DOE/GO-102006-2354 

August 2006 

Final Report on the Clean Energy/ 

Air Quality Integration Initiative Pilot 

Project of the U.S. Department of 

Energy’s Mid-Atlantic Regional Office 

D. Jacobson, George Washington University Law School 

P. O’Connor, Global Environment & Technology Foundation 

C. High, Resource Systems Group 

J. Brown, National Renewable Energy Laboratory
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any 

warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights. Reference herein to any specific commercial product, process, or service by 

trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States government or any agency thereof. The views and opinions 

of authors expressed herein do not necessarily state or reflect those of the United States government or any 

agency thereof. 

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy 

and its contractors, in paper, from: 

U.S. Department of Energy 

Office of Scientific and Technical Information 

P.O. Box 62 

Oak Ridge, TN 37831-0062 

phone: 865.576.8401 

fax: 865.576.5728 

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from: 

U.S. Department of Commerce 

National Technical Information Service 

5285 Port Royal Road 

Springfield, VA 22161 

phone: 800.553.6847 

fax: 703.605.6900 

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/ordering.htm

This publication is subject to government rights. 

Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste
Contents 

Preface ..................................................................................................................................................iii

Acronyms .................................................................................................................................................. iv

Executive Summary...................................................................................................................................... v 

1.0 Background ............................................................................................................................... 1

1.1 EPA Guidance Documents that Affect Energy Efficiency and Renewable Energy..................1

1.2 Clean Energy/Air Quality Integration Initiative........................................................................2

1.3 New Jersey Clean Energy Program...........................................................................................2

1.3.1 Residential Energy Efficiency ............................................................................................3

1.3.2 Commercial and Industrial Energy Efficiency....................................................................4

1.3.3 Renewable Energy ..............................................................................................................4

1.3.4 New Jersey NOx Cap-and-Trade Program..........................................................................4

2.0 Energy Savings and Emissions Reduction Quantification Methodology..................................6

2.1 Energy Efficiency......................................................................................................................6

2.1.1 Annual Electricity Savings .................................................................................................6

2.1.2 Summer Season Electricity Savings ...................................................................................7

2.1.3 Baselines.............................................................................................................................7

2.1.4 Avoided Emissions Rate and Tons NOx Avoided ..............................................................9

2.1.5 Degradation Factor .............................................................................................................9

2.2 Renewable Energy.....................................................................................................................9

2.2.1 Annual and Summer Season Renewable Energy Generation ...........................................10

2.2.2 Avoided Emissions Rate and Tons NOx Avoided ............................................................11

2.2.3 Degradation Factor ...........................................................................................................11

3.0 Analysis and Results ............................................................................................................... 12

3.1 Energy Efficiency....................................................................................................................12

3.2 Renewable Energy...................................................................................................................12

3.3 Summary Analysis ..................................................................................................................12

3.4 Preliminary Projection of NOx Emission Reductions from 2007 through 2012 .....................13

4.0 Policy Issues............................................................................................................................ 18

4.1 Interface between State Implementation Planning Process and State

Cap-and-Trade Regulations.....................................................................................................18

4.2 Claims for New Jersey Incentive Allowances.........................................................................19

4.3 Developing SIP Control Measures that Meet EPA Requirements..........................................20

4.3.1 Surplus Requirement.........................................................................................................20

4.3.2 Quantifiable Requirement.................................................................................................21

4.3.3 Enforceabity Requirement ................................................................................................21

4.3.4 Permanence Requirement .................................................................................................23

4.4 The Purpose and Uses of Allowances.....................................................................................23

5.0 Analytical Issues...................................................................................................................... 25

5.1 Energy Efficiency....................................................................................................................25

5.2 Degradation Factor..................................................................................................................27

5.3 Renewable Energy...................................................................................................................27

6.0 Lessons Learned...................................................................................................................... 29

 i
Appendixes 

Appendix 1: Methodology and Calculations for Avoided NOx Emissions 

Appendix 2: Photovoltaic Systems Installed through CORE Program 

Appendix 3: New Jersey Clean Energy Protocols 

Appendix 4: Comparison of Alternative Methodologies to Calculate Avoided NOx Emissions 

 ii
Preface 

This final report and its underlying analysis were developed through a collaboration between 

Debra Jacobson, professorial lecturer in law, George Washington University Law School and 

president, DJ Consulting LLC; Peter O’Connor, project manager, Global Environment & 

Technology Foundation; Dr. Colin High, vice president, Resource Systems Group; and John 

Brown, project leader for State and Local Initiatives, National Renewable Energy Laboratory. 

Dr. High and Messrs. O’Connor and Brown were responsible for the technical analysis, and Ms. 

Jacobson took the lead in the policy and legal analysis. For purposes of this report, these four 

individuals are referred to as the “project team.” This work was conducted with the financial 

support of the U.S. Department of Energy (DOE). This report has been revised since hard copies 

were distributed. 

The project team wishes to thank all those who reviewed and commented on the report, including 

Ellen Lutz and James M. Ferguson of DOE’s Mid-Atlantic Regional Office, Jerry Kotas of 

DOE’s Central Regional Office, the Climate Protection Partnership Division of the U.S. 

Environmental Protection Agency’s (EPA) Office of Air and Radiation, EPA Regions II and III, 

Mike Winka, director of the Office of Clean Energy of the New Jersey Board of Public Utilities, 

Sandy Krietzman, Christine Schell, Melissa Evanago, and Tom McNevin of the New Jersey 

Department of Environmental Protection, Alden Hathaway of Environmental Resources Trust, 

Joe Romm of Global Environment & Technology Foundation, Arnold W. Reitze, Jr., chairman 

of the Environmental Law Department at the George Washington University Law School and 

J.B. and Maurice C. Shapiro, professors of Environmental Law, Jonathan Miles, professor at the 

Integrated Science and Technology Department at James Madison University, and Mike 

Ambrosio of Ambrosio Associates. 

 iii
Acronyms 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CEP Clean Energy Program 

CFL compact fluorescent light 

C&I commercial and industrial 

CHP combined heat and power 

CO2 carbon dioxide 

CORE New Jersey Customer On-Site Renewable Energy Program 

DOE U.S. Department of Energy 

EE Energy Efficiency 

EPA U.S. Environmental Protection Agency 

kWh kilowatt-hour 

HVAC heating, ventilation, and air conditioning 

MARO U.S. Department of Energy Mid-Atlantic Regional Office 

MWh megawatt-hour 

NOx nitrogen oxides 

NJ BPU New Jersey Board of Public Utilities 

NJ CEP New Jersey Clean Energy Program 

NJ DEP New Jersey Department of Environmental Protection 

NREL National Renewable Energy Laboratory 

OTC Ozone Transport Commission 

PV photovoltaic 

RE renewable energy 

REC Renewable Energy Credit 

RGGI Regional Greenhouse Gas Initiative 

SIP State Implementation Plan 

SREC Solar Renewable Energy Certificate 

STAC State Technologies Advancement Collaborative 

 iv
Executive Summary 

DOE, in cooperation with EPA, in Fiscal Year 2005 initiated the Clean Energy/Air Quality 

Integration Initiative to facilitate state efforts to improve air quality and increase the use of 

renewable energy (RE) and energy efficiency (EE) technologies. The initiative also seeks to 

facilitate the development of new state policies to further these objectives. This report 

summarizes the results of one of the four pilot projects supported by the initiative in FY 2005—

the Mid-Atlantic Regional Office (MARO) pilot project. 

The MARO pilot project represents the first effort in the country to seek to obtain credit under a 

Clean Air Act (CAA) State Implementation Plan (SIP) for nitrogen oxide (NOx) emission 

reductions. This project came about because of state-funded incentive programs and projects for 

RE and EE.1

 Specifically, the pilot project focuses on the New Jersey (NJ) SIP and efforts to 

facilitate attainment of the new, 8-hour ozone standard under CAA by implementing selected 

categories of RE and EE programs and projects funded by the New Jersey Clean Energy 

Program (NJ CEP) of the New Jersey Board of Public Utilities (NJ BPU).2

 The project is 

significant because of the broad scope of the RE and EE programs and projects considered, 

including: (1) EE projects in new construction and retrofits of commercial and industrial (C&I) 

and residential buildings and schools (36,000 projects); (2) Energy Star® air-conditioning 

(50,000 units) and lighting (3.5 million units); (3) high-efficiency central air-conditioning 

(50,000 units) and ground source heat pumps (1,000 units); and (4) solar photovoltaic projects 

(344 systems that total 2.5 MW). 

During the pilot project, the project team refined and expanded an analytical framework 

developed by NJ BPU and the New Jersey Department of Environmental Protection (NJ DEP) 

and conducted its own extensive analysis. The team’s analysis, which employed conservative 

assumptions, indicates that a subset of RE and EE measures implemented under the NJ CEP in 

2002, 2003, and 2004 would result in the reduction of at least 240 tons of NOx emissions during 

the summer season of 2005 alone, based on summer electricity savings and RE generation of 

approximately 320,000 megawatt-hours (MWh).3

 Based on the expected continuation and 

growth of the NJ CEP, NOx emission reductions that result from that program and from private 

investments will likely exceed the current incentive allowance cap of 410 tons annually by 2007. 

Preliminary estimates of potential NOx reductions during the summer ozone season of 2012 are 

480−950 tons, depending on the specific assumptions that are used for program growth, duration 

1

 In May 2005, EPA approved the first-ever SIP credit for an RE measure in a SIP. This approval involved a wind 

purchase included in a revised SIP that was developed by the State of Maryland to meet the 1-hour ozone standard, 

70 Fed. Reg. 24987 (May 12, 2005). Although this wind purchase was precedent setting, it involved only RE and 

did not include EE measures. It also did not involve state programs to provide financial incentives, such as rebates, 

to spur RE and EE use. 

2 EPA is expected to require states to submit revised SIPs to meet the 8-hour ozone standard by June 2007, and New 

Jersey plans to identify its planned control measures by 2006. 3

 This number includes savings from measures actually implemented through 2004. Other measures are 

“committed” to or under development. 

 

 v
of measures, and changes in the electricity grid.4

 This analytical foundation also can be applied 

to estimate annual NOx emission reductions for the period 2008 to 2013 for the NJ SIP for fine 

particulate matter. It also can be applied to determine carbon dioxide (CO2) emission reductions 

for New Jersey’s participation in the Regional Greenhouse Gas Initiative (RGGI). 

The project expanded methodologies to evaluate: (1) the amount of electricity savings; (2) the 

summer component of the electricity savings; and (3) the NOx emission reductions.5

 Also, 

approaches were developed to integrate various elements of the federal and state regulatory 

framework to ensure that RE and EE programs result in real emission reductions.6

 Moreover, the 

analytical and policy framework developed during the pilot project provides many valuable 

lessons to other states, including Pennsylvania and New York—two states with direct 

involvement in MARO’s initial pilot project.7

 During the course of the project, the project team 

resolved challenges in estimating reductions in emissions of NOx, a pollutant that is subject to 

emissions trading (cap and trade) regulations in New Jersey and most eastern states. Thus, the 

team needed to integrate elements of: (1) EPA’s requirements for crediting NOx emission 

reductions in SIPs; and (2) the implementation of New Jersey regulations that govern NOx

emissions trading, including provisions to establish an RE and EE set-aside of NOx allowances 

for the summer ozone season. 

