Avoided Air Emissions from Energy Efficiency and 

Renewable Electric Power Generation in the PJM 

Interconnection Power Market Area 

Colin J. High and Kevin M. Hathaway

Resource Systems Group, Inc.  

White River Junction, VT

Supported by Funding from the U.S. Department of Energy

Clean Energy/Air Quality Integration Initiative

Draft Final March 15, 2007

Appendix H - Pages 52-82
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February 2007 Draft page 2 

CONTENTS 

CONTENTS..........................................................................................................................................................2

PREFACE.............................................................................................................................................................3

LIST OF ACRONYMS........................................................................................................................................4

1. INTRODUCTION AND PURPOSE OF THE REPORT ............................................................................5

2. THE PJM INTERCONNECTION POWER MARKET AREA .................................................................6

3. ANALYTICAL METHODOLOGY ..............................................................................................................8

3.1 ANALYSIS OF LED TRAFFIC SIGNAL RETROFITS .................................................................................10

3.2 ANALYSIS OF LIGHTING RETROFITS IN GOVERNMENT BUILDINGS.................................................11

3.3 ANALYSIS OF AIR CONDITIONING RETROFITS IN GOVERNMENT BUILDINGS ...............................12

3.4 ANALYSIS OF SOLAR PHOTOVOLTAIC POWER....................................................................................13

3.5 ANALYSIS OF WIND POWER ...................................................................................................................14

4. SUMMARY OF THE ANALYSIS OF AVOIDED NOX EMISSION RATES ........................................15

APPENDIX A: COMPARISON OF ALTERNATIVE METHODOLOGIES TO CALCULATE 

AVOIDED NOX EMISSIONS..........................................................................................................................18

APPENDIX B: ELECTRIC GENERATING UNITS INCLUDED IN THE ANALYSIS ...........................21

APPENDIX C: NREL SUPPORTING DOCUMENT- COMPOSITE HOURLY ELECTRICAL 

SAVINGS PROFILES FOR GOVERNMENT BUILDINGS IN THE WASHINGTON 

METROPOLITAN AREA ................................................................................................................................24

Appendix H - Pages 52-82
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PREFACE 

This report and analysis were prepared by Colin High and Kevin Hathaway of Resource Systems Group 

under contract to DJ Consulting LLC. The work was conducted with the financial support of the Clean 

Energy/Air Quality Integration Initiative of the U.S. Department of Energy’s Office of Energy 

Efficiency and Renewable Energy. 

Resource Systems Group wishes to thank staff from the National Renewable Energy Laboratory, who 

provided extensive input to the energy efficiency analyses included in this report, including Laura 

Vimmerstedt, David Kline, Gail Mosey, Sara Farrar-Nagy, and B. Griffith. In addition, we appreciate the 

review and comments on the entire report provided by the NREL staff, Debra Jacobson of DJ 

Consulting LLC, and Alden Hathaway of Environmental Resources Trust. We also would like to thank 

the staff of the Metropolitan Washington Council of Governments (MWCOG), state and local 

government members of MWCOG, and energy industry companies who provided data used in the 

analysis. 

Appendix H - Pages 52-82
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LIST OF ACRONYMS 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CEM Continuous Emission Monitors 

CHP Combined Heat and Power 

CO2 Carbon Dioxide 

DOE U.S. Department of Energy 

EERE Energy Efficiency and Renewable Energy 

EGU Electric Generating Units 

EPA U.S. Environmental Protection Agency 

kWh Kilowatt-Hour 

ISO Independent System Operator 

LED Light-Emitting Diode

MWCOG Metropolitan Washington Council of Governments 

MWh Megawatt-Hour 

NAAQS National Ambient Air Quality Standards 

NOx Nitrogen Oxides 

NREL National Renewable Energy Laboratory 

OAQPS EPA Office of Air Quality Planning and Standards 

OTC Ozone Transport Commission 

PJM PJM (Pennsylvania, New Jersey, Maryland) Interconnection 

PV Photovoltaic 

RSG Resource Systems Group, Inc 

SIP State Implementation Plan 

Appendix H - Pages 52-82
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February 2007 Draft page 5 

1. INTRODUCTION AND PURPOSE OF THE REPORT 

The purpose of this report is to provide an estimate of the avoided nitrogen oxide (NOx) emissions 

that result from electric energy efficiency (EE) and the generation of electric power from selected 

renewable energy (RE) sources in the PJM Interconnection power market area (PJM). This report is 

designed to assist the Metropolitan Washington Council of Governments (MWCOG) in efforts to 

support credit for avoided NOx emissions resulting from electric EE and RE power generation. 

Specifically, the report will support the development of the State Implementation Plans for the 

District of Columbia, the State of Maryland, and the Commonwealth of Virginia to implement 

requirements under the Clean Air Act to attain the 8-hour ozone standard. 

The report provides a prospective evaluation of the avoided emissions from several programs 

undertaken by State and local governments in the DC-MD-VA non-attainment area. These 

programs include: (1) energy efficiency programs including the retrofit of traffic signals to lightemitting diode (LED) signals; (2) wind energy purchases; and (3) the implementation of selected 

zero-emission renewable energy requirements of the District of Columbia’s Renewable Portfolio 

Standard (RPS). All of these programs will impact electric generation within the PJM 

Interconnection power market area. 

The technical methodology developed by Resource Systems Group, Inc., (RSG) in cooperation with 

Environmental Resources Trust was applied in this analysis. The methodology is based on generally 

accepted principles and procedures for estimating air emissions reductions from EE and RE power 

generation on the electric grid. The underlying assumptions and computation are consistent with the 

approach used by other experts in the field and similar to previous studies of avoided air emissions 

from EE and RE in New Jersey, Maryland and portions of PJM. The New Jersey report, including 

its methodology, was published in August 2006 and was co-authored by technical experts from the 

National Renewable Energy Laboratory (NREL), the Nation’s premiere renewable energy laboratory, 

and the Global Environment and Technology Foundation, a prominent non-profit organization in 

the energy and environmental field. In addition, the U.S. Environmental Protection Agency and the 

Department of Energy provided peer review comments that were incorporated in both the Maryland 

and New Jersey studies.1 The Maryland study also was accepted by the Environmental Protection 

Agency to support its first-ever approval of a renewable energy purchase for NOx emissions 

reduction credit in a State Implementation Plan (SIP) under the Clean Air Act.2 The methodology in 

 

1 The New Jersey report is titled as follows: United States Department of Energy, Final Report on the Clean Energy/ Air Quality 

Integration Initiative Pilot Project for the Mid-Atlantic Region, August 2006. (See 

http://www.eere.energy.gov/wip/clean_energy_initiative.html) The Maryland study is contained in the following document: , 

Metropolitan Washington Council of Governments, Plan to Improve Air Quality in the Washington, DC-MD-VA Region, February 19, 

2004. See http://www.mwcog.org/committee/committee/archives.asp?COMMITTEE_ID=14 (Scroll down to February 19, 

2004, pp. 7-77 to 7-81 and Appendix J, pp. J-71 to J-76). 

