG; Barry Gilman and Jack Peterson of 

Southern California Edison (SCE); and Michael McMullen of the Midwest Independent System 

Operator (Midwest ISO) for reviewing draft sections of the report. Finally, the authors thank 

Bruce Green of NREL for his editorial assistance. 
iv

TABLE OF CONTENTS

PAGE

I. Introduction ......................................................................................................................... 1

II. Types of Wind Power Forecasts and Wind Power Forecast Performance ......................... 3

III. Description of Individual Central Wind Forecasting Vendors ........................................... 5

IV. RTOs and Utilities with Operating Central Wind Power Forecasting Systems ................. 6

California ISO ..................................................................................................................... 6

Electric Reliability Council of Texas .................................................................................. 8

Hydro-Québec ..................................................................................................................... 9

Midwest ISO ..................................................................................................................... 10

New York ISO .................................................................................................................. 11

PJM ................................................................................................................................... 12

 Southern California Edison ............................................................................................... 13

V. Central Wind Power Forecasting Systems Under Development ...................................... 14

Alberta Electric System Operator ..................................................................................... 14

Bonneville Power Administration..................................................................................... 15

Independent Electric System Operator of Ontario ............................................................ 15

Xcel Energy ...................................................................................................................... 16

VI. Characteristics of Operating Central Wind Power Forecasting Systems.......................... 16

VII. Summary ........................................................................................................................... 22

References ................................................................................................................................... 23

LIST OF TABLES

PAGE

1 Projected Impact of Wind Forecasts on Grid Operating Costs ........................................... 1

2 Average Wind Forecast Error by Time Frame ................................................................... 4

3 Examples from the California ISO on Data Availability and Wind Forecasting 

Performance ...................................................................................................................... 17

4 Wind Forecast Errors for Various Central Wind Power Forecasting Programs (as a 

percent of installed wind capacity) ................................................................................... 21

LIST OF FIGURES

PAGE

1 Forecasted Wind Speed vs. Output (an example from the California ISO) ...................... 18 
1 

I. Introduction

Wind energy development has increased dramatically over the last decade. Nearly 10,000 

megawatts (MW) of wind came on-line in the United States in 2009, bringing the total U.S. 

installed wind capacity to over 35,000 MW (American Wind Energy Association, 2010). This 

represents nearly a twelve-fold increase in wind capacity since 2000. Installed wind capacity in 

Canada has more than tripled since 2005, from 684 MW to 2,369 MW as of the end of 2008. 

According to the North American Electric Reliability Corporation (NERC), another 145 

gigawatts (GW) of variable resources (mostly wind) are in various stages of planning (NERC, 

2009). While not all of this capacity will come on-line, the amount of variable resource capacity 

that is under consideration in North America is quite significant. 

The rapid growth in installed wind power capacity has led to an increased interest in wind 

power forecasting. Historically, given its variable nature, wind generation has been taken on an 

as-available basis, where wind simply “shows up” and grid operators take whatever measures are 

necessary to accommodate it, mainly reducing the output of other committed generation. At low 

wind penetrations, such actions are reasonable. At higher levels of wind penetration, however, 

uncertainty surrounding the amount of wind that can be expected becomes more problematic. In 

addition, there are costs associated with having excess units online, as well as from reduced unit 

efficiency and increased O&M. Improved wind forecasting can help reduce these costs. A 

sample of various wind integration studies estimates that potential annual operating cost savings 

from using wind forecasting in the day-ahead market range from $20 million to $510 million, 

depending on the amount of projected wind capacity (see Table 1). A perfect forecast may add 

$10 million to $60 million more in savings (Piwko, 2009). 

Table 1

Projected Impact of Wind Forecasts on Grid Operating Costs

Projected Annual 

 Operating Cost Savings 

Peak Load

Wind 

Generation

State-of-Art 

Forecast 

vs. 

No Forecast

Additional 

Savings from 

Perfect Forecast

vs.

State of Art 

Forecast

California 64 GW 7.5 GW $68 M $19 M

64 GW 12.5 GW 160 M 38 M

New York 33 GW 3.3 GW 95 M 25 M

Texas 65 GW 5.0 GW 20 M 20 M

65 GW 10.0 GW 180 M 60 M

65 GW 15.0 GW 510 M 10 M

Source: Piwko, 2009.
2 

 Other organizations have also emphasized the importance of and need for wind power 

forecasting. NERC stated that “enhanced measurement and forecasting of variable generation 

output is needed to ensure bulk power system reliability,” and that wind forecasting “must be 

incorporated into real-time operating practices as well as day-to-day operational planning” 

(NERC, 2009, p. iii). The U.S. Department of Energy (DOE) reported that “the seamless 

integration of wind plant output forecasting—into both power market operations and utility 

control room operations—is a critical next step in accommodating large penetrations of wind 

energy in power systems” (DOE, 2008, p. 86). Wind power forecasting gained further attention 

when a wind power forecasting system that the Electric Reliability Council of Texas (ERCOT) 

was testing accurately predicted the large fall-off in wind generation during a well-publicized 

system event in ERCOT in February 2008 (Ela and Kirby, 2008). 

In some markets, wind generators are required to offer a wind power forecast to the 

power purchaser or to the grid operator. These decentralized wind power forecasts, which are 

produced either internally or with the assistance of a wind power forecasting company, are not 

covered in this report. Instead, this report only addresses grid-wide wind power forecasts for all 

wind generators, which are administered by utilities or regional transmission organizations 

(RTOs), typically with the assistance of one or more wind power forecasting companies. Such 

forecasts are known as “central” wind power forecasts. This report focuses on the known central 

wind power forecasting programs in operation or under development by utilities and grid 

operators in North America.

Central wind power forecasts offer several advantages over their decentralized 

counterparts. First, a central wind power forecast will use a consistent wind power forecasting 

approach and methodology for all wind projects in the region, which will likely lead to more 

consistent (though not necessarily more accurate) results. Second, the grid operator will have 

access to wind generation data (and perhaps onsite weather data from all the wind plants) that 

can be used to improve the performance of centralized wind power forecasting systems, which

individual wind plant forecasts may not have access to because of proprietary or confidentiality 

reasons. Third, a centralized wind power forecasting system may be able to utilize economies of 

scale, reducing the cost of forecasting per individual wind project as compared to decentralized 

forecasting systems. 

Central wind power forecasts also have some disadvantages. Since they are often based 

on a single forecasting methodology and provider, the ‘more consistent’ result may be 

consistently wrong. They may also be biased for certain weather conditions or events, possibly 

leading to larger system errors. When a single central wind forecast is in place, competition and 

the ability to compare alternative results may be reduced or lost. The benefits of a diversity of 

forecasts and opinions may be significant; some system operators, such as those in Germany, 

have already implemented ensemble methods (i.e., systems that make use of a variety of 

methodologies or forecast providers) that use five or more forecasting services.1

 

1 A study of one such case indicated a day-ahead wind forecast error of 4.2% RMSE using an ensemble wind 

forecast in Germany (Ernst, 2010, p. 12). However, the California ISO recently found no additional value from 

using wind power forecasts from multiple providers. The California ISO suggested that this conclusion could be 

altered if payments to wind power forecasting providers were based on a two-part fee structure, with a small flat rate 

and a second payment based on the quality of the wind power forecasts (Blatchford and de Mello, 2009, p. 10).

 Clearly, system 
3 

operators will need to weigh the benefits of reduced wind forecast errors versus the cost of 

having multiple wind forecasting providers (Zavadil, 2009). 

