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

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Impacts of Improved Day-Ahead 

Wind Forecasts on Power Grid 

Operations 

September 2011

Richard Piwko and Gary Jordan

GE Energy Consulting 

Schenectady, New York

Subcontract Report

NREL/SR-5500-50907

November 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Impacts of Improved Day-Ahead 

Wind Forecasts on Power Grid 

Operations 

September 2011

Richard Piwko and Gary Jordan

GE Energy Consulting 

Schenectady, New York

NREL Technical Monitor: Debra Lew

Prepared under Subcontract No. AAM-8-77557-01

Subcontract Report

NREL/SR-5500-50907

November 2011
This publication received minimal editorial review at NREL.

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iii

Table of Contents

List of Figures ................................................................................................................... iv

List of Tables .................................................................................................................... iv

1 Background ..................................................................................................................1

2 Study Scenarios ............................................................................................................3

3 Analysis Approach .......................................................................................................5

4 Results ...........................................................................................................................9

4.1 Operating Costs .....................................................................................................9

4.1.1 Extrapolation to Entire US Power Grid .....................................................13

4.2 Operating Reserve Shortfalls ..............................................................................13

4.3 Spilled Energy .....................................................................................................14

5 Key Findings ...............................................................................................................16

6 Future Work ...............................................................................................................17

A Addendum – Examination of Climatology Forecast............................................. A.1
iv

List of Figures 

Figure 2.1 Wind sites for WWSIS 30% In Area Scenario, I30. ....................................................3

Figure 2.2 Total WECC wind generation and annual WECC wind energy penetration for 

study cases. ...................................................................................................................4

Figure 3.1 Example of Wind Turbine Power Output Curve ..........................................................5

Figure 3.2 SOA and Improved Wind Forecasts for a Group of Wind Plants in Arizona; 

Two Days in January ....................................................................................................6

Figure 3.3 Wind Forecast Errors with SOA and Improved Wind Forecasts in Arizona, 

Two Days in January ....................................................................................................7

Figure 3.4 SOA and Improved Wind Forecasts for Wind Plants in Arizona; Two Days in 

June ...............................................................................................................................7

Figure 3.5 Wind Forecast Errors with SOA and Improved Wind Forecasts in Arizona, 

Two Days in June .........................................................................................................8

Figure 3.6 Annual Wind Forecast Error Duration Curves for a Group of Wind Plants in 

Arizona, SOA and Improved Forecasts, 2006 data. .....................................................8

Figure 4.1 WECC Operating Cost Savings with Improved Wind Generation Forecasts 

(Relative to SOA Wind Forecast). ..............................................................................10

Figure 4.2 WECC Operating Cost Savings with Improved Wind Generation Forecasts 

(Relative to SOA Wind Forecast). ..............................................................................11

Figure 4.3 Average Annual Operating Cost Savings as a Function of Wind Generation 

Forecast Improvement Relative to SOA .....................................................................12

Figure 4.4 Average Annual Operating Cost Savings as a Function of Wind Generation 

Forecast Improvement Relative to SOA .....................................................................12

Figure 4.5 Reserve Shortfalls with Improved Wind Generation Forecasts, I30 Scenario ...........14

Figure 4.6 Spilled Energy with SOA and Improved Wind Generation Forecasts, I30 

Scenario ......................................................................................................................15

Figure 4.7 Reductions in Spilled Energy with Improved Wind Generation Forecasts, I30 

Scenario ......................................................................................................................15

Figure A.1 Wind plant output for all days in June 2006, and average daily output profile. ...... A.2

Figure A.2 Wind Plant Climatology Forecast Profiles for June. ................................................ A.3

Figure A.3 Wind Plant Climatology Forecasts for 12 Months ................................................... A.3

Figure A.4 Forecast Error Duration Curve for SOA and Climatology Forecasts ....................... A.4

Figure A.5 WECC Operating Cost Reductions for Using Climatology Wind Forecast 

relative to No Wind Forecast .................................................................................... A.4

Figure A.6 WECC Operating Cost Reductions for Using SOA Wind Forecast relative to 

Climatology Wind Forecast ...................................................................................... A.5

Figure A.7 Spilled Energy and Reserve Shortfalls for System Operation with 

Climatology vs SOA Wind Generation Forecast ..................................................... A.5