2 70 Fed. Reg. 24988 (May 12, 2005). 

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the Maryland report also was published by NREL as a model for air emissions assessment for other 

wind energy projects.3 

Avoided emissions result from renewable electric generation and demand-side energy efficiency 

measures because of the way that the electric power system works. Solar and wind energy generation 

are “must run” power sources because they have very low marginal operating costs and zero fuel 

costs. When these renewable sources and energy efficiency measures are in effect, they will displace 

generation at fossil-fuel units, which have much higher marginal operating costs in their power 

market area. As a result, the emissions from those fossil-fuel units are displaced. 

Renewable energy generation and energy efficiency almost never displaces generation at nuclear 

power plants or hydropower plants for several reasons. First, both nuclear and hydropower units 

have low operating costs. In addition, nuclear power is not displaced because of the high costs 

involved in shutting down and restarting such plants. Although the timing of the hydropower 

generation may shift, as a result of renewable generation, the stored water will be used to provide 

power at a later time -- resulting in a net reduction in fossil-fueled generation. 

The specific fossil-fuel units displaced vary by time of day and season and with the mix of fossil-fuel 

units operating. This report is focused on specific electric energy efficiency projects and the 

generation of electric power from zero-emission wind and photovoltaic sources. However, much of 

the discussion of the analytical process and the regulatory framework for crediting avoided NOx 

emissions also applies to avoided emissions from other energy efficiency /conservation measures and 

other zero-emission renewable energy generation. 

2. THE PJM INTERCONNECTION POWER MARKET AREA 

The PJM Interconnection has the largest generation capacity of any power market in North America. 

This power market has expanded from the original PJM core of Pennsylvania, New Jersey and 

Maryland (PJM) to include all or part of 12 states. The analysis excludes the two non-contiguous 

parts of PJM in Illinois and Michigan because it is unlikely that fossil-fueled generation will be 

displaced in these areas that are so distant from the Metropolitan Washington area. 

In this analysis, the import and export of power is not considered, and all generation displaced by 

renewable sources is assumed to occur in the PJM area. Transmission constraints, though likely 

 

3 National Renewable Energy Laboratory, Model State Implementation Plan (SIP) Documentation for Wind Energy Purchase in State with 

Renewable Energy Set-Aside, May 2005, Subcontract Report NREL/SR-500-38075. See 

http://www.windpoweringamerica.gov/sips.asp

 

Appendix H - Pages 52-82
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February 2007 Draft page 7 

significant in PJM’s large region, are not considered. Our analysis demonstrates that none of these 

simplifying assumptions likely affect the results at this level or for this report’s purposes.4 

The complete list of fossil-fueled plants in PJM utilized in our analysis is presented in Appendix B 

(Table B-1). The locations of the plants are shown in Figure B-1. We selected these plants for our 

analysis because they have the ability to provide variable levels of generation into the PJM grid at 

short notice and are not fully committed. These plants all meet the following criteria. They all are: 

(1) fossil-fuel fired, 

(2) sell power into the contiguous portions of the PJM Grid; 

(3) have average capacity factors below full capacity; and 

(4) are large enough to have continuous emissions monitors (CEMs). 

Small stand-by “peak shaving” units, emergency generators, and “behind the meter” generators are 

not included in the analysis because detailed data for these are not readily available. 

The monthly average generation at fossil-fueled units included in the analysis is shown by fuel type in 

Figure 1 below. This figure shows that generation peaks in summer and winter and that coal is the 

dominant fuel in all seasons. The proportion of oil increases in the May to September ozone season 

because electric generators need to meet the NOx emissions limitations for the summer ozone 

season. 

Figure 1 is based on plant-level fuel use reported to the Department of Energy’s Energy Information 

Administration and is intended for informational purposes only. The actual NOx avoided emission 

analysis is based on time matching analysis at the unit level as described in section 4. The marginal 

fuel use may differ, especially at peak demand periods. 

 

4 However, it should be noted that power imports and transmission constraints may affect the avoided emissions for 

specific projects. In a 2003 study of a specific wind project in western Maryland conducted by Resource Systems Group for 

Clipper Windpower, the average ozone season avoided NOx emission rate was 5.8 lbs/MWh, which was considerably 

higher than the PJM average avoided emissions at that time.4 These results are indicative of how project-specific factors 

can affect avoided emission rates.

Appendix H - Pages 52-82
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February 2007 Draft page 8 

Figure 1: PJM Average Monthly Generation Mix (MWh) for 2005 

-

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

45,000,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Generation (MWh)

Coal Oil Gas

3. ANALYTICAL METHODOLOGY 

The analysis objective was to determine the average avoided NOx emissions that occur when a

specific amount of EE saving or RE generation occurs in PJM. The five EE and RE projects 

discussed in this report include the following: 

• LED traffic signal retrofits (EE) 

• Lighting retrofits in government buildings (EE) 

• Air-conditioning retrofits in government buildings (EE) 

• Solar Photovoltaic Power (RE) 

• Wind Power (RE) 

For the three EE project categories, the avoided emissions analysis assumes that the projects will be

located in the DC-MD-VA non-attainment area and that electricity savings will displace fossil-fueled

generation in PJM. For the RE projects, it is assumed that generation could be located anywhere in

PJM and will displace fossil-fueled generation in PJM. 

Several established methods for estimating avoided emissions in any power market area have been

developed. These alternate methods are briefly reviewed in Appendix A of this report. Additional

reviews of this subject have been prepared by Synapse Energy Economics for the Ozone Transport 

Commission.5 

The methodology used in this report is the time-matched and generation-weighted average of the

emissions of plants that are variably dispatched to meet changing demand. This methodology is a

refinement of the generation-weighted average approach which was used in the New Jersey report

cited in Section 1. The first step determines the EE or RE generation profile (sections 3.1 - 3.6 

provides information on each of these profiles) for the summer ozone season and for the full year

 

5 Synapse Energy Economics, Inc. Predicting Avoided Emissions from Policies that Encourage Energy Efficiency and Clean Power, prepared 

for the Ozone Transport Commission, June 2002. 