Southern California Edison (SCE) was the first utility in North America to adopt central 

wind power forecasting in late 2000. Similarly, the California Independent System Operator

(CAISO) was the first regional transmission organization in North America to adopt central wind 

power forecasting in 2004. In the case of CAISO, this was the result of negotiations between 

CAISO and the wind power industry as a quid pro quo for the wind power industry to minimize 

sizable scheduling deviation penalties in exchange for participating in central wind power 

forecasting. Since then, Hydro-Québec adopted central wind power forecasting in 2006; 

followed by the Midwest ISO, the New York ISO (NYISO) and ERCOT in 2008, and PJM in 

2009. Xcel Energy, the Alberta Electric System Operator (AESO), and the Ontario Independent 

Electric System Operator (IESO) all have plans to implement central wind power forecasting in 

2010. The Bonneville Power Administration (BPA) is considering whether to implement central 

wind forecasting as well. Among RTOs, only ISO New England and the Southwest Power Pool 

have not implemented central wind power forecasting. 

 This report describes the status of central wind power forecasting in North America, both 

planned and operating. This report does not discuss the types of wind power forecasting in any 

depth, as this has been covered elsewhere (Monterio et al., 2009; Zavadil et al., 2009; Grant et 

al., 2009; Ernst et al., 2007). The report begins with a summary of wind forecasting companies 

active in currently-operating central wind forecasting systems. The report reviews the individual 

central wind power forecasting systems in North America, both planned and operating, in more 

detail. The report then discusses some common characteristics of operating central wind power 

forecasting systems, reviews some preliminary wind power forecasting performance data, and 

closes with a summary.

II. Types of Wind Power Forecasts and Wind Power Forecast Performance

There are several types of wind power forecasts. A persistence forecast (i.e., assuming 

that future values equal the current value) can be reasonably accurate for the next one or two 

hours, although the accuracy decreases significantly over time. Climatological forecasts (the 

long-term or average value) may also be used and outperform persistence forecasts looking 

further out than four to six hours; however, at any time, the actual wind production may be quite 

different from what is predicted by a climatological forecast.

The wind power forecasting systems discussed in this paper use numerical weather 

forecast models that monitor and predict weather systems in three dimensions using the physical 

laws that govern atmospheric motion and represent the known state of the atmosphere. 

Numerical weather forecast models have limitations in that small atmospheric features cannot be 

accurately predicted. Numerical weather forecast models also have difficulty predicting largescale features that evolve over a one-to-two-week period. Therefore, statistical methods are still 

necessary for very short-term time spans (1-2 hours) and the long-term (>14 days). 

Other strategies can be implemented with numerical weather forecast models to improve 

forecast accuracy, such as using artificial intelligence software to decrease systematic model 
4 

forecast error or to determine the relationship between wind speed and wind production. 

Similarly, an ensemble of multiple wind forecasts can reduce wind forecast errors, as the forecast 

errors from individual wind forecasting models will tend to cancel out (Smith, 2008). 

Different wind power forecasts can be used for different time periods. A situational 

awareness wind forecast is used in real-time for severe weather events. Hour-ahead wind 

forecasts apply updates to generate forecasts as frequently as 5-minutes for the next four to six 

hours ahead. Day-ahead wind power forecasts provide hourly forecasts for the next two-to-four 

days and are typically updated every 6 to 12 hours (Smith, 2009). Additional wind forecasts may 

be prepared focusing on the potential for wind ramps. 

Standard statistical analysis tools are used to evaluate the success of wind forecasting 

systems in predicting actual wind power generation. The Mean Absolute Error (MAE) takes the 

simple average of the absolute values of the individual wind forecast errors. Another measure, 

the Root Mean Square Error (RMSE), involves obtaining the total square error first, then 

dividing by the total number of individual errors, and then finally taking the square root. RMSE 

is more sensitive than MAE to outliers, giving a high weight to large errors since they are 

squared prior to being averaged. The RMSE will always be equal to or greater than the MAE, 

with a large difference between them signaling a high variance in the individual sample errors 

(Matsuura and Willmott, 2005; Ontario IESO, 2009d, p. 3-5). Still other means of evaluating 

wind power forecasts is to compare the performance of the wind power forecast to another 

forecasting method such as persistence or climatology (Zavadil, 2009). 

Table 2 provides some general wind forecast error results. This report will also provide 

wind forecast error results from nearly all of the RTOs and utilities with wind forecasting, 

expressed in MAE or RMSE.2

 

Table 2

Average Wind Forecast Error

by Time Frame

 Forecast Error 

Single Plant Region 

Hour Ahead 

Energy (% Actual) 10 – 15% 6 – 11%

Capacity (% Rated) 4 – 6% 3 – 6%

Day Ahead 

Hourly Energy (% Actual) 25 – 30% 15 – 18%

Hourly Capacity (% Rated) 10 – 12% 6 – 8%

 Source: Smith, 2009.

 

2 These can be found in Table 4, and should be analyzed with caution, as discussed in Section VI.
5 

III. Description of Individual Central Wind Forecasting Vendors

There are four central wind forecasting vendors discussed in this paper that are presently 

providing central wind power forecasting systems in North America – AWS Truewind, Energy 

& Meteo Systems GmbH, Environment Canada, and WEPROG. AWS Truewind is a forecast 

vendor that uses its ‘eWind®’ system to produce forecasts. When producing wind power 

forecasts for ERCOT, AWS Truewind uses a composite of the individual elements of an 

ensemble of forecasts for each wind project in the ERCOT territory. The ensemble includes both 

numerical weather model data and statistical prediction procedures. Currently, AWS Truewind is 

using statistical modeling for all sites providing valid data; a wind generation resource (WGR) 

output model with a mixed approach; and three Numerical Weather Prediction (NWP) models, 

one of which is run every three hours, while the others are run every six hours. AWS Truewind 

plans to add a nine-NWP model ensemble that will run every six hours; a single NWP model that 

will run every hour (known as a Rapid Update Cycle); a statistically optimized ensemble

procedure that weighs each ensemble member according to its performance in a rolling training 

sample; and a statistical WGR power output model for all wind generation resources for which 

the data quality and quantity are adequate (Zack, 2009, p. 13). 

When producing forecasts for the NYISO, CAISO, and SCE, AWS Truewind draws on 

meteorological models, adaptive statistical models, and a forecast delivery system. AWS 

Truewind also utilizes multi-variate linear regression and neutral network statistical models, as 

well as atmospheric models. The atmospheric models include a non-hydrostatic model known as 

the Mesoscale Atmospheric Simulation System, the Weather Research and Forecasting model, 

and ARPS, a model developed and optimized by the University of Oklahoma to forecast severe 

convection and rapidly changing atmospheric conditions (AWS Truewind; Kenneth Pennock, 

pers. comm.). 

PJM and the Midwest ISO rely on Energy & Meteo Systems GmbH, a German-based 

company that uses the Previento forecast model. The Previento model is a physical model that 

relies upon NWP forecasts (Focken and Lange, 2007, p. 3). These NWP inputs include a 

combination of numerical weather models weighted according to the weather situation, sitespecific power curves based on historical data, and a shortest-term model (0-10 hours) based on 

power measurements. Wind turbine de-rating data is integrated in the PJM forecast (Ulrich 

Focken, pers. comm.). 

Environment Canada serves as the forecast vendor for Hydro-Québec, and utilizes a 

GEM 15-km NWP forecast. The wind power forecast uses multiple models, including the 

Anemos/WPPT model, Hydro-Québec’s proprietary models, and forecasting tools installed at 

and operated by Hydro-Québec. The wind power forecast utilizes statistical models that draw on 

NWP forecasts, and wind project generation and turbine availability data as inputs. There is also 

an extensive research and development program on additional and complementary forecasting 

tools at the Institut de recherché d’Hydro- Québec (IREQ), Hydro-Québec’s research institute. 