List of Tables

Table 2.1. Total Wind Plant Capacity and Wind Energy Penetration for Study Scenario ............4

Table 4.1. Key to Simulation Case Assumptions and Codes ........................................................9

Table 5.1 Annual Operating Cost Reductions due to Improved Day-Ahead Wind 

Generation Forecasts ..................................................................................................16
1 

1 Background 

With increasing penetration of wind generation on interconnected power systems, system 

operators are faced with increased levels of variability and uncertainty. Given that the 

power output of wind plants is a function of wind speed, the level of wind generation on a 

power system varies from hour-to-hour and from day-to-day. And given that wind speed 

is a function of the weather, the amount of wind that a power system operator can expect 

for the next day is subject to the level of uncertainty in weather-related forecasts for the 

next day. 

Power system operators presently use day-ahead load forecasts to predict how much 

energy must be delivered for each hour of the next day. This forecast enables day-ahead 

commitment of generation resources, some of which may need many hours advance 

notice to be ready to generate power during the next day. Power systems with high 

penetrations of wind generation depend on day-ahead wind forecasts to predict how much 

of the wind power will be available for each hour of the next day. Combining wind 

forecast with the load forecast enables operators to commit the balance of the generation 

fleet to economically and securely serve load on the next day.

Forecasts are not perfect. Load forecasting is a very mature science since power system 

operators have been using day-ahead load forecasts in their security constrained unit 

commitment (SCUC) processes for several decades. Day-ahead hourly load forecast 

errors are typically in the range of 1% to 3%1. State-of-the-art (SOA) wind forecasts 

typically have errors in the range of 15% to 20% mean absolute error (MAE) for a single 

wind plant2

Previous large-scale wind integration studies have demonstrated that using day-ahead 

SOA wind power forecasts for unit commitment can dramatically improve system 

operation by reducing overall operating costs, reducing unserved energy, and reducing 

spilled energy (wind curtailment), while maintaining required levels of system reliability. 

This study analyzed the potential benefits of improving the accuracy (reducing the error) 

of day-ahead wind forecasts on power system operations, assuming that wind forecasts 

were used for day ahead security constrained unit commitment.

. 

The following wind forecasts were considered:

 State-of-the-art (SOA) wind forecasts, based on mesoscale simulation methods

 SOA with 10% improvement 

 SOA with 20% improvement 

 

1 CAISO publishes historical information on load forecasts and other operational data at 

http://oasishis.caiso.com/ 2 ISO-NE report, “Technical Requirements for Wind Generation Interconnection and Integration”. Section 

4 addresses Wind Generation Forecasting.

http://www.iso-ne.com/committees/comm_wkgrps/prtcpnts_comm/pac/reports/2009/index.html 
2 

 SOA with 100% improvement (i.e., a perfect forecast) 

When wind forecasts are lower than actual wind plant output, more conventional 

generation is committed in the day ahead than is actually needed during the day of 

operation. This means that the committed conventional generation will be operated at 

lower power output than planned, which would be a less efficient operating point for the 

system (primarily due to lower efficiency at lower power levels for thermal units). If the 

wind forecast error is large enough, it may be necessary to spill some of the excess wind 

(or other) generation. 

When wind forecasts are higher than actual wind plant output, less conventional 

generation is committed in the day ahead than is actually needed during the day of 

operation. Turning on quick-start peaking units normally mitigates the shortage in 

committed generation, but this drives up system operating cost significantly because of 

their lower efficiency. If the wind forecast error is large enough, there is also a risk of 

operating reserve shortfalls or possibly load-shedding. 

This study uses the WECC system as the basis for evaluating the operating cost impacts 

of improved day-ahead wind forecasts. In order to estimate the potential impacts for the 

entire US power grid, the WECC results are extrapolated according to the relative sizes 

of the WECC and US power grids, as measured by annual load energy. 
3 

2 Study Scenarios

The WECC system is used as the basis for this analysis, building on the In-Area scenario 

developed for the Western Wind and Solar Integration Study, WWSIS. Figure 2.1 shows 

the locations of wind generating plants assumed for this study. Red dots are “preselected” 

or existing wind generation plants at the time the study scenario was developed. Blue 

dots represent future wind plants that were added to provide the required level of wind 

generation for the I30 case with 30% wind energy in the Westconnect footprint (NV, AZ, 

NM, CO, WY) and 20% wind energy in the rest of WECC. Table 2.1 summarizes the 

wind plant capacities and wind energy penetration levels for the cases included in this 

study. The same data is shown graphically in Figure 2.2. The study considered WECC 

wind energy penetration levels of 3%, 10%, 14%, and 24%. 

