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

Flexibility Reserve Reductions 

from an Energy Imbalance Market 

with High Levels of Wind Energy 

in the Western Interconnection 

J. King and B. Kirby

Consultants

M. Milligan

National Renewable Energy Laboratory

S. Beuning

Xcel Energy

Technical Report

NREL/TP-5500-52330

October 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

Flexibility Reserve Reductions 

from an Energy Imbalance Market 

with High Levels of Wind Energy 

in the Western Interconnection 

J. King and B. Kirby

Consultants

M. Milligan

National Renewable Energy Laboratory

S. Beuning

Xcel Energy

Prepared under Task Nos. OE101000 and WE110830

Technical Report

NREL/TP-5500-52330

October 2011
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i

Executive Summary

The anticipated increase in variable generation in the Western Interconnection (WI) over the next 

several years has raised concerns about how to maintain system balance, especially in smaller 

Balancing Areas (BAs). Given renewable portfolio standards in the West, it is possible that more 

than 50 gigawatts (GW) of wind capacity will be installed by 2020. Significant quantities of solar 

generation are likely to be added as well. The consequent increase in variability and uncertainty 

that must be managed by the conventional generation fleet and responsive load make it attractive 

to consider ways in which Balancing Area Authorities (BAAs) can pool their variability and 

response resources, thus taking advantage of geographic and temporal diversity to increase 

overall operational efficiency. 

Our analysis considers several alternative forms of an Energy Imbalance Market (EIM) that have 

been proposed in the non-market areas of the WI. The proposed EIM includes two changes in 

operating practices that independently reduce variability and increase access to responsive 

resources: BAA cooperation and sub-hourly dispatch. As proposed, the EIM does not consider 

any form of coordinated unit commitment; however, over time it is possible that BAAs would 

develop formal or informal coordination plans. This report examines the benefits of several 

possible EIM implementations, both separately and in concert. 

The proposed EIM uses a security-constrained economic dispatch to provide two functions:

• Balancing service: This service re-dispatches generation every 5 minutes to maintain the 

balance between generation and load. The effect is that the market supplies deviations 

from schedules in generator output and errors in load schedules.

• Congestion re-dispatch service: This will re-dispatch generation to relieve overload 

constraints on the grid. Information provided to the EIM from the Enhanced Curtailment 

Calculator (ECC) ensures correct allocation of the re-dispatch service costs.

Our analysis focuses on the:

• Impact of the EIM on operating reserves, which include regulating reserves, following 

spin, and following non-spin

• Reduction in ramping requirements that are caused by the larger effective electrical 

operating footprint of the EIM

• Impact of alternative scheduling constraints on operating reserves

• Role of coordinated planning.

Although the analysis presented here focuses on high penetrations of wind energy, it can easily 

be adapted to solar data and combined contributions from wind and solar energy. 

We used data from the recent Western Wind and Solar Integration Study (WWSIS), managed by 

the National Renewable Energy Laboratory (NREL) on behalf of the U.S. Department of Energy 

(DOE) [1], focusing on the local priority case of 30% wind energy penetration (percentage of 

electricity production for the year). A new dataset developed by NREL and adopted for the 

Transmission Expansion Planning and Policy Committee (TEPPC) for the Western Electricity 
ii

Coordinating Council (WECC) was not available for this analysis; however, we are updating this 

work to include the current 2020 TEPPC case.1

Figure 1. Operating reserve requirements with alternative EIM implementations

We find that average reserve values, calculated for each hour of the study year, decrease by 51%-

54%, depending on the reserve type. Reductions in maximum reserve levels range from 58%-

67%, also depending on reserve type.

A lesser but significant reduction occurs with a regional implementation, with 32%-41% average 

reserve reduction and 42%-46% maximum reserve reductions, depending on reserve type.

 1 WECC’s current cost-benefit analysis of the EIM uses the new TEPPC wind and solar base scenario, and NREL 

has calculated reserve requirements for WECC using the same methods described in this report.

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

Footprint 1198 969 1938 4105

Regional 1547 1420 2840 5807

BAU 2440 2096 4192 8729

Reserve Requirement (MW)

Reserve Requirement Comparisons for Footprint EIM, Regional EIM 

Total, and BAU (Average, Max, and Min)

Footprint

Regional

BAU
iii

List of Abbreviations and Acronyms

AGC: Automatic generation control

BA: Balancing Area

BAA: Balancing Area Authority

BAU: Business as Usual

BCTC: British Columbia Transmission Corporation

BPA: Bonneville Power Administration

CAISO: California Independent System Operator

CG: Columbia Grid

CPS: Control Performance Standards

DCS: Disturbance Control Standard

DOE: U.S. Department of Energy

DSM: demand-side management

ECC: Enhanced Curtailment Calculator

EDT: Efficient Dispatch Toolkit

EIM: Energy Imbalance Market

ERCOT: Electric Reliability Council of Texas

EWITS: Eastern Wind Integration and Transmission Study

FERC: Federal Energy Regulatory Commission

GW: gigawatts

ISO-NE: Independent System Operator New England

MISO: Midwest Independent Transmission System Operator

NERC: North American Electric Reliability Corporation

NREL: National Renewable Energy Laboratory 
iv

NTTG: Northern Tier Transmission Group

NWP: numerical weather prediction

NYISO: New York Independent System Operator

PJM Interconnection: Pennsylvania-New Jersey-Maryland Interconnection

RPS: renewable portfolio standards

SCED: security-constrained economic dispatch

SPP: Southwest Power Pool

WAPA: Western Area Power Administration

WI: Western Interconnection

WC: WestConnect

WECC: Western Electricity Coordinating Council

WWSIS: Western Wind and Solar Integration Study
v

Ancillary Service Descriptions (2010 Price Data)

The following table describes the ancillary services referenced in this report (adapted from [6]).

Service

Service Description

Response Speed Duration Cycle Time Market Cycle

Price Range 

(average/max)

$/MW-hr

Normal Conditions

Regulating 

Reserve

Online resources on automatic generation control that can respond rapidly to systemoperator requests for up and down movements; used to track the minute-to-minute 

fluctuations in system load and to correct for unintended fluctuations in generator 

output to comply with Control Performance Standards (CPSs) 1 and 2 

~1 min Minutes Minutes Hourly 33-60#

 300-

620

Load 

Following or 

Fast Energy 

Markets

Similar to regulation but slower. Bridges between the regulation service and the hourly 

energy markets. 

~10 min 10 min to hours 10 min to 

hours Hourly -

Contingency Conditions

Spinning 

Reserve

Online resources synchronized to the grid that can increase output or decrease 

consumption immediately in response to a major generator or transmission outage and 

can provide full response within 10 minutes to comply with NERC’s Disturbance 

Control Standard (DCS)

Seconds to <10 

min 10 to 120 min Hours to Days Hourly 6-27 60-2000

Non-Spinning 

Reserve

Same as spinning reserve but need not respond immediately; resources can be offline 

but still must be capable of fully responding within the required 10 minutes

<30 min* 30 to 120 min Hours to 

Days Hourly 1-4 10-2000

Replacement 

or 

Supplemental

Reserve

Same as non-spinning reserve but with a 30- to 60-minute response time; used to restore 

spinning and non-spinning reserves to their pre-contingency status

<30 min 2 hours Hours to 

Days Hourly 1-2 4-244

# Up and down regulation prices for California and ERCOT are combined to facilitate comparison with 

the full-range New York prices. 

* Non-Spinning Reserve frequently refers to a 10-minute service. Here it is assumed to be available 

within 30 minutes.

2008 ancillary service prices are shown because 2009 and 2010 prices appear anomalous.
vi

Table of Contents

Executive Summary......................................................................................................................... i

List of Abbreviations and Acronyms.............................................................................................iii

Ancillary Service Descriptions (2010 Price Data).......................................................................... v

1 Introduction............................................................................................................................. 1

2 Data ......................................................................................................................................... 1

2.1 Wind Production Data...................................................................................................... 2

2.2 Load Data ......................................................................................................................... 3

2.3 Balancing Area Authorities and Regional Modeling ....................................................... 6

3 Overview of the Proposed Energy Imbalance Market and Efficient Dispatch Toolkit .......... 7

4 Analysis Methods.................................................................................................................. 11

4.1 Reserve Analysis............................................................................................................ 11

4.2 Ramp Analysis ............................................................................................................... 15

5 Impact of Energy Imbalance Markets on Reserves and Ramping........................................ 19

5.1 Alternative Market Scenarios......................................................................................... 19

5.2 Variability Analysis........................................................................................................ 21

5.3 Footprint EIM Scenario – Base Case ............................................................................. 24

5.3.1 Reserves.................................................................................................................. 24

5.3.2 Ramp Demand Reduction....................................................................................... 25

5.3.3 Ramp Magnitude and Duration Probability............................................................ 28

5.3.4 Implication for Planning ......................................................................................... 30

5.4 Regional EIM Scenario Results..................................................................................... 33

5.4.1 Columbia Grid EIM................................................................................................ 33

5.4.2 Northern Tier Transmission Group EIM ................................................................ 35

5.4.3 WestConnect EIM................................................................................................... 38

5.5 Results with BPA and WAPA Not Participating in EIM............................................... 41

5.5.1 BPA Not Participating ............................................................................................ 41

5.5.2 WAPA Not Participating ........................................................................................ 47

5.5.3 BPA and WAPA Not Participating......................................................................... 53

5.6 Results for Wind-Only and Load-Only Balancing Authorities...................................... 54

5.7 Summary Results............................................................................................................ 56
vii

5.8 Effects Alternative Scheduling....................................................................................... 60

6 Conclusions........................................................................................................................... 63

7 References............................................................................................................................. 64

Appendix A. Ramp Savings Plots................................................................................................. 65

Appendix B. Ramp Timing Plots.................................................................................................. 79

List of Tables

Table 1. BAAs and Regional Groups Considered in this Study..................................................... 6

Table 2. Ancillary Service Prices from Five Regions Show that Contingency Reserves Are Much 

Less Expensive than Regulation ................................................................................................... 16

Table 3. Scenario Descriptions..................................................................................................... 20

Table 4. Alternative Scheduling/Dispatch and Wind Forecast Assumptions............................... 20

Table 5. Summary of Load and Wind Data Variability................................................................ 21

Table 6. Reduction in Reserves Maximum Values....................................................................... 24

Table 7. BPA Reserve Requirements in or out of Regional EIM................................................. 42

Table 8. BPA Reserve Requirements in or out of Footprint EIM ................................................ 45

Table 9. WAPA Reserve Requirements in or out of WestConnect Regional EIM ...................... 48

Table 10. WAPA Reserve Requirements in or out of Footprint EIM .......................................... 51

Table 11. Reserve Reductions at the Footprint Level Seen in EIM Implementations.................. 57

Table 12. Comparison of Reserve Requirements for Regional EIMs (MW) ............................... 58

Table 13. Average Total Regulation Requirements Are a Constant Percentage of BAU Relative 

to Various Dispatch/Lead Time Schedules................................................................................... 63

List of Figures

Figure 1. Operating reserve requirements with alternative EIM implementations......................... ii

Figure 2. Example correction of the third-day anomaly in the WWSIS wind dataset.................... 3

Figure 3. Graphical representation of determining load variability component............................. 4

Figure 4. BA variability versus BA size for estimating 10-minute variability............................... 4

Figure 5. Comparison of actual and synthesized load data............................................................. 5

Figure 6. Comparison of interval changes for actual and synthesized data.................................... 5

Figure 7. WECC BA map with regional groups............................................................................. 7

Figure 8. Operation timeline for the EIM toolkit............................................................................ 9

Figure 9. EIM SPP schedule for calculating setpoints and moving generation within 10 minutes 9
viii

Figure 10. SPP repeats the calculations and unit ramping every 5 minutes based on system 

snapshots that are only 10 minutes old ......................................................................................... 10

Figure 11. The EIM would effectively pool variability within the operating footprint, similar to a 

single BA ...................................................................................................................................... 11

Figure 12. Forecast for 10-minute dispatch .................................................................................. 12

Figure 13. Forecast for 1-hour dispatch made 40 minutes prior to the beginning of the 

operational period ......................................................................................................................... 12

Figure 14. Short-term forecast error sigma as a function of wind production level..................... 13

Figure 15. Hour-ahead forecast error sigma as a function of wind production level ................... 14

Figure 16. Sample of ramp magnitude and duration envelopes at 90th percentile....................... 17

Figure 17. Sample plot of several magnitude/duration probability envelopes ............................. 17

Figure 18. Example ramp duration curves for net load show the benefit of combined planning for 

ramping adequacy ......................................................................................................................... 19

Figure 19. Coincidence of wind, load, and hourly penetration..................................................... 23

Figure 20. Comparison of reserve requirements for the footprint EIM to BAU .......................... 24

Figure 21. Comparison of footprint-wide EIM to BAU total reserve requirement ...................... 25

Figure 22. Reduction in total regulation reserve requirements by implementing a footprint-wide 

EIM ............................................................................................................................................... 25

Figure 23. Footprint-wide ramping that can be eliminated by the EDT for a sample 1-week 

period ............................................................................................................................................ 26

Figure 24. Frequency and magnitude of annual ramping reductions............................................ 26

Figure 25. Average timing of ramp events during the year.......................................................... 27

Figure 26. Average timing of ramp saving over the year ............................................................. 28

Figure 27. BAU ramp magnitude and duration plot, 95th percentile ........................................... 29

Figure 28. Footprint EIM ramp magnitude and duration plot, 95th percentile............................. 29

Figure 29. Reduction in ramp magnitudes for the footprint EIM................................................. 29

Figure 30. Magnitude and duration probability graph for the footprint EIM case ....................... 30

Figure 31. Footprint daily maximum load ramping requirements for 1 year ............................... 31

Figure 32. Footprint daily wind maximum ramping requirements............................................... 32

Figure 33. EDT footprint daily net load maximum ramping requirement.................................... 32