Part of this R&D effort includes collaboration with Environment Canada on the development of 

a high-resolution GEM LAM 2.5-km forecast, the next generation forecasting tool, which is 

currently under evaluation (Alain Forcione and Jacques Bourret, pers. comm.). 
6 

Finally, AESO contracted with the forecast vendor WEPROG in January 2010. 

WEPROG, which stands for Weather and wind Energy PROGnosis, is a company based in 

Germany and Denmark that uses a short-range ensemble prediction system based on a multischeme approach. The Multi-Scheme Ensemble Prediction System (MSEPS) is an integrated 

weather forecasting system that uses 75 individual forecasts to replicate weather uncertainty for 

the next six-days. The difference of WEPROG’s 75-member ensemble to a multi-model 

ensemble using multiple different NWP models is that the ensemble members are based on a 

single NWP model kernel, where the ensemble members are generated by varying dynamic and

physical processes within the NWP model (WEPROG; Corinna Möhrlen, pers. comm.). 

IV. RTOs and Utilities with Operating Central Wind Power Forecasting Systems

California ISO

CAISO, which serves 75% of California’s load, hit a record peak demand of 50,270 MW 

on July 24, 2006. CAISO has an available generating capacity of 48,954 MW, which does not 

include 10,350 MW of net imports. Of CAISO’s 2,953 MW of installed wind capacity, 1,005 

MW are involved in the ISO’s wind forecasting program (CAISO, 2009a; Jim Blatchford, pers. 

comm.). 

CAISO was the first ISO to implement centralized wind power forecasting in North 

America in June 2004. Its program is known as the Participating Intermittent Resource Program, 

or PIRP. Intermittent generators that participate pay CAISO a $0.10 per megawatt-hour (MWh) 

fee; agree to stay in PIRP for one year; install CAISO’s telemetry equipment; schedule 

consistently with the CAISO’s forecast of wind generation, and do not make advance energy 

bids into the California market in order to mitigate concerns that wind generators would try to 

game the market. The positive and negative imbalances associated with wind power generators 

are netted out monthly, with any remaining imbalances paid or charged at a monthly weighted 

Locational Marginal Price (LMP).

AWS Truewind provides the wind power forecasts to the PIRP scheduling coordinator, 

including: 

• Hour-ahead forecasts for each of the next seven hours, by 15 minutes after each hour 

(hour-ahead is defined as 1 hours and 45 minutes before real time);

• Next day energy forecasts for each hour of the next day, submitted by 5:30 a.m.; and 

• Extended hourly capacity forecasts for days two, three, and four, also delivered by 

5:30 a.m. on Thursdays and Fridays and selected days before holidays. 

CAISO uses the hour-ahead wind power forecast and treats the day-ahead wind power 

forecast as advisory. CAISO does not currently use a wind ramp forecast, but is working with 

DOE and BPA to develop a short-term event predictor and a ramp forecast tool (CAISO, 2009c, 

p. 3; Botterud and Wang, 2009, p. 10; Jim Blatchford, pers. comm.). Though the wind 
7 

forecasting fee charged to participating intermittent resources is currently $0.10 per MWh, the 

wind power forecasting costs have been higher than anticipated, and CAISO covers about 

$0.09/MWh from within its operating budget (Ontario IESO, 2009d, p. 8). In addition, CAISO 

charges an export fee for energy from PIRP facilities exported outside of CAISO. CAISO has 

proposed to the Federal Energy Regulatory Commission (FERC) that eligible intermittent 

resources not participating in PIRP also pay the $0.10 per MWh fee, though this has not yet been 

approved by FERC. 

Wind generators must provide real-time data to CAISO, including wind speed, wind 

direction, barometric pressure, and ambient temperature. There is a minimum requirement of one 

meteorological tower, though CAISO plans to require a second meteorology tower; one which 

could be located in the same location as the first, but at 30 meters below average hub height. 

CAISO also has applied to FERC to require the reporting of outages of one MW or more if the 

generator’s overall capacity is greater than ten MW (CAISO, 2009b). 

CAISO also requires wind generators to supply real-time MW production and MW 

production revenue metering, as well as locational information for a designated turbine. A 

designated turbine is the turbine designated to send in anemometry data to represent a 

surrounding group of turbines. Wind companies must provide this turbine’s longitude, latitude, 

and elevation of its hub height (Blatchford, 2008a, p. 9; Jim Blatchford, pers. comm.). 

CAISO recently concluded a year-long wind power forecasting competition among three 

companies for providing day-ahead and hour-ahead wind power forecasts. Each wind power 

forecasting provider was responsible for supporting day-ahead and hour-ahead forecasts for four 

wind projects. AWS Truewind was selected as the winner of the competition and received a new 

contract to continue wind power forecasting for CAISO. As part of the competition, CAISO 

conducted a statistical analysis of the wind power forecasts and found the following: 

• Aggregate day-ahead wind forecast error was decreased to less than 15% RMSE

• Aggregate hour-ahead wind forecast error was reduced to less than 10% RMSE, 

which is a 20% improvement over current hour-ahead forecasts used by CAISO for 

PIRP.

• Geographic diversity and aggregation of wind power forecasts for individual wind 

projects improved forecasting accuracy in both the day-ahead and hour-ahead time 

frames. This result suggests poor correlation among the direction of the wind power 

forecast errors for each wind project, and that the errors may offset each other. 

• Wind forecast performance is higher at wind production levels over 80% of installed 

wind capacity and under 20% of installed wind capacity. This corresponds to a sharp 

rise in the power curves of wind turbines, and that the amount of energy in wind 

increases with the curve of wind speed, i.e., 10% increases in wind speed creates a 

33% increase in available wind energy (DOE, 2008). Because of that, wind power 

forecasting is less predictable during the wind projects’ mid-range of wind production 

(Blatchford and de Mello, 2009, p. 9). 
8 

Electric Reliability Council of Texas (ERCOT)

ERCOT serves 85% of the load in Texas, representing 75% of Texas geographically. 

ERCOT’s record peak demand reached 63,400 MW in July of 2009. The ISO has an available 

generating capacity of 80,076 MW, with 8,916 MW of installed wind capacity (ERCOT, 2009; 

Saathoff, 2009). Total installed wind capacity in Texas is 9,410 MW as of the end of 2009 

(American Wind Energy Association, 2010). 

ERCOT’s centralized wind power forecasting system became operational in July 2008. 

ERCOT pays for the wind power forecasts and uses them for managing grid operations. An 80% 

exceedance is applied to the wind power forecast that is used in day-ahead planning, meaning 

there is an 80% chance that wind production meets or exceeds the forecast (Zack, 2008, p. 18; 

Botterud and Wang, 2009, p. 10). ERCOT is considering whether to decrease the 80% 

exceedance in its day-ahead wind power forecast to 50%, as the 80% results in more reserves 

than are probably needed. Qualified scheduling entities (QSEs) are required to use an ERCOTprovided day-ahead wind forecast as the planned operating level for wind power in their dayahead resource plan, but QSEs are permitted to provide a lower value if wind capacity will be 

unavailable or operating at a lower capacity level. QSEs are not required to use the ERCOT dayahead wind power forecast once ERCOT has completed its capacity studies for the next day 

(Maggio, 2010a). 

The short-term wind power forecast is produced hourly, is delivered fifteen minutes after 

the hour, covers the next forty-eight hours and represents a 50% exceedance that wind 

production meets or exceeds the forecast. During the operating day, QSEs may use their own 

short-term wind power forecast, on the condition that it must outperform ERCOT’s wind power 

forecast. As with the day-ahead wind power forecast, QSEs may provide a lower value if wind 

capacity is unavailable or operating at a lower capacity level (Maggio, 2010a). 