The work accomplished during the pilot project has already proven useful to other states in 

developing new NOx emissions trading programs that are required under EPA’s Clean Air 

Interstate Rule.8

 Such work should help states achieve the full air quality benefits of their RE 

and EE programs.9 

This report contains a detailed list of “lessons learned” that other states can replicate. The pilot 

project has facilitated the resolution of numerous analytical and policy issues, and provides 

direction for other states to follow. 

4

 See pp. 13−17 (infra). 5

 The basic methodology for determining energy savings, the NJ Clean Energy Protocols, is attached as Appendix 3. 

Modifications and expansions are detailed on pp. 27−30 of this report. 6

 See pp. 20−27 (infra). 7

 Originally, the Mid-Atlantic pilot project was expected to include New Jersey, New York, and Pennsylvania and 

would parallel a separate State Technologies Advancement Collaborative (STAC) project that focused on regulatory 

barriers faced by distributed generation in the mid-Atlantic States. Following delays by outside parties in the 

issuance of the contract for the STAC project and the Request for Proposals associated with this project, MARO 

decided to revise the scope of the pilot project. 

8

 70 Fed. Reg. 25162 et seq. (May 12, 2005). 

9

 New Jersey has regulatory advantages that facilitated the integration of clean energy and air quality goals that 

some other states may not possess. For example, New Jersey is one of only seven states that have adopted an EERE 

set-aside in their NOx emission trading regulations, and New Jersey’s regulations contain a stipulated allocation rate 

that aided the conversion of energy savings into emission reductions. Lessons from the New Jersey experience that 

are relevant to other states are addressed in substantial detail in the final section of this report, titled “Lessons 

Learned.” 

 

 vi
 1

1.0 Background 

1.1 EPA Guidance Documents that Affect Energy Efficiency and 

Renewable Energy 

In 2004, the U.S. Environmental Protection Agency (EPA) issued two important guidance 

documents to encourage innovative air pollution control measures, including renewable energy 

(RE) and energy efficiency (EE). EPA issued the first guidance document titled, Guidance on 

State Implementation Plan (SIP) Credits for Emission Reduction Measures from Electric-sector 

Energy Efficiency and Renewable Energy Measures in August 2004.10 The purpose of this 

guidance is to “promote the testing of promising new pollution reduction strategies, such as 

energy efficiency and renewable energy, within the air quality planning process.”11

 

10 U.S. Environmental Protection Agency, Guidance on State Implementation Plan (SIP) Credits for Emission 

Reduction Measures from Electric-sector Energy Efficiency and Renewable Energy Measures, August 2004 

(hereinafter cited as EPA SIP Guidance). This document, the September 2004 voluntary measures guidance, and 

other documents are available at www.epa.gov/cleanenergy/stateandlocal/guidance.htm. 11 EPA SIP Guidance, p. 1. 

Highlights: Findings, Issues, and Lessons Learned 

• The U.S. Department of Energy (DOE) Mid-Atlantic Regional Office 

(MARO) pilot project represents the first effort in the country to seek 

to obtain credit under a Clean Air Act State Implementation Plan (CAA 

SIP) for nitrogen oxide (NOx) emission reductions that result from 

state-funded incentive programs and projects for renewable energy 

(RE) energy efficiency (EE). 

• Preliminary estimates of NOx reductions during the 2009 summer 

ozone season in New Jersey that result from the New Jersey Clean 

Energy Program (NJ CEP) are 370−560 tons. By 2012, NOx

reductions are expected to be 480−950 tons. 

• A state with a NOx emissions trading program will not be able to claim 

NOx emission reduction credit in its SIP to meet the 8-hour ozone 

standard unless several criteria are met. In most cases, two of the key 

criteria include: (1) adopting regulations under the Clean Air Interstate 

Rule (CAIR) that allocate a percentage of NOx allowances to support 

RE/EE measures; and (2) retiring allowances to ensure that the 

emission reductions are surplus. 

• Most of the procedures developed by New Jersey and the pilot project 

to convert RE generation and EE savings into emission reduction 

estimates will be replicable in other states. These procedures allow 

NOx emission reductions from RE/EE measures to be estimated with a 

relatively small investment.
EPA issued the second guidance document titled, Incorporating Voluntary and Emerging 

Measures in a State Implementation Plan (SIP), in September 2004. The purpose of this 

guidance is to facilitate efforts by state and local governments to include nontraditional control 

measures in their SIPs. 

The major purpose of the two guidance documents is to assist areas of the country that are facing 

challenges in meeting air quality standards. EPA has designated 474 counties or portions of 

counties as nonattainment areas for the 8-hour ozone standard and 225 counties or portions of 

counties as nonattainment areas for fine particulate matter.12

1.2 Clean Energy/Air Quality Integration Initiative 

DOE established the Clean Energy/Air Quality Integration Initiative in late 2004 after the two 

EPA guidance documents were issued. This DOE initiative was undertaken in cooperation with 

EPA, several energy and environmental organizations,13 and the National Renewable Energy 

Laboratory (NREL). Its purpose was “to demonstrate how state energy and environmental 

officials can work together on energy efficiency and renewable energy technologies and policies 

that improve air quality while they address energy goals.”14 DOE designated four of its Regional 

Offices to develop pilot projects to pursue clean energy/air quality integration in the first phase 

of this initiative. This report summarizes the results of the pilot project in MARO. 

The MARO pilot project represents the first effort in the country to seek to obtain credit under a 

Clean Air Act (CAA) SIP for nitrogen oxide (NOx) emission reductions that result from statefunded incentive programs and projects for both RE and EE. Specifically, the pilot project 

focuses on the New Jersey (NJ) SIP and efforts to facilitate attainment of the new, 8-hour ozone 

standard under CAA through SIP credit for selected categories of RE and EE programs and 

projects funded by the Clean Energy Program (CEP) of the NJ Board of Public Utilities (BPU). 

The project is significant because of the broad scope of the RE and EE programs and projects 

considered, including: (1) EE projects in new construction and retrofits of commercial and 

industrial (C&I) and residential buildings and schools (36,000 projects); (2) Energy Star® airconditioning (50,000 units) and lighting (3.5 million units); (3) high-efficiency central airconditioning (50,000 units) and ground-source heat pumps (1,000 units); and (4) solar 

photovoltaic (PV) projects (344 systems that total 2.5 MW).15 

1.3 New Jersey Clean Energy Program 

The current NJ CEP began in 2001. It is funded by a “societal benefits charge” of more than 

$100 million annually,16 and includes a wide range of RE and EE programs and projects across 

12 U.S. Environmental Protection Agency, Green Book for Nonattainment Areas. See 

http://www.epa.gov/oar/oaqps/greenbk/. Figures are from the October 5, 2005 update. 13 These organizations include the National Association of State Energy Officials, the Environmental Council of the 

States, and the Global Environment & Technology Foundation. 

14 DOE fact sheet, “Integration Pilots: Improving Air Quality through Energy Efficiency and Renewable Energy 

Technologies,” 2004. 

15 See infra. 16 Actual program expenditures were $100 million in 2002, $98 million in 2003, and $108 million in 2004. Funding 

increased to $140 million for 2004 and 2005 and is expected to increase to $235 million by 2008. 

 

 2
the state. New Jersey is one of nearly 20 states that fund energy incentive programs under a 

systems benefit charge. The New Jersey “societal benefits charge” is about 3 mills/kWh, of 

which NJ CEP receives about 1 mill/kWh.17 Growth in electricity savings18 has increased 70% 

from 2002 to 2003 and 14% from 2003 to 2004, to outpace growth in expenditures. Because 

these RE and EE improvements are long lasting, New Jersey will see a cumulative benefit, as 

measures implemented in previous years continue to save or generate energy. 

The project team focused on programs and projects completed under the NJ CEP in 2002, 2003, 

and 2004 in the following specific categories: 

1.3.1 Residential Energy Efficiency 

• Comfort Partners Low-Income Customers Program – This program improves EE in the 

homes of low-income customers and includes a pilot program, implemented in 2004, for 

weatherizing the homes of senior citizens. Projects can include a wide range of EE 

measures such as lighting, appliances, insulation, and duct sealing and repair. Program 

expenditures were $14.3 million in 2004. From 2002 through 2004, the program made 

improvements in more than 19,000 homes. 

• NJ Energy Star Homes – This program works with residential builders to ensure that new 

homes are built to New Jersey Energy Star® standards, which exceed the standards of the 

national Energy Star program. Homes must use at least 30% less energy than homes built 

to the model national energy code and must be located in a “smart growth” area. Nearly 

13,000 such homes were built between 2002 and 2004; in 2004, qualified homes 

represented 16% of all new homes in New Jersey. Program expenditures were $21.7 

million in 2004. 

• Cool Advantage and Warm Advantage – This program focuses on energy-efficient 

cooling and heating equipment. Program expenditures were $15.6 million in 2004. 

Between 2002 and 2004, the program led to the installation of more than 50,000 highefficiency central air conditioners and more than 1,000 high-efficiency heat pumps.19 

• New Jersey for Energy Star – This program promotes the use of Energy Star appliances 

and other products. The largest component of this program promotes compact 

fluorescent lights (CFLs) and other efficient residential lighting fixtures; more than 3.5 

million units have been sold (commercial lighting is a separate program). Another 

element of this program provides rebates for high-efficiency room air conditioners; 

nearly 50,000 units were sold through 2004. In addition, New Jersey added a new 

component to this program in 2004 that promotes energy-efficient clothes washers. 

Lighting represented 97.8% of the electricity savings from 2004 projects in this category. 

In 2004, program expenditures totaled $8.4 million. 

17 See Atlantic City Electric tariff, effective July 1, 2005. The benefits charge may vary from year to year, according 

to the NJ CEP funding levels set by the NJ BPU. 

18 The program also implements several measures that can reduce natural gas demand and emissions. However, 

since our analysis focuses only on electricity savings, we have avoided use of the term energy savings to clarify that 

our analysis does not include natural gas savings. 

19 The program also funds furnaces and water heaters. These programs provide natural gas savings rather than 

electricity savings and are not included in our analysis. 

 

 3
1.3.2 Commercial and Industrial Energy Efficiency 

New Jersey SmartStart Buildings – This program includes all C&I EE programs, grouped into 

three categories: (1) C&I new construction ($3.9 million in 2004), (2) C&I retrofits ($22.7 

million in 2004), and (3) new school construction and retrofits ($3.1 million in 2004). Measures 

may include lighting, motors, traffic signals, heat pumps, chillers, variable frequency drives, and 

other improvements. This program conducted more than 17,000 projects between 2002 and 

2004. 

1.3.3 Renewable Energy 

Customer On-Site Renewable Energy (CORE) is the only RE program in New Jersey that has 

achieved energy generation to date. The project team’s analysis focused on the PV component 

of this program. Though other renewable technologies such as wind and biogas have been 

installed, available data are insufficient for the project team to determine the resulting generation 

or emission reductions. Three hundred forty-four PV systems were installed under the program 

from mid-2003 through 2004, with an aggregate capacity of 2.5 MW.20 This capacity represents 

five to six acres of PV panels. 