Figure 34. Net reserve savings for Columbia Grid regional EIM................................................. 33

Figure 35. Columbia Grid EIM 1-hour ramp savings................................................................... 34

Figure 36. Columbia Grid EIM ramp savings over a typical week .............................................. 34

Figure 37. Average timing of net ramps in Columbia Grid.......................................................... 35
ix

Figure 38. Average timing of net ramp savings in Columbia Grid .............................................. 35

Figure 39. Net reserve reductions from NTTG regional EIM ...................................................... 36

Figure 40. NTTG EIM 1-hour ramp savings................................................................................ 36

Figure 41. NTTG regional EIM ramp savings over a typical week.............................................. 37

Figure 42. Average timing of net ramps in the NTTG ................................................................. 37

Figure 43. Timing of ramp savings in the NTTG regional EIM................................................... 38

Figure 44. WestConnect regional EIM net reserve reductions..................................................... 38

Figure 45. WestConnect EIM 1-hour ramp savings ..................................................................... 39

Figure 46. WestConnect EIM ramp savings over a typical week................................................. 39

Figure 47. WestConnect regional EIM ramp timing .................................................................... 40

Figure 48. Timing of ramp savings in the WestConnect regional EIM....................................... 40

Figure 49. Net reserve requirements for Columbia Grid regional EIM without BPA.................. 41

Figure 50. Reserve requirements for BPA alone and participating in Columbia Grid EIM......... 42

Figure 51. Summary of average reserve for Columbia Grid with and without BPA.................... 43

Figure 52. Ramping savings for BPA by participating in Columbia Grid EIM ........................... 44

Figure 53. Reserve requirement for the footprint EIM without BPA participation...................... 44

Figure 54. Reserve savings for the footprint EIM with and without BPA ................................... 45

Figure 55. Summary of reserves for BPA alone and in the footprint EIM................................... 46

Figure 56. Summary of reserves for the footprint EIM with and without BPA ........................... 46

Figure 57. Ramp savings for BPA by participating in the footprint EIM..................................... 47

Figure 58. Reserve requirements for WestConnect regional EIM without WAPA participation 47

Figure 59. Summary of reserves for WAPA alone or participating in WestConnect EIM .......... 48

Figure 60. Summary of average reserve for WestConnect with and without WAPA .................. 49

Figure 61. Ramping savings for WAPA by participating in WestConnect regional EIM............ 50

Figure 62. Reserve requirements for the footprint EIM without WAPA participation ................ 50

Figure 63. Summary of reserves for WAPA alone and in the footprint EIM............................... 51

Figure 64. Summary of reserve requirements for the footprint EIM with and without WAPA... 52

Figure 65. Ramp reduction resulting from WAPA participating in the footprint EIM ................ 52

Figure 66. Reserve savings for the footprint EIM without BPA or WAPA participation............ 53

Figure 67. Summary of reserves for WAPA and BPA alone and in the footprint EIM ............... 53

Figure 68. Summary of reserves for the footprint EIM with and without BPA and WAPA........ 54

Figure 69. Reserve requirements for wind-only/load-only EIM implementations....................... 55

Figure 70. Detail of regulation requirements for wind-only/load-only EIM implementations .... 55
x

Figure 71. Reserve requirement reductions for wind-only/load-only EIM implementations....... 56

Figure 72. Reserve requirements across EIM implementations ................................................... 57

Figure 73. Regulation requirements for various scenarios........................................................... 58

Figure 74. Reserve requirements comparison with and without optional participants for the 

footprint EIM ................................................................................................................................ 59

Figure 75. Reserve requirements across all “full-participation” scenarios................................... 60

Figure 76. Faster scheduling and larger aggregation greatly reduce the total required regulating 

reserves ......................................................................................................................................... 61

Figure 77. Effect of forecast lead time and dispatch schedule on reserve requirements.............. 62

Figure 78. Comparison of regulation requirements with various cooperation regimes and 

dispatch/forecast lead times.......................................................................................................... 62

List of Equations

Equation 1. Sample calculation of hourly wind standard deviation ............................................. 13

Equation 2. Calculation of intra-hour regulation requirement...................................................... 14

Equation 3. Sample calculation of hour-head wind standard deviation........................................ 14

Equation 4. Calculation of spinning reserves requirement ........................................................... 15
1

1 Introduction

The anticipated increase in variable generation in the Western Interconnection (WI) over the next 

several years has raised concerns about how to maintain system balance, especially in smaller 

Balancing Areas (BAs). Given renewable portfolio standards in the West, it is possible that more 

than 50 gigawatts (GW) of wind capacity will be installed by 2020. Significant quantities of solar 

generation will likely be added as well. The consequent increase in variability that must be 

managed by the conventional generation fleet and responsive load makes it attractive to consider 

ways in which Balancing Area Authorities (BAAs) can pool their variability and response 

resources, thus taking advantage of geographic and temporal diversity to increase overall 

operational efficiency. 

There are several possible approaches to implementing this type of variability pooling, each of 

which would involve alternative levels of operational coordination beyond today’s efforts. A full 

pooling of variability could potentially result in fully coordinated unit commitment, after 

blending the load and wind forecasts. Closer to real-time, economic dispatch could also be 

implemented across the entire electrical footprint. An alternative to this fully coordinated 

operational case would consist of using existing practice for unit commitment, which is largely 

uncoordinated between BAAs in most cases but allowing for economic dispatch over a wide 

area.

Our analysis considers several alternative forms of an Energy Imbalance Market (EIM) that have

been proposed in the non-market areas of the WI. The proposed EIM includes two changes in 

operating practices that independently reduce variability and increase access to responsive 

resources: BAA cooperation and sub-hourly dispatch. As proposed, the EIM does not consider 

any form of coordinated unit commitment; however, over time it is possible that BAAs would 

develop formal or informal coordination plans. This report examines the benefits of several 

possible EIM implementations, both separately and in concert. Although the analysis presented 

here focuses on high penetrations of wind energy, it can easily be adapted to solar data and 

combined contributions from wind and solar energy. 

2 Data

We used data from the recent Western Wind and Solar Integration Study (WWSIS), managed by 

the National Renewable Energy Laboratory (NREL) on behalf of the U.S. Department of Energy 

(DOE) [1]. The study outlined several alternative build-out scenarios of wind plants: (a) the “in 

area” scenario, which assumes all renewable portfolio standards (RPS) requirements are met by 

resources within the state; (b) the “mega-project” scenario, which locates wind plants based on 

wind regime quality, as measured by the annual capacity factor; and (c) the “local priority” case 

that blends (a) and (b). Interestingly, there were no dramatic differences in total costs for the 

different scenarios. For this study, we utilize scenario (a). Our method can easily be applied to 

the other scenarios or to entirely different mixes of wind and/or solar energy. The wind energy 

penetration from our selected case is 30% of all electricity within the WestConnect (WC)

footprint and 20% of all electricity in the remaining Interconnection.

The 2006 time series wind dataset was paired with the 2006 time series load data so that the 

common weather impacts on load and wind would be consistent. We aggregated the data into 
2

regional2 footprints: Columbia Grid (CG), Northern Tier Transmission Group (NTTG), WC, and 

British Columbia. Other areas within the WI (California and Alberta) were not modeled because 

markets are already in place in those areas and they likely would not participate in the initial 

EIM analyzed in this report.

3

2.1 Wind Production Data

3Tier Group developed a large wind speed and wind power database using a numerical weather 

prediction (NWP) model applied to the West. Because the model allows the re-creation of the 

weather at any time or space, wind speed data were sampled every 10 minutes for a 3-year period 

on a 2-km spatial resolution at representative hub heights for modern wind turbines. The 

resulting dataset captures the chronological behavior of the wind that would be seen at locations 

around the West. The high-resolution dataset was then used to construct the various wind 

scenarios described above.

The NWP model of the WI contained geographic and temporal seams that were not possible to 

resolve. This resulted in unrealistic wind energy ramps near the temporal boundaries, which 

occurred every 3 days. To make the reserves and ramping analysis complete, a continuous annual 

record was needed, so a method to smooth those ramps below statistical significance was 

required. To do this, the wind data were analyzed in detail surrounding the anomalies.

The anomalies were seen to occur at approximately the same time, 3 p.m. every third day,

starting with the first day of data for all wind plants in the dataset. Anomalous data were seen up 

to 3 hours before this time and 3 hours after, a side effect of the blending of model runs by the 

wind data contractor. These anomalous data caused 10-minute ramps more than double that seen 

anywhere else in the datasets. Figure 2 shows a scatter plot of 10-minute interval changes versus 

the interval number from a 3-day period. The red dots show where the anomalous data are found.

The spikes near 90 on the x axis show the peak interval changes. The similar times (3 p.m.) on 

the second and third days are near 230 and 380 and do not show similar peaks.

The time range where they occurred and the magnitude characteristics were observed. Statistics 

for similar time periods not affected by the anomaly were computed. Several moving average 

filters were designed to push the magnitude of the anomalies below a threshold consistent with 

statistics from the non-affected times. The blue dots show the results of the filtering. While some 

artifacts of the filtering are observed, the overall shape of the envelope is similar to the same 

time on days 2 and 3 of the sequence.

 2 We use the term “regional” to describe aggregation of generation, load, and wind in an area such as CG. We also 

use the term “sub-regional.” 3 Even greater benefits could be realized if the EIM is expanded to include California and Alberta.
3

Figure 2. Example correction of the third-day anomaly in the WWSIS wind dataset

2.2 Load Data

Load time series data from 2006 were chosen from Ventyx Velocity Suite and were increased to 

represent 2017 loads. The load information was only available at an hourly resolution. 

To provide adequate temporal resolution to observe diversity effects and to match the resolution 

of the wind data, 10-minute data were synthesized from the hourly load data. The intra-hour 

variability was statistically characterized using multiple high-resolution datasets from Bonneville 

Power Administration (BPA) and other Eastern Interconnection balancing authority sources.

These datasets ranged from 5- to 10-minute sampling resolution for balancing authorities ranging 

from about 2,000 MW up to 15,000 MW. The size range was chosen to cover the range seen in 

the subset of Western Electricity Coordinating Council (WECC) member BAs used in the 

broader analysis discussed later in this report.

The load-following trend was removed from the datasets by applying a 50-minute, 0 delay 

moving average filter to each of the load data series. The filtered data were subtracted from the 

load data to leave the variability component. Figure 3 shows a graphical representation of this 

process.
4

Figure 3. Graphical representation of determining load variability component

Once the variability component is isolated from the trend, the variability was characterized as a 

normally distributed random variable whose mean is 0. The standard deviation for each BA was 

calculated and plotted against the BA size.

Figure 4. BA variability versus BA size for estimating 10-minute variability

The 10-minute load data are synthesized from the hourly by forming a linear interpolation at 10-

minute intervals between each hourly point. Then a normally distributed random number is 

generated with mean of 0 and standard deviation calculated from Figure 4 and added to each of 

the trend points.

-20

-15

-10

-5

0

5

10

15

20

1000

1050

1100

1150

1200

1250

1300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Load Variability Component (MW)

Load (MW)

10-Minute Period

Example of Filtering Process

10-Min Load

Filtered Load

Variability Signal

y = 0.0017x + 2.3583

R2 = 0.9587

0

5

10

15

20

25

30

0 2000 4000 6000 8000 10000 12000 14000 16000

Load Variability (MW)

BA Size (MW)

10-Minute Load Variability without Trend vs. BA Size 

(for Synthesizing 10-Minute Data)
5

Figure 5 compares the results of this procedure for one of the BAs used to develop the curve in 

Figure 4. The 5-minute data for one of the reference BAs were averaged to generate a 1-hour 

dataset. The 10-minute data were synthesized as described above. The blue curve shows these 

results, and the red curve is the original 10-minute measured data.

Figure 5. Comparison of actual and synthesized load data

Figure 6 shows the interval-to-interval changes for the full time range for the same BA. Note that 

there is a good match with the exception of the short duration peaks of less than 1 hour. The 

short duration peaks are lost in the averaging to 1-hour data and cannot be reconstructed through 

the statistics.

Figure 6. Comparison of interval changes for actual and synthesized data

3800

4000

4200

4400

4600

4800

5000

5200

5400

5600

0 2 4 6 8 10 12 14 16 18 20 22

Load (MW)

Hours

Comparison of Measured and Synthesized Load Data

Synth. Load

Actual Load
6

2.3 Balancing Area Authorities and Regional Modeling

This study modeled portions of the WI not already covered by a market structure. Table 1 shows 

the BAAs considered as part of the study as well as regional groupings used for regional 

implementations of the EIM operations. Balancing authorities without load were not considered.

Table 1. BAAs and Regional Groups Considered in this Study

Columbia Grid

Avista PUD No 1 of Grant County

Bonneville Power Administration Puget Sound Energy

PUD No 1 of Chelan County Seattle City Light

PUD No 1 of Cowlitz County4 Tacoma Power

PUD No 1 of Douglas County

Northern Tier Transmission Group

Idaho Power Corp Pacificorp West

Northwest Energy Portland General Electric

Pacificorp East

WestConnect

Arizona Public Service Sierra Pacific Power (NV Energy)

El Paso Electric Salt River Project

Imperial Irrigation District Tucson Electric Power

Nevada Power Turlock Irrigation District

Public Service Company of New Mexico WAPA - Colorado Missouri Region

Public Service Company of Colorado WAPA - Lower Colorado Region

Sacramento Municipal Utility District WAPA - Upper Great Plains West

Canada

British Columbia Transmission Corporation

Figure 7 shows a map of the reduced BA structure considered for this study. The color of the BA 

name indicates in which regional group it belongs: Orange indicates CG, light blue is NTTG, 

white is WC, and black is BCTC.