Additional information about the In-Area Scenario is included in the WWSIS final 

report3. 

Figure 2.1: Wind sites for WWSIS 30% In Area Scenario, I30.

 

3

 NREL Report, “Western Wind and Solar Integration Study”, 

http://www.nrel.gov/wind/systemsintegration/wwsis.html 
4 

Table 2.1: Total Wind Plant Capacity and Wind Energy Penetration for Study Scenario

Case Name Case 

Code

Wind 

Capacity 

(MW)

Wind 

Energy 

(GWh)

Wind Energy 

Penetration for 

Study Footprint

Wind Energy 

Penetration for 

all of WECC

No Wind 0 0 0% 0%

Preselected Pre 10,230 22,526 3% 3%

10% In-Area I10 33,240 93,339 10% 10%

20% In-Area I20 42,900 122,336 20% 14%

30% In-Area I30 75,390 214,381 30% 24%

Figure 2.2: Total WECC wind generation and annual WECC wind energy penetration for 

study cases.

 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

Pre I10 I20 I30

Scenario

Renewable Generation (MW)

0%

5%

10%

15%

20%

25%

WECC Energy Penetration (%)

Wind Capacity (MW)

% Total WECC Energy
5 

3 Analysis Approach

This study builds upon the methods and models developed during the WWSIS. The same 

Multi-Area Production Simulation (MAPS) model used to simulate hourly operation of 

the WECC system for the WWSIS was used for this study. Chapter 6 of the WWSIS 

report explains the details of that model. 

The WWSIS used day-ahead SOA wind forecasts developed by 3-TIER for the purpose 

of that study. The dataset includes a separate day ahead wind forecast for each individual 

wind plant. The forecasts exhibited mean absolute errors in the range of 12% to 16% 

when aggregated on a state-by-state basis (i.e., calculated by aggregating the hourly dayahead forecasts and hourly actual outputs for all wind plants in a given state). Section 5.6 

of the WWSIS report shows more information about the SOA wind forecast data. 

The primary objective of this study was to examine the impact of improving the SOA 

wind generation forecasts by 10% and 20%. This was done by modifying the day-ahead 

SOA wind generation forecasts used in the WWSIS by reducing the wind generation 

forecast error by 10% and 20% for each hour of the year, and then repeating the 

production simulations to evaluate impacts on overall WECC system operations. Wind 

generation and load profiles from three calendar years were analyzed (2004, 2005, and 

2006). 

Note that the wind generation forecast improvements are expressed in power (MW), not 

wind speed (meters/second). For typical pitch-controlled wind turbines, power output 

varies as the cube of wind speed over a significant portion of the power output curve (see 

Figure 3.1). In this region, small improvements in forecasted wind speed would lead to 

significantly larger improvements in wind power forecasts.

Figure 3.1: Example of Wind Turbine Power Output Curve

Figure 3.2 shows forecast and actual aggregated wind plant output for a region of 

Arizona with 1920 MW of wind generation, for the 30% In-Area Scenario, for two days 

0

200

400

600

800

1,000

1,200

1,400

1,600

0 5 10 15 20 25

Wind Speed (m/s)

Electrical Power (kW)

Power proportional 

to (wind speed)3
6 

in January. The black curve represents the actual wind plant output. The red curve labeled 

“Base Forecast” represents the day-ahead SOA generation forecast provided by 3-TIER. 

The blue and green curves represent the SOA generation forecast with 10% and 20% less 

error respectively. Figure 3.3 shows the errors in each of the three wind generation 

forecasts, calculated as the difference between the forecast value and the actual value.

