CHAPTER 1 5

. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION

................................ ................................ ................................ ......... 15

- 1

15.2 METHODOLOGY

................................ ................................ ................................ ........ 15

- 2

15.3 UTILITY IMPACT RESUL TS

................................ ................................ ..................... 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 Reduct ion

................................ . 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 Red uction

......................... 15

- 9 Figure 15.3.10 Commercial Clothes Washers: Gas Combined Cycle Generation

Reduction

................................ ................................ ................................ ........... 15

- 9 Figure 15.3.11 Commercial Clothes Washers: Oil Generation Reduction

.............................. 15

- 10 Figure 15.3.12 Commercial Clothes Washers: Renewables Generation Reduction

................ 15

- 10

15

- iCHAPTER 15. UTILITY IMPACT ANALY SIS

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 201 4 (AEO 201 4 ) R eference 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 change s 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

transp arency 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 f rom 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

- 115.2 METHODOLOGY

DOE estimate s the marginal impacts of re duction 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 us es 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 inter

related effects: the annual generation (TWh) from the stock of electric generating capacity changes, the total generation capacity itself (GW) may chan ge, 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 sensit ive 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 SO 2 , NO

x , Hg

and CO 2

.

DO E ’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 significan t changes to equipment efficiencies relative to the R eference

case are:

• 2013 Technology (leaves all technologies at 2013 efficiencies) ; • Best Available Technology (highest efficiency irrespective of cost) ; • High Technology (higher penetration rates f or 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 R eference case.

2. The changes in electricity u se on the demand

- side data are a llocated 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 i n 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 s houlder period.

15

- 23. 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 g as 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 c oefficients 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 produc es 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 t o 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 f or generation except for peak : NEMS uses two peak capacity types (combustion turbin e/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.

Thi s analysis ignores p umped storage , fuel cells and distributed generation , as t hese 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 S ector I mpacts of E lectricity

D emand R eductions” (Coughlin, 2014). 4

15

- 315.3 UTILITY IMPACT RESUL TS

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 140201820232028203320382043204820532058206320682073MW Total

TSL 1 TSL 2 TSL 3 TSL 4

15

- 4Figure 15.3.2 Commercial Clothes Washers : Coal Capacity Reduction

Figure 15.3.3 Commercial Clothes Washers : Nuclear Capacity Reduction 0 5 10 15 20 25201820232028203320382043204820532058206320682073MW Coal

TSL 1 TSL 2 TSL 3 TSL 4

0 0 0 1 1 1 1 1 2 2201820232028203320382043204820532058206320682073MW Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

15

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

Reduction

Figure 15.3.5 Commercial Clothes Washers : Peaking Capaci ty Reduction 0 5 10 15 20 25 30 35 40 45201820232028203320382043204820532058206320682073MW NGCC

TSL 1 TSL 2 TSL 3 TSL 4

0 10 20 30 40 50 60201820232028203320382043204820532058206320682073MW Peaking

TSL 1 TSL 2 TSL 3 TSL 4

15

- 6Figure 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 18201820232028203320382043204820532058206320682073MW Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15

- 7Figure 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 400201820232028203320382043204820532058206320682073GWh Total

TSL 1 TSL 2 TSL 3 TSL 4

0 20 40 60 80 100 120201820232028203320382043204820532058206320682073GWh Coal

TSL 1 TSL 2 TSL 3 TSL 4

15

- 8Figure 15.3.9 Commercial Clothes Washers : Nuclear Generation Reduction

Figure 15.3.10 Comm ercial Clothes Washers : Gas Combined Cycle

Generation Reduction 0 2 4 6 8 10 12 14201820232028203320382043204820532058206320682073GWh Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

0 50 100 150 200 250201820232028203320382043204820532058206320682073GWh NGCC

TSL 1 TSL 2 TSL 3 TSL 4

15

- 9Figure 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 1201820232028203320382043204820532058206320682073GWh Oil

TSL 1 TSL 2 TSL 3 TSL 4

0 10 20 30 40 50 60201820232028203320382043204820532058206320682073GWh Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15

- 1015.3.3 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

- 11REFERENCES

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 201 4 with Projections to

2040 , 201 4 . Washington, DC. < http://www .eia.gov/forecasts/aeo/pdf/0383(201 4 ).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 , 201 4

. Lawrence Berkeley National Laboratory. Report No. LBNL

- xxxx

.

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