 4 PUD No 1 of Cowlitz County is not shown as an independent BAA in the WI in Figure 7. However, the original 

WWSIS dataset (on which this study is based) contained independent data for this entity and is included here.
7

Figure 7. WECC BA map with regional groups

3 Overview of the Proposed Energy Imbalance Market and 

Efficient Dispatch Toolkit

In the WI, areas outside of California and Alberta do not presently have a common energy 

market, although there is bilateral transaction activity in the region. The Seams Issues 

Subcommittee of the WECC is currently investigating an Efficient Dispatch Toolkit (EDT) that 

would achieve many of the benefits of a large-scale energy market but without a coordinated unit 

commitment or regulation market.

The proposed EDT would use two primary tools. An Enhanced Curtailment Calculator (ECC), 

which can prioritize and allocate transmission service curtailments based on service priority for 

power flow impacts on the grid, will evaluate tagged and untagged flows (most deliveries inside 

balancing areas are not tagged). The ECC would pass relevant curtailment information to the 

second tool, the EIM.

The EIM uses a security-constrained economic dispatch to provide two functions:
8

• Balancing service: This service re-dispatches generation every 5 minutes to maintain the 

balance between generation and load. For deliveries scheduled in advance, the effect is 

that the market supplies deviations from schedules in generator output and errors in load 

schedules.

• Congestion re-dispatch service: This will re-dispatch generation to relieve overload 

constraints on the grid. Information provided to the EIM from the ECC ensures correct 

allocation of the costs of re-dispatch service.

Federal Energy Regulatory Commission (FERC) Pro Forma Tariff Schedules 4 (energy 

imbalance) and 9 (generation imbalance) provide the current approach that is used by the WECC 

BAAs for balancing services. The proposed EIM replaces part of the BAA services and results in 

a “virtual consolidation” due to a wide-area security-constrained economic dispatch that covers 

imbalances. The congestion re-dispatch service is new to the non-market portions of the WI. 

The EIM design includes a feature different from most regional markets in the United States 

where internal resources are subject to a “must offer” requirement. Instead, the default operating 

assumption is that each market participant provides sufficient resources to cover its own 

obligations (as is the case today) and the regional economic dispatch is provided by any resource 

that voluntarily offered responsive capability and that is cleared by the security-constrained 

economic dispatch process. Most transmission service deliveries would continue to use 

traditional reserved transmission service, but the EIM would not use pre-reserved transmission. 

Instead, the EIM flow would receive the lowest transmission service curtailment priority. By this 

mechanism, EIM flows would not displace reserved transmission service. 

Unlike other regional markets where transmission service for market delivery is provided under a 

regional network service tariff, the EIM flows would pay an imputed service compensation after

the fact to participating transmission providers. At this stage of the EDT development, the terms 

for the transmission service revenue target and revenue allocation among participating 

transmission providers have not yet been established.

The EIM function adds some operational steps to the current practices used in the WI today. 

Functionally, the operating steps for the proposed EIM track closely with the operating process 

established in the Southwest Power Pool (SPP) in its Energy Imbalance Service Market. Figure 8

illustrates the timeline for operation of the proposed EDT.

Figure 9 shows the sequence for the SPP taking the current system data, calculating the expected 

conditions and required setpoints for the next interval, communicating those setpoints to 

generators and responsive loads, and the responsive resources moving to the new setpoints, all in 

10 minutes. Figure 10 shows how continuously repeating the process shown in 

Figure 9 results in meeting a new system dispatch point every 5 minutes, based on information 

that is only 10 minutes old.
9

Figure 8. Operation timeline for the EIM toolkit

Figure 9. EIM SPP schedule for calculating setpoints and moving generation within 10 minutes
10

Figure 10. SPP repeats the calculations and unit ramping every 5 minutes based on

system snapshots that are only 10 minutes old

The EIM would effectively implement some aspects of a virtual BA across the WI by performing 

a security-constrained economic dispatch (SCED) on energy imbalances (California and Alberta 

would not be included because they already have energy markets). Imbalances would be netted 

out, much as they would be in a single BA. As proposed, the EIM does not result in a 

coordinated unit commitment, nor does it pool regulation, which remains a service at the local 

BA level. However, the netting of energy imbalance, which would include impacts of load and 

wind, is expected to be significant. Figure 11 illustrates the concept, with each of the small 

bubbles representing a single BA. The arrows between the BAs indicate bilateral tagged energy 

flows that would not be precluded in the EIM. However, under the EIM, only the footprint net 

imbalance must be managed, resulting in less net variability within the local BAs and less 

required ramping across the footprint.
11

Figure 11. The EIM would effectively pool variability within the operating footprint, similar to a 

single BA

4 Analysis Methods

4.1 Reserve Analysis

The increased variability and uncertainty from wind and solar power causes an increase in 

operating reserve requirements that can be provided by some combination of flexible generation 

and responsive load.5 Together these can contribute to the operating reserve that is available to 

help manage the wind and load variability. This reserve is calculated dynamically and is a 

function of the time-synchronized anticipated variability of the wind power and the load. A 

methodology was developed to estimate the increased requirements for regulation with wind 

variability in the Eastern Wind Integration and Transmission Study (EWITS) [4]. The EWITS 

methodology focused on fast dispatch updates of 10 minutes or faster. For the purposes of this 

work, that method was extended to cover hourly dispatch updates as well.

Short-term variability is challenging because it is difficult to fully anticipate the scheduling 

changes and fluctuations that must be covered with reserves. In a system with 10-minute or faster 

markets or dispatch updates, a common approach is to forecast a flat value for wind output for 

the next interval based on the past 10 to 20 minutes.6 The wind varies on that time scale, and an

understanding is needed of how it will vary during the forecast interval. Figure 12 and Figure 13

illustrate how the forecast error is calculated for both 10-minute and 1-hour dispatch schedules.

The forecast error is the difference between the actual data and the forecast value.

 5 We note that wind power plants do not constitute a contingency because of the relatively slow rate at which wind 

power changes compared to a unit tripping offline. The operating reserve we refer to is further discussed in Ela et al. 

(2011).

6 The short-term load trend can be forecast somewhat more accurately, but the load regulation movement cannot.

EIM Footprint

EIM Tool: 

SCED

Intra-hour variability is captured and allocated 

in real-time within the entire region, limited by 

Diversity benefit the physical capability of the wires.

reduces operating 

costs for balancing.
12

Figure 12. Forecast for 10-minute dispatch

Figure 13. Forecast for 1-hour dispatch made 40 minutes prior to 

the beginning of the operational period

With a statistical approach that is based on detailed wind and load and forecast data, an estimate 

of the required reserves can be calculated based on the standard deviation or other variability 

metric derived from the data.

For our purposes, the reserve requirements are broken down into three classes by the resource 

types required to fulfill them.

• Regulation is required to cover fast changes within the forecast interval. These changes 

can be up or down and can happen on a minute-to-minute time scale. Regulation requires 

resources on automatic generation control (AGC).

• Spinning reserve is required to cover larger, less frequent variations that are primarily due 

to longer-term forecast errors. Spinning reserve is provided by resources (generation and 
 

responsive load) that are spinning and can fully respond within 10 minutes. These 

resources do not necessarily require AGC. 

•	 Non-spinning and supplemental reserves are used to cover large, slower-moving, 

infrequent events such as unforecasted ramping events. For the purposes of this study, 

non-spinning reserve can be made available within 30 minutes7

 and can come from 

quick-start resources and responsive load. Supplemental reserves can be made available 

within 30 minutes. 

The variability of wind plant output is a function of its production level. The EWITS method 

recognizes that the short-term variability in wind plant output and thus short-term forecast error 

is a normally distributed value over a large geographic footprint. Through analysis, an equation 

can be written for the standard deviation (sigma) of variability that varies with production level. 

That equation is derived by analyzing the wind production data over some long period of time (a 

year or more) and calculating the standard deviation for the variability in various ranges of wind 

output. Figure 14 shows an example of this function. 

0 

20 

40 

60 

80 

100 

120 

0 1000 2000 3000 4000 5000 6000 7000 

Sigma of Forecast Error (MW) 

Wind Production Level (MW) 

Short-Term Forecast Error Sigma vs. Production Level 

Figure 14. Short-term forecast error sigma as a function of wind production level 

The curve fit polynomial curve fit shown as the smoothed line in this example is shown in 

Equation 1. 

Equation 1. Sample calculation of hourly wind standard deviation 

ሻࢊ࢔࢏ࢃ ࢟࢒࢛࢘࢕ࡴሺ ࢀࡿ࣌

ൌ െ ૟. ૠ૛ࡱ െ ૙૟ · ሺ࢟࢒࢛࢘࢕ࡴ ࢊ࢔࢏ࢃሻ૛ ൅ ૙. ૙૝૜ૠ · ሺ࢟࢒࢛࢘࢕ࡴ ࢊ࢔࢏ࢃሻ

൅ ૛૟. ૠ૝ 

7

 Non-spin frequently refers to a 10-minute service but is intended as a 30-minute service here. 

13 
The equation is used to calculate the standard deviation (sigma) of the wind power for each hour. 

A component to cover load variability is calculated as a fixed percentage of the hourly load. That 

fixed percentage is calculated based on the load size in the BA as described in the Data section 

above and is calculated to cover 1 sigma of the load variability. The wind and load components 

are scaled to 3 sigma and combined as the square root of the sum of the squares, as shown in 

Equation 2. 

Equation 2. Calculation of intra-hour regulation requirement

ሻܹ݅݊݀ ݄ݐܹ݅ሺ ݐ݁݉݁݊ݎ݅ݑݍܴ݁ ݊݋݅ݐ݈ܽݑܴ݁݃

ܽ݀݋ܮ ݕ݈ݎݑ݋ܪ 1.5%

3 

మ ଶ

ൌ3 ඨ൬ ൰ ൅ ሺߪௌ் ሺݎݑ݋ܪ݈ݕ ܹ݅݊݀ሻሻଶ ·
	

The 3-sigma approach estimates reserve values that will cover 99.7% of all short-term variability 

for normal distributions; for non-normal distributions, adjustments can be made accordingly. 

This component must be covered by regulation like reserves under AGC. 

An additional uncertainty component due to hour-ahead wind forecasting error was calculated as 

part of the EWITS method. This component is calculated in a similar manner to the short-term 

forecast error described above, using an equation to describe the standard deviation of hourahead forecast error. Figure 15 shows the development of the equation for hour-ahead forecast 

error standard deviation. 

0 

100 

200 

300 

400 

500 

0 1000 2000 3000 4000 5000 6000 7000

Sigma of Forecast Error (MW)

Wind Production Level (MW) 

Hour-Ahead Forecast Error Sigma vs. Production 

Level 

Figure 15. Hour-ahead forecast error sigma as a function of wind production level 

The curve fit polynomial curve fit shown as the smoothed line, in this example, is shown in 

Equation 3. 

Equation 3. Sample calculation of hour-head wind standard deviation 

ߪு௢௨௥ି௔௛௘௔ௗ ݈ݕ ܹ݅݊݀ݑ݋ܪݎ ሻ

2ൌ െ .985ܧ െ 05 

ሺ

·.18950൅ ሺݎݑ݋ܪ݈ݕ ܹ݅݊݀ ൅ 103.2 ሻ ଶ ሺ· ݎݑ݋ܪ݈ݕ ܹ݅݊݀ሻ

14 
 

 

With that equation, the expected sigma for the forecast error is calculated based on the previous 

hour’s production (persistence forecast). This component helps to insure the system is positioned 

with enough maneuverability to cover the probable forecast error and divided as 1 sigma 

assigned to spinning reserves and 2 sigma assigned to non-spin/supplemental reserves. 

Equation 4 shows the function for the spinning reserves. The equation for nonspinning/supplemental reserves is the same except that 2 * sigma is used. 

Equation 4. Calculation of spinning reserves requirement 

ݎ݋ݎݎ݁ ݐݏ݁ܿܽݎ݋݂ ݄݅݊݀݁ܽ݀ܽݓ െ ݎݑ݋ܪሺ ݐ݁݉݁݊ݎ݅ݑݍܴ݁ ݅݊݊݅݊݃݌ܵ

ሻܹ݅݊݀ ݎݑ݋ܪ ݏݑ݋݅ݒ݁ݎܲሺ ௗ௔௛௘௔ି௥௨௛௢ߪ · 1ൌ

ሻ 

Finally, to find the total reserve requirement, each of these three components (regulation, spin, 

and non-spin) is added arithmetically. 

Both conventional contingencies and the increased variability and uncertainty associated with 

wind and solar generation increase the need for responsive reserves. Because the response 

requirements are similar in terms of the required response speed, response frequency, and 

response duration, they are both expressed in terms of the same set of required reserves: 

regulation, spinning reserve, non-spinning reserve, and supplemental reserve. The same 

resources can supply the services for either need. That does not mean that dedicated contingency 

reserves would be used to respond to wind variability or uncertainty. It does mean that wind 

variability and uncertainty results in an increased need for the same types of reserves that are 

required for contingency response. Our analysis does not evaluate contingency reserves, nor do 

we consider whether resources that provide one type of reserve can be activated to provide 

another type of reserve. The issue of whether reserve types can be shared among different uses is 

under discussion in several forums, and we do not take a position on this issue. For the analysis 

in this report, we do not reduce the contingency reserve margin nor deploy contingency reserve 

to manage the increased variability and uncertainty of wind and solar. 

4.2 Ramp Analysis 

The reduction in variability and uncertainty implies that the need for ramping will be reduced 

under the various coordination approaches. Three approaches were used to understand the full 

implications of ramp reduction. 

We followed a similar approach as Milligan and Kirby [2, 3] in developing ramp-reduction 

estimates based on the chronological wind and load data available for this study. The approach 

calculates hourly individual area ramp requirements, separating up-ramp and down-ramp 

demand for load alone and for net load (load minus wind). BAs that operate without coordination 

may simultaneously have ramps occurring in the opposite direction. With coordinated 

operations, such as would be available with the EDT, some of this ramping requirement, and 

therefore generator and responsive load ramping, could be reduced or eliminated. Similarly, 

different BAs can experience peak ramping requirements during different hours or on different 

days. By sharing ramping reserves, they can reduce the total reserve requirements, similar to the 

savings provided by contingency reserve sharing groups. 