A long-term climatology report, which is not currently required but will be required when 

the nodal market begins in late 2010, is a facility-by-facility daily wind power forecast, by hour, 

for the next thirty-six months. The long-term wind power forecasts will be required monthly, and 

ERCOT is also in the process of developing a large ramp alert forecast (ERCOT, 2008a, p. 4-7; 

Zack, 2009, p. 16; David Maggio, pers. comm.; Doggett, 2009). 

Several data inputs are used for each wind project, including observed generation, the 

capacity rating and model of each individual turbine, the total number of wind turbines, and the 

average hub heights and geographic location of the center of the wind project. Additionally, 

ERCOT requires meteorological data such as wind speed in miles per hour, wind direction in 

degrees, temperature in degrees Celsius, and barometric pressure measured by millibars. Turbine 

outage and availability data is currently provided on a voluntary basis, but ERCOT will likely 

require this data in the future. Observed availability and observed base point for each wind 

project is required, and intended to be available and used under ERCOT’s Nodal Market, 

however they are not currently available to the wind power forecaster. The base point is the 

dispatch signal that is sent to the resource based on ERCOT’s Security Constrained Economic 

Dispatch algorithm, and is intended to be used by the wind power forecast vendor to determine if 

a wind resource is curtailed or not (ERCOT, 2008a, p. 17; ERCOT, 2008b, p. 5; David Maggio, 
9 

pers. comm.). ERCOT is also developing rules for forecasting potential wind capacity if a wind 

generator is being curtailed in order to estimate future wind output when ERCOT releases wind 

generation from curtailment. Such a requirement would allow system operators to have a better 

sense of future wind output after curtailment and may result in releasing wind generation from 

curtailment earlier (Maggio, 2010b). 

ERCOT also uses the wind power forecast as an input for determining the need for 

monthly non-spinning reserve service — an ancillary service that ERCOT procures which must 

be able to reach a specified capacity level within 30 minutes. To determine the monthly 

requirement for non-spinning reserve service, ERCOT divides the month into four-hour blocks 

and establishes the monthly requirement to ensure that the combination of regulation up and nonspinning reserve service will cover the 95th percentile of the net load forecast error. Should the 

net load forecasts be conservative, ERCOT will remove any bias from the net load forecasts and 

add that value to non-spinning reserve service when determining the monthly requirement 

(Maggio, 2010a).

Hydro-Québec 

Hydro-Québec, which serves the Québec province of Canada, reached a record peak 

demand of 37,230 MW on January 16, 2009. Hydro-Québec has an available generating capacity 

of 43,664 MW, with 657 MW of installed wind capacity (Hydro-Québec, 2009; Francis 

Gosselin, pers. comm.). 

The first phase of Hydro-Québec’s wind power forecasting system was launched in 

November 2006, with Environment Canada as the wind power forecast vendor (Balvet et al., 

2009). At present, the NWP models are updated twice daily, at midnight and 12 p.m. (UTC), and 

look forward 48 hours. Hydro-Québec recently augmented their wind power forecasts with 

additional runs at 6 a.m. and 6 p.m. There is no wind ramp forecast currently. The wind power 

forecasts are updated hourly, and meteorologists send alarms to grid operators and wind project 

operators if weather conditions are present that could lead to conditions where wind turbines stop 

generating because of high wind speeds. These wind power forecasts are paid for by HydroQuébec (Alain Forcione and Jacques Bourret, pers. comm.). 

Hydro-Québec uses the wind power forecasts for day-ahead scheduling (including 

transaction schedules), reserve requirements, as well as for intra-day rescheduling. HydroQuébec also sends the wind power forecast to wind project operators for use in short-term 

maintenance scheduling (Alain Forcione and Jacques Bourret, pers. comm.). 

Wind companies must provide Hydro-Québec operational planning data for each wind 

turbine, including planned turbine availability, the wind turbine power curves, control system 

information, and information on “cold weather packages” that boost thermal limit capabilities 

during periods of cold weather (Alain Forcione and Jacques Bourret, pers. comm.). HydroQuébec also requires real-time data for each wind turbine, including the average, maximum, 

minimum, and standard deviation of the following: kW of active power; nacelle direction in 

degrees relative to true North; blade position in degrees; temperature at the nacelle level in 
10 

degrees Celsius; wind speed in meters per second, which is measured by the nacelle 

anemometer; and wind direction in degrees, relative to true North, measured by the nacelle wind 

vane. The wind status of each wind turbine in real-time must also be supplied (Hydro-Québec 

Distribution, 2009, p. 5).

For each wind project, Hydro-Québec also requires information regarding the wind 

project layout and facility level real-time data, including the average, minimum, and maximum 

of the following: the kW of active power, for which the standard deviation is also required; the 

kW of power available from the substation, the facility, and from individual wind turbines; the 

number of available wind turbines; and the number of wind turbines not operating because of 

low or high wind speeds, or low temperatures (Alain Forcione and Jacques Bourret, pers. comm.; 

Hydro-Québec Distribution, 2009, p. 2). Hydro-Québec also wants the average, minimum, 

maximum, and standard deviation of a range of data drawn from meteorological towers, 

including the horizontal and vertical wind speeds in meters per second at each mast anemometer, 

the wind direction in degrees relative to true North at each weather vane, the temperature in 

degrees Celsius at each mast anemometer, the percent relative humidity, and the atmospheric 

pressure in kilopascals (Hydro-Québec Distribution, 2009, p. 3). 

Midwest ISO

The Midwest ISO footprint covers all or most of North Dakota, South Dakota, Nebraska, 

Minnesota, Iowa, Wisconsin, Illinois, Indiana, Michigan and parts of Montana, Missouri, 

Kentucky, and Ohio. The Midwest ISO has a total available generating capacity of 138,556 MW, 

and about 7,200 MW of installed wind capacity. On July 31, 2006, the Midwest ISO experienced 

its record peak demand of 116,030 MW (Midwest ISO, 2009a; Kris Ruud, pers. comm.). 

The Midwest ISO began using centralized wind power forecasting in June 2008. The 

Midwest ISO uses the wind power forecast for next day and multi-day-ahead transmission 

security planning and outage coordination, as well as next-day and intraday reliability analysis. 

The Midwest ISO also uses the wind power forecast to project the impact of wind variability on 

transmission flowgates and to manage transmission constraints (Botterud and Wang, 2009, p. 10; 

Midwest ISO, 2010). 

Energy & Meteo Systems uses three NWP models, each of which is used at four levels. 

The four levels include commercial pricing (CP) nodes, zones, regions, and the entire Midwest 

ISO. The CP nodes typically signify a single wind project, while the regions match up 

geographically with the Midwest ISO’s Reliability Regions (East, Central, and West), and the 

zones represent smaller areas, such as states. In addition, Energy & Meteo Systems also provides 

a wind power forecast of the MW power output for the optimal combination of all three wind 

power forecasts, and a statistical power curve for each wind project (Ontario IESO, 2009b, p. 6; 

Michael McMullen, pers. comm.; Ulrich Focken, pers. comm.). 

The Midwest ISO receives forward hourly projections updated from Energy & Meteo

Systems providing projected wind power output for each hour over the next seven days. The first 

six hours are considered the short-term forecast, while the remainder of the wind power forecast 

period is considered medium- or long-term. Several wind power forecasts indicating possible 
11 

wind ramps are provided, but the ramp forecasting metrics are still under development (Ulrich 

Focken, pers. comm.; Michael McMullen, pers. comm.). The Midwest ISO shares its wind 

forecast with PJM for coordinating transmission security between the two RTOs (Midwest ISO, 

2010). 