1.3.4 New Jersey NOx Cap-and-Trade Program 

Pursuant to the 1990 Amendments to CAA, EPA issued the NOx SIP Call, which required certain 

states to issue regulations that impose limits (a cap) on NOx emissions. The regulations also 

established a NOx emissions trading program to, among other things, reduce the cost of 

implementation to electric utilities. 

In response to the NOx SIP Call, New Jersey issued NOx budget regulations21 that included 

several components, such as an incentive reserve for RE and EE.22 The New Jersey Department 

of Environmental Protection (NJ DEP) has incorporated the total emissions cap under its NOx

budget regulations into its attainment demonstration for the 1-hour ozone standard. 

The New Jersey incentive reserve set aside 410 NOx allowances that could be claimed by 

customers who saved electricity and owners and operators of RE projects. These incentive 

allowances can be traded or sold as an inducement to encourage RE and EE measures.23 A 

project owner or energy customer can also “retire” such allowances, which will reduce the total 

emissions cap and help the state attain the ozone standard. 

20 See Appendix 2 for a complete list of CORE projects during this period. 

21 NJAC 7:27-31 et seq. 

22 NJAC 7:27-31.8. 

23 Art Diem and Debra Jacobson, Options for New Jersey to Obtain and Retire Allowances in Order to Obtain SIP 

Credit for Energy Efficiency and Renewable Energy Measures, April 14, 2005, p. 4. Since its inception, the EERE 

incentive allowance pool has been undersubscribed in New Jersey, and the owners and operators of most of the 

projects subsidized through the NJ CEP have not yet applied for allowances under the Incentive Reserve. One of the 

major reasons for the limited use of the reserve appears to be that most small individual projects, such as small PV 

arrays and small EE projects, are not large enough to meet eligibility requirements for allowances on their own, and 

only one company has effectively pursued aggregation of projects to overcome this hurdle. Most projects involve 

emission reductions far below one ton, and allowances are only granted in one-ton increments. Discussion with 

Tom McNevin, NJ DEP, May 2005. 

 

 4
Under EPA’s Clean Air Interstate Rule (CAIR), issued in the spring of 2005, NJ DEP is required 

to issue new NOx cap-and-trade regulations to replace its NOx SIP Call regulations for electric 

generating units.24 NJ DEP has not yet developed these new regulations, which will govern NOx

emission trading in New Jersey for the 2009 ozone season and thereafter. Therefore, the state 

has not yet announced its plans for allowance allocation under the new rules, including any 

decision on the continuation of its RE/EE incentive allowance. 

24 70 Fed. Reg. 25290 (May 12, 2005). 

 

 5
2.0 Energy Savings and Emissions Reduction 

Quantification Methodology 

2.1 Energy Efficiency 

The project team refined and expanded methodologies that were developed by the NJ CEP to 

estimate NOx emission reductions during the summer ozone season that result from measures 

implemented under the NJ CEP. This methodology: 

1. Calculates the annual electricity savings resulting from the energy efficiency measures. 

2. Estimates the electricity savings during the summer ozone season. 

3. Calculates the NOx emission reductions during the summer ozone season. 

4. Estimates the current electricity savings and ozone season NOx emission reductions of 

previously implemented projects. 

The project team relied on calculations that were conducted by the NJ CEP for step 1; step 2 

involved applying a summer season “allocation factor” to the estimate of annual electricity 

savings, and step 3 required that a “conversion factor” be applied to convert summer electricity 

savings into summer emission reductions. During this process, we identified step 4, and we 

employed a “degradation factor” to determine this quantity. Details of the analysis follow. 

2.1.1 Annual Electricity Savings 

The first step—the calculation of annual electricity savings—was based on the official protocols 

developed by the NJ CEP.25 New Jersey has directed extensive effort into data tracking and 

estimating the electricity savings of various installed EE measures. For each type of technology, 

the protocols spell out the methodology used to estimate annual electricity savings. This 

approach is used in the program’s annual reports and in periodic cost-benefit analyses. 

Thus, under the New Jersey energy saving protocols, electricity savings are not measured 

directly for each piece of equipment, but are calculated based on the characteristics of the 

installed technology. This approach greatly simplifies the data tracking and measurement 

procedure. The protocols compare a piece of equipment, such as a CFL, an Energy Star air 

conditioner, or a highly efficient industrial motor, to the average new model of that type and size. 

The protocols also take into account the typical hours of operation of that type of equipment at a 

specified facility (school, C&I, or residential). The savings stipulated by the protocols are based 

on almost 10 years of direct measurement. 

For example, a CFL installed at a residential location is assumed to save 42 watts compared to a 

standard new light bulb,26 and to provide these savings for 2.5 hours/day. Thus, each CFL is 

25 New Jersey Clean Energy Program, Protocols to Measure Resource Savings, September 2004. See 

Appendix 3 for a more detailed explanation. 26 This figure is based on a minimum electricity saving of 66% for Energy Star CFLs (as specified in the Energy Star 

labeling requirement), combined with NJ CEP assumptions about the typical wattage of incandescent bulbs 

replaced. The protocols indicate that this figure can be adjusted as the NJ CEP obtains better information. 

 

 6
assumed to save 38 kWh/year at the customer side, and 42 kWh of generation.27 For the Energy 

Star homes program, home energy rating software is used to evaluate energy savings for each 

building constructed.28

The value of the New Jersey protocols is that they eliminate the need for tracking electricity 

savings for each piece of equipment and greatly simplify the energy savings estimation process. 

Such an approach would be unnecessarily burdensome in most cases. However, individual 

tracking may be a valid approach where there are only a few instances of a technology, or where 

the energy generation or savings of individual units are particularly large. 

2.1.2 Summer Season Electricity Savings 

The second step—estimating summer ozone season electricity savings—is derived from 

“summer season allocation factors” specified by the protocols. In its annual reports, the NJ CEP 

uses the summer season allocation factors to determine the cost savings associated with various 

EE measures. This allocation is required because electricity tends to cost more in the summer. 

The summer season, as defined by the allocation factors, runs from May 1 through September 

30, matching the ozone season. The project team used the allocation factors to identify NOx

emission reductions during the summer ozone season when such factors were available. 

In some cases the New Jersey protocols do not provide allocation factors, and the project team 

used other resources to identify the fraction of electricity savings that occurs during the summer 

season. In particular, the team employed allocation factors provided in the Emission Reduction 

Workbook developed for the Ozone Transport Commission (OTC).29 Factors listed in the OTC 

Emission Reduction Workbook are well established and accepted by industry practitioners. 

2.1.3 Baselines 

A baseline must be determined to establish electricity savings from EE programs. New Jersey 

officials, as well as our own project team, measured the electricity savings of the New Jersey EE 

programs against a “business-as-usual” baseline case that assumes the program was not 

implemented. For example, these baseline methodologies quantify the electricity savings benefit 

of a new high-efficiency air-conditioning system by comparing this system to the average new 

system of that size that conforms to current applicable codes and standards. 

The New Jersey and pilot team methodologies are consistent with the standard methods for 

computing baselines for electricity savings. Such methods do not compare the new system to the 

previous system or to the average system, but rather to current standard technology. If, for 

example, a building consumes 4000 MWh/year less than before the energy-efficient equipment 

was installed, this amount is not used as the total savings. 

Improved technology and standards dictate that some degree of energy savings improvement 

must be used to determine the baseline. Models used by the Energy Information Administration 

27 Assumed transmission and distribution losses are 11%, according to the protocols. 28 This approach will be required in other states, as it is required by the Energy Star homes program. 

29 The OTC Emission Reduction Workbook 2.1, Synapse Energy Economics, 2001. The allocation factors used for 

the NJ CEP’s cost-benefit analysis could not be directly employed in certain cases. However, they did confirm the 

accuracy of other resources, such as the OTC Emission Reduction Workbook. 

 

 7
and other energy experts assume that old equipment will wear out and be replaced by newer and 

more efficient equipment. Under these models, the installation of a typical new system is 

considered business as usual and not surplus energy savings, even if the new system results in 

some savings from the previous levels of electricity consumption. Some degree of improvement 

is already incorporated into projections used to develop SIPs. 

A second component of the baseline determination involves identifying the impact of the 

electricity savings. If New Jersey did not implement its EE programs, additional electricity 

generation would be necessary to meet the increased load. Therefore, the type of technology that 

would have been used to meet this additional load needs to be projected. As recommended by 

EPA, the pilot team looked at the direct and immediate impact on fossil fuel plants.30

Modified Excerpts from 

New Jersey Clean Energy Program Protocols to Measure Resource Savings

Basic Methodology 

Electric Demand Savings = ∆kW = kWbaseline - kWenergy efficient measure

Electric Energy Savings = ∆kW × EFLH 

 EFLH = Equivalent Full Load Hours of operation for the installed measure. 

Electric Peak Coincident Demand Savings = ∆kW × Coincidence Factor 

Electric Loss Factor: 

The electric loss factor applied to savings at the customer meter is 1.11 for both energy and demand. 

The electric system loss factor was developed to be applicable to statewide programs. Therefore, New 

Jersey used average system losses at the margin, based on PJM grid data (referes to the PJM 

Interconnection region, which includes New Jersey, Pennsylvania, Maryland, and neighboring states). 

This approach reflects a mix of losses that occur relative to delivery at various voltage levels. The 1.11 

factor used for both energy and capacity is a weighted average loss factor and was adopted by 

consensus. 

Example: Central Air Conditioner 

Energy Impact (kWh) = CAPY/1000 × (1/SEERb – (1/SEERq × (1-ESF)) × EFLH 

 CAPY = Cooling capacity (output) of system 

SEERb = The Seasonal Energy Efficiency Ratio of the Baseline Unit (set at 10). 

SEERq = The Seasonal Energy Efficiency Ratio of the qualifying unit installed. These data are 

obtained from the application form based on the model number. 

ESF = The Energy Sizing Factor or the assumed saving that results from proper sizing and installation 

(set at 17%). 

EFLH = Equivalent Full Load Hours of operation for the installed measure (set at 600 hours for 

cooling). 

Note: This text is based on the September 2004 New Jersey Protocol document and has been modified 

for illustrative purposes. A complete detailed list of the New Jersey energy-saving protocols may be 

found in Appendix 3 and at www.njcleanenergy.com/media/Protocols.pdf. 

30 A large and lasting EE program might defer the need for new power plants. However, this consideration is 

beyond the scope of the analysis required by EPA. 

 

 8
2.1.4 Avoided Emissions Rate and Tons NOx Avoided 

The third step in the energy savings and emission reduction quantification methodology is to 

determine the NOx emission reductions that are achieved for a given electricity saving. Under 

the New Jersey NOx emission trading regulations, this conversion factor is specified as 1.5 lb for 

each megawatt-hour of energy savings (1.5 lb/MWh). Under the regulations, this conversion 

factor is fixed even if the actual emission reductions are greater than this amount.31 Therefore, 

the actual avoided emissions need to meet or exceed the stipulated emission reductions. 

2.1.5 Degradation Factor 

When calculating the ongoing electricity savings from projects installed in previous years, the 

team decided that employing a degradation factor would be conservative and useful. The 

degradation factor accounts for some changes that may occur to an EE improvement: (1) 

equipment deteriorates over time, especially if maintenance is inadequate; (2) some equipment 

may be removed before the end of its useful life; and (3) a degradation factor can offer a 

substitute for estimating the lifetime effectiveness for specific types of efficiency measures. 