15 
16

Contingency Reserves & Ramping Reserves

Contingency reserves and ramping reserves share a number of similarities but also have a few 

differences. The power system maintains a series of contingency reserves in sufficient quantity 

to ensure that it will be able to maintain the generation/load balance even if a large generator or 

transmission line suddenly fails. These reserves are made up of generating capacity that is held 

back from energy supply and/or responsive load that is available to respond. 

Contingency reserves are time synchronized. Spinning reserve begins responding immediately 

while non-spinning reserve is fully deployable within 10 minutes and supplemental operating 

reserve is typically available within 30 minutes. An important characteristic of these 

contingency reserves is that they are used relatively infrequently (every few days as opposed to 

every hour) because contingency events are relatively infrequent. Consequently, the cost to 

stand ready to respond is more important than the response cost itself. A fast-start combustion 

turbine may be an economic source of non-spinning reserve even if it has a relatively high fuel 

cost.

Wind ramping requirements are similar to conventional contingency requirements in that large 

wind ramp events are relatively rare. The standby costs are often more important than the 

response costs, just as with conventional contingency reserves. 

Wind ramps differ from conventional contingencies both in the event speed and duration. A 

large generator can trip and remove 1,000 MW from the power system in a cycle. Because of 

geographic diversity, even a fast, large wind ramp will take an hour or more to drop 1,000 MW. 

This means that non-spinning and supplemental operating reserves can often be used for wind 

ramps rather than spinning reserves. Wind ramp events are also longer than conventional 

contingency events. North American Electric Reliability Corporation (NERC) standards require 

BAs to restore their contingency reserves within 105 minutes of an event, and they typically 

restore their reserves much faster. [6] Slower wind ramps may require longer reserve 

deployments. 

The important point is that wind ramps require the same types of responsive resources as 

conventional contingencies. Reserves may or may not be able to be shared between 

contingencies and wind ramps; the issue is still being investigated. Partial sharing may be both 

economic and reliable. Contingency-like reserves, however, should be used for wind ramps 

because they cost much less than alternatives like regulation. Table 2 presents annual average 

prices for regulation and the contingency reserves from five regions for 2010. Spinning reserve 

is only 25% to 50% of the cost of regulation while non-spinning reserve is 6% to 16%. 

Table 2. Ancillary Service Prices from Five Regions Show that

Contingency Reserves Are Much Less Expensive than Regulation

California

(Reg = 

up+dn)

ERCOT

(Reg = up+dn)

New York New England

(Reg + 

“mileage”)

MISO

2010 Annual Average $/MW-hr

Regulation 10.6 18.1 28.8 7.07 12.2

Spin 4.1 9.1 6.2 1.75 4.0

Non-Spin 0.6 2.3 1.15 1.5

Replacement 4.3 0.1 0.42
17

Another approach involved calculating all possible ramps at 13 durations from 10 minutes to 12 

hours for load, wind, and net load (load minus wind). For each duration, every possible ramp 

(including overlapping ramps) is calculated and tabulated. From that list, the magnitude 

corresponding to some percentile (90%, 99%, 99.9%, etc.) of ramps is calculated. From this data, 

the expectation of the magnitude and duration of ramps can be bounded by looking at the 

envelope formed when the negative and positive ramp results are plotted, as in Figure 16. Here 

we can see the relationships among the load, wind, and net load ramps at the 90th percentile.

From this plot we can tell, for instance, that 90% of all 8-hour wind up-ramps will be less than 

13,243 MW.

Figure 16. Sample of ramp magnitude and duration envelopes at 90th percentile

By plotting the envelopes for a series of probability levels, we can get an idea about the shape of 

the tails of the distributions. In Figure 17, we can see how the expected magnitude at a particular 

duration changes with the probability level. For instance, 90% of all up-ramps at 4 hours will be 

less than 13,422 MW, and 100% of those ramps will be less than 22,989 MW. The arrows on the 

figure indicate these examples.

Figure 17. Sample plot of several magnitude/duration probability envelopes

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

0 120 240 360 480 600 720

Ramp Magnitude (MW)

Ramp Duration (Minutes)

90-Percentile Ramp Envelopes

Load

Net

Wind

-60000

-40000

-20000

0

20000

40000

60000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Probability of Net Ramp Magnitude and Duration

100% Prob.

99.9% Prob.

99% Prob.

95% Prob.

90% Prob.
18

Ramping requirements can be thought of in the planning horizon as well. Each BAA must have 

sufficient ramping capability to cope with ramps whenever they occur. To estimate the impact of 

separate vs. combined planning for ramping capability, we proceed in a similar fashion as above. 

First, we analyze each BAA separately to determine the ramping needs it will have, given the 

load and wind data. We again calculated ramping requirements for various time steps that ranged 

from 10 minutes to 12 hours. For this phase of the analysis, each ramp occurs entirely within a 

single calendar day, and as before, every third day is excluded from the analysis. That is, we 

calculate the maximum daily ramping requirement in terms of ramp size (MW) and ramp rate 

(MW/min) for each ramp duration (10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, 

and 12 hours). Naturally, the ramps are not independent. The maximum 1-hour ramp is likely a 

part of the maximum 2-hour ramp on the same day. Still, the ramp size and rate metrics for 

different durations provide insight into the flexibility requirements imposed on the conventional 

generation and demand response fleet. 

Ramps are classified by size, duration, and direction. From that information, maximum, 

minimum, and average ramps can be calculated, retaining the classification by size, duration, and 

direction. “Average” in this case means the average daily maximum ramp of the specific 

duration. To obtain the total ramping needs based on separate planning, the curves are added 

together. Estimating the combined ramp requirements follows the same basic algorithm as the 

separate analysis. The only exception is that the load and wind data are combined first, and then 

the various ramp statistics are calculated. The procedure is carried out for load alone, wind alone, 

or for net load, as Figure 18 illustrates. While the previous analysis focused on real-time 

aggregation benefits in which an up-ramp in one BAA is countered by a simultaneous downramp in the opposite direction, this analysis focuses on the ramping capacity that each BAA must 

access. Similar to the analysis of non-coincident peak loads, if one BAA needs 100 MW of 2-

hour up-ramp capability on one day and another BAA needs 200 MW of 2-hour up-ramp 

capability on another day, then they separately need 300 MW of 2-hour up-ramp capability 

because they have no way of sharing the resource. When combined, they may only require 200

MW of 2-hour up-ramp capability if the ramps in fact happen on different days because they can 

now share the resource. 

The four traces on the graph appear for both positive and negative ramp requirements. The upper 

quadrant of the graph shows the aggregated maximum and un-aggregated maximum ramps (top 

two traces), which represent the separate (un-aggregated) and combined (aggregated) ramping 

needs. The difference between the curves shows the maximum ramping capability that could be 

avoided by coordinated planning and subsequent operations. 
19

Figure 18. Example ramp duration curves for net load show the benefit of combined planning for 

ramping adequacy

5 Impact of Energy Imbalance Markets on Reserves and 

Ramping

Per-unit wind variability is reduced with increased geographic diversity, reducing the level of 

reserves needed to compensate for that variability. Forecast errors are also reduced by diversity 

[5].

5.1 Alternative Market Scenarios

We analyzed a large number of possible market footprints and variations on participation levels, 

based on current discussions with the WECC. 

Table 3 shows the combinations we used. Although the EIM may cover all of the non-market 

areas of the Interconnection, there may instead be regional implementations of the market that 

correspond to the regional transmission planning groups, which include Columbia Grid, 

WestConnect, and Northern Tier Transmission Group. For our study, we did not include wind in 

British Columbia because no wind data were available. Federal Power Marketing Agencies such 

as the Bonneville Power Administration (BPA) and Western Area Power Administration 

-100,000

-75,000

-50,000

-25,000

0

25,000

50,000

75,000

100,000

0 2 4 6 8 10 12

Ramp Capacity (MW)

Ramp Duration (Hours)

Un-Aggregated Max

Aggregated Max

Un-Aggregated Average

Aggregated Average
20

(WAPA) may not participate in the EIM because of various potential institutional constraints.8

We therefore constructed cases that excluded one or both of these entities as variations from the 

all-inclusive participation cases. The full footprint includes all of the WI except for Alberta and 

California.

The proposed EIM would operate at the 5-minute level, aggregating energy settlements to hourly

(similar to the 5-minute markets currently operated by Pennsylvania-New Jersey-Maryland 

Interconnection [PJM], Midwest Independent Transmission System Operator [MISO], New York 

Independent System Operator [NYISO], Independent System Operator-New England [ISO-NE], 

Electric Reliability Council of Texas [ERCOT], and California Independent System Operator

[CAISO]); however, our analysis evaluated alternative dispatch intervals of 10 minutes, 30 

minutes, and 60 minutes because of data limitations. As discussed in [5], faster markets improve 

access to generation that may be available to alter its output, whereas slower markets restrict 

units on economic dispatch so that they cannot respond to demand changes within the dispatch 

period. Our 10-minute analysis understates the benefits of the actual 5-minute EIM. Table 4

illustrates the scheduling and dispatch intervals and forecast assumptions for wind.

Table 3. Scenario Descriptions

Market Footprint

Full Footprint (Base 

Case) Regional Business as Usual (BAU)

Wind and load in same BA

Full participation X X X

Excludes BPA X X

Excludes Western X X

Excludes Western and BPA X X

Wind in separate BA from load

Full participation X X X

Excludes BPA X X

Excludes Western X X

Excludes Western and BPA X X

Table 4. Alternative Scheduling/Dispatch and Wind Forecast Assumptions

Schedule Dispatch Interval Forecast Set Time

10m 10 minutes 10 minutes prior

30m 30 minutes 40 minutes prior to start of hour and half-hour

(40 minutes lead)

60m 60 minutes 40 minutes prior to start of the hour

 8 It is also possible that a version of EIM may eventually be extended to include all of the WECC.
21

The 10-minute schedule with the 10-minute dispatch interval is the basis for the analysis in this 

report and is implied in all scenarios unless otherwise noted.

5.2 Variability Analysis

Larger operating footprints improve the ability of the system to respond to variability [2], [3]. 

This occurs for two reasons: (1) Pooling of variability of loads and wind generation increases 

diversity, which reduces the overall per-unit variability, and (2) a broader resource mix increases 

ramping capability linearly. The result is that aggregation provides an increased ability to 

manage variability, which itself is reduced with aggregation. This principle can be applied to 

many facets of power system operation and is one driver for the formation of reserve-sharing 

pools that reduce the total level of contingency reserve needed to maintain reliability.

Although the aggregation may cover all of the non-market areas of the Interconnection, there 

may instead be regional implementations of the market that correspond to the regional 

transmission planning groups, which include Columbia Grid, WestConnect, and Northern Tier 

Transmission Group. For our study we did not include wind in British Columbia because wind 

data were not available. The full footprint includes all of the WI except for Alberta and 

California.

Table 5. Summary of Load and Wind Data Variability

Footprint West 

Conn.

North. 

Tier

Colum. 

Grid

# of BAs 28 14 5 9

Load MW

Max Non-Coincident 109,587 62,512 23,971 23,104

Max Coin. 102,399 60,820 23,116 22,681

Average 64,095 33,512 15,820 14,587

Min Coincident 44,652 22,511 10,623 10,201

Min Non-Coincident 40,218 21,286 10,200 8,731

Wind MW

Max Non-Coincident 48,933 26,167 10,914 11,853

Max Coin. 40,392 24,398 10,430 11,702

Average 16,413 8,604 3,802 4,006

Min Coincident 239 124 69 2

Min Non-Coincident 4 1 3 1

Non-Coincident CF 34% 33% 35% 34%

Coincident CF 41% 35% 36% 34%

Wind Penetration

Max in Area 835% 835% 180% 227%

Max Coincident 77% 95% 80% 108%

Energy 26% 26% 24% 27%

Wind penetration refers to the ratio of annual wind energy to total generation.
22

Figure 19 shows the peak load and wind coincidence for all of the WI and the three regions. 

Aggregation provides a host of benefits for load as well as for wind. Aggregation reduces the 

peak capacity requirements for load alone. Coincident peak load is 7% lower for the overall 

footprint than the sum of the non-coincident peak loads, which each BA must support on its own. 

Minimum loads are also improved (raised) through aggregation: 9.9% for WI and 14.4% for 

Columbia Grid. Load factor is 6% better for the aggregated footprint (65% versus 59%). 

Aggregation also benefits wind. Peak WI wind is reduced by 15.3% through aggregation. WI 

aggregated minimum wind is 420 MW, compared with zero to 43 MW for the individual subregions. Footprint wind capacity factor increases by 7% with aggregation. Aggregating wind also 

reduces the maximum wind penetration. One BA in WestConnect (WAUW) has a maximum 10-

minute wind penetration of 835%, which is reduced to a maximum of 95% for the aggregated 

WestConnect and a maximum 66% for the aggregated footprint.
23

Figure 19. Coincidence of wind, load, and hourly penetration

0

20000

40000

60000

80000

100000

120000

Footprint WC NTTG CG

MW

Peak Load Coincidence

Non-Coincident Coincident

0

10000

20000

30000

40000

50000

60000

Footprint WC NTTG CG

MW

Wind Coincidence

Non-Coincident Coincident

0%

200%

400%

600%

800%

1000%

Footprint WC NTTG CG

Wind Penetration Coincidence

Max in Area Max Coin.
24

5.3 Footprint EIM Scenario – Base Case

The base scenario for our analysis compares the footprint-wide EIM with each balancing 

authority managing the variability wind and load located in their borders, the Business as Usual

(BAU) case. The footprint-wide EIM includes all of the BAs included in the study cooperating to 

manage that variability.