The Midwest ISO pays for the central wind-power forecasting service, and market 

participants are required to provide the Midwest ISO with non-binding, day-ahead intermittent 

resource forecasts. These consist of an hourly forecast of projected next-day output and are not 

currently used for dispatch purposes. For each wind plant, the Midwest ISO provides Energy & 

Meteo Systems with the latitude and longitude, the hub height, the maximum and historical MW 

output, and the real-time output. Wind turbine outages are not currently factored into the wind 

power forecast, but the Midwest ISO anticipates doing so in the future (Ontario IESO, 2009b, p. 

6; Midwest ISO, 2009b, p. 2; Michael McMullen, pers. comm.). 

New York ISO

The New York ISO (NYISO), which serves the state of New York, reached a record peak 

demand of 33,939 MW on August 2, 2006. The ISO has a total available generating capacity of 

38,190 MW, with an installed wind capacity of 1,275 MW (Edelson, 2009c; NYISO, 2009). 

NYISO implemented central wind power forecasting in June 2008, using AWS 

Truewind’s eWind® forecast. NYISO uses wind power forecasts to review day-ahead unit 

commitment schedules to ensure that there is enough generation committed to meet predicted 

load and reserve requirements, and to make real-time commitment and dispatch decisions 

(Botterud and Wang, 2009, p. 10; David Edelson, pers. comm.). In May 2009, NYISO also 

began using the wind power forecasts to make individual wind plant economic dispatch 

decisions, also known as economic curtailments (David Edelson, pers. comm.; NYISO Market 

Issues Working Group, 2007, p. 4). 

NYISO receives an updated, day-ahead wind power forecast twice daily – once at 4 a.m., 

and again at 4 p.m. – that covers the next two operating days. They also receive a real-time wind 

power forecast that is updated every fifteen minutes and provides fifteen-minute interval data for 

the next eight hours (Edelson, 2009b, p. 5). The wind power forecasts are blended with 

persistence forecasts to generate a wind forecast for the next 21⁄2 hours at fifteen-minute intervals. 

These forecasts are used when making real-time commitments and when scheduling external 

transactions. Wind power and persistence forecasts for the next hour at five- to fifteen-minute 

intervals are also used in the real-time dispatch. NYISO may vary the actual blend between 

persistence and the wind power forecast depending on grid conditions and the wind power 

forecast. There currently is no wind ramp forecast; however, it is under consideration (Edelson, 

2009a, p. 7; David Edelson, pers. comm.). 

NYISO assesses a fee to each wind project to pay for central wind power forecasting. The 

charge, which is subject to change as more wind projects are added, includes a fixed monthly fee 

of $500 and a separate monthly charge of $7.50 per MW of installed wind capacity (NY SO, 

2010). The wind projects are also required to supply data for the wind power forecast, with daily 

penalties of the greater of $500 or $20/MW if there is a persistent lack of data provided (Federal 

I
12 

Energy Regulatory Commission, p. 4). The required information includes turbine manufacturer 

specifications, the latitude and longitude coordinates of each turbine, the manufacturer’s power 

curve, plant/turbine availability, and plant or turbine level power output data. On-site 

meteorological data is also required, including wind speed, wind direction, pressure, temperature, 

humidity and dew point. NYISO wants data to be taken from multiple heights and from as many 

points at the wind plant as possible. NYISO prefers that several measurements be taken from a 

number of stand-alone meteorological towers. While NYISO will accept turbine-mounted 

sensors for data retrieval, they are not considered ideal because they are impacted by wake 

effect. Also, while NYISO does not require off-site meteorological towers, it notes that upwind, 

off-site towers are helpful in predicting large ramp events. Currently, the meteorological data is 

required from at least one point every 15 minutes, but this requirement will be increased in June 

2010 to require data every thirty seconds, and from locations such that no individual turbine is 

more than 5 km from a reporting sensor (Edelson, 2009b, p. 6-12; David Edelson, pers. comm.). 

PJM 

The PJM footprint covers all or parts of thirteen states and the District of Columbia, 

including Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North 

Carolina, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia. The record peak demand 

for the RTO is 144,644 MW, which was reached on August 2, 2006. PJM has a total available 

generating capacity of 164,895 MW, with an installed wind capacity of about 2,500 MW (PJM, 

2009a; Sanjay Patil, pers. comm.). 

PJM launched its centralized wind power forecasting program in April of 2009. Wind 

power forecasting is done for 28 of the 32 wind projects in PJM; the other four are considered 

too small to include (Ontario IESO, 2009d, p. 9; PJM, 2009c; PJM, 2009d). PJM pays for the 

centralized wind power forecast, and uses them in its reliability assessment. The wind power 

forecast uses a PJM-defined confidence interval, is conducted on five aggregation levels, and 

includes a statistical power curve for each wind project (Ulrich Focken, pers. comm.). 

For the real-time reliability assessment, PJM utilizes a short-term wind power forecast to 

assess current-day congestion and to ensure that there is enough generation available to account 

for variability in wind power output. The short-term wind power forecast is updated every ten 

minutes with a forecast interval of five minutes for the next six hours. For the day-ahead

reliability assessment, PJM uses a medium-term wind power forecast, which runs from six hours 

ahead to 48 hours ahead. The medium-term wind power forecast is used to assess day-ahead 

congestion and to check that there is enough generation scheduled to supply the forecasted load, 

transaction schedules, and reserve requirements. In addition to this, PJM also employs a longterm wind power forecast that runs hourly from 48 hours ahead to 168 hours ahead, and is used 

for forecasting wind power over holidays and weekends. A ramp forecast is also provided, 

updated every ten minutes in intervals of five minutes for a six-hour time horizon. All wind 

power forecasts are prepared for individual or aggregate wind projects, as designated by PJM

(PJM Power System Coordination Department, 2009, p. 58-60). PJM uses the same wind power 

forecasting vendor as the Midwest ISO, and both RTOs are exchanging forecasts. Energy &

Meteo Systems is also using telemetered data across both RTOs to increase the accuracy of the 

wind power forecast. 
13 

PJM requires certain data and information from wind projects. For each wind turbine, 

wind generators must provide general information such as the class of the turbine, power 

generation threshold rates (i.e. minimum and maximum wind speeds), the turbine’s capacity, the 

manufacturer power curves for each individual wind turbine, the longitude and latitude of the 

wind project site or each turbine, and the hub height of the wind power facility. In addition, wind 

projects are expected to supply the historic data on measured MW output, outages, and wind 

speeds at hub height for existing facilities that connect to PJM or bid into the PJM market (PJM 

Intermittent Resource Working Group, 2009; PJM Power System Coordination Department, 

2009, p. 58-59).3

PJM also wants data regarding ambient temperature operating limits and information on 

cold-weather package data. In addition, PJM requires the aggregate reactive capability curve, or 

‘D-Curve,’ and real-time aggregate wind project MW output, telemetered at the low-side net and 

high-side net of the wind project. The ‘low-side net’ and ‘high-side net’ refers to the transformer 

at the point of interconnection to the grid. Metering is required for both the high-voltage and 

low-voltage sides (PJM Power System Coordination Department, 2009, p. 58; Ken Schuyler, 

pers. comm.). 

Finally, PJM requires that wind projects install at least one meteorological tower for 

providing real-time meteorological data, including wind speed in meters per second and wind 

direction measured in degrees from true north. Alternatively, a wind plant may use wind speed 

and direction data from select turbines’ anemometers and wind vanes. PJM prefers, but does not 

require, temperature in degrees Fahrenheit and pressure in hectopascals. Percent humidity data is 

also accepted, though not required (PJM Power System Coordination Department, 2009, p. 

58-59). 