Our calculations include a degradation factor of 15%/year for EE measures. For example, 

energy savings from 2004 measures are credited fully in 2005, but at 85% of their previous value 

in each successive year. To be conservative, a high figure is used as a placeholder until more 

precise calculations are conducted. Though using a single overall factor to all measures 

simplifies the calculation process, using different factors for different types of measures may be 

more accurate.32

2.2 Renewable Energy 

For RE generation,33 the methodology for calculating the annual and summer ozone season 

avoided NOx emissions consists of three steps: 

1. Calculate the annual and summer ozone season electricity generation of the renewable 

source. 

2. Estimate the annual and summer ozone season NOx emission reductions. 

3. Estimate the electricity generation and NOx reductions of previously implemented 

projects. 

Thus, the emission reductions that result from RE are easier to quantify than those that result 

from EE. The baseline is simply the case in which the project was not implemented. The 

business as usual scenario assumes a negligible amount of RE and does not include projects that 

are implemented as a result of the NJ CEP. Therefore, all RE generation that is implemented 

through the NJ CEP is surplus and not included in the baseline. 

31 N.J.A.C. 7:27-31.7(e)3.i. 

32 Examining varying degradation factors is beyond the scope of this initial pilot project. 

33 The New Jersey regulations limit claims for incentive allowances to equipment that commenced operation in 1992 

and thereafter and that generates electricity through one of the following “environmentally beneficial techniques”: 

(1) Generation through the burning of landfill gas or digester gas; (2) generation by a fuel cell; (3) generation using 

solar energy or wind power; or (4) generation through another environmentally beneficial technique approved by 

DEP. N.J.A.C. 7:27-31.8(c) 

 

 9
 10

2.2.1 Annual and Summer Season Renewable Energy Generation 

The first step is to calculate the annual and summer season electricity generation of the 

renewable source. The project team employed standard methodologies to estimate annual and 

summer season energy generation from PV projects based on the rated installed capacity. 

The annual and summer ozone season electricity generation for the 344 completed solar projects 

supported by the CORE program was calculated with the PVWATTS model (see Figure 1),34

which was developed by NREL. NREL developed this model to provide a calculator for 

estimating the monthly generation of specified PV units on a per-kilowatt basis of installed 

capacity. With this tool, annual and monthly generation are provided simultaneously, so 

identification of annual and summer season energy production can be considered in a single step. 

The calculation is based on measurements of incident solar radiation recorded at observation 

stations in all 50 states. 

PV Watts 

PVWATTS calculates electrical energy produced by a grid-connected photovoltaic (PV) system. Researchers at the 

National Renewable Energy Laboratory developed PVWATTS to permit non-experts to quickly obtain performance 

estimates for grid-connected PV systems within the United States and its territories. The grid cells indicate solar 

resource in kWh/m2

/day. 

Figure 1. PVWATTS model 

 

34 PVWATTS program version 2, NREL. http://mapserve1.nrel.gov/website/PVWATTSLITE/viewer.htm.
Because New Jersey is relatively small on a geographic scale (compared to Florida or 

California), the project team used a representative factor based on examination of three areas 

across the state to apply the PVWATTS model to New Jersey. The PV systems were grouped 

into 21 categories according to orientation and inclination. For example, the largest group (by 

capacity) consisted of systems that are oriented generally southward (160°−220°) at a tilt of 

30°−39°. This group was modeled as orientation of 180° (south) and an inclination of 35°. The 

second-largest group consisted of systems with an inclination less than 10°; these were modeled 

as flat-roof systems (which, in fact, most were).35

We calculated an average annual generation rate of 1,191 kWh/kW of installed capacity for the 

New Jersey solar PV projects.36 The generation rate for the five-month ozone season is 597 

kWh/kW of installed capacity. The total annual generation from the solar electric capacity of 

2,521 kW is 3,003 MWh and the ozone season generation is 1,505 MWh. 

2.2.2 Avoided Emissions Rate and Tons NOx Avoided 

A solar electric system produces no direct emissions. Moreover, solar electric displaces 

emissions from fossil fuel generating sources such as natural gas or coal-fired generation. As 

with EE measures, the New Jersey NOx regulations stipulate the allocation of incentive reserve 

allowances at a rate of 1.5 lb/MWh. This rate is applied to all nonemitting RE systems. 

2.2.3 Degradation Factor 

Similar to EE, when calculating the ongoing renewable generation from previous years, the team 

decided that a degradation factor should be applied. The actual deterioration of solar panels can 

be quite small: well-maintained systems may experience output declines of less than 1%/year. 

However, systems that are not well maintained may be shaded, soiled, or have inverter failures or 

other problems. For this analysis, we used a degradation factor of 5%/year for solar PV systems. 

As with EE, this is a conservative factor that accounts for deterioration and system failures, and 

obviates the need for a fixed system lifetime. 

A degradation factor of 5%/year implies that, by the end of 2012, the systems installed in 2004 

will be, on average, at two-thirds of their original capacity. Some systems will have failed or 

been removed, but many will still be operating at almost their original capacity. NJ CEP 

conducts quality assurance tests through a random routine inspection of 10% of the larger (10 

kW or greater) systems by comparing the estimated energy production with the inverter display. 

This sampling is conducted as part of the state’s Solar Renewable Energy Certificate (SREC) 

trading system. These data could be used in the future to more precisely estimate the actual 

degradation factor. These refined estimates might provide the basis for a lower degradation 

factor. 

35 For variations and the uncertainties associated with the weather data and the model used to model the PV 

performance, future months and years may be encountered where the actual PV performance is less than or greater 

than the values shown in the table. The variations may be as much as 40% for individual months and up to 20% for 

individual years. Compared to long-term performance over many years, the values in the table are accurate to 

within 10%−12%. The model also assumes a standard combined default factor of 0.77 for inefficiencies in 

conversions from DC power to AC power. 36 The category with the highest annual generation included south-facing systems of 30°−39° inclination; these were 

deemed to have an annual generation of 1,263 kWh/DC kW. 

 

 11
3.0 Analysis and Results 

3.1 Energy Efficiency 

Our findings indicate that the efficiency measures installed pursuant to the NJ CEP in calendar 

years 2002, 2003, and 2004 should result in electricity savings for the summer of 2005 of 

approximately 320,000 MWh. Using the conversion factor specified in the New Jersey 

emissions trading regulations of 1.5 lb/MWh, this electricity saving translates into emission 

reductions of 240 tons of NOx. 

3.2 Renewable Energy 

Our analysis indicates that the solar electric projects installed under the CORE program in 2003 

and 2004 generated 1,505 MWh during the summer ozone season of 2005. Based on the 

stipulated conversion factor in the New Jersey regulations, this generation accounted for 

emission reductions of approximately 1.13 tons of NOx during the 2005 summer ozone season.37 

3.3 Summary Analysis 

Table 1 illustrates the electricity savings, renewable generation, and avoided NOx emissions for 

the 2005 ozone season. It includes the savings from measures implemented in 2002 through 

2004 with a 15% annual degradation factor for EE and a 5% annual degradation factor for RE. 

Table 1. Summary of 2005 Energy Savings, RE Generation, and Avoided NOx Emissions

 2005 Summer MWh 2005 Summer NOx (tons) 

Residential HVAC 

Central Air-Conditioning 38,457 28.84 

Heat Pumps 407 0.31 

Residential New Construction 4,715 3.54 

Room Air-Conditioning 2,594 1.95 

Lighting 63,464 47.60 

Clothes Washers 276 0.21 

Comfort Partners 6,733 5.05 

C&I New Construction 62,794 47.10 

C&I Retrofit 136,034 102.03 

New School Construction and Retrofit 6,019 4.51 

Combined Heat & Power N/A N/A 

Total Energy Efficiency 321,493 241 

Renewable Energy 

Solar Electric 1,505 1.13 

Wind TBD* TBD* 

Fuel Cells TBD* TBD* 

Landfill Gas TBD* TBD* 

Total Renewable Energy 1,505 1.13 

TOTAL 322,998 242 

* See discussion of analytical issues and data gaps on pages 27−31, infra. 

37 If a power plant dispatch study were conducted, the actual displacement of NOx emissions might be calculated at 

a higher rate. 

 

 12
Although we have stated avoided emissions in tons per summer ozone season, SIP submissions 

to EPA to implement the ozone standard generally present NOx emissions avoided in tons/day. 

The 242 tons is equivalent to 1.6 tons/day.38

3.4 Preliminary Projection of NOx Emission Reductions from 2007 through 2012 

If NJ DEP includes NOx emission reductions that result from the NJ CEP in its SIP for the 8-

hour ozone standard, the agency will be required to project such reductions for the summer 

ozone season for the period 2007 through 2009 to demonstrate attainment of the ozone standard 

in 2010. The project team has not conducted a detailed analysis of the projected emission 

reductions for this period. However, we have developed preliminary projections based on the 

methodology and analysis conducted to date. We used the following assumptions to estimate 

NOx emission reductions for the summer ozone seasons from 2007 through 201239: 

• The determination of the baseline for this analysis was limited only to the specific NJ 

CEP programs that were covered in our analysis of emission reductions for the 2005 

ozone season.40

• Estimates provided by the NJ CEP for future years project a growth of 20%/year for EE 

programs and 40%/year for RE programs. These projections are based on a New Jersey 

goal of a 10% increase in energy savings per dollar invested and annual increases in 

funding of approximately 10% for EE and 30% for RE.41

• The fraction of summer electricity savings remains at 46.38% of annual savings, and the 

fraction of summer PV generation remains at 50.13% of annual PV generation. 

• The actual avoided emissions rate is 1.85 lb/MWh in 2004 and is projected to be 1.65 

lb/MWh in 2005 and 2006, 1.24 lb/MWh in 2007, and 0.97 lb/MWh in 2008. This rate is 

estimated to decrease by 5%/year after 2008; the credited value will be the lesser of the 

actual rate or the stipulated rate of 1.5 lb/MWh.42 The annual degradation factor is 15% 

for EE and 5% for RE, which reflects the relatively short lifetimes of many EE measures 

such as lighting, which accounts for a significant fraction of savings; and renovation or 

remodeling may also reduce EE measures. 

To illustrate the sensitivity of our assumptions, four alternative scenarios are presented for 

comparison: 

Base Case Scenario: Table 2 summarizes avoided NOx emissions under the assumptions 

presented earlier: 

38 Total emission reductions during the summer ozone season in tons can be converted into tons/day by dividing by 

153 (the number of days in the summer season). 

39 Although EPA is expected to require data for 2007 to 2009 only, the project team has provided projected 

scenarios through 2012 for informational purposes. 

40 See pp. 2−3, supra. 41 The EE budget grows 43% over 2004 to 2008, and the RE budget grows 129% over that time. Program 

effectiveness per dollar has increased by more than 10% from 2002 to 2004. 

42 Estimated rates for 2004 to 2008 were provided by NJ DEP. These emission rates represent the generationweighted average emissions rate for all NOx budget units in New Jersey; that is, all fossil fuel units greater than 15 

MW. An analysis for 2009 may be necessary for SIP crediting, and may need to account for the impacts of CAIR. 