Appendix H - Pages 52-82
Avoided Emissions Analysis PJM Interconnection Resource Systems Group, Inc. 

February 2007 Draft page 9 

(by time of day, week, and month). Secondly, these hourly profiles are matched with the hourly 

generation of the variably dispatched fossil-fuel units at plants listed in Appendix B (Table B-1) using 

SQL (structured query language) software. This customized tool identifies the fossil-fueled units 

operating when renewable power is generated or energy savings are taking place, forming the list of 

generating units available to be displaced in each hour of the year. 

Unit-level generation is estimated using the hourly carbon dioxide (CO2) emissions from the 

continuous emissions monitors (CEMs) required by EPA for each fossil-fueled electric generating 

unit (EGU).6 The relationship between CO2 emissions and generation is described by different linear 

models for each fuel (oil, coal, and gas) and combustion design (boilers, turbines, and internal 

combustion engines). These linear models provide reasonably accurate estimates of hourly 

generation of the EGUs in PJM. 

Hourly NOx emissions for each fossil-fueled EGU are also derived from the CEM data. Estimated 

avoided emissions for each hour are based on a generation-weighted average of the units operating at 

that time.7 These results are reported for both the ozone season (May 1 to September 30) and the 

full year. The current analysis is based 2005 data. 

The hourly, annual, and ozone season avoided emissions rates for NOx (lbs/MWh) are shown in 

Figure 2. These avoided emission rates are almost identical for each of the EE and RE 

technologies.8 The monthly and daily total avoided emissions (affected by the seasonal variability in 

energy savings and wind and PV power generation) are described in the following sections. 

 

6 U.S. Environmental Protection Agency, Office of Air and Radiation, Continuous Emission Monitoring Database, ,Acid Rain Hourly 

Emissions Data 2005, CD 2 disks. Distributed by National Technical Information Service, Springfield, VA. 

7 The analysis includes only the variably dispatched fossil fueled units at plants which are included in the analysis area of PJM 

and listed in Table 1 of Appendix B. 

8 The similarity in annual and ozone season avoided NOx emission rates (lbs/MWh) extends to five or more decimal places. The 

reason for this similarity is because of PMJ’s large generation base and the small increment of EE/RE that is being added to the 

system in this analysis. A more refined analysis of specific projects for smaller areas and larger increments of EE/RE may show

some differences between technologies. The seasonal differences in generation patterns between EE/RE technologies are, 

however, significant and result in marked differences on a tons per day basis for specific increments of generation or electricity 

savings. 

Appendix H - Pages 52-82
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February 2007 Draft page 10

Figure 2: Annual and Ozone Season Hourly Avoided Emission Potential in the PJM Area

-

1

2

3

4

5

6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Avoided E

missions

lbs/MWh

NOx

OZONE SEASON: 1.7 lbs/MWh

ANNUAL: 3.2 lbs/MWh

3.1 ANALYSIS OF LED TRAFFIC SIGNAL RETROFITS 

The energy savings from retrofits of LED traffic signals has been compiled from the reports of the 

state and municipal agencies in the MWCOG that have conducted such retrofits. The energy savings

have been calculated by using the protocol established by the EPA ENERGY STAR Program by the

use of data derived from the EPA ENERGY STAR Calculator Worksheet.9 The results of these 

calculations are reported in Section 6.5 of the SIP document. The electricity savings from the LED

traffic signal retrofits occur at a nearly constant level over a 24-hour period and throughout the year.

Therefore, the analysis does not contain any daily or seasonal variation. The electricity savings profile

is constant at all hours as shown below in Figure 3. 

 

9 U.S. Environmental Protection Agency, ENERGYSTAR LED Traffic Signals Calculator. See 

http://www.energystar.gov/index.cfm?c=traffic.pr_traffic_signals

Appendix H - Pages 52-82
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February 2007 Draft page 11

Figure 3: Monthly Percentage of Annual NOx Emissions Avoided & Annual Electricity Savings from 

LED Traffic Signals Retrofits

12% 12% 12%

10%

11% 12%

11%

4% 4% 4% 4% 4%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual total

NOx Emissions Avoided

8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33% 8.33%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

%

of annual total

Electricity Savings from LED Traffic Signal Retrofits

 

3.2 ANALYSIS OF LIGHTING RETROFITS IN GOVERNMENT BUILDINGS 

The energy savings from lighting retrofits in government buildings has been compiled from the

reports of the state and municipal agencies in the MWCOG that have conducted such retrofits. The

methodology is described in a memorandum included in Appendix C 10. The summary results of the 

savings calculations are reported in Section 6.5 of the SIP document. The hourly energy savings

profile has been simulated by NREL using the performance of a sample of 20 government buildings

in the Metropolitan Washington area and on meteorological data for 2003. The monthly electricity 

savings profile is shown in Figure 4. 

 

10 B. Griffith, Composite Hourly Electrical Savings Profiles from Government Buildings in the Washington DC. Area, Buildings and 

Thermal Systems Center, National Renewable Energy Laboratory, December 5, 2006.

Appendix H - Pages 52-82
Avoided Emissions Analysis PJM Interconnection Resource Systems Group, Inc. 

February 2007 Draft page 12

Figure 4: Monthly Percentage of Annual NOx Emissions Avoided & Annual Energy Savings from 

Lighting Retrofits of Government Buildings

10%

9%

12%

10% 10% 11% 11%

4% 5% 5% 6%

5%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

NOx Emissions Avoided

7% 6%

8% 8%

9% 10% 9% 8% 8% 7%

10%

10%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

Electricity Savings from Lighting Efficiency Improvements

3.3 ANALYSIS OF AIR CONDITIONING RETROFITS IN GOVERNMENT BUILDINGS 

The energy savings from retrofits of air conditioning in government buildings has been compiled

from the reports of the state and municipal agencies in the MWCOG that have conducted such

retrofits. The methodology is described in a memorandum included in Appendix C.

11 The summary

results of savings calculations are reported in Section 6.5 of the MWCOG SIP document. The

hourly energy savings profile has been simulated by NREL using the performance of a sample of 20 

government buildings in the Metropolitan Washington area and on meteorological data for 2003. The 

monthly electricity savings profile is shown in Figure 5. The energy savings show a very marked 

summer peak. 

 

11 B. Griffith, Composite Hourly Electrical Savings Profiles from Government Buildings in the Washington DC. Area. Buildings and Thermal 

Systems Center, National Renewable Energy Laboratory, December 5, 2006. 