5.3.1 Reserves

As described in the Analysis Methods section above, three categories of reserve requirements 

were calculated for the footprint EIM and BAU scenarios. Figure 20 shows the comparison of 

the regulation, spin, and non-spin/supplemental reserves. The whiskers show minimum and 

maximum values and the bar shows the average value for all hours of the year.

For each category of reserves, the requirement is cut approximately in half. Total regulation is 

cut from 2,440 MW for the BAU case to 1,198 in the footprint EIM.

Figure 20. Comparison of reserve requirements for the footprint EIM to BAU

Table 6. Reduction in Reserves Maximum Values

Regulation Spin Non-Spin Total

BAU 3,826 3,791 7,582 15,200

EIM 1,626 1,262 2,524 5,412

Reduction in 

Max Value 58% 67% 67% 64%

To understand how often various amounts of reserves are required, we developed regulation and 

total reserves duration plot. Total reserves are the sum of the total regulation, spin, and non-spin

requirements. Figure 21 shows total reserves duration for the BAU and footprint-wide EIM case.

The black line shows the saving in total reserves that are realized when the footprint EIM is 

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

BAU 2440 2096 4192 8729

EIM 1198 969 1938 4105

Reserve Requirement (MW)

Reserve Savings for Footprint EIM

(Average, Max, and Min) 

BAU

EIM
25

implemented. The plot shows the large decrease in the overall requirements but particularly for 

the large, infrequent tails events.

Figure 21. Comparison of footprint-wide EIM to BAU total reserve requirement

Interestingly, the total reserve requirement for the large aggregation is flatter as well as lower 

than when reserves are supplied for each BA individually. The same pattern is seen for 

regulation for the scenario regions, as shown in Figure 22.

Figure 22. Reduction in total regulation reserve requirements by implementing a footprint-wide 

EIM

5.3.2 Ramp Demand Reduction

The reduction in variability implies that the need for ramping will be reduced under the various 

coordination approaches. We followed a similar approach as Milligan and Kirby [2, 3] in 

developing ramp-reduction estimates based on the chronological wind and load data available for 

this study. The approach calculates hourly individual area ramp requirements, separating up0

2000

4000

6000

8000

10000

12000

14000

Total Reserve Requirement (MW)

0 1000 2000 3000 4000 5000 6000 7000 8000

Hours

Total Reserve Requirement Duration for Footprint EIM

BAU

EIM

Savings

0

500

1000

1500

2000

2500

3000

3500

4000

0 1000 2000 3000 4000 5000 6000 7000 8000

Regulation Requirement (MW)

Hours

Regulation Requirement Duration for Footprint EIM

BAU

EIM

Savings
26

ramp and down-ramp demand for load alone and for net load (load minus wind). BAs that 

operate without coordination may simultaneously have ramps occurring in the opposite direction. 

With coordinated operations, such as would be available with the EDT, some of this ramping 

requirement, and therefore generator ramping, could be reduced or eliminated. 

Figure 23 illustrates the concept for a sample 1-week period. This graph assumes that the EDT 

would operate across the entire footprint. As shown in the graph, there is a benefit even without 

any wind because of the load diversity. However, as also can be seen in the graph, there is a 

much larger ramp saving at a high wind penetration rate, largely because a high wind penetration 

will cause a significant increase in ramping demand for many hours of the year and the greater 

amount of geographic diversity in wind ramps as compared with load ramps.

Figure 23. Footprint-wide ramping that can be eliminated by the EDT for a sample 1-week period

Figure 24 shows a duration plot for the load and net ramp savings for the entire 8,760 annual 

hours. For 134 hours per year the savings in net ramp exceeds 2,000 MW and averages about 

550 MW for the year. Load ramp savings over the year average about 90 MW. Again, the effect 

of aggregation on wind ramp savings is clearly higher than for load alone.

Figure 24. Frequency and magnitude of annual ramping reductions

-3000

-2000

-1000

0

1000

2000

3000

1 25 49 73 97 121 145 169

Reduction in Ramp Demand 

(MW/hr)

Hours

1-Hour Ramp Reduction - Footprint EIM Savings over BAU

Typical Week

Net

Load

-4000

-2000

0

2000

4000

0 1000 2000 3000 4000 5000 6000 7000 8000

Reduction in Ramp Demand 

(MW/hr)

Hours

1-Hour Ramp Reduction - Footprint EIM Savings over 

BAU

Net

Load
27

Also of interest is the timing of the ramps and ramp savings.

Figure 25 and Figure 26 show by week of the year (1 to 52 on the vertical axis) and hour of the 

day (on the horizontal axis) when the ramp and ramp savings occur. Note that the hours are 

relative to Mountain Standard Time and there is a 1-hour delay in the savings plot because 

savings is referenced to the beginning of the hour. An interesting observation is that the 

maximum ramp savings do not always line up with the peak daily up-ramping, as seen in 

Figure 25. In the summer, the peak savings seem to occur at or just after the peak load periods 

where the slope of the afternoon ramp has started to decline. In the winter months, the peak 

savings is nearly coincident with the peak morning ramp. The savings don’t align with the peak 

ramp because load dominates the morning ramps and there is little diversity in the load ramps.

The best load diversity across the footprint occurs just before and just after the major daily load 

ramps because of the two time zones involved. Also, wind ramp savings tend to be at a minimum 

on summer mornings and maximum in the late afternoon.

Figure 25. Average timing of ramp events during the year

Average Timimg of Net Ramps in Footprint EIM

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the Year

5614

3167

720

-1727

-4174

-6621
28

Figure 26. Average timing of ramp saving over the year

5.3.3 Ramp Magnitude and Duration Probability

Ramping requirements are also reduced with aggregation. Figure 27 through Figure 29 show the 

ramping requirements of load, wind, and net load for the non-coincident case in which all 29 

BAs meet their own requirements (Figure 27) and the coincident case in which all 29 BAs 

cooperate to meet the total system ramping requirement (Figure 28). The figures show the 95th

percentile to provide a conservative comparison Control Performance Standard (CPS) 2 

requirement. Naturally the ramp magnitude increases with longer duration. The curvature 

indicates that the average ramp rate declines with ramp duration, as expected. Aggregation 

reduces wind-ramping requirements by around 55% and net-load-ramping requirements by about 

35%. Load does not get as much ramping benefit from aggregation because the daily load pattern 

is highly correlated across the region. Figure 29 shows the reduction in ramp magnitude at the 

95th percentile.

Average Timing of Net Ramp Reduction in Footprint EIM

Hourly average of the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

697

557

418

279

139

0
29

Figure 27. BAU ramp magnitude and duration plot, 95th percentile

Figure 28. Footprint EIM ramp magnitude and duration plot, 95th percentile

Figure 29. Reduction in ramp magnitudes for the footprint EIM

-40000

-20000

0

20000

40000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

95th Percentile BAU Ramps

Load

Net

Wind

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

95th Percentile Footprint EIM Ramps

Load

Net

Wind

-15000

-10000

-5000

0

5000

10000

15000

0 2 4 6 8 10 12

Ramp Magnitude Savings (MW)

Ramp Duration (Hours)

Ramp Savings Footprint EIM to BAU at 95th Percentile

Load

Net

Wind
30

Extreme ramp events are relatively rare, as can be seen in Figure 30. At 12 hours, the worst net 

ramp events are greater than 44,000 MW, but 99% of all ramps at the same duration are less than 

34,000; 90% of all 12-hour ramps are less than about 20,000 MW.

Figure 30. Magnitude and duration probability graph for the footprint EIM case

5.3.4 Implication for Planning 

System planners must ensure that there is sufficient ramping capability to follow the net load, 

just as they must ensure that there is sufficient generation to meet the peak net load. Table 5

showed the significant capacity benefits that can be realized through BA cooperation or 

aggregation in operations. There is a similar benefit that accrues when planning is coordinated 

over a broader region. With un-coordinated planning, each BAA (or entities within the BAA) 

must plan for sufficient generation to cover expected peak conditions and also to provide 

sufficient flexibility: ramping, minimum turn-down, and short minimum up- and down-times. 

With coordinated planning, aggregation reduces needed ramping requirements. Figure 31 shows 

both the maximum ramping capacity required for each ramp duration for the year as well as the 

average of the daily peak ramping requirements. The un-aggregated curves represent the ramping 

requirements that would be necessary with uncoordinated planning, whereas the aggregated 

curves show the needed ramping requirements with coordinated planning. Interestingly,

aggregation reduces the maximum annual ramping requirement significantly more than it 

reduces the average daily ramping requirement. This is because the daily load shape is highly 

correlated between the BAs in the aggregation.

-60000

-40000

-20000

0

20000

40000

60000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Probability of Net Ramp Magnitude and Duration Footprint EIM 

100% Prob.

99.9% Prob.

99% Prob.

95% Prob.

90% Prob.
31

Figure 31. Footprint daily maximum load ramping requirements for 1 year

Figure 32 shows the wind ramping requirements for the EDT footprint while Figure 33 shows 

the net load ramping requirements. For convenience, the ramping sign convention for loads is 

used in all of the figures. A positive ramp represents a load increase or a wind decrease since 

both require conventional generation to ramp up in response. Several characteristics are apparent 

in the ramping data. One is that the wind ramping requirements are significantly lower than the 

load ramping requirements. Second is that there is a greater ramping aggregation benefit for 

wind than for load indicating greater diversity in wind patterns than in load patterns. Third, the 

maximum annual ramping requirements are significantly greater for both load and wind than the 

average daily maximum ramping requirements. Especially in the case of wind, large ramps are 

rare. Finally, aggregation benefits remain strong for net load.

-100,000

-75,000

-50,000

-25,000

0

25,000

50,000

75,000

100,000

0 2 4 6 8 10 12

Ramp Capacity (MW)

Ramp Duration (Hours)

Un-Aggregated Max

Aggregated Max

Un-Aggregated Average

Aggregated Average
32

Figure 32. Footprint daily wind maximum ramping requirements

Figure 33. EDT footprint daily net load maximum ramping requirement

-100,000

-75,000

-50,000

-25,000

0

25,000

50,000

75,000

100,000

0 2 4 6 8 10 12

Ramp Capacity (MW)

Ramp Duration (Hours)

Un-Aggregated 

Max

Aggregated 

Max

-100,000

-75,000

-50,000

-25,000

0

25,000

50,000

75,000

100,000

0 2 4 6 8 10 12

Ramp Capacity (MW)

Ramp Duration (Hours)

Un-Aggregated 

Max
33

5.4 Regional EIM Scenario Results

Results in the previous section show how reserves are affected when the entire study footprint 

operates as a single EIM. We also evaluated the effect of operating three distinct regional EIMs 

in the same footprint.

5.4.1 Columbia Grid EIM

Columbia Grid was evaluated as an EIM with all of the members participating and also with 

BPA not participating in the EIM. Figure 34 shows the net reserve requirement reduction results 

for Columbia Grid with all of the members participating in the EIM.

Reserve reduction for a Columbia Grid EIM is relatively modest compared to the full footprint 

and the other regional EIMs. This is primarily due to the relatively high correlation between 

wind sites in the footprint with approximately 73% of the nameplate (measured as maximum 

zonal output not actual machine nameplate) located in the BPA Western Montana zone. This 

dilutes much of the advantage of aggregating the wind across the regional EIM. Total regulation 

is reduced by 25% over each of the Columbia Grid BAs operating independently. Total reserves 

are reduced from 2,221 MW to 1,929 MW, or about 13%.

Figure 34. Net reserve savings for Columbia Grid regional EIM

Figure 35 shows the load and net load ramp savings seen by implementing the regional EIM. The 

savings are relative to ramping that would be seen in the same set of BAs without the EIM.

For net ramps, the maximum up-ramp reduction seen is about 13%, and down-ramp is about 

20%. Ramping energy or average ramp saving is about 15%. The average ramp is reduced by 60 

MW. For load ramps, the maximum up-ramp reduction is 11%, and down is 7%. The average 

ramp savings is about 3%, or 7 MW.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Total Reg Spin Non-spin Total

BAU 586 545 1090 2221

EIM 439 497 993 1929

Reserve Requirement (MW)

Net Reserve Savings for Columbia Grid Regional EIM

(Average, Max, and Min) 

BAU

EIM
34

Figure 35. Columbia Grid EIM 1-hour ramp savings

Figure 36 shows the ramp savings for a typical week. The net savings are much larger than the 

load only because the load in Columbia Grid is highly correlated and the wind is less so,

resulting in net ramp reductions of greater magnitude.

Figure 36. Columbia Grid EIM ramp savings over a typical week

Figure 37 and Figure 38 show the average timing of net ramps seen in Columbia Grid and the 

timing of the ramp savings. The ramp timing shows the typical high ramp rate in the morning 

with the ramp continuing into the afternoon before dropping in the evening. The ramp savings 

plot does not show a clear trend as to when ramps are reduced because it is dominated by wind 

ramp reductions.

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

0 1000 2000 3000 4000 5000 6000 7000 8000

Reduction in Ramp Demand (MW/hr)

Hours

1-Hour Ramp Reduction - Columbia Grid Savings over BAU for Regional EIM

Net

Load

-1000

-500

0

500

1000

1 25 49 73 97 121 145 169

Reduction in Ramp Demand (MW/hr)

Hours

1-Hour Ramp Reduction - Columbia Grid Savings over BAU for Regional EIM

Typical Week

Net

Load
35

Figure 37. Average timing of net ramps in Columbia Grid

Figure 38. Average timing of net ramp savings in Columbia Grid

5.4.2 Northern Tier Transmission Group EIM

NTTG was evaluated as a regional EIM. Figure 39 shows the net reserve requirement reduction 

results for NTTG with all of the members participating in the EIM.