 

 13
Table 2. Avoided NOx Emissions 

Year 

Summer Electricity 

Savings (MWh) 

Credited NOx 

Rate (lb/MWh) 

Credited NOx 

Emissions (tons) 

2005 322,999 1.50 242 

2006 459,635 1.50 345 

2007 613,387 1.24 380 

2008 789,413 0.97 383 

2009 993,723 0.92 458 

2010 1,233,412 0.88 540 

2011 1,516,942 0.83 631 

2012 1,854,483 0.79 733 

First Alternative Scenario − Low Growth: Table 3 shows the avoided emissions with the 

program electricity savings growing only as fast as the program budget, rather than the higher 

rates assumed in the base case scenario. All other assumptions are the same as the base case. 

Table 3. Avoided Emissions with Program Electricity Savings 

Level with Program Budget

Year 

Summer Electricity 

Savings (MWh) 

Credited NOx 

Rate (lb/MWh) 

Credited NOx

Emissions (tons) 

2005 322,999 1.50 242 

2006 444,249 1.50 333 

2007 564,845 1.24 350 

2008 686,788 0.97 333 

2009 812,013 0.92 374 

2010 942,436 0.88 413 

2011 1,080,005 0.83 449 

2012 1,226,749 0.79 485 

This scenario illustrates the impact of the state’s goal for the NJ CEP of improving energy saved 

per dollar invested by 10%/year. If that goal is not met, and energy savings grow only as fast as 

the program budget, avoided emissions in 2012 are one-third less than they are in the base case. 

Second Alternative Scenario − Sustained Measures: Table 4 shows the avoided emissions 

with the degradation factors set at 5%/year for EE and 2.5%/year for RE instead of 15% and 5%, 

respectively. All other assumptions are the same as the base case. 

Table 4. Avoided Emissions with Degradation Factors at 5% per Year

Year 

Summer Electricity 

Savings (MWh) 

Credited NOx 

Rate (lb/MWh) 

Credited NOx

Emissions (tons) 

2005 350,334 1.50 263 

2006 517,790 1.50 388 

2007 714,334 1.24 443 

2008 946,181 0.97 459 

2009 1,220,841 0.92 563 

2010 1,547,405 0.88 677 

2011 1,936,894 0.83 805 

2012 2,402,698 0.79 949 

 14
This scenario shows avoided emissions in 2012 that are nearly one-third higher than those in the 

base case. The lower degradation factors are reasonable (and in fact conservative) for systems 

that are properly maintained and monitored.43 NJ CEP can ensure a higher level of avoided 

emissions in future years by demonstrating the continued performance of previously 

implemented RE/EE measures. C&I facilities that benefit from EE improvements through NJ 

CEP should be encouraged to take steps to ensure the continued performance of their new 

systems. 

Third Alternative Scenario − Clean Grid: Table 5 shows the avoided emissions with the 

actual avoided emissions rate falling by 10%/year instead of 5%/year after 2008. All other 

assumptions are the same as the base case. 

Table 5. Avoided Emissions with Actual Avoided Emissions Rate 

Falling by 10% per Year

Year 

Summer Electricity 

Savings (MWh) 

Credited NOx 

Rate (lb/MWh) 

Credited NOx

Emissions (tons) 

2005 322,999 1.50 242 

2006 459,635 1.50 345 

2007 613,387 1.24 380 

2008 789,413 0.97 383 

2009 993,723 0.87 434 

2010 1,233,412 0.79 485 

2011 1,516,942 0.71 536 

2012 1,854,483 0.64 590 

Expedited reductions in NOx emission rates from power plants would lower the amount of NOx

avoided by RE and EE measures. However, many power plants have already implemented the 

most cost-effective pollution controls available and have less room for improvement. NJ DEP 

considers that the reduction in NOx emission rates will probably slow after 2008. 

Fourth Alternative Scenario – Full Renewable Energy: Table 6 shows the impact of New 

Jersey meeting its RE goals by 2012. These goals are 300 MW of RE, including 90 MW of PV. 

The non-PV RE is assumed to be 50% wind and 50% biomass (landfill gas, possibly in fuel 

cells), with a wind capacity factor of 35%, a wind seasonal allocation factor of 40%, a biomass 

capacity factor of 75%, and a biomass seasonal allocation factor of 45%.44 This case involves 

considerable speculation about the performance of wind and biomass systems. 

If met, New Jersey’s RE goal would lead to a significant reduction in emissions by 2012, 

primarily because of the non-PV RE component, which attains a larger capacity and a higher 

capacity factor than PV. 

43 See G. Kats, A. Rosenfeld, T. McIntosh, and S. McGaraghan, Energy Efficiency as a Commodity: The Emergence 

of an Efficiency Secondary Market for Savings in Commercial Buildings, Published in ACEEE 1997 Summer Study, 

Part I, Panel 2. 

44 Only the avoided electricity generation emissions, not the direct emissions, are considered for biomass. This 

would be the case when the biomass emissions equal the avoided flaring emissions. 

 

 15
Table 6. Impact of New Jersey Meeting Its Renewable Goals by 2012

Year 

Summer Electricity 

Savings (MWh) 

Credited NOx 

Rate (lb/MWh) 

Credited NOx

Emissions (tons) 

2005 322,999 1.50 242 

2006 477,500 1.50 358 

2007 655,576 1.24 406 

2008 865,085 0.97 420 

2009 1,115,850 0.92 514 

2010 1,420,342 0.88 622 

2011 1,794,610 0.83 746 

2012 2,259,530 0.79 893 

Figure 2 shows the NOx emission reductions achieved by each component of the NJ CEP: EE, 

PV systems, and other RE. 

Credited NOx Reductions with 300 MW RE by 2012

0

100

200

300

400

500

600

700

800

900

1000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Year

Tons of NOx 

EE Wind/Biomass PV

Figure 2. Credited NOx Reductions with 300 MW RE by 2012

Summary of Scenarios: Varying each of these assumptions leads to markedly different results. 

The projection of the avoided NOx emissions rate under each alternative assumption is 

particularly important. Because of these differences, NJ DEP should consider an analysis of the 

projected summer season NOx rate of dispatchable fossil fuel electric generation facilities. Also, 

demonstrating the sustained performance of previously implemented EERE measures and 

continued improvements in the cost effectiveness of the NJ CEP will be important. 

Figure 3 illustrates NOx credited to NJ CEP: 

 16
Figure 3. NOx Credited to NJ CEP 

NOx Credited to NJ Clean Energy Program

0

100

200

300

400

500

600

700

800

900

1000

2005 2006 2007 2008 2009 2010 2011 2012

Year

Tons of NOx

Base Case Low Growth Sustained Measures

Incentive Reserve Cap Full RE Clean Grid

A rapid decrease in the avoided NOx rate causes avoided emissions to level off or decline slightly 

from 2007 to 2008. In most scenarios, avoided emissions consistently approach or exceed 400 

tons/year, and in some cases, they double that level. When non-BPU claimants to allowances are 

included,45 the current Incentive Allowance Reserve of 410 tons is likely to be oversubscribed in 

most years of the program.46 One possible option available to NJ DEP is to transfer unused 

allowances from the Growth/New Source Reserve to the Incentive Allowance Reserve.47 NJ 

DEP has indicated that the Growth/New Source Reserve is unlikely to be fully utilized. 

45 The DEP issued 47 incentive allowances to private parties in 2004. 

46 The limit of 410 tons per season is set forth in New Jersey Administrative Code, Title 7, Chapter 27, Subchapter 

31.7, Part(d)ii. See www.state.nj.us/dep/aqm/Sub31v2004-04-05.htm. 47 The New Jersey NOx budget regulations allocate 820 allowances into this reserve from 2004 to 2008. NJAC 7:27-

31.7(d)(1). New Jersey has not yet issued regulations to implement the EPA’s CAIR, and New Jersey’s CAIR rule 

will determine the size of any new source allocations for 2009 and thereafter. 

 

 17
4.0 Policy Issues 

4.1 Interface between State Implementation Planning Process and State 

Cap-and-Trade Regulations 

One of the major challenges of this pilot project was to develop an approach to ensure that RE 

and EE programs result in real reductions in emissions of NOx, a pollutant that is subject to 

emissions trading (cap-and-trade) regulations in New Jersey and most eastern states. The SIP 

process under CAA Section 110 is the mechanism to account for emission reductions.48 Under 

EPA’s 2004 guidance, states can receive emission reduction credit in their SIPs for RE and EE 

measures that reduce NOx emissions and help achieve the 8-hour ozone health standard, under 

specified circumstances.49 

Currently, states in the eastern United States have caps on NOx emissions from electric 

generating units (EGUs) through regulations that implement EPA’s NOx SIP Call. Beginning in 

2009, these caps in 28 states and the District of Columbia will be governed by new regulations 

that are being developed by the states that implement EPA’s new CAIR.50 As a result, credit for 

RE and EE projects must be provided in a way that avoids double counting. According to EPA’s 

Guidance, the states will need to ensure that the emissions trading cap for NOx and the number of 

allowances allocated to fossil-fuel generators are reduced commensurate with the level of 

emission reductions that result from EERE projects and programs. 

This can be accomplished in one of two ways: 

• Baseline Approach − Incorporates the estimated effect that the RE and EE programs 

have on emissions within the projected emissions inventory baseline and provides a 

corresponding decrease in the emissions cap. This decrease in the emissions cap to 

account for the RE and EE programs can be accomplished by issuing CAIR regulations 

that adjust the EPA-established state cap at the outset through an attainment reserve, 

public health reserve, or similar mechanism. 

• Control Measures Approach − Incorporates emission reductions from individual

control measures such as a regional wind purchase or solar programs in schools (or as 

part of a voluntary bundle of control measures), and provides a corresponding decrease in 

the emissions cap. The decrease in the cap can be accomplished by retiring allowances 

that have been allocated to RE and EE projects through a set-aside or output-based 

regulations issued under the state’s NOx budget or CAIR regulations. 

According to guidance issued by EPA in August 2004, both approaches are acceptable.51 Of 

course, under any approach, the state’s SIP will require approval by the relevant EPA Regional 

Office (Region II with respect to New Jersey). 

48 42 U.S.C. 7410 (2005). 

49 EPA SIP Guidance. 

50 70 Fed. Reg. 25162 (May 12, 2005). 

51 EPA SIP Guidance, pp. 13–14. 

 

 18
In other words, to achieve SIP credit under either approach, the state must either omit a certain 

fraction of allowances from distribution (thereby lowering the NOx emissions cap at the outset) 

or require the retirement of any such allowances allocated to EERE owners and operators. 

Otherwise, the emissions would be allowed within the trading program and could be double 

counted.52 

The justification for EPA’s approach is that EERE activities are unlikely to result in emission 

reductions of a capped pollutant, particularly in the near term, unless the state lowers the cap 

directly or retires allowances (the authorization to emit a ton of NOx) to account for the reduction 

in demand from fossil fuel generators caused by the EERE measures. According to EPA, the 

cap-and-trade program allows the same emissions from fossil fuel-fired generation, no matter 

how much generation these sources are called upon to meet demand. EPA is concerned that 

fossil fuel generators are likely to take the allowances made available when coal, natural gas, or 

oil generation is displaced by RE and EE measures and either use such allowances or sell them to 

other generators. This results in the continued emissions of NOx at the capped amount and the 

failure to provide surplus emission reductions. 

