15

- i CHAPTER 1 5 . UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION

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

- 1 15.2 METHODOLOGY

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

- 215

- 1 CHAPTER 15. UTIL ITY I MPACT A NALYSIS

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 ) 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 an alysis 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

. 115

- 2 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 bett er 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 cu rves, 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 in terawatt hours (TWh) from the sto ck of electric generating capacity changes, the total generation capacity itself in gigawatts (GW) may change, and the mix of capacity by fuel type may change. b Each of these effects can vary for different types of end use. The change in total generating c apacity 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 sulfur dioxide ( SO 2 ) , nitrogen oxides ( NO X ) , mercury ( Hg ), and carbon dioxide ( CO 2 )

. 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. c The side cases, or scenarios, that incorporate significant 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 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 ste ps:
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.

b T he terawatt is equal to one trillion (10 12 ) watts. The total power used by humans worldwide (about 16 TW in 2006) is commonly measured in this unit. The gigawatt is equal to one billion (10 9 ) watts or 1 gigawatt = 1000 megawatts. This unit is often used for large power plants or power grids.

c Appendix E of AEO 2014 is available at http://www.eia.gov/forecasts/aeo/section_appendices.cfm and http://www.eia.gov/forecasts/aeo/data_side_cases.cfm15

- 3 2. The changes in electrici ty 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 explicit ly 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 12:00 noon to 5 :00 p

. m

. , June through September. Off

- peak hours are nights and Sundays. All other hours ar e assigned to the shoulder period.
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 S tep 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 en d

- 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 calc ulated in S tep 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 inst alled 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 S tep 4 to produce coefficients that relate installed capacity changes to changes in end

- use demand, as a function of time.15

- 4 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 uti lity impacts. This analysis does not take into account pumped 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 Sector Impacts of Electricity Demand Reductions” (Coughlin, 2014). 315

- 5 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. U.S. Departmen t of Energy

- Energy Information Administration, Annual Energy Outlook 2014 with Projections to 2040 , 2014. Washington, DC.
< http://www.eia.gov/forecasts/aeo/ >

3. Coughlin, K., Utility Sector Imacts of Reduc ed Electricity Demand , 2014. Lawrence Berkeley National Laboratory. Report No. LBNL

- XXXX.