Average Timing of Net Ramps In Columbia Grid

Hourly average over the weeks of the year	

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1920

1198

476

-246

-968

-1690

Average Timing of Net Ramp Reduction in Columbia Grid

Effect of WestConnect operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

355

284

213

142

71

-0
36

For NTTG, the savings are more substantial because of additional diversity in the wind resources 

in the region. The wind is spread across a large area without large concentrations. Net regulation 

requirement is reduced from 497 MW on average to 338 MW, a 32% reduction. Average total 

net reserves are reduced from 1,744 MW to 1,191 MW, also a 32% reduction.

Figure 39. Net reserve reductions from NTTG regional EIM

Figure 40 shows the load and net load ramp savings seen by implementing the regional EIM. The 

savings are relative to ramping that would be seen in the same set of BAs without the EIM.

For net ramps, the maximum up-ramp reduction seen is about 29%, and down-ramp is about 

31%. Ramping energy or average ramp saving is about 15%. The average ramp is reduced by 

259 MW. For load ramps, the maximum up-ramp reduction is 10%, and down is 9%. The 

average ramp savings is about 3%, or 8 MW.

Figure 40. NTTG EIM 1-hour ramp savings

0

500

1000

1500

2000

2500

3000

Total Reg Spin Non-spin Total

BAU 497 416 831 1744

EIM 338 284 569 1191

Reserve Requirement (MW)

Net Reserve Savings for NTTG Regional EIM

(Average, Max, and Min) 

BAU

EIM

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

0 1000 2000 3000 4000 5000 6000 7000 8000

Reduction in Ramp Demand (MW/hr)

Hours

Ramp Reduction - NTTG Savings over BAU for Regional EIM

Net

Load
37

Figure 41. NTTG regional EIM ramp savings over a typical week

Figure 42 and Figure 43 show the average timing of net ramps seen in NTTG and the timing of 

the ramp savings. The ramp timing shows the typical high ramp rate in the morning with the 

ramp continuing into the afternoon and an evening bump in the winter weeks. The ramp savings 

plot does show a trend, with the sharpest reductions in early evening.

Figure 42. Average timing of net ramps in the NTTG

-500

-300

-100

100

300

500

0 24 48 72 96 120 144

Reduction in Ramp Demand (MW/hr)

Hours

Ramp Reduction - NTTG Savings over BAU for Regional EIM

Typical Week

Net

Load

Average Timing of Net Ramps in NTTG

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1658

1002

345

-312

-969

-1626
38

Figure 43. Timing of ramp savings in the NTTG regional EIM

5.4.3 WestConnect EIM

WestConnect was evaluated as an EIM with all of the members participating and also with three 

regions of WAPA not participating in the EIM. Figure 44 shows the net reserve requirement 

reduction results for WestConnect with all of the members participating in the EIM. 

Of the three regional EIM implementations modeled, WestConnect realizes the greatest benefits 

both in absolute and relative terms. This is due to the large load and footprint of the region and 

high geographic diversity of the wind resources. Net load regulation is reduced from 1,242 MW 

for the BAs operating independently to 662 MW for the EIM, a 50% reduction. Total net reserve 

requirements are reduced from 4,649 MW to 2,579 MW for the EIM, a 45% reduction.

Figure 44. WestConnect regional EIM net reserve reductions

Average Timing of Net Ramp Reduction in NTTG

Effect of NTTG operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 1011 12 1314 1516 1718 19 2021 2223 24

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

247

197

148

99

49

0

0

2000

4000

6000

8000

10000

Total Reg Spin Non-spin Total

BAU 1242 1136 2271 4649

EIM 662 639 1278 2579

Reserve Requirement (MW)

Net Reserve Savings for WestConnect Regional EIM

(Average, Max, and Min) 

BAU

EIM
39

Figure 45 shows the load and net load ramp savings seen by implementing the regional EIM. The 

savings are relative to ramping that would be seen in the same set of BAs without the EIM.

For net ramps, the maximum up-ramp reduction seen is about 47%, and down-ramp is about 

48%. Ramping energy or average ramp saving is about 29%. The average ramp is reduced by 

270 MW. For load ramps, the maximum up-ramp reduction is 13%, and down is 16%. The 

average ramp savings is about 6%, or 35 MW.

Figure 45. WestConnect EIM 1-hour ramp savings

Figure 46. WestConnect EIM ramp savings over a typical week

Figure 47 and Figure 49 show the average timing of net ramps seen in WestConnect and the 

timing of the ramp savings. The ramp timing shows the typical high ramp rate in the morning 

with the ramp continuing into the afternoon and an evening bump in the winter weeks. The ramp 

savings plot does show a clear trend for the sharpest reductions: early evening.

-2000

-1500

-1000

-500

0

500

1000

1500

2000

0 1000 2000 3000 4000 5000 6000 7000 8000

Reduction in Ramp Demand (MW/hr)

Hours

1-Hour Ramp Reduction - WestConnect Savings over BAU for Regional EIM

Net

Load

-1500

-1000

-500

0

500

1000

1500

0 24 48 72 96 120 144

Reduction in Ramp Demand (MW/hr)

Hours

1-Hour Ramp Reduction - WestConnect Savings over BAU for Regional EIM - Typical 

Week

Net

Load
40

Figure 47. WestConnect regional EIM ramp timing

Figure 48. Timing of ramp savings in the WestConnect regional EIM

Average Timing of Net Ramps in WestConnect

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

3586

2146

705

-735

-2176

-3616

Average Timing of Net Ramp Reduction in WestConnect

Effect of WestConnect operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

902

722

541

361

180

0
41

5.5 Results with BPA and WAPA Not Participating in EIM

One of the important elements in this work was to understand the effect of non-participation of 

BAAs with large wind production. To do this, cases were run with BPA and WAPA managing 

their wind individually.

As part of this analysis, it was necessary to calculate the value of participation in the EIM 

structures to BPA and WAPA. This would be calculated as the reserve reductions left on the 

table by not participating. To do this, it is necessary to calculate the proportion of saving that the 

two areas can claim from the overall savings of the footprint EIM. A method using a share of net 

variability was devised. This measure weights the benefits to areas with higher variability.

Net variability, for the purposes of this calculation, is defined as the standard deviation of net 

load. The net load is calculated for each BA in the footprint and the standard deviation calculated 

and tabulated. The sum of the net variability is formed and a ratio calculated for each BAs share.

The result of this calculation is that BPA has a 13.7% share and WAPA an 8.3% share of the 

footprint EIM. BPA has a 66% share of the Columbia Grid EIM and WAPA has a 14.9% share 

of the WestConnect EIM. Using average or peak load to apportion the reserves savings yielded 

similar results.

5.5.1 BPA Not Participating

As we saw with the full Columbia Grid EIM, the savings are somewhat less than seen for the 

other EIMs relative to the total size. BPA represents around 40% of the load and around 90% of 

the total wind in Columbia Grid. Removing BPA from the EIM further reduces the reductions, as 

can be seen in Figure 49. Note that BPA reserves are not included in either set of bars as they are 

assumed to be managing their variability independently.

Figure 49. Net reserve requirements for Columbia Grid regional EIM without BPA 

When BPA participates in the Columbia Grid regional EIM, significant reserve requirement 

reductions can be realized. Table shows this effect. The first column shows the reserve 

requirement responsibility for BPA if participating in the EIM. The second shows the 

requirement when BPA operates on its own, as is does today. Finally, the last columns show the 

0

100

200

300

400

500

600

700

800

900

Total Reg Spin Non-spin Total

BAU 205 63 126 394

EIM 129 48 96 273

Reserve Requirement (MW)

Net Reserves for Columbia Grid Regional EIM w/o BPA

(Average, Max, and Min)

BAU

EIM
42

savings that could be realized for BPA if it participated in the EIM. While the effect on the other 

participants was shown to be modest, the savings to BPA are significant, cutting regulation 

requirements by more than one-third. Figure 50 shows the summary of the average reserve 

requirements for BPA in or out of the Columbia Grid regional EIM.

Table 7. BPA Reserve Requirements in or out of Regional EIM

Portion of 

Regional EIM 

Requirement

Individual 

Requirement

Unrealized Savings

MW MW MW % 

BPA (Columbia Grid)

Average Regulation 291 383 92 24%

Max Regulation 447 635 188 30%

Average Total 

Reserves 1,278 1,829 551 30%

Max Total Reserves 2,206 3,363 1,157 34%

Figure 50. Reserve requirements for BPA alone and participating in Columbia Grid EIM

Figure 51 summarizes the reserve requirement results for Columbia Grid EIM with and without 

BPA participating. In the figure, the bars represent the following conditions:

• CG BAU is the operation of the Columbia Grid members independently managing the 

variability in their own regions. This is the sum of the member requirements.

• CG wo BPA BAU is the operation of the Columbia Grid members without BPA’s 

contribution to the total requirements.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Regulation Spin Non-spin Total

Reserves (MW)

Reserve Comparison for BPA Optionally Participating in CG EIM

BPA Alone

BPA in CG EIM
43

• CG Regional EIM is the implementation of a regional EIM for all members of the 

Columbia Grid

• CG wo BPA EIM is the Columbia Grid EIM operating with all members except BPA.

Because BPA dominates the variability in the Columbia Grid, the cases in which BPA is not 

involved show relatively low requirements compared to the cases with BPA.

Figure 51. Summary of average reserve for Columbia Grid with and without BPA

In addition to the reserve savings not realized by BPA, there are also ramp consequences. Figure 

52 shows a comparison of the ramp requirements for BPA when managing its net ramps itself 

and when participating with a Columbia Grid EIM. As seen with the reserves, the ramp savings 

are less than in other regions for BPA because BPA dominates the variability and wind ramping 

seen in Columbia Grid. The possible reduction in BPA ramping by participating in the EIM 

ranges from about 33% at short durations to 15% at longer ramp lengths.

0

500

1000

1500

2000

2500

Regulation Spin Non-spin Total

Reserves (MW)

Reserve Comparison for Columbia Grid with and without BPA

CG BAU

CG wo BPA BAU

CG Regional EIM

CG wo BPA EIM
44

Figure 52. Ramping savings for BPA by participating in Columbia Grid EIM

The full-footprint EIM case was also evaluated with BPA not participating. When the fullfootprint EIM is operated without BPA, there is still a very significant reduction in reserves, as 

seen in Figure 53. Note that BPA reserve responsibilities are not shown as part of either set of 

bars. They are assumed to be operating independently.

Figure 53. Reserve requirement for the footprint EIM without BPA participation

Without BPA participating in the footprint EIM, the reserve savings are still quite significant to 

the rest of the BAs in the EIM but are reduced, as shown in Figure 54.

-15000

-10000

-5000

0

5000

10000

15000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Reduction of Ramping for BPA by Participating in CG Regional EIM 

(99.9th Percentile)

BPA In CG EIM

BPA Alone

0

2000

4000

6000

8000

10000

12000

14000

Total Reg Spin Non-spin Total

BAU 2057 1614 3228 6900

EIM 1045 780 1560 3385

Reserve Requirement (MW)

Reserves for Footprint EIM w/o BPA

(Average, Max, and Min) 

BAU

EIM
45

Figure 54. Reserve savings for the footprint EIM with and without BPA

Table 8 shows the effect of participation in the footprint EIM for BPA. The savings are nearly 

double that for participating in the Columbia Grid EIM. Figure 55 shows a summary for BPA in 

and out of the footprint EIM.

Table 8. BPA Reserve Requirements in or out of Footprint EIM

Portion of 

Footprint EIM 

Requirement

Individual 

Requirement Unrealized Savings

MW MW MW % Savings

BPA

Average Regulation 164 383 219 57%

Max Regulation 222 636 414 65%

Average Total Reserves 561 1,829 1,268 69%

Max Total Reserves 739 3,363 2,624 78%

Total Reg Spin Suppl. Total

With BPA 1242 1127 2254 4623

Without BPA 1013 834 1668 3515

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Reduction in Reserve 

Requirement (MW)

Comparison of Reserve Savings for Footprint EIM with and without BPA 

Compared to BAU

With BPA

Without BPA
46

Figure 55. Summary of reserves for BPA alone and in the footprint EIM

A summary of all reserve requirements for the footprint EIM with and without BPA can be seen 

in Figure 56.

Figure 56. Summary of reserves for the footprint EIM with and without BPA

Figure 57 shows the reduction in ramping duty for BPA. Ramp magnitudes are decreased by 

more than 70% at short durations to just under 40% at longer durations. These reductions are 

approximately twice the reductions seen for the Columbia Grid regional EIM.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary of Reserves for BPA Optionally Participating

in Footprint EIM

BPA Alone

BPA In footprint EIM

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary of Footprint EIM with and without BPA

Footprint BAU

Footprint BAU w/o BPA

Footprint EIM

Footprint EIM w/o BPA
47

Figure 57. Ramp savings for BPA by participating in the footprint EIM

5.5.2 WAPA Not Participating

The three WAPA regions in the model represent approximately 14% of the load and 22% of the 

total wind modeled in WestConnect. With these proportions, WAPA participation in the 

WestConnect EIM contributes a significant amount to the reserve savings for the EIM. Without 

WAPA, the requirements are also reduced significantly. Figure 58 shows these requirements 

with WAPA not represented in either set of bars.

Figure 58. Reserve requirements for WestConnect regional EIM without WAPA participation

-15000

-10000

-5000

0

5000

10000

15000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Reduction of Ramping for BPA by Participating in Footprint EIM

(99.9th Percentile)

BPA In footprint 

EIM

BPA Alone

0

1000

2000

3000

4000

5000

6000

7000

8000

Total Reg Spin Non-spin Total

BAU 982 891 1781 3654

EIM 564 547 1094 2206

Reserve Requirement (MW)

Reserve Requirements for WestConnect Regional EIM w/o WAPA

(Average, Max, and Min)

BAU

EIM
48

When WAPA participates in the WestConnect EIM, very significant reserve requirement 

reductions can be realized over operating independently. Table shows these significant possible 

savings. The first column shows the proportion of the total EIM requirements that WAPA would 

be responsible for based on the net variability apportionment discussed earlier in this section.