As EPA states in its Guidance: 

Cap and trade programs are enforced through the issuance of a limited number 

of allowances (authorizations to emit) that are equal to the emissions cap. 

Through trading and banking of these allowances, individual sources can vary 

their emissions as long as the aggregate emissions for all sources do not 

exceed the allowances issued. By limiting total mass emissions for the 

category of sources, cap and trade programs automatically account for any 

action that reduces emissions, including energy efficiency and renewable 

energy.53

Under the base case scenario for the NJ CEP, NJ DEP could lower the NOx emissions cap by 520 

allowances on average for the summer ozone season from 2007 through 2012, which is 

equivalent to an average emission reduction of 3.4 tons of NOx per summer day. Because NJ 

CEP will increase the market for RE and EE and the integration pilot will raise the visibility of 

the Incentive Allowance Reserve, private parties will probably claim additional allowances. 

4.2 Claims for New Jersey Incentive Allowances 

Under its current emissions trading regulations, New Jersey has included a set-aside of NOx

allowances for certain RE and EE activities.54 This is called the “incentive allowance” pool and 

52 Id., p. 18. 53 Id. p. 9. 54 Under the incentive allowance regulations, the following two categories of entities are specifically listed as 

eligible to submit an annual claim for allowances: (1) New Jersey electricity consumers who reduce electricity 

consumption by implementing an EE measure initiated in 1992 or thereafter (subject to certain additional 

conditions); and (2) owners and operators of equipment that commenced operation after 1992 that generates 

electricity through certain environmentally beneficial techniques defined as generation by burning of landfill gas or 

digester gas, fuel cell, solar energy or wind power, or equipment that generates electricity by another 

environmentally beneficial technique approved by the DEP. N.J. 7:27-31.8. 

 

 19
is distributed at the rate of 1.5 lb/MWh with a current cap of 410 allowances.55 If the incentive 

allowance pool is oversubscribed, these allowances are distributed pro-rata.56 Under current NOx

budget regulations, unused allowances can be transferred from the Growth/New Source Reserve, 

thereby increasing the number of incentive allowances distributed. However, the adequacy of 

the post-2008 allowances for RE and EE will depend on the specifics of the NJ CAIR 

regulations, which have not yet been issued. 

The regulations do not directly authorize the issuance of allowances to an entity that aggregates allowances on 

behalf of energy-saving electric consumers or owners or operators of environmentally beneficial techniques. 

However, the regulatory history of the regulation, contained in the NOx Budget Rule Adoption Document 

(government response to comment 123), indicates that “the rules adopted herein do not preclude the submittal of a 

claim on behalf of the owner or operator of [a] project eligible for submitting a claim. Neither do the rules preclude 

aggregating several different projects into a single claim.” 

In addition, a precedent has been established for the award of allowances from the incentive reserve to an energy 

services company named SYCOM on behalf of its clients who contracted for EE projects. 

4.3 Developing SIP Control Measures that Meet EPA Requirements 

If a state proceeds with a SIP to seek approval of individual control measures for RE and EE, it 

will need to demonstrate to EPA that its emission reductions will be surplus, quantifiable, 

enforceable, and permanent.57 

4.3.1 Surplus Requirement 

EPA notes in its SIP Guidance that “the surplus requirement is especially important in areas 

subject to a cap and trade program.”58 However, the Guidance emphasizes that: 

One acceptable way of achieving additional emission reductions from energy 

efficiency and renewable energy measures in the presence of a cap and trade 

program is through the retirement of allowances commensurate to the 

emissions expected to be reduced by the energy efficiency measures. The 

retirement of allowances provides some level of assurance that the energy 

efficiency measures will achieve emission reductions that are surplus to the 

emission reductions under the cap and trade program.59 (emphasis added) 

As a result of this guidance, NJ BPU and DEP have worked together under this pilot project to 

plan an approach for retiring allowances that are obtained by BPU under the incentive allowance 

program. This should provide a key element to help meet the surplus requirement. 

In addition, if emission reductions from RE and other measures are included in individual control 

measures, they cannot be included in the baseline emissions inventory for a SIP to meet the 8-

hour ozone standard. This element is crucial to ensure that emission reductions are not double 

counted and that they are surplus and have not been otherwise relied on to meet air quality 

attainment requirements. 

55 N.J.A.C. 7:27–31.7(e).1. In 2004, applicants claimed 47 of the 410 incentive allowances (NJ DEP). 

56 N.J.A.C. 7:27–31.7(e)3.iv. 

57 EPA SIP Guidance, pp. 4–7. 

58 Id., p. 5. 59 Id., p. 10. 

 

 20
4.3.2 Quantifiable Requirement 

Another key component of EPA’s regulations and SIP Credit Guidance is a demonstration that 

the NOx emission reductions that result from the NJ CEP are quantifiable. During the course of 

the pilot project, EPA officials advised informally that this test might be simplified because of 

the stipulation of the New Jersey regulations that fossil fuel emissions are displaced by RE and 

EE at a rate of 1.5 lb/MWh. EPA officials have indicated that some basic analysis should be 

conducted to demonstrate that the avoided emissions associated with the displaced fossil fuel 

generation are no less than the presumed rate of 1.5 lb/MWh. 

The project team evaluated several methodologies to support the quantifiable requirement and to 

demonstrate that the avoided emissions were in fact greater than 1.5 lb/MWh. The team 

determined that the best methodology available for the purposes of the pilot project was to 

estimate the generation-weighted average NOx rate of New Jersey fossil fuel plants. This 

methodology would provide a reasonable approximation of the marginal emissions rate without 

the time and expense of a complete grid system dispatch analysis. 

The analysis included only facilities that were fossil fuel powered for their primary source of 

input energy, including those that burn coal, natural gas, and petroleum fuels. Under this 

generation-weighted approach, the estimated avoided emissions are driven by facilities that 

contribute the most generation to the system. 

Initially the primary data source for this methodology was EPA’s eGRID database 2002, which 

was last updated with emission rates from 2000.60 However, the mix of fossil fuel generating 

facilities in New Jersey has changed significantly since 2000. As a result, the project team used 

more recent estimates, provided by NJ DEP, of NOx emission rates for 2003 to 2008.61 These 

rates represent the generation-weighted average of NOx budget units in the state; they include all 

fossil fuel generating plants with a capacity greater than 15 MW. These and other methodologies 

are discussed in greater detail in Appendix 4. 

Under the New Jersey regulations, RE generation uses the same 1.5 lb/MWh rate for avoided 

NOx emissions as EE. This rate is fully applicable for zero-emission renewable sources such as 

solar electric generation.62 

4.3.3 Enforceabity Requirement 

If a state pursues SIP credit for RE and EE projects as individual control measures, it must also 

meet the enforceability test under EPA’s voluntary measures policy.63 RE and EE measures 

60 The project team initially relied on data from the U.S. Energy Information Administration and EPA (the 

Emissions & Generation Integrated Database or eGRID). 

61 Tom McNevin, Bureau Air Quality Planning, NJ DEP, personal communication, September 2005. 

62 In comparison, certain RE sources such as landfill gas systems not only produce electricity but also produce some 

NOx emissions of their own and reduce emissions that would be produced by the alternative disposal of that landfill 

gas (typically flaring). In such cases, the net avoided emissions—the emissions produced by the landfill gas engine 

minus the sum of 1.5 lb/MWh (for the avoided generation of fossil fuel-fired electricity and the emissions that would 

be produced from flaring the landfill gas) must be calculated. 

63 See EPA’s, “Incorporating Voluntary and Emerging Measures in a SIP,” September 2004 for the voluntary 

measures policy. Such EERE measures would need to meet all applicable requirements of the voluntary measures 

policy (hereinafter cited as Voluntary and Emerging Measures Policy). See www.epa.gov/ttn/oarpg/tl/pgm.html. 

 

 21
typically result in emission reductions at fossil fuel generating plants located some distance from 

the RE or EE activities. Although such measures are not enforceable against the direct emitting 

sources, they are enforceable against the entities such as state and local governments that 

undertake such activities.64 

If a SIP revision is approved under the voluntary measures policy, a state is responsible to ensure 

that the reductions credited in the SIP are made. The state would need to make an enforceable 

SIP commitment to monitor, assess, and report on the emission reductions that result from the 

voluntary measure, and remedy any shortfalls from forecasted emission reductions in a timely 

manner. For voluntary and emerging measures that cover stationary sources, a presumptive limit 

of 6% of the total reductions is needed to meet any requirements related to attainment or 

maintenance of the air quality standards or reasonable further progress or rate of progress, as 

described in the policy.65 A separate limit of 3% applies to voluntary mobile source programs. 

Thus, there is a presumptive 9% limit on the inclusion of voluntary and emerging measures in a 

SIP, although a state may seek case-by-case EPA approval of a higher limit.66 

Recently, EPA issued a new guidance document on incorporating bundled measures in a SIP,67

which should facilitate efforts by states to meet the enforceability requirement with voluntary 

SIP control measures. As stated in EPA’s transmittal memorandum to regional air directors: 

The guidance supports the development of additional emissions reductions 

from innovative approaches by describing how States can identify individual 

voluntary and emerging measures and “bundle” them into a single SIP 

submission. The emissions reductions for each measure in the bundle would 

be quantified and, after applying an appropriate discount factor for 

uncertainty, the total reductions would be summed together in the SIP 

submission. After SIP approval, each individual measure would be 

implemented according to its schedule in the SIP. It is the performance of the 

entire bundle (the sum of emissions reductions from all the measures in the 

bundle) that is considered for SIP evaluation purposes, not the effectiveness of 

any individual measure. 

In other words, by grouping a set of voluntary control measures into a bundle, the state 

minimizes the chance that it will experience a shortfall later when the effectiveness of the 

measures is evaluated. By averaging the contribution of multiple measures, overperformance of 

some measures will likely compensate for underperformance of others. 

64 See EPA SIP Guidance, pp. 5–7. 

65 Ibid. 66 EPA,Voluntary and Emerging Measures Policy, p. 9. 

67 Memorandum from Stephen D. Page, director, Office of Air Quality Planning and Standards, EPA, and Margo 

Tsirigotis Oge, director, Office of Transportation and Air Quality, EPA, to Air Division Directors, “Guidance on 

Incorporating Bundled Measures in a State Implementation Plan,” August 16, 2005. See 

www.epa.gov/ttn/oarpg/t1pgm.html. 

 

 22
4.3.4 Permanence Requirement 

EPA’s SIP Guidance requires that a control measure “should be permanent throughout the term 

for which the credit is granted unless it is replaced by another measure or the State demonstrates 

in a SIP revision that the emission reductions from the measure are no longer needed to meet 

applicable requirements.” The guidance emphasizes that the emission reductions will qualify as 

meeting the permanence test even if the emission reductions change over time or vary from 

season to season.68 

Thus, NOx emission reductions that result from the BPU’s CEP will satisfy the permanence test 

even if the reductions from efficiency measures decline over time or the RE generation varies 

from one season to another. However, New Jersey will need to ensure that the estimated 

emission reductions are delivered. 

