Electric System Impacts of Utility Large-Scale Investment ... · and constraints, and market system operations. ” from the Ventyx website. Allows zonal or nodal market (bus level)

Electric System Impacts of Utility Large-Scale Investment in Building Energy Efficiency

Dave AndersonPNNL

2011 ACEEE National Conference on Energy Efficiency as a Resource

9/26/2011

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Electric System Impacts of Utility Large-Scale Investment in Building Energy Efficiency

Forthcoming PNNL report:Authors

Dave AndersonBrian BoydJim CabeBenton RussellLaurel Schmidt

Sponsored by DOE Building Technologies ProgramNot affiliated with nor addressing Smart Grid

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The questions

How much can adding large-scale energy efficiency resource deployment actually delay or avoid new “bricks and mortar” generation resources at the utility system level?What happens to dispatch in four geographically representative utility service areas, accounting for transaction behavior?How does the emissions profile change in each of the four example utility service areas?

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Issues for Energy Efficiency Resources

Institutional barriers abound for large-scale deploymentCompensation model for cost recoveryRate-payer financing of new resourcesRPS in many states, but no accompanying recovery systemWhat to do in deregulated states?How to acquire LARGE amounts of EE resource? – program designPerceptions that favor central station for reliability and risk avoidance

Market barriers also abound – this is just RESIDENTIALImpose major appliance decisions on consumers

Consumer choice versus command and controlRate-payer financed?Mobility of the resourceReliability of the resource

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The Model: PROMOD IV by Ventyx

“PROMOD IV is the industry-leading Fundamental Electric Market Simulation software, incorporating extensive details for understanding generating unit operating characteristics, transmission grid topology and constraints, and market system operations.” from the Ventyx website.

Allows zonal or nodal market (bus level) behavior to be simulatedComprehensive database of generators by utility service areaDatabase is fully customizable if you have the dataVisibility at all points of the analysisAs much or as little summary detail as desiredUtility system load forecasting and resource acquisition Simulates purchases and sales in the wholesale market

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Criteria for Example Utilities

Large enough to potentially consider new central station baseloadDiverse fuel and technology mix of generation resourcesGeographically representativeRegulated, no merchant generationLoad forecasts are available

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Example Utilities: 2010 Installed Capacity by Fuel

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Southwest Southeast Midwest Northeast

OtherWindSolarRenewOilNuclearHydroGasCoal

Source: Ventyx PowerBase

Implementation Assumptions

Utility can finance (and recover) costs of acquisition and installation of new major appliances for upwards of 500,000 residences or moreAppliance upgrades would meet current Energy Star labeling criteria“Project” budget would be set at $3-4 billion (overnight) and require a 10-year build-out, similar to a large central station power plantAdequate supply of installers and inspectors could be trained and available to support the build outVolume purchase contracts with manufacturers would be implemented and annual unit installation goals would be met.Annual maintenance program to recommission

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Residential Measures Applied

Current Energy Star-compliant models installed across residential customer base

CFL LightingRefrigeratorStove, Range, OvenWater heaterHVAC unitTelevision

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Allowance for unrealizable installations or previous adoptersAllowance for measure degradationStock accounting to 2020 only

The Model: Getting Residential Loads Right

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0 5 10 15 20 25 30 35 40

Load

(MW)

Temperature (°C)


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Temperature-Independent

Load

Heating Load

Cooling Load


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End Use Load Shapes: Some Examples

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region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 33 RSFTVEW7com COCCAL01

Cht_vctr LoadShapes!$F$8793:$AC$8793LoadShapes!$F$8793:$AC$8793LoadShapes!$F$35397:$AC$35397

Res Shape 8793Com Shape 35397

March WEEK‐DAY

RSFTVEW7: Residential Television

0

2

4

6

8

10

12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Percentage of Daily End‐Use Consumption

Hr

Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt

region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 36 RSFWHO07com COCCAL01