Figure 3.4 and Figure 3.5 show similar data for two days in June. Figure 3.6 shows a 

duration curve of the wind forecast error for all hours in a year. For 80% of the hours, 

the forecast errors are less than 500 MW. But for a few hours each year, at the positive 

and negative tails of the curves, forecast errors are in the range of 1500 MW. 

In this study, the day-ahead unit commitment process assumed that the forecasted wind 

generation would be available, and committed other generation resources to cover the net 

load (load minus forecasted wind generation) plus reserves. 

Figure 3.2: SOA and Improved Wind Forecasts for a Group of Wind Plants in Arizona;

Two Days in January

 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0 6 12 18 24 30 36 42 48

Hour

Generation (MW)

Actual

SOA Forecast

10% Improvement

20% Improvement
7 

Figure 3.3: Wind Forecast Errors with SOA and Improved Wind Forecasts in Arizona, 

Two Days in January

Figure 3.4: SOA and Improved Wind Forecasts for Wind Plants in Arizona; Two Days in 

June 

 

-1,000

-800

-600

-400

-200

0

200

400

600

800

1,000

0 6 12 18 24 30 36 42 48

Hour

Error (MW)

SOA Error

10% Error Reduction

20% Error Reduction

 

0

200

400

600

800

1,000

1,200

1,400

0 6 12 18 24 30 36 42 48

Hour

Generation (MW)

Actual

SOA Forecast

10% Improvement

20% Improvement
8 

Figure 3.5: Wind Forecast Errors with SOA and Improved Wind Forecasts in Arizona, 

Two Days in June

Figure 3.6: Annual Wind Forecast Error Duration Curves for a Group of Wind Plants in 

Arizona, SOA and Improved Forecasts, 2006 data.

 

-1,000

-800

-600

-400

-200

0

200

400

600

800

1,000

1,200

0 6 12 18 24 30 36 42 48

Hour

Error (MW)

SOA Error

10% Error Reduction

20% Error Reduction

Annual Forecast Error Duration Curve

-2000

-1500

-1000

-500

0

500

1000

1500

2000

1 1001 2001 3001 4001 5001 6001 7001 8001

Hours

Error (MW)

SOA Error

10% Error Reduction

20% Error Reduction

80% of Hours
9 

4 Results

The potential benefits of improved day-ahead wind generation forecasts were evaluated 

with respect to three critical measurements: operating costs, unserved energy, and spilled 

energy. 

Table 4.1 summarizes the production cost simulation cases from which the study results 

were derived. The first three characters of the Case Code refers to the amount of wind 

generation, as defined in Table 2.1. The remaining characters refer to the type of wind 

forecast; 10% improvement, 20% improvement, or perfect (i.e., 100% improvement). 

Table 4.1: Key to Simulation Case Assumptions and Codes

Case Code Case Assumptions

Pre m10 Preselected wind generation, modified SOA wind forecast with 10% less error

Pre m20 Preselected wind generation, modified SOA wind forecast with 20% less error

Pre P Preselected wind generation, perfect wind forecast

I10 m10 10% In-Area Scenario, modified SOA wind forecast with 10% less error

I10 m20 10% In-Area Scenario, modified SOA wind forecast with 20% less error

I10 P 10% In-Area Scenario, perfect wind forecast

I20 m10 20% In-Area Scenario, modified SOA wind forecast with 10% less error

I20 m20 20% In-Area Scenario, modified SOA wind forecast with 20% less error

I20 P 20% In-Area Scenario, perfect wind forecast

I30 m10 30% In-Area Scenario, modified SOA wind forecast with 10% less error

I30 m20 30% In-Area Scenario, modified SOA wind forecast with 20% less error

I30 P 30% In-Area Scenario, perfect wind forecast

4.1 Operating Costs

For this analysis, operating costs include the variable costs associated with operating the 

WECC power system for a year, including fuels costs, unit start-up costs, and unit 

variable operation and maintenance (O&M) costs. Operating costs exclude capital costs, 

debt service costs, and other fixed costs.

Figure 4.1shows the annual operating cost savings for the WECC system with improved 

forecasts, relative to operation using a SOA wind forecast, for increasing levels of wind 

energy penetration, and for three years of operation. It shows the savings in operating 

costs for a 10%, 20% and 100% improvement to the day-ahead wind generation forecast. 