One way states have sought to address the fact that the impact of emission reductions from RE 

and EE measures is often variable during the term of the SIP is through the bundled measures 

approach addressed earlier. Maryland incorporated this creative approach in its SIP revision to 

meet the 1-hour ozone standard,69 and this example was showcased by EPA at its 2004 Air 

Innovations Conference.70

4.4 The Purpose and Uses of Allowances 

The initial purpose of the New Jersey incentive allowance for EE and EE (and similar NOx

allowance set-asides adopted by six other states)71 was to provide a financial incentive to entities 

that adopt such pollution prevention projects. Thus, issuance of such allowances to RE and EE 

developers was designed to offset the cost of installing such projects and spur increased use. 

In the past few years, state officials have recognized that the reduction of the NOx emissions cap 

(either through a direct reduction at the outset or by retiring NOx allowances) is a prerequisite to 

SIP credit in a state with a NOx cap-and-trade program. As stated previously,72 RE and EE 

activities are unlikely to result in emission reductions of a capped pollutant unless the state 

lowers the EPA-established cap directly or retires allowances to account for the reduction in 

demand from fossil fuel generators that results from the RE and EE measures. 

During the pilot project, participants discussed their interest in ensuring that two goals—spurring 

increased EERE development and achieving improved air quality—could be accomplished. For 

example, an approach that balances both goals might allow applicants for NOx allowances to 

either sell or retire such allowances. 

68 EPA SIP Guidance, p. 7. 

69 See 70 Fed. Reg. 24987 (May 12, 2005) for final EPA approval of the voluntary bundle and 

www.mwcog.org/committee/committee/archives.asp?COMMITTEE_ID=14 (February 19, 2004) for the detailed 

SIP revisions, Chapter 7, pp. 77−-81 and Appendix J, J-71-76. 70 www.epa.gov/ttnmain1/airinnovations/aiconf.2004.html Presentation by Brian Hug, Maryland Department of 

Environmental Protection. 71 These states are Maryland, New York, Massachusetts, Indiana, Ohio, and Missouri.

72 See p. 18, infra. 

 

 23
A recent presentation by Kevin Rackstraw of Clipper Wind Energy has underscored the 

monetary value in the Renewable Energy Credit (REC) market of NOx allowances awarded to 

RE generators under a well-structured allowance allocation program.73 According to Mr. 

Rackstraw, this monetary value accrues in the REC market with the retirement of NOx

allowances, and can greatly enhance financing opportunities.74

73 It is important to recognize the difference between the RECs and emission trading markets that involve NOx

allowances. In essence, RECs and allowances are two separate trading currencies. However, energy marketers have 

begun to recognize that the value of a REC can be increased if the REC is sold in conjunction with an allowance that 

can be retired because this REC will then ensure emission reduction of the capped pollutant. 

74 See the Web site of the American Wind Energy Association for a copy of this presentation. 

www.awea.org/seminars/past_events.html. 

 

 24
5.0 Analytical Issues 

5.1 Energy Efficiency 

During this project, the team worked to resolve analytical challenges in a number of areas. 

These challenges included estimating the electricity savings, the summer season electricity 

savings, and the emission reductions. 

In many cases, the reporting mechanisms established by the NJ CEP resolved important issues. 

In other cases, experts from NJ BPU and NJ DEP were able to clarify key points. However, the 

project team sometimes had to refine and expand the methodologies. 

One of the major challenges was the estimation of summer season energy savings. This factor is 

essential because the current ozone regulations focus on NOx emissions during the summer 

ozone season.75

The seasonal allocation factors applied by the Clean Energy Protocols form a sound 

methodological tool and were applied by the project team, where available. The NJ CEP 

seasonal factors generally agree with the findings of other analyses, such as the OTC Emission 

Reduction Workbook. These allocation factors are particularly appropriate for consumer 

products and appliances such as light bulbs and clothes washers. 

However, NJ CEP could not provide data on seasonal load profiles for every category of energysaving measure considered by the project team. For example, the Clean Energy Protocols 

include “custom measures” as a catch-all category that encompasses any type of measure not 

detailed elsewhere. In such cases, the Clean Energy Protocols allocate savings evenly across the 

year. This specific set of seasonal allocation factors is acceptable but not preferable.76 NJ CEP 

recognizes this limitation, noting that “[t]hese allocations may change [when] actual penetration 

numbers are available.”77

In addition, the annual and quarterly reports currently prepared by the NJ CEP do not provide 

sufficient detail on C&I EE improvements that are necessary to estimate summer season energy 

savings. The reports provide aggregated data on C&I electricity savings combined with 

information on the number, but not the size of electricity savings or of specific measures such as 

motors, chillers, or lighting controls. Unfortunately, many types of C&I measures have 

significantly different seasonal load profiles that have result in varying summer season electricity 

savings. 

In this situation, the project team employed the OTC Workbook seasonal allocation factors 

specified for “Commercial Comprehensive New Construction Design.” This approach was 

selected because this category includes a range of efficiency measures. Based on this factor, we 

75 EPA’s new regulations under CAIR, issued in May 2005, focus on both summer season and annual NOx

emissions. 70 Fed. Reg. 25162. 

76 Because electricity use for most loads is either summer peaking (as for HVAC) or equal year-round (as for office 

computers), assuming equal year-round energy savings is unlikely to overstate summer energy savings. 

77 Protocols to Measure Resource Savings, Appendix 3, p. 27. 

 

 25
assumed that 43% of C&I energy savings occurred during the summer season. For some types 

of commercial measures, such as the 364 high-efficiency electrical chillers and heating, 

ventilation, and air-conditioning (HVAC) systems installed in 2004, summer season savings are 

expected to be much higher. For other types of commercial measures, such as the 358 motors 

installed, summer season savings could be slightly lower (the OTC Workbook suggests that 

industrial motors achieve 39% of their savings in the summer season). 

NJ CEP has access to data on C&I energy savings by type of measure through the utilities. 78 In 

future years, the program will have the data readily available. NJ CEP could conduct an analysis 

to determine whether the project team’s estimated allocation factors are reasonable. A 

significant overrepresentation or underrepresentation of measures with high seasonal variability, 

such as HVAC systems, could lead to revisions of the projected emission reductions. 

Another area of uncertainty that the project team confronted was the definition of HVAC.” NJ 

CEP provided data on the number of units that were categorized as central air-conditioning or 

heat pumps, as well as the annual electricity savings that result from HVAC as a whole. 

However, NJ CEP did not provide the project team with information in its database on the 

relative size of these units, or on the summer season electricity savings for the heat pumps. 

These data are important because some heat pumps save electricity during the winter. 

In an effort to address these information gaps, the project team applied the following 

assumptions: 

• Each measure saved the same amount of energy per unit, so the fraction of electricity 

savings attributable to central air conditioning was the same as the fraction of units that 

were central air conditioners.79

• Half the heat pumps were air-source and half were ground-source (affects peak versus 

off-peak calculations). 

• Each heat pump achieved half of its annual electricity savings in the summer. 

Our analysis is not significantly affected by these assumptions because the number of central air 

conditioners was 50 times greater than the number of heat pumps. 

Another analytical challenge arose because the New Jersey protocols did not specify how to 

allocate electricity savings by season for Energy Star homes in the Residential New Construction 

program. We applied the allocation factors specified under the Comfort Partners Low Income 

Customers program because that program also focuses on whole-home energy efficiency 

78 In this and other instances, NJ CEP had access to more extensive data but could not provide the data to the project 

team because of confidentiality concerns. With appropriate confidentiality agreements, such information might be 

used in a future analysis. 

79 In 2004, there were 16,986 CAC units and 339 heat pumps, saving 15,499 MWh of electricity. If electricity 

savings are proportional to the number of units, CAC savings are 15,196 MWh and heat pump savings are 303 

MWh. Information on the capacity of units installed was not available; a directional bias, such as heat pumps 

tending to have larger capacity than CAC units, would produce slightly different results. 

 

 26
improvement.80 For Energy Star homes, employing energy modeling software to generate an 

estimate of summer and annual electricity savings would be preferable and not burdensome. 

New School Construction & Retrofit was another area of interest that lacked data. Some schools 

could be closed during most of the summer season and thus be a marginal source of summer 

season electricity savings. However, we were informed that many schools in New Jersey, 

particularly new schools or those that were renovated during the program, would continue to be 

used during the summer for a variety of programs. We therefore assessed schools according to 

the OTC Workbook factor for Commercial Comprehensive New Construction Design. 

The project team also identified analytical challenges that will need to be addressed with respect 

to combined heat and power (CHP) projects. At the time of our review, no CHP systems had 

been installed under the NJ CEP, but several had been approved for funding. When New Jersey 

officials begin to evaluate potential NOx emission reductions from CHP, they will need to 

address the following issues: 

• CHP projects usually result in some additional emissions of NOx, which have to be 

measured or calculated, to determine the net emission reductions. 

• The energy savings are variable, depending on season and the allocation of the energy 

savings between electricity and process heat. 

We recommend that New Jersey officials develop a data collection and analysis protocol to 

facilitate analysis of the CHP technology. 

5.2 Degradation Factor 

The Clean Energy Protocols include assumed system lifetimes. In many cases, New Jersey 

officials have accounted for system degradation by shortening the estimated system lifetimes or 

adjusting the deemed energy savings. We recommend using an annual degradation factor rather 

than fixed lifetimes to better reflect degradation in the near term. 

An annual degradation factor also helps to account for the fact that some equipment is removed 

before the end of its useful life. Commercial buildings may be renovated, or new tenants may 

override the high-efficiency lighting or HVAC controls. 

5.3 Renewable Energy 

The RE analysis encountered numerous challenges, including limitations on available data and 

the need to supplement the New Jersey data with additional data sources. In analyzing solar 

electric generation, the project team supplemented the data provided by NJ CEP with a model 

developed by NREL to estimate electricity generation from the solar PV systems. Since no 

metered data were readily available to us for any of the solar generation funded under the CORE 

80 This factor allocates the electricity savings as follows: summer/on-peak 21%; summer/off-peak 22%; winter/onpeak 28%; and winter/off-peak 29%. Because electricity consumption in New Jersey peaks during the summer 

season, and because the summer season (May−September) takes up 42% of the year, we consider it conservative to 

allocate 43% of the energy savings to the summer period. 

 

 27
program, the project team employed the PVWATTS Version 2 Tool to meet this need.81 The 

PVWATTS calculation was based on typical solar radiation from a location in south-central New 

Jersey. Other locations were examined as well: the highest resource location in the state showed 

summer generation as 4% higher; the lowest resource location showed summer generation as 4% 

lower. 

NJ CEP collects data through a sampling process of PV systems that are 10 kW or larger for 

SREC trading. The program is considering a more active remote-reading approach to encompass 

a broader range of systems. Comparing the projected summer production from our analysis with 

the observed summer production from the NJ CEP sampling would be advantageous. 

The team also employed the PVWATTS default factors to allow for power to be converted from 

DC to AC. This tool requires information about the direction of orientation and angle of tilt for 

the PV system considered. NJ CEP requires applicants to provide this information to receive a 

rebate, and tracks this information in a database. Because there were 344 systems, the project 

team grouped systems into categories of orientation and inclination to facilitate use of the PV 

WATTS tool. System orientation and inclination have a greater impact for year-round than for 

summer generation. 