Southern California Edison

SCE serves a 50,000-square-mile area of California and reached a record peak demand of 

23,303 MW on August 31, 2007. SCE considers its available generating capacity data to be 

confidential, but has reported its 1,073 MW of installed wind capacity (Barry Gilman, pers. 

comm.; Arthur Canning, pers. comm.). Although SCE is a participating transmission owner in 

CAISO, SCE has its own central wind forecasting system and does not participate in PIRP. 

SCE began using wind power forecasting in November of 2000 and uses AWS Truewind 

as their wind power forecast vendor. SCE uses the wind power forecasts for scheduling wind 

energy, and pays for the wind power forecasting service internally. The wind power forecasts are 

updated twice daily, once at 5 a.m. and again at 5 p.m. Both forecasts look ahead seven days. 

While no wind ramp forecast is currently prepared, SCE is considering developing such a 

forecast (Barry Gilman, pers. comm.; Bailey et al., 2001; AWS Truewind). 

 

3 ‘Outages’ refers to any planned, forced, or unplanned outage of turbines of 1 MW or more, or an outage lasting 

1 hour or more. Outages related to wind speed, however, will be modeled by the wind power forecasting system, and 

do not have to be reported (PJM, 2009c). 
14 

SCE uses an outside company to collect wind data, which is taken directly off the meters 

and remotely read. They then make it available to both AWS Truewind and SCE. The data 

includes MW production metering and meteorological information, including wind speed, 

direction, temperature, and humidity. SCE uses twelve meteorology towers, with six each in 

Tehachapi and San Gorgonio. Data on outages and curtailments are incorporated into the wind 

power forecast system both before wind power production (for forecast correction) and postproduction (for calibration of the wind power forecast) (Barry Gilman, pers. comm.). 

V. Central Wind Power Forecasting Systems under Development

At least four utilities or regional grid operators have central wind power forecasting 

programs under development, including Xcel Energy, the Independent Electric System Operator 

of Ontario (IESO), the Alberta Electric System Operator (AESO), and the Bonneville Power 

Administration (BPA). These are described in this section. 

Alberta Electric System Operator

AESO, which serves the Alberta province of Canada, reached a record peak demand of 

9,806 MW in December 2008. AESO has an available generating capacity of about 12,700 MW, 

with about 560 MW of installed wind capacity (Alberta Electric System Operator; Alberta 

Electric System Operator, 2009b). 

Following completion of a wind forecasting pilot project in 2008, AESO issued a twoyear contract in January 2010 with WEPROG to provide a long-term wind power forecast of up 

to six days and a wind ramping forecast out several hours and updated every 10 minutes. 

WEPROG is also to provide AESO with visualization tools, uncertainty forecasts, and 

notifications of any potential system events such as multi-hour wind ramping (Kehler, 2010). 

The AESO eventually wants to expand hourly wind power forecasting up to 72, 96, or 120 hours 

ahead, and to have intra-hour forecasts at ten-minute intervals for the next six hours (Alberta 

Electric System Operator, 2009d, p. 21-28). 

AESO plans to begin consulting with industry participants on rules, procedures, 

standards, technical requirements, communication protocols, and data requirements for central 

wind power forecasting. AESO also proposes to make near-term and real-time aggregated wind 

power forecasts available to market participants and to establish a standing work group on wind 

power forecasting.

All wind generators interconnected with AESO will have to participate in the wind power 

forecast. AESO has proposed that wind generators pay the costs of central wind power 

forecasting through a $/MWh charge, escalating the charge by 10% annually, reconciling the 

differences between wind power forecast costs and revenues from the surcharge annually 

(Alberta Electric System Operator, 2009a). AESO also has proposed data requirements for wind 

projects, such as ten-minute meteorology tower and wind generation data, and available capacity. 

The wind power forecasts will incorporate up to two years of historical meteorological and 

power data (Frost, 2009). 
15 

Bonneville Power Administration

BPA serves an area of 300,000 square miles that includes all of Washington, Oregon, 

Idaho, western Montana, and small contiguous portions of California, Nevada, Utah, Wyoming 

and eastern Montana. BPA has a record peak demand of 10,500 MW. It has an available 

generating capacity of 21,580 MW, with 2,284 MW of installed wind capacity (Bonneville 

Power Administration, 2009a; Bonneville Power Administration, 2009b, Silverstein, 2009). 

Though BPA does not currently have an operational centralized wind-power forecasting

system, it is considering whether to prepare its own wind power forecast or to contract out to a 

third party. A decision is expected in 2010. BPA also installed fourteen meteorology devices in 

its balancing authority area. Thirteen of these meteorology sites are currently posting data 

publicly, with the fourteenth under study.4

 Dispatchers and schedulers are expected to be 

working with wind generation and forecast displays in late 2010. BPA will also be developing a 

“wind desk” over the next two years to aid dispatchers (Bart McManus and Matthew Neel, pers. 

comm.; Bonneville Power Administration, 2009c, p. 25). 

Independent Electric System Operator of Ontario

The Ontario IESO, which serves the Ontario province in Canada, reached a record peak 

demand of 27,005 MW on August 1, 2006. It has an available generating capacity of about 

35,465 MW, with about 1,200 MW of installed wind capacity (Ontario IESO, 2009c; Ontario 

IESO, 2009g; Ontario IESO, 2009f). 

IESO does not currently have central wind power forecasting. IESO uses a decentralized 

wind forecasting approach by requiring each wind generator to provide a day-ahead wind power 

forecast by 11 a.m. Wind generators are required to update their forecasts where actual output is 

reasonably expected to differ from original forecasts by 2% or 10 MW, whichever is greater. 

Wind companies comply with this requirement differently, with various companies submitting 

updates to their forecasts hourly, while other wind companies only submit the day-ahead 

forecast. The Market Assessment and Compliance Division of IESO, which is charged with 

enforcement of the market rules, assesses whether generators have exercised due diligence in 

revising and updating dispatch data/forecasts to reflect changed conditions or expected injections 

(Ontario IESO, 2009b, 2; Martin Hastings, pers. comm.). 

Presently, day-ahead wind power forecasts are used to help assess expected system 

conditions leading up to real-time. The wind power forecasts are inputted into the run-through of 

the pre-dispatch every hour, and the results are used to help make decisions regarding day-ahead 

unit commitment, spare generation on-line actions, and intertie transaction scheduling (Ontario 

IESO, 2008, p. 2). Real-time power scheduling in Ontario is done on a 5-minute basis. IESO 

uses 5-minute forecasting in real-time, relying upon a telemetry snapshot of wind output from 10 

minutes prior to setting the schedule in real-time. 

 

4

Bonneville Power Administration, “Meteorological Information from BPA Weather Sites,” 

http://www.transmission.bpa.gov/Business/Operations/Wind/MetData.aspx. 
16 

IESO announced it will convert to central wind power forecasting and will issue an RFP 

for a wind power forecasting vendor by the end of the second quarter of 2010 (Ontario IESO, 

2009f; Deven Huber, pers. comm.). IESO also anticipates issuing new telemetry requirements for 

wind production data, metrological data and for planned and forced wind plant outages 

(Rochester, 2010). IESO plans to assign the wind forecasting costs to wind companies, with 

costs expected to be about $0.135/MWh, in Canadian dollars (about $0.127/MWh in U.S. dollars 

as of January 2010) (Ontario IESO, 2009a). 

Xcel Energy

Though Xcel Energy serves parts of eight states, the information contained in this paper 

is only applicable for Xcel Energy’s operations in Colorado. The record peak demand for this 

area is 6,884 MW, which was reached in the summer of 2005. It has an available generating 

capacity of 7,738 MW, with 1,234 MW of installed wind capacity (Public Service Company of 

Colorado, 2009; Craig Cox, pers. comm.). 