The data for the 100% improved, or perfect, forecast shows the maximum possible 

benefit to the WECC operating cost from wind forecasts. Although a perfect wind 

forecast is not realistically possible, the data serves as a calibration for the amount of 

benefit gained from more realistic levels of forecast improvements.
10 

Figure 4.2 shows the same information as Figure 4.1, but without the perfect forecast. If 

we consider the cases with 14% WECC wind energy penetration, the results show: 

 A 10% improvement in day-ahead wind generation forecast yields an average of 

$28M savings in annual operating costs. 

 A 20% improvement in day-ahead wind generation forecast yields an average of 

$52M savings in annual operating costs. 

With 24% wind energy penetration in WECC, the results show: 

 A 10% improvement in day-ahead wind generation forecast yields an average of 

$100M savings in annual operating costs. 

 A 20% improvement in day-ahead wind generation forecast yields an average of 

$195M savings in annual operating costs. 

Figure 4.1: WECC Operating Cost Savings with Improved Wind Generation Forecasts 

(Relative to SOA Wind Forecast).

g 

0

100

200

300

400

500

600

700

Prem10Prem20

PreP

I10m10

I10m20

I10P

I20m10I20m20

I20P

I30m10

I30m20

I30P

Annual Operating Cost Savings ($M)

2004

2005

2006

10% Wind

Energy in 

3% Wind WECC

Energy in 

WECC

14% Wind

Energy in 

WECC

24% Wind

Energy in 

WECC
11 

Figure 4.2: WECC Operating Cost Savings with Improved Wind Generation Forecasts 

(Relative to SOA Wind Forecast).

These same results are also plotted in Figure 4.3 and Figure 4.4. These figures show 

average annual operating cost savings as a function of forecast improvement. Figure 4.3

is scaled to 100% improvement (perfect wind forecast). Figure 4.4 is scaled to 30% 

forecast improvement to show higher resolution. A critical observation here is that the 

slope of the curves becomes lower as the level of forecast improvement increases, 

indicating that there is a diminishing benefit for greater forecast improvements. The 

initial 10% or 20% improvements in wind forecasts provide the greatest relative benefits. 

Further improvement will provide diminishing marginal benefits, approaching the perfect 

forecast.

g 

0

50

100

150

200

250

Prem10Prem20

I10m10I10m20I20m10I20m20I30m10I30m20

Annual Operating Cost Savings ($M)

2004

2005

2006

10% Wind

Energy in 

WECC

3% Wind

Energy in 

WECC

14% Wind

Energy in 

WECC

24% Wind

Energy in 

WECC
12 

Figure 4.3: Average Annual Operating Cost Savings as a Function of Wind Generation 

Forecast Improvement Relative to SOA

Figure 4.4: Average Annual Operating Cost Savings as a Function of Wind Generation 

Forecast Improvement Relative to SOA

 

0.0

100.0

200.0

300.0

400.0

500.0

600.0

0% 20% 40% 60% 80% 100%

Forecast Improvement (%)

Avg Annual Operating Cost Savings 

($M)

Pre

I10

I20

I30

 

0.0

25.0

50.0

75.0

100.0

125.0

150.0

175.0

200.0

225.0

250.0

0% 5% 10% 15% 20% 25% 30%

Forecast Improvement (%)

Avg Annual Operating Cost Savings 

($M)

Pre

I10

I20

I30
13 

4.1.1 Extrapolation to Entire US Power Grid

According to historical data for year 2009 in the Ventyx database, annual energy demand 

for WECC was 714 TWh and annual energy demand for the entire USA was 3836 TWh. 

Therefore, the WECC system serves slightly less than 20% of the total US electrical 

energy demand. 

Assuming that the operational characteristics of the WECC system are generally 

representative of other operating areas in the USA, it would seem reasonable to roughly 

estimate the operating cost impacts of improved day-ahead wind forecasts on the entire 

USA by multiplying the WECC results by a factor of 5.

With 14% wind energy penetration in the US, extrapolation of the WECC results implies 

that:

 A 10% improvement in day-ahead wind generation forecast yields an average of 

$140M savings in annual operating costs. 

 A 20% improvement in day-ahead wind generation forecast yields an average of 

$260M savings in annual operating costs. 