Appendix H - Pages 52-82
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February 2007 Draft page 13

Figure 5: Monthly Percentage of Annual NOx Emissions Avoided & Annual Energy Savings from Air 

Conditioning Efficiency Retrofits in Government Buildings

7% 7%

10%

8%

9% 10% 10%

5%

8%

10%

11%

7%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

NOx Emissions Avoided

4%

5%

6%

8%

12%

11%

6% 6%

4% 4%

17%

15%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual total Electricity Savings from Air Conditioning Efficiency Improvements

3.4 ANALYSIS OF SOLAR PHOTOVOLTAIC POWER 

The analysis for solar photovoltaic power is based on generation data derived from the performance 

of existing facilities. The photovoltaic data are based on the performance of a standard silicon PV 

system using Typical Meteorological Years (TMY2) solar radiation data compiled by NREL for the

Metropolitan Washington area.12 Other locations in PJM would have similar seasonal patterns, and

therefore, they would have similar avoided emission rates (lbs/MWh) even if the actual generation

rate in watts per meter differed. The summary results of the avoided NOx calculations are reported

 

12 National Renewable Energy Laboratory, TMY2 Users Manual . See http://rredc.nrel.gov/solar/old_data/nsrdb/tmy2/

Appendix H - Pages 52-82
Avoided Emissions Analysis PJM Interconnection Resource Systems Group, Inc. 

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in Section 6.5 of the MWCOG SIP document. The monthly percentage of total annual generation 

and the hourly NOx avoided emission rate for photovoltaic power is shown in Figure 6. 

Figure 6: Monthly Percentage of Annual NOx Emissions Avoided & Annual Photovoltaic Energy

Produced

7%

13%

15%

5%

9%

7%

10%

6% 8% 8% 7%

5%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

NOx Emissions Avoided

5%

8%

10%

12%

13%

7%

4% 3%

9%

4%

13%

11%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual total

Photovoltaic Energy Produced

3.5 ANALYSIS OF WIND POWER

The analysis for wind power is based on wind generation data derived from existing wind energy 

facilities.13 The data are for hourly electric generation for a one-year period. The wind data are based

on the performance shown in the complete annual records of several wind turbines, in the 

mountainous interior areas of the Appalachian Mountains in PJM. This location is typical of the wind 

power purchases planned by municipalities in the DC-MD-VA non-attainment area. 

 

13 The location and ownership of the wind turbines are confidential to protect the commercial interests of the owners who 

provided performance data. 

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February 2007 Draft page 15

The monthly percentage of total annual generation and the hourly NOx avoided emission rate for

wind power is shown in Figure 7 with a marked winter peak. The summary results of the avoided 

NOx emissions calculations are reported in Section 6.5 of the SIP document. 

Figure 7: Monthly Percentage of Annual NOx Emissions Avoided & Annual Wind Energy Produced

14%

13%

14%

10%

15%

2% 2% 1% 2%

3%

8%

16%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

NOx Emissions Avoided

12%

11%

12%

9%

8%

5%

4%

7%

13% 13%

3% 3%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

% of annual t

otal

Wind Energy Produced

4. SUMMARY OF THE ANALYSIS OF AVOIDED NOx EMISSION RATES 

The monthly and seasonal analysis of the avoided emissions for each of the five NOx reduction 

measures described in Section 3 were computed by an Microsfoft® ExcelTM workbook calculator

prepared by Resource Systems Group, Inc.14 The calculator will be posted on the website of the 

 

14 Resource Systems Group Inc. MWAQC EERE Avoided NOx Emissions Calculator 2007. available at 

http://www.mwcog.org/environment/air 

Appendix H - Pages 52-82
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Metropolitan Washington Air Quality Committee of the Metropolitan Washington Council of 

Governments at http://sharepoint.mwcog.org/airquality/default.aspx . 

The calculator consists of three core worksheets plus other linked worksheets, as follows: 

• The first sheet (named “Instructions and Documentation”) provides instructions & general 

methodology while graphically displaying the seasonal variation in hourly and daily avoided 

NOx emissions rates. Seasonal patterns in energy efficiency savings and power generation for 

each of the five EE/RE measures are also provided. 

• The second worksheet (named “Emissions Standard”) calculates the monthly, annual and 

ozone season avoided NOx emissions (in tons per day) based on the seasonal profiles of the 

five EE/RE measures described in Section 3. There are separate sections for input and 

results for Maryland, Virginia and the District of Columbia. 

• The third worksheet (named “Emissions Season Specific”) provides the same calculations as 

the ”Emissions Standard”, except that the user can specify the energy generated or saved in 

the ozone and non-ozone seasons. This worksheet can be used when the energy amounts are 

known or specified, such as in a power purchase agreement or a contract to purchase 

Renewable Energy Certificates (RECs). This worksheet provides a more accurate result than 

the “Emissions Standard” worksheet where the specified ozone season and non-ozone 

season MWh differ from the regional generation or savings profile. This worksheet would be 

most applicable where a contract for RECs or energy efficiency savings binds the provider to 

supply a specified number of RECs during the summer ozone season. 

The average avoided NOx emission rates for the five NOx reduction measures are 1.7 lb/MWh for 

the ozone season and 3.2 lb/MWh annually. The average avoided ozone season and annual NOx

emissions (in tons per day) for each of the five measures and each of the three jurisdictions are 

provided in Section 6.5 of the SIP document. The average avoided emissions that may be applied in 

the SIP depend on the absolute amount of generation or savings and the monthly profile of these 

measures. Avoided emissions apply to PJM described in Section 2 for 2005 and are based on the 

methodology described in this report and the set of fossil-fueled generating units at power plants 

listed in Appendix B. They are based on electricity savings from energy efficiency and renewable 

generation located in PJM without regard to location. Renewable energy project location will 

influence the avoided emission rate, especially in areas with significant transmission constraints. 

For projects located in PJM, but not in the immediate MWCOG area, the upwind or downwind 

location may also affect the ozone impact of the avoided emissions. Avoided emissions from wind 

plants that are located in the Appalachian Mountains to the west will provide upwind reductions for 

the MWCOG area. The average avoided NOx emission rates of western PJM, where more generation 

is fueled by coal, are typically higher than in eastern PJM (i.e. more natural gas). If fossil-fueled units 

in eastern PJM’s were excluded from the analysis (i.e. they are unlikely to be displaced by wind 

power), the avoided NOx emission rates would be higher. In this instance, the analysis 

underestimates the avoided emission from wind plants upwind of the Washington DC-MD-VA nonattainment area. 