The second column shows the WAPA requirement when operating as three independent regions.

The last columns show the savings that could be realized by operating in the regional EIM. The 

potential savings are large, with nearly two-thirds reduction in regulation requirement and nearly 

75% reduction in total reserves. Figure 59 shows a summary of the average reserve requirements 

for WAPA in or out of the WestConnect EIM.

Table 9. WAPA Reserve Requirements in or out of WestConnect Regional EIM

Portion of 

Regional EIM 

Requirement

Individual 

Requirement

Unrealized Savings

MW MW MW % 

WAPA (WestConnect)

Average Regulation 98 261 163 62%

Max Regulation 143 364 221 61%

Average Total 

Reserves 282 996 714 72%

Max Total Reserves 549 1,524 975 64%

Figure 59. Summary of reserves for WAPA alone or participating in WestConnect EIM

Figure 60 shows the average reserve savings comparing the BAU case to the WestConnect 

regional EIM without WAPA. The graph shows that the savings reduction is significant for the 

remaining participants.

• WC with WAPA BAU is the operation of the WestConnect members independently 

managing the variability in their own regions. This is the sum of the member 

requirements.

0

200

400

600

800

1000

1200

Regulation Spin Non-spin Total

Reserves (MW)

Reserve Comparison for WAPA Optionally Participating

in WC Regional EIM

WAPA Alone

WAPA in WC EIM
49

• WC wo WAPA BAU is the operation of the WestConnect members without BPA’s 

contribution to the total requirements.

• WC Regional EIM is the implementation of a regional EIM for all members of the 

WestConnect.

• WC wo WAPA EIM is the WestConnect EIM operating with all members except WAPA.

While all participants in the EIM benefit when WAPA participates, the benefit seen by WAPA is 

very significant.

Figure 60. Summary of average reserve for WestConnect with and without WAPA

In addition to the reserve savings not realized by WAPA, there are also ramp consequences.

Figure 61 shows a comparison of the ramp requirements for WAPA when managing its net 

ramps itself and when it participates with a WestConnect EIM. As seen with the reserves, the 

ramp savings are significant for WAPA. The possible reduction in ramping ranges from about 

70% at short durations to 25% to 30% at longer ramp lengths.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Regulation Spin Non-spin Total

Reserves (MW)

Reserve Comparison for WestConnect with and without WAPA

WC with WAPA BAU

WC wo WAPA BAU

WC Regional EIM

WC wo WAPA EIM
50

Figure 61. Ramping savings for WAPA by participating in WestConnect regional EIM

When the full-footprint EIM is operated without WAPA, there is still a very significant reduction 

in reserves, as seen in Figure 62. Note that WAPA reserve responsibilities are not shown as part 

of either set of bars. They are assumed to operate independently.

Figure 62. Reserve requirements for the footprint EIM without WAPA participation

Table shows the reserve requirements and savings for WAPA participating in the footprint EIM.

The savings are slightly higher for this case compared to WAPA participation in the 

-6000

-4000

-2000

0

2000

4000

6000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Reduction of Ramping for WAPA by Participating in WC Regional EIM 

(99.9th Percentile)

WAPA In WC EIM

WAPA Alone

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

BAU 2179 1851 3702 7732

EIM 1110 894 1788 3792

Reserve Requirement (MW)

Reserve Savings for Footprint EIM w/o WAPA

(Average, Max, and Min)

BAU

EIM
51

WestConnect regional EIM. Figure 63 shows a summary of the average reserve requirements for 

WAPA in or out of the footprint EIM.

Table 10. WAPA Reserve Requirements in or out of Footprint EIM

Portion of 

Footprint EIM 

Requirement

Individual 

Requirement

Unrealized Savings

MW MW MW % Savings

WAPA

Average Regulation 100 261 161 62%

Max Regulation 136 364 228 63%

Average Total Reserves 342 996 654 66%

Max Total Reserves 451 1,524 1,073 70%

Figure 63. Summary of reserves for WAPA alone and in the footprint EIM

Figure 64 shows a summary of all reserve requirements for the footprint EIM with and without 

WAPA participation.

0

500

1000

1500

2000

2500

3000

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary for Reserves for BPA and WAPA Optionally Participating in 

Footprint EIM

BPA WAPA Alone

Both in Footprint EIM
52

Figure 64. Summary of reserve requirements for the footprint EIM with and without WAPA

Figure 65 shows the reduction in ramping duty for WAPA. Ramp magnitudes are decreased by 

75% at short durations and by as much as 35% at longer durations.

Figure 65. Ramp reduction resulting from WAPA participating in the footprint EIM

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary of Footprint EIM with and without WAPA

Footprint BAU

Footprint BAU w/o WAPA

Footprint EIM

Footprint EIM w/o WAPA

-6000

-4000

-2000

0

2000

4000

6000

0 2 4 6 8 10 12

Ramp Magnitude (MW)

Ramp Duration (Hours)

Reduction of Ramping for WAPA by Participating in Footprint EIM 

(99.9th Percentile)

WAPA in Footprint EIM

WAPA Alone
53

5.5.3 BPA and WAPA Not Participating

The case in which neither BPA nor WAPA participate in a footprint-wide EIM was also

evaluated. These BAs represent a significant portion of the wind in the entire footprint at 

approximately 33% of the total wind resource modeled. Figure 66 shows reserve requirements 

for the footprint with these two BAs not participating in the EIM. There are very significant 

savings for the remaining participants in the EIM.

Figure 66. Reserve savings for the footprint EIM without BPA or WAPA participation

The savings for BPA and WAPA participating in the footprint EIM are roughly the sum of the 

savings for the individual BAs seen in the previous two sections. Figure 67 shows a summary of 

the average reserves for BPA and WAPA both in and out of the footprint EIM.

Figure 67. Summary of reserves for WAPA and BPA alone and in the footprint EIM

0

2000

4000

6000

8000

10000

12000

Total Reg Spin Non-spin Total

BAU 1796 1369 2738 5904

EIM 952 691 1383 3026

Reserve Requirement (MW)

Reserve Savings for Footprint EIM w/o BPA, WAPA

(Average, Max, and Min)

BAU

EIM

0

200

400

600

800

1000

1200

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary of Reserves for WAPA Optionally Participating

in Footprint EIM

WAPA Alone

WAPA in Footprint EIM
54

Figure 68 shows a summary of the reserve requirements for the footprint EIM with and without 

BPA and WAPA.

Figure 68. Summary of reserves for the footprint EIM with and without BPA and WAPA

5.6 Results for Wind-Only and Load-Only Balancing Authorities

One possible operating structure for the study footprint with wind energy involves segregating 

the wind into wind-generation-only balancing authorities (see for example [7]), which have no 

load. The load would be segregated into load and conventional generation balancing authorities

with no wind included.

In a wind-only/load-only BA arrangement, the load BAs would be responsible for balancing their 

load and would not have any burden in balancing the wind. The wind-only BAs, with no load or 

any other generation, would be forced to acquire regulating, spin, and non-spin reserves from 

other market participants. An arrangement such as this exposes all costs of the reserve burden 

imposed by wind and foregoes the benefits of aggregating wind and load variability and 

uncertainty.

Three scenarios were evaluated. Two of the scenarios concentrate all wind into a single 

footprint-wide EIM with the load either in a single footprint-wide EIM or managed by each 

individual BA as is the current business practice. The third scenario has three wind-only and 

three load-only EIMs implemented, one each in the defined regional breakdown of Columbia 

Grid, Northern Tier Transmission Group, and WestConnect.

The results for these scenarios are compared with the BAU scenario in Figure 69. Note that the 

BAU reference is as defined earlier and models each existing BA as managing its own wind and 

load. There are very significant savings from these implementations. In fact, they differ from the 

integrated wind and load EIM implementations only in the load regulation components and the 

additional increment caused to the total by the load regulation. These results will be compared 

later in the summary.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Reg Spin Non-spin Total

Reserve Requirements (MW)

Summary of Footprint EIM with and without BPA and WAPA

Footprint BAU

Footprint BAU w/o Both

Footprint EIM

Footprint EIM w/o Both
55

Figure 69. Reserve requirements for wind-only/load-only EIM implementations

With the regulation components small in comparison to the total reserves, those values are 

difficult to discern. Figure 70 presents the regulation bars in isolation so the detail can be seen.

Figure 70. Detail of regulation requirements for wind-only/load-only EIM implementations

Figure 71 shows the reserve reductions for the three wind-only/load-only scenarios over the 

BAU scenario. The savings are dominated by the scenarios with footprint-wide wind EIM

implementations. The load regulation savings contributes an extra 250 MW to total regulation 

savings.

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

Reserve Requirements (MW)

Reserve Requirements in Wind-Only/Load-Only EIM Implementations

(Average, Max, and Min)

Fooprint Load and 

Wind EIMs

Regional Load and 

Wind EIMs

BAU Load/ Footprint 

Wind EIM

BAU Reference

0

500

1000

1500

2000

2500

3000

Load Reg Wind Reg Total Reg

Regulation Requirement (MW)

Detail of Regulation Requirements in Wind-Only/Load-Only EIM 

Implementations (Average)

Fooprint Load and 

Wind EIMs

Regional Load and 

Wind EIMs

BAU Load/ Footprint 

Wind EIM

BAU Reference
56

Figure 71. Reserve requirement reductions for wind-only/load-only EIM implementations

5.7 Summary Results

As has been shown, implementation of an EIM over any size region leads to reduction in reserve 

requirements and ramping demand. As the size of the EIM increases, the benefits to all 

participants increase because the variability and uncertainty associated with the wind resources 

are spread across a greater footprint with more diversity.

Figure 72 illustrates how diversity affects the EIM reserve requirements for the three levels of 

aggregation. The BAU scenario refers to each BA operating independently. The bars represent 

the arithmetic sum of all the BA requirements. The whiskers on the plots show the minimum and 

maximum values for each parameter. For the regional EIM, the bars represent the sum of the 

requirements computed at the regional level. The footprint EIM aggregates all of the load and 

wind into one EIM. As can be clearly seen, the wind regulation reserve requirements for the 

footprint EIM are less than half those required with the BAU case. Spinning and non-spin 

reserve requirements are similarly reduced. It is interesting to note that the load-only regulation 

is also reduced significantly by this aggregation, 21% on average for the footprint EIM.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Load Reg Wind Reg Total Reg Spin Non-spin Total

Reserve Requirement 

Savings (MW)

Reserve Requirement Reductions in Wind-Only/Load-Only EIM 

Implementations vs. BAU (Average)

Regional Load and 

Wind EIMs

Fooprint Load and 

Wind EIMs

BAU Load/ Footprint 

Wind EIM
57 

Figure 72. Reserve requirements across EIM implementations

Table 

reflected to the footprint level.

11 shows the reductions in reserve requirements for the footprint and regional EIMs 

Table 11. Reserve Reductions at the Footprint Level Seen in EIM Implementations 

Footprint EIM Regional EIM

BAU EIM Reduction EIM Reduction

Maximum Value (MW)

Reg. 3,826 1,626 58% 2,221 42%

Spin 3,791 1,262 67% 2,206 42%

Nonspin 7,582 2,524 67% 4,412 42%

Total 15,200 5,412 64% 8,839 42%

Average Values (MW)

Reg. 2,440 1,198 51% 1,547 37%

Spin 2,096 969 54% 1,420 32%

Nonspin 4,192 1,938 54% 2,840 32%

Total 8,729 4,105 53% 5,807 33%

At the regional EIM implementation, significant savings are seen also. Table 

comparison and summary for the three EIM regions considered. 

12 shows a 

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

Footprint 1198 969 1938 4105

Regional 1547 1420 2840 5807

BAU 2440 2096 4192 8729

Reserve Requirement (MW)

Reserve Requirement Comparisons for Footprint EIM, Regional EIM 

Total and BAU (Average, Max, and Min)

Footprint 

Regional

BAU
58 

Table 12. Comparison of Reserve Requirements for Regional EIMs (MW) 

Regional EIM

Columbia Grid NTTG WestConnect

BAU EIM Reduction BAU EIM Reduction BAU EIM Reduction

Maximum Values

Reg. 991 674 32% 677 425 37% 2,019 960 52%

Spin 1,059 885 16% 664 409 38% 2,068 912 56%

Non-Spin 2,118 1,771 16% 1,328 818 38% 4,137 1,824 56%

Total 4,167 3,330 20% 2,669 1,652 38% 8,224 3,695 55%

Average Values

Reg. 586 439 25% 497 338 32% 1,242 662 47%

Spin 545 497 9% 416 284 32% 1,136 639 44%

Non-Spin 1,090 993 9% 831 569 32% 2,271 1,278 44%

Total 2,221 1,929 13% 1,744 1,191 32% 4,649 2,579 45%

It is interesting to note that the load-only regulation is also reduced significantly by this 

aggregation. Spinning and non-spin reserve requirements are similarly reduced, as seen in Figure 

73. 

Figure 73. Regulation requirements for various scenarios

One of the important elements in this work was to understand the effect of non-participation of 

BAs with large wind production. To do this, we ran cases with BPA and Western managing their 

wind individually. Aggregation helps all participants, and when one entity does not participate, 

0

1000

2000

3000

4000

5000

6000

7000

8000

Load Reg Wind Reg Total Reg Spin Non-spin

Reserve Requirement (MW)

Reserve Requirements by Class for Three EIM Scenarios

(Average, Max, and Min)

Footprint EIM

Regional EIM

Business As Usual
59

the benefits for all participants are reduced. Failing to participate has a greater impact on the 

non-participants. 

As Figure 74 shows, the remaining participants' regulation requirement is reduced to 51% for 10-

minute scheduling if either BPA or Western does not participate as opposed to 49% if everyone 

participates. The non-participant’s requirement is still 100%, however, so the non-participant 

loses the most. If neither BPA nor Western participates, the remaining BA's regulation 

requirement is reduced to 53% for 10-minute scheduling, instead of 49%. Results are essentially 

the same for half-hour and hour scheduling.