Xcel Energy expects to launch centralized wind power forecasting in August 2010. The 

National Center for Atmospheric Research (NCAR) is working with Xcel to develop the wind 

power forecast system. NCAR plans to develop a prototype system and run test forecasts during 

an eighteen month period before transferring the wind power forecasting system over to Xcel; 

receipt of these ‘test’ forecasts from NCAR began on September 25, 2009 (University 

Corporation for Atmospheric Research, 2009; Keith Parks, pers. comm.). 

NCAR’s wind power forecasting system will use observations of current atmospheric 

conditions from several sources. Turbine-level and onsite meteorology tower information will 

be collected from the wind projects into an Xcel Energy scheduling system, and Xcel will also 

deploy remote sensing technologies for short-term forecasting purposes. The data will then be 

fed into three NCAR-based tools: the Real-Time Four-Dimensional Data Assimilation System, 

which constantly renews the simulations with observation updates; the Weather Research and 

Forecasting computer model, which generates detailed simulations of future atmospheric 

conditions; and the Dynamic Integrated Forecast System, which statistically optimizes the output 

based on recent performance. The wind forecasting system involves a 3-kilometer nested 

forecasting grid, which is updated every three hours (University Corporation for Atmospheric 

Research, 2009; Parks, 2009, p. 5). 

VI. Characteristics of Operating Central Wind Power Forecasting Systems

Centralized wind power forecasting has evolved over time as more utilities and RTOs 

consider adopting and using forecast data. This section compares and discusses some of the 

characteristics of central wind power forecasting systems.

Wind Power Forecasting Services: Wind forecasting systems have become more evolved 

and more complex over time. For instance, the California ISO emphasizes hour-ahead wind 

power forecasting for the hour-ahead market, and a separate day-ahead wind power forecast is 

prepared, but is considered advisory. The California ISO also receives extended hourly forecasts 
17 

for two to four days ahead. In contrast, both the Midwest ISO and PJM, for instance, receive 

hourly wind forecasts for the next seven days, with PJM’s shorter term wind forecasts of 5-

minute intervals over the next six hours updated every 10 minutes. The Midwest ISO’s wind 

forecast is prepared at four aggregation levels (commercial pricing nodes, zones, regions, and the 

entire Midwest ISO footprint) while PJM’s covers five aggregation levels. 

Who Pays: Two RTOs, the California ISO and NYISO, at least partly recover the wind 

power forecasting costs directly from wind generators, and AESO and IESO have also proposed 

charging wind generators for their central wind forecasting programs. Other RTOs and utilities 

have absorbed the full costs of central wind power forecasting, including PJM, ERCOT, the 

Midwest ISO, and SCE. Those with central wind power forecasting initiatives under 

development, other than AESO and IESO, have not announced whether they will assess a fee to 

wind generators or absorb the costs of central wind power forecasting.5

 

Data Quality and Availability: Data quality and availability has emerged as an important 

issue in implementing central wind power forecasting. Turbine availability is often assumed at 

100% in central wind-power forecasting models, and the reporting of wind turbines being offline has not always been incorporated in wind power forecast models. Furthermore, the 

communication links between wind projects and the central wind power forecaster have not 

always functioned well. The performance of central wind-power forecasting systems has 

sometimes suffered as a result. Consequently, RTOs and utilities have made data access and 

quality priorities for launching and maintaining a central wind-power forecasting system. Table 

3 shows that wind projects with greater amounts of data available to forecasters have a lower 

annual MAE for forecasting the next operating hour. 

Table 3

Examples from the California ISO on

Data Availability and Wind Forecasting Performance

Facility Data Availability

Next Operating Hour 

Forecast

Annual MAE

(% Capacity)

Next Operating Hour

Forecast

Annual Net Deviation

A1 98.37% 11.30% -0.18%

A2 87.18% 14.59% 2.18%

Source: Derived from Blatchford, 2008b, p. 13.

Figure 1 illustrates the forecasted wind speed and power output as compared to a wind 

turbine power curve. A wind forecast with good data quality and availability will have projected 

output that tracks close to the manufacturer’s power curve, as seen in the figure on the far left. 

Poorer quality data results in more spread around the power curve, as seen in the figure on the 

right. 

 

5 This includes the Bonneville Power Administration and Xcel Energy. 
18 

Figure 1

Forecasted Wind Speed vs. Output

(an example from the California ISO)

Source: Derived from Blatchford, 2008b, p. 12. 

 

Ramp Forecasts: Few of the operating central wind power forecasting systems include a 

forecast for ramps from wind generation, although several are considering adding such a 

forecast. PJM receives an updated wind ramp forecast every ten minutes, covering 5-minute 

intervals for the next six hours. The Midwest ISO receives wind forecasts that indicate potential 

wind ramps, but ramp forecasting metrics are still under development. ERCOT, NYISO, and 

CAISO are all considering incorporating a wind ramp forecast. 

How Central Wind Power Forecasts Are Used: The day-ahead markets provide a 

financial vehicle for market participants to buy and sell energy. In most regions, the day-ahead 
19 

market uses a security constrained unit commitment (SCUC) that determines the most economic 

selection of resources to meet bid in load requirements with consideration of the transmission 

network. Additionally, a second SCUC estimates what additional resources are necessary (if 

any) over and above what has already been self-committed for bilateral contracts and committed 

by other markets, such as the day-ahead market and the ancillary services market, to meet the 

expected real-time demand. This is often referred to as a reliability unit commitment and will 

usually differ from the day-ahead market in that an ISO provided forecast is used as input. 

Centralized wind power forecasting is not generally used to affect day-ahead market

schedules. Instead, wind power forecasts are normally used to ensure that enough generation is 

committed to meet expected load. The Midwest ISO also uses its wind power forecast to 

conduct week-ahead and intra-day reliability analysis and to determine wind’s impact on 

transmission flowgates. ERCOT uses an 80% exceedance factor (i.e., there is an 80% 

probability that the projected wind power will exceed a certain level) for the wind power forecast 

in evaluating whether there are sufficient generating resources for the next day. NYISO uses its 

wind power forecasts to review day-ahead commitments to ensure there is sufficient generation, 

but in addition, uses wind power forecasts to assist in real-time commitment and dispatch. Wind 

power forecasts in the California ISO are used in the hour-ahead market.

When the unit commitment process is centralized, wind integration studies have shown 

very significant benefits of using a wind power forecast in day-ahead market schedules. If the 

contributions from wind power are not taken into account when producing the day-ahead market 

schedule, then conventionally fueled generators may be used inefficiently when wind energy is 

added to the system in real-time. Indeed, the economic gains reported from use of the wind 

power forecast are savings to other generators and to retail electric customers (not necessarily the 

wind generators themselves) due to more efficient dispatch, and saved fuel and lower O&M 

costs. In addition, if wind power forecasts are used in the centralized reliability unit commitment, 

it will help identify the additional reserves needed to maintain system reliability, as well as the 

congestion points that need to be relieved. 

The economic and reliability gains from using wind power forecasting can only be 

realized if wind power forecasts are integrated with day-ahead schedules. Some may contend that 

this would represent a significant change in procedure for ISOs and RTOs, or a preferential 

treatment of wind relative to other types of generation. However, similar to the ISO’s and RTO’s 

creation of a load forecast today, the ISO’s and RTO’s creation of a combined “load net wind” 

forecast could be used after clearing the financial day-ahead market in the reliability 

commitment process (usually considered the first stage of the next day’s real-time market). The 

ISO and RTO process to commit sufficient resources to supply anticipated load may have to 

account for the increased uncertainty around the wind power forecast. That said, the “load net 

wind” forecast should contribute to more efficient market operation and dispatch, improve 

overall operating reliability, and should not financially benefit wind generators over what they 

would otherwise receive as price-takers in the real-time market, so this is quite analogous to the 

use of an improved system load forecast that is created by the ISO or RTO.