Appendix H - Pages 52-82
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These avoided emission rates are likely to have been similar in 2006. In future years, the avoided 

emission rates will be affected by changes in power generation, emissions control technology, and 

emissions control regulations. 

Appendix H - Pages 52-82
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February 2007 Draft page 18

APPENDIX A: COMPARISON OF ALTERNATIVE METHODOLOGIES TO CALCULATE 

AVOIDED NOX EMISSIONS15 

To model the avoided emissions or marginal emission reductions, several methods may be employed. 

These methods include the following: 

1) A system mix analysis of all electric generating units; 

2) A surrogate plant analysis; 

3) A generation-weighted average of variably dispatched fossil-fueled generating units; 

4) A time-matched and generation-weighted average of variably dispatched fossil-fueled generating 

units; and 

5) A complete grid-system dispatch analysis of fossil-fueled generating units. 

The analysis in this report relies on methodology (4) for the reasons set forth below. 

1) The system mix analysis takes the generation-weighted average of all the fossil-fueled units in the 

electric generating system. This is a simple method. However, this method includes nuclear and 

hydropower plants that are almost never displaced by EE/RE measures. As a result, this approach 

significantly underestimates the emissions displacement, which occurs almost entirely at fossil-fueled 

plants. The EPA eGrid data relies on this approach.16

2) The surrogate plant analysis calculates the emissions of the next new plant or unit that is likely to be 

added to the electric grid as a basis for determining what emissions would be avoided if the demand were 

reduced by energy efficiency measures or displaced by renewable energy generation. In the Mid-Atlantic, 

the most likely new plant in recent years would be combined-cycle natural gas with best available NOx

control technology. The result is a very low NOx avoided emission rate. 

This approach is unrealistic in the short-term because actual generation and energy efficiency 

displacement are spread across a wide range of fossil-fueled generation units, some of which have 

relatively high NOx emission rates. This approach may provide a reasonable estimate of the long-term 

avoided emissions if current trends continue. However, the actual mix of plants may be very different in 

the future depending on fuel prices and public policy. 

3) The generation-weighted average of the emissions of the fossil-fueled electric generating units 

that are var ably dispatched to meet changing demand. This approach provides a reasonable 

approximation of the marginal emissions rate without the time and cost of a complete grid-system 

dispatch analysis, as described below. This method was used in the New Jersey Report.

i

 

17

15 This description is based, in large part, on Appendix 4 of the Final Report on the Clean Energy/ Air Quality Integration Initiative 

Pilot Project for the Mid-Atlantic Region, August 2006. (See http://www.eere.energy.gov/wip/clean_energy_initiative.html) 

16 U.S. Environmental Protection Agency Emissions & Generation Resource Integrated Database (eGRID) 2002 

http://www.epa.gov/cleanenergy/egrid/index.htm 

Appendix H - Pages 52-82
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4) The time-matched and generation-weighted average of the emissions of the fossil-fueled 

generating units that are variably dispatched to meet changing demand. This methodology has been 

developed by Resource Systems Group in cooperation with Environmental Resources Trust. It is a 

refinement of the generation-weighted average approach. It matches the hour-by-hour output of the 

renewable energy source or energy saving units with the generation from fossil-fuel units in each power 

market area that can be displaced by “must run” renewable energy or energy efficiency savings. Under 

the RSG methodology, the profiles of the EE/RE for the summer ozone season (by time of day, week, 

and month) and for the full year are determined. The next step matches the EE/RE hourly profile data 

for each source type against the hourly generation of the variably dispatched fossil-fueled units at plants 

in the power market analysis area. This comparison forms the basis for matching and identifying the set 

of generation units which can be displaced in each hour. 

The method requires intensive computational power and use of the CEM’s CO2 data to estimate hourly 

generation for individual units (because exact data is not publicly available). This method should provide 

a better approximation of the true marginal emission rate without the time and cost of a complete gridsystem dispatch analysis. 

5) A complete grid-system dispatch analysis of fossil-fuel units considers the dispatch order and 

scheduling of specific combustion units at each facility in the power market area in detail, providing the 

most comprehensive estimate of the avoided emissions. An analysis of this type may be based on 

historical data on dispatch schedules or on a proprietary unit dispatch model. This approach allows for 

matching the EE/RE measures at specific hours with the actual generation of variably dispatched fossilfuel fired units. In principle, this method takes into consideration the actual dispatch order at every hour, 

including operational cost, system reliability, transmission constraints and other operational factors. 

This approach was utilized by the State of Maryland to support NOx emissions reduction credit for a 

regional wind purchase led by Montgomery County, Maryland in the SIP to meet the 1-hour ozone 

standard.18 In 2005, this wind purchase received the first-ever approval by the U.S. EPA for NOx

emission reduction credit for a renewable energy purchase in a State Implementation Plan.19 

This grid-system dispatch analysis approach is workable for the analysis of a renewable energy purchase 

from a single renewable energy plant (as in the case of the Montgomery County wind purchase). 

However, this approach is extremely difficult and very expensive to apply when multiple EE/RE projects 

or an entire EE/RE program is the subject of the analysis. This expense is hard to justify, particularly if 

it is intended for the sole purpose of validating an avoided emissions rate stipulated in a State NOx

 

17 U.S. Department of Energy, Final Report on the Clean Energy/ Air Quality Integration Initiative Pilot Project for the Mid-Atlantic Region,

August 2006. (See http://www.eere.energy.gov/wip/clean_energy_initiative.html) 

18 See http://www.mwcog.org/environment/committee/committee/archives.asp?COMMITTEE_ID=14 Scroll down to 

February 19, 2004 and Click SIP Chapter 7 (pages 7-77 to 7-80) and Appendix J, pp. 7-71 to 7-76. 

Appendix H - Pages 52-82
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emissions trading regulation, such as the Virginia Clean Air Interstate Rule, which stipulates an avoided 

emissions rate of 1.5 pounds per MWh for 2009 to 2014. However, this detailed dispatch analysis 

approach can be justified to provide accurate estimates of displaced NOx emissions resulting from a large 

renewable energy project, such as a single wind farm.20 

The use of a proprietary economic unit dispatch models also makes this approach non-transparent, which 

may create problems for public agencies in reviewing the results. The system simulation dispatch models 

were primarily designed for optimizing new construction and operating decisions of relatively large plants 

in competitive markets. In many power markets, there is insufficient experience with the dispatch of 

small intermittent renewable power, such as wind and PV, to establish clear precedents for how the 

system will respond at the level of detail implied by the dispatch simulation models. 