March WEEK‐DAY

RSFWHO07: Residential Water Heating Base/Conventional ‐ NEMS Default

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24


Hr


region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 29 RSFRFW07com COCCAL01



March WEEK‐DAY

RSFRFW07: Residential Refrigeration Base/Conventional ‐ NEMS Default

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24


Hr


region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 1 Januarydaytype 2 WEEK‐DAYres 17 RSFEHO67com COCCAL01



January WEEK‐DAY

RSFEHO67: Residential Elec Resistance Heat Heat Pump (North) ‐ NEMS Default

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24


Hr


region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 8 Augustdaytype 2 WEEK‐DAYres 43 RSFWSW07com COCCAL01



August WEEK‐DAY

RSFWSW07: Residential Clothes Washer Base/Conventional

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24


Hr


region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 8 Augustdaytype 2 WEEK‐DAYres 30 RSFSCE67com COCCAL01



August WEEK‐DAY

RSFSCE67: Residential Space Conditioning Heat Pump (South) ‐ NEMS Default

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24


Hr


AC

TV & Gadgets

Water Heating

Heating Refrigeration Clothes Washer

Load Duration Curves for Example Utilities

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0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

NE

SW

MW

SE

Proportion of Annual Peak MW

Proportion of Annual Hours

Modeling 2020 Savings

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0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1,144 Prop

ortio

n of 202

0 Maxim

um Savings


MW

Savings Duration: Southwest Example

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0

0.2

0.4

0.6

0.8

1

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

0

0.2

0.4

0.6

0.8

1

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

Summer Winter

0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

2,509 Prop

ortio

n of 202

0 Maxim

um Savings


MW

Savings Duration: Midwest Example

17

0

0.2

0.4

0.6

0.8

1

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

0

0.2

0.4

0.6

0.8

1

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

Summer Winter

0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

2,735 Prop

ortio

n of 202

0 Maxim

um Savings


MW

Savings Duration: Northeast Example

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0

0.2

0.4

0.6

0.8

1

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

0

0.2

0.4

0.6

0.8

1

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

Summer Winter

0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

5,480 Prop

ortio

n of 202

0 Maxim

um Savings


MW

Savings Duration: Southeast Example

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0

0.2

0.4

0.6

0.8

1

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

0

0.2

0.4

0.6

0.8

1

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

Summer Winter

Savings Proportions by End Use

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Southwest Midwest Northeast Southeast

Cooking

Water Heating

HVAC

Television

Refrigeration

Lighting

Results Summary

Example Utility

2020 Peak-Hr MW %

Savings

2020 MeanSavings Capacity

Factor

2020 MWh % Savings

2020Net change in Transactions

MWh (%)

2020 Carbon Savings

%

Southwest 11.9 23.3 6.8 17.2 1.8

Midwest 24.9 11.8 3.1 19.9 0.6

Northeast 27.6 18.3 7.6 28.9 3.9

Southeast 9.4 17.7 2.9 2.4 2.0

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Relative Carbon Emissions Savings by Fuel

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113 122 121 122

210

207

Southwest Midwest Northeast Southeast

Oil

Coal

Biomass

Gas

(CO2 lbs/MMBTU)

Conclusions and Discussion

Large-scale ($3-4 Billion) acquisition of efficiency provides baseload savings in 2020 during 70-75 percent of the year for utilities that might afford itBaseload savings amount to 5-15% of maximum hourly savings (150-500 MW in the example utilities)Modeling shows that utilities will sell excess generation saved by efficiency when possible – reducing savingsStarting point of load forecast matters – results vary widely depending on forecast trendsBarriers to large-scale acquisition are significant, but worth discussing and analyzingNext up: Commercial sector

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Contact:

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Dave AndersonEmail: [email protected]

Feedback is requested and welcome

mailto:[email protected]

Documents

Electric System Impacts of Utility Large-Scale Investment ... · and constraints, and market system operations. ” from the Ventyx website. Allows zonal or nodal market (bus level)