With 24% wind energy penetration in the US, extrapolation of the WECC results implies 

that:

 A 10% improvement in day-ahead wind generation forecast yields an average of 

$500M savings in annual operating costs. 

 A 20% improvement in day-ahead wind generation forecast yields an average of 

$975M savings in annual operating costs. 

4.2 Operating Reserve Shortfalls

A shortfall in operating reserve occurs when there is insufficient generation available to 

serve the load and meet operating reserve requirements. When a reserve shortfall occurs, 

there is still adequate generation to serve the load, but there is not enough generation to 

supply all required reserves. The magnitude of the reserve shortfall is the cumulative 

shortage in reserve energy for all hours over a calendar year of operation. In a system that 

has adequate installed capacity margin, reserve shortfall events are extremely rare. When 

such an event does occur, a likely cause is a large error in the day-ahead forecast used for 

unit commitment. These events would typically develop in the following sequence: 

 The day-ahead unit SCUC commits adequate generation to meet the forecast load 

for the next day using forecasted wind generation and other dispatchable 

generation resources. The commitment also includes required operating reserves.

 When the next day arrives, actual wind generation falls significantly below the 

forecasted level, or actual load is significantly above the forecasted load, or both. 

 Quick start generation is committed and dispatched to fill the shortfall to the 

extent possible, but there is still not enough generation available to completely 

meet operating reserve requirements.
14 

Study results indicate that improved day-ahead wind generation forecasts have no 

significant impact on reserve shortfalls if WECC wind energy penetration is below 14% 

because there were sufficient quick-start generators available to cover the forecast errors. 

With wind energy penetration of 24%, improved wind forecasts significantly reduce 

reserve shortfalls (see Figure 4.5). For the three calendar years analyzed, average annual 

operating reserve shortfalls would be reduced from 43 GWh to 24 GWh with a 10% wind 

forecast improvement. Reserve shortfalls would be further reduced to 15 GWh with a 

20% wind forecast improvement. 

Figure 4.5: Reserve Shortfalls with Improved Wind Generation Forecasts, I30 Scenario

4.3 Spilled Energy

Spilled energy is a measure of generation that is available at no cost but cannot be used 

due to necessary restrictions on power system commitment and dispatch. For example, if 

thermal generation required to meet forecasted load on the next day is turned down to 

minimum load overnight, and wind generation in the overnight hours exceeds predicted 

levels, it may become necessary to spill (or curtail) excess generation. Most often, wind 

generation is curtailed in such circumstances.

Spilled energy may also be caused by congestion (i.e., generation in one area that cannot 

be delivered to load in another area due to transmission capacity limits). The results in 

this study include congestion on inter-area transfer paths. However, the system model 

does not include full transmission representation within balancing areas, so not all 

possible spilled energy due to congestion is captured in the analysis. 

Study results indicate that improved day-ahead wind generation forecasts have no 

significant impact on spilled energy if WECC wind energy penetration is below 14%. 

With wind energy penetration of 24%, improved wind forecasts reduce the amounts of 

spilled energy (see Figure 4.6). For the three calendar years analyzed, average annual 

spilled energy would be reduced from 655 GWh to 628 GWh with a 10% wind forecast 

 

0

10

20

30

40

50

60

2004 2005 2006 Average

Reserve Shortfalls (GWh)

SOA Forecast

10% Improvement

20% Improvement
15 

improvement. It would be further reduced to 613 GWh with a 20% wind forecast 

improvement. 

Figure 4.7 shows the percentage reduction in spilled energy relative to that with a SOA 

day-ahead wind generation forecast. Spilled energy is reduced by 4% with a 10% 

improvement in wind forecast. Spilled energy is reduced by 6.3% with a 20% wind 

forecast improvement.

Figure 4.6: Spilled Energy with SOA and Improved Wind Generation Forecasts, 

I30 Scenario

Figure 4.7: Reductions in Spilled Energy with Improved Wind Generation Forecasts, 

I30 Scenario

 

0

100

200

300

400

500

600

700

800

900

2004 2005 2006 Average

Spilled Energy (GWh)

SOA Forecast

10% Forecast Improvement

20% Forecast Improvement

 

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

2004 2005 2006 Average

Spilled Energy Reduction (%)

10% Forecast Improvement

20% Forecast Improvement
16 

5 Key Findings

The study results show that improved day-ahead wind forecasts can significantly reduce 

operating costs and increase the reliability of large interconnected power systems. Even a 

relatively modest 10% improvement in wind generation forecasts would reduce WECC 

operating costs by about $28M per year with 14% wind energy penetration. For the entire 

US power system, the corresponding operating cost reduction would be about $140M per 

year.