 

19 70 Fed. Reg. 24987 (May 12, 2005). 

20 See National Renewable Energy Laboratory, Model State Implementation Plan (SIP) Documentation for Wind Energy Purchase in State 

with Renewable Energy Set-Aside, May 2005, Subcontract Report NREL/SR-500-38075 See, 

http://www.eere.energy.gov/windandhydro/windpoweringamerica/sips.asp 

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APPENDIX B: ELECTRIC GENERATING UNITS INCLUDED IN THE ANALYSIS 

The list of plants with fossil-fueled units included in the analysis of the PJM Interconnection in this 

report is shown in Table B-1. In a few cases, renewable-fueled units, such as wood-fired units, are 

included because they also burn fossil fuels. We conducted the analysis at the level of the individual 

electric generation unit. 

The NOx and CO2 emissions data was obtained from the Continuous Emission Monitor database of the 

U.S. EPA for calendar year 2005.21 This information is supplemented by data from the U.S. Energy 

Information Administration (EIA), the Federal Energy Regulatory Commission, and from utility 

company websites. 

RSG used data from the EPA’s Emissions & Generation Resource Integrated Database (eGRID) 2002 to 

check and supplement the list of electric generating units.22 The emissions data in eGRID 2002 are based 

on data collected in 2000. Additional information has been obtained from technical staff at the Virginia 

Department of Environmental Quality and the New Jersey Department of Environmental Protection. 

 

21 U.S. Environmental Protection Agency, Continuous Emission Monitoring Database, Office of Air and Radiation, “Acid Rain 

Hourly Emissions Data 2005,” CD 2 disks. Distributed by National Technical Information Service, Springfield VA. 

22 U.S. Environmental Protection Agency Emissions & Generation Resource Integrated Database (eGRID) 2002 

http://www.epa.gov/cleanenergy/egrid/index.htm 

Appendix H - Pages 52-82
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Table B-1: Plants Included in PJM Interconnection Analysis 

