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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 $3-4 billion (overnight) and require a 10-year build-out, similar to a large central station power plantAdequate supply of installers and inspectors could be trained and available to support the build outVolume purchase contracts with manufacturers would be implemented and annual unit installation goals would be met.Annual maintenance program to recommission
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Residential Measures Applied
Current Energy Star-compliant models installed across residential customer base
CFL LightingRefrigeratorStove, Range, OvenWater heaterHVAC unitTelevision
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Allowance for unrealizable installations or previous adoptersAllowance for measure degradationStock accounting to 2020 only
The Model: Getting Residential Loads Right
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0 5 10 15 20 25 30 35 40
Load
(MW)
Temperature (°C)
The Model: Getting Residential Loads Right
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Temperature-Independent
Load
Heating Load
Cooling Load
The Model: Getting Residential Loads Right
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End Use Load Shapes: Some Examples
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region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 33 RSFTVEW7com COCCAL01
Cht_vctr LoadShapes!$F$8793:$AC$8793LoadShapes!$F$8793:$AC$8793LoadShapes!$F$35397:$AC$35397
Res Shape 8793Com Shape 35397
March WEEK‐DAY
RSFTVEW7: Residential Television
0
2
4
6
8
10
12
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 36 RSFWHO07com COCCAL01
Cht_vctr LoadShapes!$F$8973:$AC$8973LoadShapes!$F$8973:$AC$8973LoadShapes!$F$35397:$AC$35397
Res Shape 8973Com Shape 35397
March WEEK‐DAY
RSFWHO07: Residential Water Heating Base/Conventional ‐ NEMS Default
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 3 Marchdaytype 2 WEEK‐DAYres 29 RSFRFW07com COCCAL01
Cht_vctr LoadShapes!$F$8649:$AC$8649LoadShapes!$F$8649:$AC$8649LoadShapes!$F$35397:$AC$35397
Res Shape 8649Com Shape 35397
March WEEK‐DAY
RSFRFW07: Residential Refrigeration Base/Conventional ‐ NEMS Default
0
1
2
3
4
5
6
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 1 Januarydaytype 2 WEEK‐DAYres 17 RSFEHO67com COCCAL01
Cht_vctr LoadShapes!$F$8031:$AC$8031LoadShapes!$F$8031:$AC$8031LoadShapes!$F$35391:$AC$35391
Res Shape 8031Com Shape 35391
January WEEK‐DAY
RSFEHO67: Residential Elec Resistance Heat Heat Pump (North) ‐ NEMS Default
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 8 Augustdaytype 2 WEEK‐DAYres 43 RSFWSW07com COCCAL01
Cht_vctr LoadShapes!$F$9276:$AC$9276LoadShapes!$F$9276:$AC$9276LoadShapes!$F$35412:$AC$35412
Res Shape 9276Com Shape 35412
August WEEK‐DAY
RSFWSW07: Residential Clothes Washer Base/Conventional
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
region 4 Los Angelesbldtyp 5 Large Officeenduse 1 Space Coolingvintage 1 Existingbundle 1 Standard HVACmonth 8 Augustdaytype 2 WEEK‐DAYres 30 RSFSCE67com COCCAL01
Cht_vctr LoadShapes!$F$8700:$AC$8700LoadShapes!$F$8700:$AC$8700LoadShapes!$F$35412:$AC$35412
Res Shape 8700Com Shape 35412
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
Percentage of Daily End‐Use Consumption
Hr
Source: AEO 2010 NEMS input file: all1lsr.v1.1.txt
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
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0.35
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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
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0.35
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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
Proportion of Annual Hours
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
Proportion of Annual Hours
MW
Savings Duration: Midwest 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
2,735 Prop
ortio
n of 202
0 Maxim
um Savings
Proportion of Annual Hours
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
Proportion of Annual Hours
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
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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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