CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-2

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-4

15.3.1 Installed Capacity........................................................................................................... 15-4

15.3.2 Electricity Generation .................................................................................................... 15-7

15.3.3 Results Summary ......................................................................................................... 15-11

LIST OF TABLES

Table 15.3.1 Commercial Clothes Washers: Summary of Utility Impact Results ............... 15-11

LIST OF FIGURES

Figure 15.3.1 Commercial Clothes Washers: Total Electric Capacity Reduction ................... 15-4

Figure 15.3.2 Commercial Clothes Washers: Coal Capacity Reduction ................................. 15-5

Figure 15.3.3 Commercial Clothes Washers: Nuclear Capacity Reduction ............................ 15-5

Figure 15.3.4 Commercial Clothes Washers: Gas Combined Cycle Capacity Reduction....... 15-6

Figure 15.3.5 Commercial Clothes Washers: Peaking Capacity Reduction ............................ 15-6

Figure 15.3.6 Commercial Clothes Washers: Renewables Capacity Reduction...................... 15-7

Figure 15.3.7 Commercial Clothes Washers: Total Generation Reduction............................. 15-8

Figure 15.3.8 Commercial Clothes Washers: Coal Generation Reduction.............................. 15-8

Figure 15.3.9 Commercial Clothes Washers: Nuclear Generation Reduction......................... 15-9

Figure 15.3.10Commercial Clothes Washers: Gas Combined Cycle Generation 

Reduction ........................................................................................................... 15-9

Figure 15.3.11Commercial Clothes Washers: Oil Generation Reduction .............................. 15-10

Figure 15.3.12Commercial Clothes Washers: Renewables Generation Reduction ................ 15-10

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CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL). 

The utility impact analysis is based on output of the DOE/Energy Information 

Administration (EIA)’s National Energy Modeling System (NEMS).a NEMS is a public domain, 

multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses 

NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO). 

The EIA publishes a reference case, which incorporates all existing energy-related policies at the 

time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully 

documented and receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among 

the various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the 

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be 

reduced under the new approach.

On the average, however, over the full analysis period, the results from the new approach 

are comparable to results from the old approach.

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview.

1

15-1

 
15.2 METHODOLOGY 

DOE estimates the marginal impacts of reduction in energy demand on the energy supply 

sector. In principle, marginal values should provide a better estimate of the actual impact of 

energy conservation standards. 

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (GW) may change, and the mix of capacity by fuel 

type may change. Each of these effects can vary for different types of end use. The change in 

total generating capacity is sensitive to the degree to which the end-use is peak coincident, while

the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies);

• Best Available Technology (highest efficiency irrespective of cost);

• High Technology (higher penetration rates for efficiency and demand management);

• Extended Policies (includes efficiency standards that are not in the reference).

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to 

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period.

15-2
3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules:

3.1. All petroleum-based generation is allocated to peak periods;

3.2. Natural gas generation is allocated to any remaining peak reduction; this is consistent 

with the fact that oil and gas steam units are used in NEMS to meet peak demand;

3.3. Base-load generation (nuclear and coal) is allocated proportionally to all periods;

3.4. The remaining generation of all types is allocated to the remaining off-peak and shoulder 

reductions proportionally.

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types.

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a 

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time.

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts.

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Electricity 

Demand Reductions” (Coughlin, 2014).4

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15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. Note that a negative number means an 

increase in capacity under a TSL.

Figure 15.3.1 Commercial Clothes Washers: Total Electric Capacity 

Reduction

0

20

40

60

80

100

120

140

201820232028203320382043204820532058206320682073

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4

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Figure 15.3.2 Commercial Clothes Washers: Coal Capacity Reduction

Figure 15.3.3 Commercial Clothes Washers: Nuclear Capacity Reduction

0

5

10

15

20

25

201820232028203320382043204820532058206320682073

MW

Coal

TSL 1 TSL 2 TSL 3 TSL 4

0

0

0

1

1

1

1

1

2

2

201820232028203320382043204820532058206320682073

MW

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

15-5
Figure 15.3.4 Commercial Clothes Washers: Gas Combined Cycle Capacity 

Reduction

Figure 15.3.5 Commercial Clothes Washers: Peaking Capacity Reduction

0

5

10

15

20

25

30

35

40

45

201820232028203320382043204820532058206320682073

MW

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

0

10

20

30

40

50

60

201820232028203320382043204820532058206320682073

MW

Peaking

TSL 1 TSL 2 TSL 3 TSL 4

15-6
Figure 15.3.6 Commercial Clothes Washers: Renewables Capacity 

Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each TSL by plant type. Note that a negative number means an increase in generation under a 

TSL.

0

2

4

6

8

10

12

14

16

18

201820232028203320382043204820532058206320682073

MW

Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15-7
Figure 15.3.7 Commercial Clothes Washers: Total Generation Reduction

Figure 15.3.8 Commercial Clothes Washers: Coal Generation Reduction

0

50

100

150

200

250

300

350

400

201820232028203320382043204820532058206320682073

GWh

Total

TSL 1 TSL 2 TSL 3 TSL 4

0

20

40

60

80

100

120

201820232028203320382043204820532058206320682073

GWh

Coal

TSL 1 TSL 2 TSL 3 TSL 4

15-8
Figure 15.3.9 Commercial Clothes Washers: Nuclear Generation Reduction

Figure 15.3.10 Commercial Clothes Washers: Gas Combined Cycle

Generation Reduction

0

2

4

6

8

10

12

14

201820232028203320382043204820532058206320682073

GWh

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

0

50

100

150

200

250

201820232028203320382043204820532058206320682073

GWh

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

15-9
Figure 15.3.11 Commercial Clothes Washers: Oil Generation Reduction

Figure 15.3.12 Commercial Clothes Washers: Renewables Generation 

Reduction

0

0

0

1

1

1

1

1

201820232028203320382043204820532058206320682073

GWh

Oil

TSL 1 TSL 2 TSL 3 TSL 4

0

10

20

30

40

50

60

201820232028203320382043204820532058206320682073

GWh

Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15-10
15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results for commercial clothes 

washers.

Table 15.3.1 Commercial Clothes Washers: Summary of Utility Impact Results

TSL

1 2 3 4

Installed Capacity Reduction (MW)

2020 11.7 12.1 27.6 27.3

2025 32.9 34.0 77.5 76.8

2030 45.4 47.1 107.4 106.6

2035 50.6 52.7 120.4 119.6

2040 52.8 55.3 126.3 125.6

Electricity Generation Reduction (GWh)

2020 44.2 45.5 103.8 102.9

2025 106.2 109.7 250.2 248.1

2030 138.4 143.5 327.4 324.9

2035 144.3 150.4 343.3 341.0

2040 141.9 148.7 339.4 337.6

15-11
REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<http://www.eia.gov/oiaf/aeo/overview/>

2. Energy Information Administration, Annual Energy Outlook 2014 with Projections to 

2040, 2014. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>

3. Coughlin, K., H. Shen, P. Chan, B. McDevitt, and A. Sturges, Marginal Utility Sector 

Impacts of Reduced Electricity Demand, 2012. 

4. Coughlin, K., Utility Sector Impacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-xxxx. 

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