Name State Name State Name State Name State

BENNING DC NATIONAL PARK STA. NJ CROYDON STATION PA BIRCHWOOD POWER VA

CENTRAL HEATING DC NEWARK BAY COGEN PRT NJ EBENSBURG POWER COMP PA BREMO VA

CHRISTIANA SUBSTATION DE NORTH JERSEY ENERGY NJ EDDYSTONE PA BUCHANAN GENERATING VA

CONECTIV, EDGE MOOR DE OCEAN PEAKING POWER NJ EDGAR THOMSON PLANT PA CCC POWER STATION VA

DELAWARE CITY DE PEDRICKTOWN COGEN NJ EDISON STATION PA CELANESE ACETATE VA

HAY ROAD DE PELP NJ ELRAMA PA CHESAPEAKE VA

INDIAN RIVER GENERATION DE SALEM STATION NJ ENTRIKEN PA CHESTERFIELD VA

MADISON STREET DE SAYREVILLE NJ FAIRLESS ENERGY, LLC PA CLINCH RIVER VA

MCKEE RUN DE SHERMAN AVENUE NJ FAIRLESS HILLS SGS PA CLOVER VA

NRG ENERGY DOVER DE VALERO REF.CO.- N.J. NJ FAYETTE ENERGY FACILITY PA COGENTRIX HOPEWELL VA

PREMCOR REFINING GRP DE WERNER NJ FPL ENERGY MH50 L.P. PA COGENTRIX PORTSMOUTH VA

VANSANT DE WEST STATION NJ FPLE MARCUS HOOK PA COGENTRIX RICHMOND VA

WARREN F. (SAM) BEAS DE FULTON COGENERATION NY GANS PA DAN RIVER VA

WEST SUBSTATION DE HILLBURN GT NY GILBERTON POWER COMP PA DARBYTOWN CT VA

MONTPELIER ELECTRIC IN NISSEQUOGUE COGEN NY GLATFELTER, P.H. CO. PA DOSWELL LP VA

BIG SANDY KY WATERSIDE GENERATING NY GREYS FERRY COGEN PA ELIZABETH RIVER VA

CATLETTSBURG RFNG KY AK STEEL OH HANDSOME LAKE ENERGY PA GLEN LYN VA

RIVER SIDE GENERATION KY CARDINAL OH HATFIELD'S FERRY PS PA GORDONSVILLE VA

AES WARRIOR RUN MD CARGILL INC OH HAZELTON GENERATION PA GRAVEL NECK CT VA

BRANDON SHORES MD CHILLICOTHE PAPERINC OH HOMER CITY PA HOPEWELL VA

CHALK POINT MD CONESVILLE PLANT OH HUNLOCK POWER PA HOPEWELL VA

CHARLES P. CRANE MD DARBY ELECTRIC GENERATION OH HUNTERSTOWN PA HOPEWELL COGEN VA

DICKERSON MD DAYTON POWER AND LIGHT OH KEYSTONE PA INTERNATIONAL PAPER VA

H.A. WAGNER MD DICK'S CREEK STATION OH KIMBERLY-CLARK PA LADYSMITH VA

MEADWESTVACO MD, INC MD GAVIN OH LIBERTY ELECTRIC LLC PA LOUISA GENERATION FA VA

MORGANTOWN MD GRNVILLE ELECTRIC OH LOWER MOUNT BETHEL PA MARSH RUN GENERATION VA

PANDA-BRANDYWINE MD HAMILTON GENERATING OH MARTINS CREEK PA MEADWESTVACO OF VA VA

PERRYMAN MD HAMILTON MUNICIPAL OH MERCK & COMPANY INC PA MECKLENBURG P.S VA

R P SMITH P S MD HANGING ROCK ENERGY OH MITCHELL P S PA POSSUM POINT VA

RIVERSIDE MD KYGER CREEK OH MONTOUR PA POTOMAC RIVER VA

ROCK SPRINGS MD MAD RIVER OH MOUNTAIN PA REMINGTON VA

VIENNA GENERATING ST MD MADISON STATION OH MT. CARMEL COGEN PA SOUTHAMPTON VA

WESTPORT MD MUSKINGUM RIVER OH NORTH EAST COGEN PA TASLEY PEAKING STATION VA

INTERNATIONAL PAPER NC O.H.HUTCHINGS OH NORTHAMPTON GEN CO PA TENASKA VIRGINIA GEN VA

WEYERHAEUSER CO. NC OSU-MCCRACKEN OH NORTHEASTERNPOWERCO PA WOLF HILLS ENERGY VA

GRANITE RIDGE ENERGY NH PICWAY OH ONTELAUNEE ENERGY CE PA YORKTOWN VA

AES RED OAK FACILITY NJ RE BURGER OH PANTHER CREEK PA ALBRIGHT P S WV

B.L. ENGLAND NJ RICHARD GORSUCH GENE OH PINEY CREEK L. P. PA BIG SANDY PEAKER PL WV

BELVIDERE PLANT NJ ROBERT P. MONE OH PORTLAND PA CEREDO WV

BERGEN GEN. STATION NJ ROLLING HILLS GENER. OH PROCTER&GAMBLE PAPER PA DUPONT BELLE PLANT WV

BURLINGTON STATION NJ SMART PAPERS LLC OH RICHMOND STATION PA ELKEM, STEAM PLANT WV

CALPINE NEWARK, INC NJ SUNOCO, INC. (R&M) OH SCHUYLKILL PA FORT MARTIN P S WV

CALPINE PARLIN, INC. NJ TAIT ELECTRIC GENER OH SCHUYLKILL STATION PA GRANT TOWN POWER PLT WV

CARLLS CORNER NJ W. H. SAMMIS OH SCRUBGRASS GEN PLANT PA HARRISON P S WV

CARNEY POINT GENERATION NJ WASHINGTON ENERGY OH SEWARD PA INSTITUTE PLANT WV

CEDAR STATION NJ WATERFORD OH SHAWVILLE PA JOHN E. AMOS WV

COASTAL EAGLE POINT NJ WOODSDALE STATION OH SHENANGO INC PA KAMMER WV

COGEN TECH, LINDEN NJ AE 3,4&5 PA SHERMANS DALE PA KANAWHA RIVER WV

CUMBERLAND NJ AES BEAVER VALLEY PA ST. NICHOLAS COGEN. PA MITCHELL WV

DEEPWATER STATION NJ AES IRONWOOD, INC PA SUN COMPANY PHILA PA MORGANTOWN ENERGY WV

EAGLE POINT COGEN NJ ALLEGHENY ENERGY LLC PA SUNBURY PA MOUNTAINEER WV

EDISON STATION NJ ARCHBALD POWER PA SUNOCO INC. (R&M) PA MT. STORM WV

EF KENILWORTH NJ ARMAGH PA TITUS PA NATRIUM PLANT WV

ESSEX GEN. STATION NJ ARMSTRONG ENRGY,LLP PA TOLNA PA NORTH BRANCH WV

FORKED RIVER NJ ARMSTRONG P S PA U.S. STEEL CLAIRTON PA PHILIP SPORN WV

GILBERT NJ B MANSFIELD PA WARREN PA PLEASANTS ENERGY,LLC WV

GLEN_GARDNER NJ BERNVILLE PA WESTWOOD OPER CO. PA PLEASANTS P S WV

HOWARD M DOWN NJ BETHLEHEM POWER PLANT PA WHEELABRATOR PA RIVESVILLE P S WV

HUDSON STATION NJ BRUNNER ISLAND PA WILLAMETTE IND. INC. PA UNION CARBIDE CORP. WV

LINDEN STATION NJ CAMBRIA COGEN PA ZINC CORP OF AMERICA PA WILLOW IS P S WV

LOGAN GENERATING PLANT NJ CHESWICK PA EASTMAN CHEMICAL COM TN

MERCER GEN. STATION NJ COLVER POWER PROJECT PA KINGSPORT PAPER MILL TN

MICKLETON NJ CONEMAUGH PA ALTAVISTA VA

MIDDLE STREET NJ CONOCOPHILLIPS COMP. PA BELLEMEADE VA

MISSOURI AVENUE NJ CROMBY PA BIG ISLAND MILL VA

Appendix H - Pages 52-82
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Figure B-1: Locations of Plants Used in Analysis 

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APPENDIX C: NREL SUPPORTING DOCUMENT- COMPOSITE HOURLY ELECTRICAL 

SAVINGS PROFILES FOR GOVERNMENT BUILDINGS IN THE WASHINGTON 

METROPOLITAN AREA 

Composite Hourly Electrical Savings Profiles for Government Buildings in the Washington 

Metropolitan Area 

B. Griffith 

Buildings and Thermal Systems Center, Commercial Buildings Group 

National Renewable Energy Laboratory 

January 16, 2005 

INTRODUCTION 

Building energy efficiency programs can reduce electricity use and demand. Because of the variability in 

the mix of power plants and their operating conditions over time, emission reductions are actually a 

function of the time of day and time of year. The Metropolitan Washington Air Quality Committee 

requested NREL’s Commercial Buildings Group to provide a composite profile for the hourly electricity 

savings during the summer ozone season in the Metropolitan Washington Area for use in estimating the 

associated emissions reductions. This report provides a brief summary of the methodology used to 

develop the hourly profiles. 

METHODOLOGY 

The NREL analysis relies on a database compiled by the U.S. Energy Information Administration (EIA) 

entitled the “Commercial Buildings Energy Consumption Survey (CBECS).” NREL utilized a computer 

simulation program called EnergyPlus to generate composite profiles for electricity savings using a set of 

twenty building models derived from the EIA survey. The set of buildings was selected to represent 

government buildings in the Metropolitan Washington area. The larger, national survey included 

EnergyPlus models for all 4,820 buildings in the 2003 CBECS public use data. NREL developed the 

subset for the current study by selecting every government-owned building that had been assigned to 

either of two airport weather locations in the Metropolitan Washington area. Although the 2003 CBECS 

survey data actually masks the locations of the survey buildings, NREL was able to determine the 

modeled locations by fitting degree day information in the survey data to the 2003 historical weather data 

for major airports used for weather data in building simulation. The DC area airports included 

Washington Dulles (IAD, WMO#724030) and Baltimore (BWI, WMO#724060). Of the 4,820 buildings 

in the national survey, a total of 69 were assigned to IAD or BWI. Of these sixty-nine building, twenty 

were government-owned. These twenty building models were used in the current study to generate the 

composite electrical savings profiles summarized in the following table. 