The impacts are even greater at higher penetrations of wind energy. A 10% wind forecast 

improvement would reduce WECC operating costs by about $100M per year with 24% 

wind energy penetration. For the entire US power system, the corresponding operating 

cost reduction would be about $500M per year. These findings are summarized in Table 

5.1. 

Improved wind generation forecasts can reduce the amount of spilled energy by up to 

6%, thereby increasing the overall energy efficiency of the power system. Improved wind 

forecasts also increase the reliability of power systems by reducing operating reserve 

shortfalls. A 20% wind forecast improvement could decrease reserve shortfalls by as

much as 2/3 with 24% wind energy penetration. 

Table 5.1: Annual Operating Cost Reductions due to Improved Day-Ahead Wind 

Generation Forecasts

Day-Ahead Wind Generation 

Forecast Error Reduction

Wind Energy 

Penetration

WECC Annual 

Operating Cost 

Reduction ($M)

Estimated US Annual 

Operating Cost 

Reduction ($M)

10% 14% $28M $140M

20% 14% $52M $260M

10% 24% $100M $500M

20% 24% $195M $975M
17 

6 Future Work

The study results suggest several areas that could warrant further exploration: 

1. This study assumed that wind power forecasts were improved by the same 

percentage in all hours of the year. But what if it was possible to obtain bigger 

improvements for periods with the biggest forecast errors? Large forecast errors 

lead to most of the problems and costs with system operations, so reducing the 

largest errors would be very beneficial. 

2. In general, over-forecasting wind power causes more severe problems for system 

operations than under-forecasting wind power. When wind is over-forecast 

(predicting more wind power than actually occurs), the power grid experiences a 

shortage in unit commitment and expensive peaking units are turned on to fill the 

gap. There is also the risk of reserve shortfalls. Would it be possible to improve 

wind forecasts such that over-forecast errors are reduced by greater amount? 

What would be the value of such an improvement? 

3. Wind forecast providers are moving towards ensemble forecasts and other 

methods that enable confidence bands to placed around forecast values. How 

would improved wind forecasts techniques affect those confidence values? And 

how could the forecast confidence data be used in the unit commitment process? 
A.1

A Addendum – Examination of Climatology Forecast

Although climatology-based wind generation forecasts are not often (if ever) used for 

day-ahead unit commitment and real time power system operations, the effectiveness of 

climatology forecasts was investigated as part of this study. The results are reported in 

this addendum. The information in this addendum builds upon the study cases and 

simulation results presented in the main report.

Climatology forecasts are based on trends, and therefore provide an expectation of what 

should happen based on historical records of what has normally happened in the past. For 

the purpose of this analysis, it was assumed that wind generation followed daily trends, 

and that those trends are different for different months of the year. 

Climatology forecasts were derived from the three years of “actual” wind data for all the 

wind plants included in this study. This was done by calculating the average hourly wind 

plant output for each month of the year. For example, a climatology forecast of a selected 

plant was calculated by averaging the hourly profiles for all the days in January. The 

colored lines in Figure A.1 show the output for that plant for all days in January. The 

heavy black line shows the hourly average plant output, which represents one method for 

calculating a climatology forecast for the month of January. 

Figure A.2 shows the results of applying the same method to the wind plant output data 

for three years. It shows that the average January daily output profiles are similar for 

years 2004 and 2005, but that 2006 is significantly different. 

For the purpose of this analysis, climatology-based wind forecasts were calculated as the 

average hourly plant output for each wind plant, for each of the three years of wind data. 

Figure A.3 shows those forecasts for an example wind plant for all 12 months of the year. 

For each month, the plot shows the average daily output profile that was subsequently 

used in production simulations as the climatology-based forecast for that wind plant. 

These types of profiles were calculated for each year of wind data.

