14A-i INTERPOLATION OF UTILITY AND ENVIRONMENTAL RESULTS FROM NEMS-BT OUTPUT

LIST OF TABLES

Table 14A.1 Set of Multipliers for Various IRL Standards ........................................... 14A-1 Table 14A.2 Set of Multipliers for Various GSFL Standards ....................................... 14A-2

LIST OF FIGURE

Figure 14A.1 An Example of Interpolation of a Trial Standa rd Level: Difference in Total Generating Capacity ........................................................................ 14A-3 Figure 14A.2 An Example of Estimates Based on Regression Results of a Sa mple Trial Standard Level: Differen ce in Total Generating Capacity ............... 14A-4A PPENDIX 14 A. INTERPOLATION OF UTILITY AND ENVIRONMENTAL

RESULTS FROM NEMS-BT OUTPUT

The effects of proposed incandescent re flector lamp (IRL) and general service fluorescence lamp (GSFL) efficiency standards on the electric power industry were analyzed using a variant of the U.S. DOE/EIA Nationa l Energy Modeling System (NEMS) called NEMSBT, together with some exogenous calculations. Because the relative size of the energy savings being implemented in NEMS-BT was, in general, too small to be reliably estimated in the context of the whole electricity sector, adju stments to the NEMS-BT runs were necessary.

To run a simulation in NEMS-BT, the Residential Demand Module, Commercial Demand Module and Industrial Demand Modules el ectric lighting end us e is reduced annually according to the energy savings estimated by the National Energy Savings model (see Chapter 13) for a given trial standard level. These energy savings varied over time.

The magnitude of the energy decrement that would be required for NEMS-BT to produce stable results safely out of the range of numerical noise made it necessary, in both the utility analysis and environmental assessment, to estimat e results in the range of the standard levels effects using interpolation. Interpolated values are derived from a series of higher decrement simulations based on the standard levels. The standard levels for the IRL and GSFL analysis were modeled separately under two technology penetration scenarios.

To preserve the pattern of en ergy savings over time for a tria l standard level, savings in each year were multiplied by the same “scaling” f actor. This factor varies between standards because the magnitude of the savings changes. An appropriate set of multipliers was chosen to augment the savings to a magnitude that produces more stable results. Using professional judgment, sets of three multipliers were selected for each of the m odeled trial standard levels as shown in Table 14A.1 for IRL and Table 14A.2 for GSFL.

Table 14A.1 Set of Multipliers for Various IRL Standards

IRL Existing Technologies, Shift, R-CFL Substitution

Trial Standard Level 1 30, 61, 91 Trial Standard Level 2 13, 26, 39 Trial Standard Level 3 7, 15, 22 Trial Standard Level 4 6, 12, 17 Trial Standard Level 5 5, 10, 15

IRL Emerging Technologies, Rollup, BR Lamp Substitution

Trial Standard Level 1 90, 180, 269 Trial Standard Level 2 38, 75, 113 Trial Standard Level 3 18, 37, 55 Trial Standard Level 4 16, 32, 49 Trial Standard Level 5 14, 27, 41

14A-1Table 14A.2 Set of Multipliers for Various GSF L Standards

GSFL Existing Technologies, Shift, High Lighting Expertise

Trial Standard Level 1 5.4, 10.7, 16.1 Trial Standard Level 2 4.9, 9.8, 14.6 Trial Standard Level 3 2.1, 4.3, 6.4 Trial Standard Level 4 1.3, 2.6, 3.8 Trial Standard Level 5 1.0, 2.1, 3.1

GSFL Emerging Technologies, Roll Up, Market Segment Based Lighting Expertise

Trial Standard Level 1 10.5, 21.0, 31.5 Trial Standard Level 2 7.6, 15.2, 22.8 Trial Standard Level 3 4.5, 9.0, 13.5 Trial Standard Level 4 3.5, 7.1, 10.6 Trial Standard Level 5 2.5, 5.1, 7.6

The output for electricity generation and cap acity by fuel type for each of the scaled model runs (e.g., 5.4, 10.7, 16.1 times the standard level) is then regressed, with the y-intercept forced through the origin, and the actual standard level forecast is interpolated along this regression line. The linear regression is forced through the origin because a zero change must be the case with no standard in place and because the target points of interpolation are close to the origin (i.e., at low energy decrements).

Figure 14A.1 shows an example of the inte rpolation approach for a proposed trial standard level. The magnitude of the energy sa vings multiplier is plotted on the x-axis against the reduction in total instal led generating capacity for the years 2020, 2025 and 2030, as shown by the various plotted lines. In general, resu lts for the various NEMS-BT runs are reasonably stable and linear. Using these in terpolation techniques, the results of the NEMS-BT model runs have been used to estimate the a nnual values of select ed variables for each of the trial standard levels. Figure 14A.2 shows estimates of total ge nerating capacity reducti ons associated with a sample trial standard level for the years 2012 to 2030 (the last year ou tput from NEMS-BT).

As was discussed more fully in Chapter 16 of the TSD, NEMS-BT was not used directly to estimate emissions impacts for mercury (H g). Instead, DOE established a range of Hg reductions due to standards. DOE’s high estima te used an Hg emission rate based on an historical emissions rate. Because virtually a ll Hg emitted from electricity generation is from coal-fired power plants, DOE based the emission rate from the tons of Hg emitted per TWh of coal-generated electricity and used the average value reported in the AEO2008 for the year 2006. This emission rate is 0.0230 metric tons per TWh (0.0253 short tons/TWh). DOE’s low estimate was based on the use of an emission rate for new combined-cycle gas plants as provided in the NEMS-BT. The emission rate for this genera tion technology is 0.000 tons per TWh. To estimate the high end reduction in Hg emissions from the power sector, DOE multiplied the high emission rate by the reduction in coal-gener ated electricity due to the sta ndards considered as determined in the utility impact analysis (see Chapter 14, U tility Impact Analysis). Again, the estimates of

14A-2the redu ction in coal electric ity generation was developed usi ng the interpolation technique described above and illustrated in Figure 14A.1

Figure 14A.1 An Example of Interpolation of a Tr ial Standard Level: Difference in Total Generating Capacity

Regression of Scaled Results Total Generating Capacity Reductions



### y = -15.681x y = -12.667x

y = -8.6159x

(100)

(90) (80) (70) (60) (50) (40) (30) (20) (10) - - 1.0 2.0 3.0 4.0 5.0 6.0 Scaling FactorGW Savings

2025 2020 2030 Linear (2025) Linear (2020) Linear (2030)

14A-3Figure 14A.2 An Example of Estimates Based on Regression Results of a Sample Trial Standard Level: Difference in Total Generating Capacity

Estimates of Savings Based on Regression for Total Generating Capacity

(18) (16) (14) (12) (10) (8) (6) (4) (2) -2012201320142015201620172018201920202021202220232024202520262027202820292030GW Savings

The highlighted results for th e years 2020, 2025 and 2030 were directly interpolated from the regressions shown in Figure 14A.1

14A-4