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Floor area Floor area

m2 ft2

1 22 311 3,350 Repair shop 2546.83 21: Forced air cooling (no heat)

2 472 19,045 205,000 Government office 4.28 43: Fan coil with district chilled and hot water

3 629 2,973 32,000 Other classroom education 364.5 5: PVAV w/ reheat

4 723 26,012 280,000 Government office 6.04 43: Fan coil with district chilled and hot water

5 755 715 7,700 Post office 2546.83 29: hot water baseboards (no cool)

6 2153 55,740 600,000 Vacant 6.04 44: Fan coil with chiller and district hot water

7 2193 32,515 350,000 High school 133.87 3: PSZ-AC

8 2200 399 4,300 Vehicle Storage/maintenance 2546.83 19: forced air furnace w/ OA system

9 2380 139,350 1,500,000 hospital 4.59 44: Fan coil with chiller and district hot water

10 2512 139,350 1,500,000 hospital 3.88 44: Fan coil with chiller and district hot water

11 2726 139,350 1,500,000 Government office 46.62 44: Fan coil with chiller and district hot water

12 2753 139,350 1,500,000 Government office 3.88 43: Fan coil with district chilled and hot water

13 2805 1,231 13,250 Library 419.28 7: VAV w/reheat

14 3258 55,740 600,000 Government office 6.04 44: Fan coil with chiller and district hot water

15 3296 4,506 48,500 College/university 133.87 43: Fan coil with district chilled and hot water

16 4299 139,350 1,500,000 Government office 6.47 43: Fan coil with district chilled and hot water

17 5722 6,503 70,000 Elementary school 136.88 40: Fan coil w/ district chilled water and boiler

18 5824 13,749 148,000 High school 142.39 7: VAV w/reheat

19 5970 5,110 55,000 Elementary school 364.5 27: VAV with electric baseboard

20 6151 139,350 1,500,000 Government office 3.88 8: VAV w/PFP boxes

No. HVAC system type

CBECS

No. Type of Building

CBECS

weight

For each savings scenario described below, NREL generated a composite savings profile. The profiles

are hourly and annual and include values for each of the 8760 hours in the year. Each of the twenty 

buildings used to model DC government buildings produces its own energy savings profile. Because the

buildings have different sizes, the power use by each is normalized by the floor area to obtain values that

are power intensity per unit area. We developed a single composite hourly energy savings profile by

computing a weighted average from all twenty buildings for each hour of the year. 

The energy efficiency scenario is always compared to a reference scenario, and the difference in electricity

use is “savings” attributed to the efficiency measure. Note that the use of highly integrated analyses

provided by EnergyPlus allows calculating the integrated effects of savings measures. For example, a 

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lighting measure will decrease the energy use for lighting, increase the energy use for heating and decrease 

the energy used for cooling. 

The methodology is summarized in the following equations: 

∑

∑

=

= = 20

1

20

1

j

j j

j

j j ij

i

W A

W A PSI

PSI (1) 

where, i is the index represent the hour of the year (on [1..8760]) 

j is the index representing the building in the analysis ( on [1..20]) 

PSIi is the resulting power savings intensity (W/m2 or W/ft2) at hour i 

 is the weight factor for building j from 2003 CBECS Wj

Aj is the area (m2 or ft2) of building j from 2003 CBECS 

PSIij is the power intensity (W/m2 or W/ft2) for building j at hour i 

Equation 1 is repeated for each of 8760 hours in the year using a small computer program to process

simulation results. 

The resulting composite profile is power savings intensity in units of power per unit area. When applied 

for subsequent emissions research, the profile’s values should be multiplied by the total building floor

area affected by the energy efficiency program(s). For example, once weighted, the twenty building

sample used in the modeling represent a population of government buildings in the Washington, D.C. 

region with a maximum of 25,469,458 m2 (274,159,938 ft2) of floor area. 

SCENARIOS 

NREL evaluated two separate energy efficiency scenarios: one for lighting and one for air conditioning. 

Lighting improvements were modeled by assuming a 25% improvement in the lighting equipment. The

lighting power densities in each reference building were reduced by 25% and the models rerun with the 

change and compared to the reference case. 

Air conditioning improvements were modeled by assuming a 25% improvement in cooling equipment 

efficiency and fans. The Coefficient of Performance was increased by 25% on all chillers and direct 

expansion primary cooling equipment. Fan total efficiencies were also increased by 25%. Note that six 

of the buildings happen to use district chilled water and without primary cooling equipment on-site, the 

only change was in fan efficiency. 

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RESULTS 

The results were obtained using historical weather data for the year 2003. These historical weather data 

files in EnergyPlus format were developed by Drury Crawley of the US. Department of Energy using 

data from the National Solar Resource Database (NSRDB) from NREL. 

The composite Power Savings Intensity profile for 2003 for a 25% lighting improvement is shown in the 

figure below. The figure represents all hours of the year, organized in two dimensions. Hours of the day 

proceed from left to right, starting at midnight. Because lighting is used during the day time, the highest 

power savings intensities appear in the middle as hours proceed left to right. Days of the year proceed 

from top to bottom, starting with January 1. Thus, the upper left corner represents midnight on January 

1, the upper right corner represents 11:59 on January 1, the lower left corner represents midnight on 

December 31, and the lower right corner represents 11:59 on December 31. 

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The composite Power Savings Intensity profile for 2003 for a 25% air conditioning improvement is 

shown in the figure below. 

Appendix H - Pages 52-82
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Separate computer files accompany this report with the raw data for each hour of the year, for 2003. 

Note that the time series data are in Standard Time for the entire year, but occupancy and operational

patterns included the effects of Daylight Savings Time. 

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DISCUSSION 

CBECS is a national survey and was not (necessarily) designed to represent a relatively small geographic 

area such as the Metropolitan Washington area. NREL selected the twenty government buildings in the 

Metropolitan Washington area to model the problem at hand as well as possible. For purposes of the 

SIP analysis, these estimates provide important information to help assess the hourly impact of energy 

savings. However, the set of twenty buildings should not be viewed as a comprehensive nor robust 

representation of government buildings in the Metropolitan Washington area. 

The underlying simulations are actually run with 15-minute time steps and the hourly results represent an 

average. It would be possible to provide 15-minute power savings profiles in addition to hourly profiles. 

Because DC offices appear to make considerable use of district chilled water, a separate study of the 

central plants that provide district chilled water may be needed to properly understand the implications of 

cooling equipment upgrades. This is probably beyond the scope of modeling that is appropriate for 

EnergyPlus. 

REFERENCES 

Commercial Buildings Energy Consumption Survey (CBECS), Energy Information Administration, U.S. 

Department of Energy. Public Use Microdata 2003. http://www.eia.doe.gov/emeu/cbecs/

EnergyPlus Energy Simulation Software, Building Technologies Program, Office of Energy and Renewable 

Energy, U.S. Department of Energy. http://www.eere.energy.gov/buildings/energyplus/

National Solar Resource Database (NSRDB), National Renewable Energy Laboratory. 2003 

http://rredc.nrel.gov/solar/old_data/nsrdb/

Appendix H - Pages 52-82