Figure A.4 shows a duration curve of hourly forecast errors for an entire year for the 

example wind plant. The red curve is for the state-of-art (SOA) wind generation forecast 

based on mesoscale simulation techniques. The 10% and 20% improved SOA forecasts 

are also shown in blue and green respectively. The climatology forecast is represented by 

the black line. It has significantly higher errors than the other forecasts, except at the 

extremes.

Figure A.5 shows operating cost savings if WECC uses a climatology-based wind 

forecast for day-ahead unit commitment, relative to operating with no wind forecast (i.e., 

day-ahead unit commitment assumes there will be no wind generation available the next 

day). The figure shows that with 3% wind energy penetration, the climatology forecast 

reduces operating costs by about $175M per year. With 24% wind energy penetration, the 

operating cost savings grow to about $3.8B per year. 
A.2

Figure A.6 shows a similar comparison, but for the SOA forecast relative to the 

climatology forecast. With 3% wind energy penetration, using a SOA wind forecast for 

unit commitment saves about $6M in annual operating costs compared to using a 

climatology forecast. If wind energy penetration increases to 14%, the operating costs 

savings grow to about $175M per year. And with 24% wind energy penetration, the 

operating cost savings grow to an average of $730M per year. 

Figure A.7 shows a comparison of how SOA and climatology forecasts affect reserve 

shortfalls and spilled energy. Spilled energy is about 30% lower with the SOA forecast as 

compared to the climatology forecast. Reserve shortfalls are about 85% lower with the 

SOA forecast as compared to the climatology forecast.

In general, the results of this analysis indicated that:

 Using climatology-based wind forecasts for day-ahead unit commitment can 

significantly reduce variable operating costs, as compared to assuming there will 

be no wind generation the next day. 

 SOA mesoscale simulation based wind generation forecasts can reduce variable 

operating costs even further. 

 Because SOA forecasts are uniquely derived for each day of operation, they 

perform dramatically better than climatology forecasts in reducing reserve 

shortfalls and spilled energy. 

Figure A.1: Wind plant output for all days in June 2006, and average daily output profile.

 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Wind Plant Output (MW)
A.3

Figure A.2: Wind Plant Climatology Forecast Profiles for June.

Figure A.3: Wind Plant Climatology Forecasts for 12 Months

 

0

200

400

600

800

1000

1200

0 3 6 9 12 15 18 21 24

Hour

Wind Forecast (MW)

2004

2005

2006

0

200

400

600

800

1000

1200

1400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Wind Generation (MW)

2004

2005

2006
A.4

Figure A.4: Forecast Error Duration Curve for SOA and Climatology Forecasts

Figure A.5: WECC Operating Cost Reductions for Using Climatology Wind Forecast 

relative to No Wind Forecast

 

-2000

-1500

-1000

-500

0

500

1000

1500

2000

1 1001 2001 3001 4001 5001 6001 7001 8001

Hours

Error (MW)

SOA Error

10% Error Reduction

20% Error Reduction

Climatology Forecast Error

g 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Preselected I10 I20 I30

Operating Cost Savings ($M)

2004

2005

2006

10% Wind

Energy in 

WECC

3% Wind

Energy in 

WECC

14% Wind

Energy in 

WECC

24% Wind

Energy in 

WECC
A.5

Figure A.6: WECC Operating Cost Reductions for Using SOA Wind Forecast relative to 

Climatology Wind Forecast

Figure A.7: Spilled Energy and Reserve Shortfalls for System Operation with Climatology 

vs. SOA Wind Generation Forecast

 

0

100

200

300

400

500

600

700

800

900

1,000

Preselected I10 I20 I30

Increased Operating Cost 

Savings ($M)

2004

2005

2006

10% Wind

Energy in 

WECC

3% Wind

Energy in 

WECC

14% Wind

Energy in 

WECC

24% Wind

Energy in 

WECC

Comparison of Climatology and SOA Forecast

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Climatology

Forecast, 2004

SOA Forecast,

2004

Climatology

Forecast, 2005

SOA Forecast,

2005

Climatology

Forecast, 2006

SOA Forecast,

2006

Energy (MWh)

Spilled Energy (MWh)

Reserve Shortfalls (MWh)