Certain system- or market-specific issues may require further study. For example, 

available forecasts and tools must be investigated to ensure that they are sufficiently accurate for 
20 

use in security constrained unit commitment. The forecast error may be also be reflected in the 

day-ahead commitment schedule and prices, and issues around cost responsibility for deviations 

from the forecast must be considered. Nevertheless, the value of incorporating the wind power 

forecast into day-ahead scheduling is significant. 

Wind Power Forecast Performance: Specific performance data of wind power forecasts 

is provided in Table 4, though caution should be used in its interpretation. The data in Table 4 

encompasses different time periods, making comparisons difficult. Also, many wind power 

forecasting systems have not been in operation for very long. Added to that, wind power forecast 

performance may vary significantly (5% or more of the installed capacity of wind) because of 

location, season, and weather regime. Some weather regimes are also more sensitive to small 

variations in the start-up conditions of the wind power forecast. Therefore, small differences in 

current weather conditions can lead to large differences under future weather conditions. In 

these weather regimes, the performance of wind power forecasting systems is generally not as 

good as weather regimes with less sensitivity (Zavadil, 2009). 

Most importantly, the wind power forecast error numbers in Table 4 cannot be compared 

with the typical errors for single wind plants because they are aggregated values for the entire 

system. Geographic distribution of multiple wind plants will reduce the aggregated error rates 

when compared with the errors from individual wind plants. Comparing the error values from 

various RTO regions is difficult, however, since each RTO varies in terms of its geographic 

distribution and penetration of wind plants. Conversely, the wind forecast error statistics

provided for Hydro-Québec are for a specific wind project which is located in complex terrain, 

and should not be compared to the wind forecasting errors that are aggregated values. 
21 

Table 4. Wind Forecast Errors for Various Central Wind Power Forecasting Programs6

(as a percent of installed wind capacity)

Record Peak 

Demand

Available 

Generating 

Capacity

Installed 

Wind 

Capacity

MAE: Time 

Period MAE: Forecast Description MAE

RMSE: 

Time 

Period

RMSE: Forecast Description RMSE

PJM May 2009-

July 2009

Monthly for intra-day forecast (0-

24hrs-ahead): 

4.9% -

5.1%

May 2009 - 

July 2009

Monthly for intraday forecast (0-

24hrs-ahead): 6.5% - 7.3%

144,644 MW 164,895 MW About 2,500 

MW

Monthly for day-ahead forecast 

(24-48 hrs-ahead):

5.9% -

7.9%

Monthly for day-ahead forecast 

(24-48 hrs-ahead):

8.3% –

10.3%

Monthly for evening 4pm forecast 

(8-32 hrs-ahead):

5.2% - 

5.6%

Monthly for evening 4pm forecast 

(8-32 hrs-ahead): 6.9% - 7.6%

ERCOT 62,339 MW 80,076 MW 8,916 MW

May 2009-

August 

2009

Monthly for 4:30 pm system-wide 

day-ahead forecast, all-hours 

8.28% - 

10.73% N.A. N.A. N.A.

Midwest ISO 116,030 MW 138,556 MW About 7,200 

MW

May 2009-

July 2009

Monthly for 4:30 pm day-ahead 

forecast, all-hours 

3.3% - 

4.5%

Aug 2008 - 

Aug 2009

Monthly for intraday forecast (0-

24hrs-ahead): 4% - 7%

Monthly for day-ahead forecast 

(24-48 hrs-ahead) 5% - 10%

NYISO 33,939 MW 38,190 MW 1,275 MW June 2008 - 

March 2009

One hour ahead 4.8%

N.A. N.A. N.A.

Day ahead 11.5%

CAISO 50,270 MW 48,954 MW7 1,005 MW8 N.A. N.A. N.A.

July 2008 

to July 

2009

PIRP anticipated accuracy error 

hour-ahead <7% RMSE

PIRP anticipated accuracy error 

day-ahead <15% RMSE

HydroQuébec9 37,230 MW 43,664 MW 657 MW

March 15, 

2009 – 

June 1, 

2009

1 hr-ahead 10 8.5% March 15, 

2009 – 

June 1, 

2009

1 hr-ahead 12.6%

4 hrs-ahead 13.0% 4 hrs-ahead 18.0%

8 hrs-ahead 14.2% 8 hrs-ahead 19.8%

12 hrs-ahead 14.4% 12 hrs-ahead 20.1%

24 hrs-ahead 15.0% 24 hrs-ahead 20.7%

 

6 Although the Ontario IESO does not currently have a central wind forecast, it reported that as of June 2009, the year-to-date MAE system-wide for wind 

forecasting was 6.7% for the hour-ahead forecast, and 13.6% for the day-ahead. The system-wide RMSE was 8.6% for hour-ahead and 17.9% for day-ahead. 

Forecast errors for individual wind power projects were slightly higher: MAE ranged between 11% to 17% for the hour-ahead forecast, and 17% to 30% for the 

day-ahead; RMSE ranged from 16% to 23% for the hour-ahead forecast, and from 17 % to 30% for the day-ahead (Ontario IESO, 2009d, p. 8). 7 Does not include 10,350 MW of net imports.

8 There is a total of 2,953 MW of installed wind capacity in CAISO, of which 1,005 MW is in CAISO’s wind forecasting program. 9

 Wind forecast error statistics are for a single wind project. 10 By comparison, there was a 7% MAE, and a 10.4% RMSE, one hour ahead pure persistence performance for HQ.
22 

VII. Summary

More and more utilities and RTOs are adopting, or planning to adopt, central wind 

forecasting systems as a means of more effectively integrating greater amounts of wind power. 

SCE and the California ISO adopted central wind forecasting in 2000 and 2004, respectively. 

Hydro-Québec implemented central wind forecasting in 2006 followed by ERCOT, NYISO, and 

the Midwest ISO in 2008, and PJM in 2009. At least four other entities have plans to implement 

central wind forecasting in 2010. A majority of the RTOs and utilities absorb the costs of wind 

forecasting internally, while the California ISO and New York ISO charge wind generators to 

recover at least part of the wind forecasting costs. 

Data quality and availability have emerged as important issues for RTOs and utilities in 

implementing central wind forecasting systems, though several difficulties have surfaced in data 

transfer and communication. Information such as turbine availability and whether wind plants 

were curtailed or not was not always made available to wind forecasters, affecting wind forecast 

accuracy. As a result, RTOs and utilities are increasingly imposing data requirements on wind 

generators. The New York ISO has taken the additional step of imposing penalties for failure to 

provide data. 

Day-ahead wind forecasts are being used in reliability planning, but not directly in dayahead market decisions, despite results from several wind integration studies that suggest 

significant savings from reduced fuel and O&M costs if wind forecasts were used as part of 

day-ahead unit commitment schedules. Some of this may reflect the fact that wind forecasting is 

still in the early stages of implementation. Ultimately, the RTO could create and use a “load net 

wind” forecast to clear the day-ahead market (albeit with some additional consideration for the 

increased uncertainty around the forecast), which could contribute to more efficient market 

operation and dispatch. 

Wind forecasting is widely seen as a pre-requisite for integrating larger amounts of wind 

power, in minimizing operating impacts of wind power, and providing critical information to 

system operators to help maintain grid reliability. Wind forecasting is also seen as an important 

tool for minimizing the costs of integrating more wind power. Therefore, more utilities and 

RTOs will likely adopt central wind power forecasting in North America in the near future. 

Continuing research and experimentation may also lead to increases in wind power forecasting 

accuracy, also seen as an important step towards successfully managing the power grid with 

large amounts of wind power. 
23 

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26 

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27 

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F1146-E(10/2008) 

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