Figure 74. Reserve requirements comparison with and without optional participants

for the footprint EIM

Another set of configurations that we investigated involved aggregating wind into wind-only 

BAs that would be responsible for acquiring resources to balance the wind variability without 

any load-serving responsibility.

This analysis demonstrated the additional reduction in reserves by combining load and wind into 

the same EIM structure, while modest compared to the gains seen from wind-only EIM, are 

significant. This is because load aggregation results in lower return. This lower return is, as 

discussed earlier, due to the higher correlation of load across an EIM footprint compared to 

wind. The majority of the difference seen when wind and load are combined is due to the fact 

that the load and wind regulation components are not perfectly correlated, so they do not add 

linearly. Instead, wind and load regulation components must be added statistically when 

balanced in the same EIM.

0

1000

2000

3000

4000

5000

6000

7000

8000

Load Reg Wind Reg Total Reg Spin Non-spin

Reserves Requirement (MW)

Full-Footprint EIM - Total Reserves by Class

(Average, Max, and Min)

All BAs in EIM

No BPA

No Western

No BPA No Western

Business As Usual
60

Figure 75 shows a summary of the reserve requirements across all scenarios analyzed where all 

BAs are participating in an EIM (BPA and WAPA are included). This demonstrates the 

similarities between the combined wind and load verses separate EIM structures. Compare, for 

example, the combined footprint EIM (Footprint EIM) to the separate load and wind EIM (Foot 

Load/Foot Wind). The regulation requirement for the combined is 1,198 MW while the 

requirement for the separate case is 1,672 MW. Also comparing combined regional EIM to 

regional load/regional wind implantation we see that the total reserves are 2,087 MW for the 

separate EIMs compared to 1,547 MW for the load and wind combined scenario, a similar 

difference. These differences are reflected exactly in the total reserves since the total is the 

arithmetic sum of the total regulation, the spin and the non-spin.

Figure 75. Reserve requirements across all “full-participation” scenarios

5.8 Effects Alternative Scheduling

Both faster scheduling (economic dispatch) and aggregation over a larger area reduce the 

regulation reserve requirements, as shown in Figure 76. Ten-minute scheduling requires about 

29% of the regulation reserves compared to hourly scheduling under all aggregations. Fiveminute scheduling will require even less. Similarly, when all 29 BAs cooperate (All) they need 

less than half (49% for 10-minute scheduling) of the total regulation compared to the BAU case, 

regardless of the scheduling interval. Implementing both 10-minute scheduling and regional 

cooperation will reduce the regulation requirement more than seven-fold from current practice: 

a significant potential savings.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Total Reg Spin Non-spin Total

Reserve Requirement (MW)

Comparison of Reserve Requirements across

All "Full-Participation" Scenarios

Footprint EIM

Foot Load/ Foot Wind

BAU Load/ Foot Wind

Regional EIM

Reg. Load/ Reg. Wind

BAU
61

Figure 76. Faster scheduling and larger aggregation greatly reduce the total required regulating 

reserves

Figure 77 shows the results for three dispatch and forecast lead time sets based on the base case, 

full-footprint EIM with all BAs participating. The 30- and 60-minute dispatch each use a 40-

minute lead time on the forecast, and the 10-minute dispatch uses a 10-minute forecast lead time.

The dramatic effect on regulation requirements is clear. The average total regulation requirement 

is reduced from 3,942 MW for a 60-minute dispatch to 1,198 MW for the 10-minute dispatch.

This represents a savings of 70% on regulation needs and 40% on total reserves (right most 

columns). All of these savings are from wind-related regulation requirements only. In reality, 

better load forecasts at the 10-minute lead time would improve these results marginally. Spin and 

non-spin/supplemental are the same for each case because they depend only on hour-ahead 

forecast errors.

0

2000

4000

6000

8000

10000

12000

14000

16000

All No BPA No WAPA No BPA, WAPA BAU

Total Regulation Reserves (MW)

Total Regulation Reserve Requirements for Three Schedules

Footprint EIM by Case (Average, Max, and Min)

Schedule #1 - 10-Minute Schedule

Schedule #2 - Half-Hour Schedule 40 Min Prior

Schedule #3 - Hour Schedule at 40 Min prior
62 

Figure 77. Effect of forecast lead time and dispatch schedule on reserve requirements

To further illustrate the effect of dispatch interval and forecast lead time, we ran additional cases

for the footprint and regional cooperation. Figure 78 and Table 13 show these results. 

Figure 78. Comparison of regulation requirements with various cooperation regimes and 

dispatch/forecast lead times

One interesting aspect is the relative reduction in reserves regardless of the dispatch schedule.

For a given cooperation regime (footprint or regional), the reduction in reserves is nearly the 

same for each of the dispatch/forecast lead times analyzed. A BA can capture the sub-hourly 

scheduling benefit even without coordination with neighboring BAs.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Regulation Spin Non-spin Total

10 Minute 1198 969 1938 4105

30 Minute 3184 969 1938 6091

60 Minute 3942 969 1938 6849 Reserve Requirement (MW)

Reserve Requirement Comparisons for Three Dispatch Schedule Options

Footprint EIM Case (Average, Max, and Min)

10 Minute

30 Minute

60 Minute

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Footprint Regional BAU

Average Total Regulation (MW)

Average Total Regulation for Six Dispatch/Lead Schedules by 

Cooperation (Dispatch Interval - Forecast Lead Time) 

10-10

30-10

30-30

60-10

30-40

60-40
63 

Table 13. Average Total Regulation Requirements Are a Constant Percentage of BAU 

Relative to Various Dispatch/Lead Time Schedules

DispatchForecast 

Lead (Min)

Avg. Regulation (MW) Compared to BAU

Footprint Regional BAU Footprint Regional BAU

10-10 1,198 1,547 2,440 49% 63% 100%

30-10 1,718 2,444 3,873 44% 63% 100%

30-30 2,705 3,975 6,168 44% 64% 100%

60-10 3,027 4,453 6,831 44% 65% 100%

30-40 3,184 4,696 7,205 44% 65% 100%

60-40 3,942 5,813 8,777 45% 66% 100%

6 Conclusions

This report examines alternative implementations of the proposed EIM in the non-market areas 

of the WI. We adapt the reserves method from the EWITS to analyze the implications of these 

alternative market structures. Although we use standard deviation as the variability metric, our 

approach could be easily adapted to non-normal distributions and could also be adapted to allow 

for solar generation, which would be expected to have similar qualitative impacts on variability 

and uncertainty, and thus reserve requirements. 

The proposed EIM includes two independent beneficial changes in current operating practices: 

sub-hourly scheduling and inter-BA coordination. Half of the load in the country lies in regions 

with 5-minute markets: PJM, MISO, ERCOT, NYISO, ISO-NE, and CAISO. It is likely that 5-

minute scheduling can be successfully implemented in the rest of the WI too. Inter-BA 

cooperation has been practiced for decades with contingency reserve sharing pools and energy 

transactions. The EIM simply extends this concept through an automated imbalance market. 

Based on our analysis, we conclude that full participation of all BAAs would result in maximum 

benefit across the Interconnection. Lesser participation levels (which include regional 

implementations of the EIM), various exclusions (BPA and Western), and the wind-only BAAs 

we analyzed will still improve on the BAU case but will fail to achieve the maximum benefit of 

the full participation scenario, especially for the non-participants. The participating BAs will 

capture 80% to 85% of the benefits of reduced reserves if BPA or WAPA are unable to 

participate, but the excluded BA will forgo a 60% to 70% savings. We recognize that there may 

be various institutional impediments to a full EIM implementation, but based on our analysis, the 

results suggest that potential participants should undertake a careful cost-benefit analysis to 

determine whether it may be economically efficient to implement institutional changes that can

help move toward a full EIM implementation. Expanding EIM to all of the WI may be possible 

in the future and would result in additional savings. 

Finally, we note that the proposed EIM does not consider coordinated unit commitment. We 

believe that over time, participants may conclude that some form of coordinate commitment will 

achieve additional savings, although additional analysis would be needed to determine these 

impacts. Partial coordination of unit commitment may occur naturally as participants learn to 

anticipate what generation is likely to be available from other BAs tomorrow through the EIM 
64

and incorporate those expectations in their own unit commitment. Participants may engage in 

bilateral contracts to add certainty to those expectations. Firm transmission may be necessary to 

fully capture the benefits of coordinated unit commitment.

7 References

[1] GE Energy. (2010). “Western Wind and Solar Integration Study.” 536 pp.; NREL Report No. 

SR-550-47434. Available at http://www.nrel.gov/docs/fy10osti/47434.pdf.

[2] Milligan, M.; Kirby, B. (2007). Impact of Balancing Areas Size, Obligation Sharing, and 

Ramping Capability on Wind Integration: Preprint. 43 pp.; NREL Report No. CP-500-41809. 

Available at http://www.nrel.gov/docs/fy07osti/41809.pdf.

[3] Milligan, M.; Kirby, B. (2008). Analysis of Sub-Hourly Ramping Impacts of Wind Energy 

and Balancing Area Size: Preprint. 19 pp.; NREL Report No. CP-500-43434. Available at 

http://www.nrel.gov/docs/fy08osti/43434.pdf.

[4] Enernex Corporation, “Eastern Wind Integration and Transmission Study,” Prepared for the 

National Renewable Energy Laboratory, January 2010. Available at

http://www.nrel.gov/wind/systemsintegration/pdfs/2010/ewits_final_report.pdf.

[5] Milligan, M.; Kirby, B. (2010). Market Characteristics for Efficient Integration of Variable 

Generation in the Western Interconnection: National Renewable Energy Laboratory 

Technical Report NREL/TP-550-48192. Available at 

http://www.nrel.gov/docs/fy10osti/48192.pdf.

[6] B. Kirby, 2006, Demand Response For Power System Reliability: FAQ, ORNL/TM 

2006/565, Oak Ridge National Laboratory, December.

[7] NERC (2008). “NERC Balancing Authority (BA) Certification of Naturener Glacier Wind 

Energy 1 (GWA) Operated by Constellation Energy Control and Dispatch (CECD).” 

http://www.nerc.com/files/NaturEner%20Final%20Report.pdf.

The Alliance for Sustainable Energy, LLC (Alliance) is the manager and operator of the National 

Renewable Energy Laboratory (NREL). Employees of the Alliance, under Contract No. DEAC36-08GO28308 with the U.S. Dept. of Energy, have authored this work. The United States 

Government retains and the publisher, by accepting the article for publication, acknowledges that 

the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to 

publish or reproduce the published form of this work, or allow others to do so, for United States 

Government purposes.
65

Appendix A. Ramp Savings Plots

This appendix contains ramp reduction plots for all cases we ran as part of this study. The case conditions 

for each plot are described in the plot title. There are four plots for each case. The first two plots show 1-

hour ramp savings and the second pair shows 10-minute ramp savings.
66
67
68
69
70
71
72
73
74
75
76
77
78
79

Appendix B. Ramp Timing Plots

This appendix contains ramp and ramp reduction timing plots for each of the cases in this study. 

Average Timimg of Net Ramps in Footprint EIM

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the Year

5614

3167

720

-1727

-4174

-6621

Average Timing of Net Ramp Reduction in Footprint EIM

Hourly average of the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

697

557

418

279

139

0
80

Average Timing of Net Ramps in Footprint EIM w/o BPA

Hourly average over the weeks of the year

Average net ramp in MW

0 2 4 6 8 10 12 14 16 18 20 22

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

5925

3593

1261

-1071

-3403

-5734

Average Timing of Net Ramps Reduction in Footprint EIM w/o BPA

Hourly average over the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1249

999

749

499

249

-1
81

Average Timing of Net Ramps in Footprint EIM w/o WAPA

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

6001

3646

1290

-1065

-3421

-5776

Average Timing of Net Ramps Reduction in Footprint EIM w/o WAPA

Hourly average over the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1329

1060

791

522

253

-16
82

Average Timing of Net Ramps in Footprint EIM w/o BPA, WAPA

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

5397

3279

1161

-957

-3076

-5194

Average Timing of Net Ramps Reduction in Footprint EIM w/o BPA, WAPA

Hourly average over the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1071

855

640

425

209

-6
83

Average Timing of Net Ramps in Columbia Grid

Hourly average over the weeks of the year	

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1920

1198

476

-246

-968

-1690

Average Timing of Net Ramp Reduction in Columbia Grid

Effect of WestConnect operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

355

284

213

142

71

-0
84

Average Timing of Net Ramps in Columbia Grid Regional EIM w/o BPA

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

949

609

268

-72

-413

-753

Average Timing of Net Ramps Reduction in Columbia Grid Regional EIM w/o BPA

Hourly average over the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

72

57

42

28

13

-2
85

Average Timing of Net Ramps in NTTG

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

1658

1002

345

-312

-969

-1626

Average Timing of Net Ramp Reduction in NTTG

Effect of NTTG operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 1011 12 1314 1516 1718 19 2021 2223 24

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

247

197

148

99

49

0
86

Average Timing of Net Ramps in WestConnect

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

3586

2146

705

-735

-2176

-3616

Average Timing of Net Ramp Reduction in WestConnect

Effect of WestConnect operating as an EIM versus BAU

Hourly average over the weeks of the year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

902

722

541

361

180

0
87

Average Timing of Net Ramps in WestConnect Regional EIM w/o WAPA

Hourly average over the weeks of the year

Average net ramp in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

2981

1757

533

-691

-1915

-3139

Average Timing of Net Ramps Reduction in WestConnect Regional EIM w/o WAPA

Hourly average over the weeks of the year

Average net ramp reduction in MW

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

0

4

8

12

16

20

24

28

32

36

40

44

48

52

Week of the year

736

588

440

291

143

-6