NJ CEP data were insufficient to provide a reasonable estimate of electricity generation and NOx

emission reductions during the summer ozone season in 2005 from a wind project and two 

biomass (landfill gas) projects funded by the agency. For the wind project, the location, height, 

and type of equipment are critical to estimating ozone season generation. Actual records of 

generation are preferred for making this determination, and were not available to us. The 

importance of these site-specific protocols will increase as additional wind projects come on line. 

Estimating the electric generation from landfill gas projects also requires site-specific data, 

which were not available to the project team. Actual generation is almost always metered, and 

those data could be made available in the program records. Landfill gas combustion emits 

significant direct NOx emissions, which must be compared to the emissions that are avoided 

when flaring is eliminated.82 This comparative analysis could not be performed because the 

emission rates from the specific combustion systems were not available to the project team. In 

projecting future emissions from landfill gas, the rate of decline in landfill gas releases from the 

specific landfills over time need to be forecast. 

The data available to the project team on the fuel cell projects funded by the NJ CEP were 

insufficient to estimate the 2005 generation and to determine whether such projects qualified as 

RE sources. The second problem has been resolved. NJ CEP has indicated that: “After a 

program modification in early 2004, only sustainably-fueled (landfill gas) fuel cells are eligible 

for rebates.”83 Generation records and technology-specific data also will be required to estimate 

generation and net avoided emissions of fuel cell projects. 

81 See p. 10 (infra). 

82 We understand that private companies have sought allowances for certain landfill gas projects. How they 

resolved these analytical issues is unclear. 

83 New Jersey Clean Energy Programs Report, submitted to the New Jersey Board of Public Utilities, May 6, 2005, 

p. 30. 

 

 28
6.0 Lessons Learned 

The MARO pilot project provides numerous lessons learned about emissions trading programs 

that are applicable to other states. These lessons involve issues of regulatory policy as well as 

methodological approaches. 

First, the project underscores the importance of the state regulatory framework that governs NOx

emissions trading. If state regulations limit the allocation of allowances to only fossil fuel-fired 

generating units, the state and local government agencies will be precluded, for all practical 

purposes, from obtaining SIP credit for RE or EE purchases or development. In other words, 

unless the state’s NOx trading regulations provide a pool of allowances to reward RE and EE 

development or purchases and provide a mechanism for lowering the total cap of allowances, an 

important market driver and revenue stream for RE and EE will be lost. 

State focus on NOx allocation regulations is particularly timely. EPA’s new CAIR requires most 

states in the ozone transport region to submit to EPA enforceable plans by September 2006 for 

complying with the CAIR requirements. The new CAIR requirements will replace the state 

regulations under the NOx SIP Call for electric generating units and will govern NOx allowance 

allocation in 2009 and thereafter for such units. 

The pilot project underscored several regulatory options that provide a pool of NOx allowances 

to encourage EERE measures. These options include: 

• Allocate a percentage of NOx allowances into a so-called RE and EE set-aside to 

encourage RE and EE measures. 

• Allocate a percentage of NOx allowance into a so-called attainment/public health reserve. 

• Allocate allowances to all sources on an output basis (tons of emissions reduction per 

MWh) instead of a heat-input basis (used currently in most states and which covers only 

electric generators that burn fossil fuels). 

• Allocate allowances to new sources on an output-basis.84 

A recent document prepared by the State and Territorial Air Pollution Program Administrators 

(STAPPA) and the Association of Local Air Pollution Control Officials (ALAPCO) highlights 

these options.85 

In addition, lessons learned from RE and EE set-aside regulations under the NOx SIP Call are 

helpful in crafting new CAIR rules for NOx trading. These lessons are highlighted in an EPA 

draft report titled State Set-Aside Programs for Energy Efficiency and Renewable Energy 

84 This approach was adopted by the National Association of State Regulatory Commissioners in a resolution in the 

summer of 2004. This approach can encompass energy efficiency if a conversion factor is specified in the 

regulation. 

85 “Alternative NOx Allowance Allocation Language for the Clean Air Interstate Rule,” August, 2005. See 

www.4cleanair.org/Bluestein-cairallocation-final.pdf. 

 

 29
Projects Under the NOx Budget Trading Program: A Review of Programs in Indiana, Maryland, 

Massachusetts, Missouri, New Jersey, New York, and Ohio.

86 

The pilot project and these two recent reports underscore several important provisions that states 

should consider including in new CAIR rules that provide allowances to spur EERE. These 

provisions include: 

• Authority for a developer and the state to aggregate small projects in an application for 

allowance allocation. For example, small solar PV projects alone will not qualify for 

allowances since such projects result in emission reductions far lower than one ton (the 

minimum allocation in most states). If project aggregation authority is not provided in 

the state rules, the RE and EE set-aside will likely be underused. 

• Authority for a state energy office that administers incentive programs to apply for 

allowances relating to state-funded clean energy projects for owners and operators who 

have not applied on their own behalf. The state energy office could then retire these 

allowances to obtain SIP credit. 

 

The pilot project highlights various methodological approaches that can facilitate the 

implementation of state regulations to reward RE and EE. Other states can benefit from 

employing some or all of the methodologies developed by the NJ CEP (and refined and 

expanded by the pilot project) to determine the emission reductions achieved by RE and EE 

measures. New Jersey’s clean energy protocols and incentive allowance regulations allow NOx

emission reductions to be estimated with a relatively small investment.87 

States may want to consider the following specific methodological approaches in their CAIR 

regulations to facilitate the integration of energy and air quality goals: 

• Employ protocols with stipulated electricity savings to assess the benefits of specific EE 

measures and use the New Jersey clean energy protocols as a starting point. Some 

variables may need to be adjusted, depending on the specific state and region (e.g., the 

assumed equivalent full load hours of operation of an air-conditioning unit), but the 

equations included in the protocols appear to be sound and transferable. 

86 EPA, Climate Protection Partnerships Division, Office of Atmospheric Programs, Office of Air and Radiation, 

EPA Document No. 430-R-03-005, September 2005, available at http://www.epa.gov/cleanenergy/pdf/eere_rpt.pdf. 

87When using a simplified approach, such as that employed by the pilot project, be conservative in assumptions and 

default values. 

States may alternatively follow an approach developed by the Texas Energy Systems Laboratory (in partnership 

with EPA) that is more detailed and includes more in-depth analysis. This latter approach may result in a greater 

emission reductions credit but at a much higher cost. This methodology, which identifies county-level emissions 

impacts of RE/EE measures based on the county in which the measure was implemented, is both possible and 

necessary in a statewith regions that are not subject to NOx caps. (Emissions & Energy Calculator (eCalc), Energy 

Systems Laboratory, Texas A&M University. See http://ecalc.tamu.edu/.) 

 

 30
• Employ a stipulated avoided emissions rate (lb/MWh) for RE and EE measures that is 

based on the generation-weighted average NOx emissions rate of the relevant region’s 

variable dispatch fossil fuel plants. 

o The CAIR regulations could require that the state update this stipulated allocation rate 

periodically to reflect changes in the composition of the fuel mix and control 

technologies. (This approach would significantly reduce the resources required to 

analyze specific facilities.) 

o In addition, the state’s CAIR regulations could, if adequate evidence is presented, 

grant the state air agency authority to approve a higher allocation rate than the 

stipulated amount. (This approach allows parties who are responsible for developing 

large RE or EE projects to devote greater resources and claim higher actual emissions 

reductions.) 

• Include seasonal allocation factors that define assumed electricity savings for specific 

types of measures in state protocols. 

• Seek to fulfill the requirements for Energy Star ratings for residential construction and 

renovation and employ Energy Star energy modeling for home EE improvements. This 

approach can ascertain the likely effect of a combination of efficiency measures and will 

help promote the Energy Star brand. 

• Determine electricity savings for specific measures by comparison to a typical new 

system for that load, and define the reference system in protocols. Determine electricity 

savings for comprehensive building improvements through energy modeling with an 

energy code as a reference. 

• Examine the state’s emission inventory and SIP and identify any assumptions about RE 

and EE that are already in the inventory. If the state intends to rely on RE/EE measures 

for SIP credit, it must ensure those measures are surplus to those included in the baseline. 

 31
Appendix 1: Methodology and Calculations 

This appendix outlines the methodology used in developing an estimate of the potential NOx emission reductions from sele

New Jersey Clean Energy Program. Excel workbooks were the primary calculation tool used in this process. The NJ Clea

NREL’s PVWATTS tool provided key inputs. More detailed information can be obtained from Peter O’Connor, Global Environment & Technology Foundation, at pete.o

1. Assessment of 2005 Electricity Savings and Emission Reductions from Selected Categories of the NJ Clean 

2004 Projects

Original Discounted Original Discounted Original On-Peak Off-Peak

Residential HVAC 15,703 11,345 14,621 12,428 15,499

CAC 15,373 11,107 14,299 12,154 15,196 64.9% 35.1% 38,457 Heat Pumps 330 238 322 273 303 28.0% 22.0% 407 

Residential New Construction 3,262 2,357 4,773 4,057 4,551 21.0% 22.0% 4,715 Room Air Conditioning 0 0 1,432 1,217 1,377 65.1% 34.9% 2,594 Lighting 0 0 61,630 52,386 95,206 21.0% 22.0% 63,464 Clothes Washers 0 0 0 0 740 24.5% 12.8% 276 Comfort Partners 5,196 3,754 5,774 4,908 6,995 21.0% 22.0% 6,733 C&I New Construction 144,635 104,499 11,760 9,996 31,538 24.0% 19.0% 62,794 C&I Retrofit 0 0 179,679 152,727 163,631 24.0% 19.0% 136,034 New School Construction & Retrofit 0 0 5,908 5,022 8,975 24.0% 19.0% 6,019 

EE Subtotal 168,796 121,955 285,577 242,740 328,512 321,493 

PV 0 0 0 0 3,003 1,505 

Total 168,796 121,955 285,577 242,740 331,515 322,999 

EE Degradation Factor 15% EE Summer Fraction 4

RE Degradation Factor 5% PV Summer Fraction 5

2005 Electricity Savings (MWh)

Summer Fraction 2005 Summer 

MWh

200

N

2002 Projects 2003 Projects

This table illustrates the overall electricity savings for 2005 resulting from selected categories of the NJCEP implemented f2004. The table also includes the summer electricity savings and summer NOx emission reductions resulting from these pr

base figures are the annual electricity savings resulting from each category of measure, as identified in the quarterly reports Board of Public Utilities on the Clean Energy Program. The annual electricity savings from the energy efficiency projects by the square of (1-degradation factor), or 0.7225. The savings from the 2003 projects are multiplied by (1-degradation facsavings from the 2004 projects are fully credited for the summer of 2005. The total annual savings (the sum of the 2004 va2002 and 2003 values) are then multiplied by the sum of the summer season allocation factors. For example, clothes wash

1-1 
Appendix 1: Methodology and Calculations 

achieve 24.5%1 of their annual energy savings in the summer peak period and 12.8% of their annual energy savings in the s

period, for a total of 37.3% of their annual savings in the summer. For PV systems, the discount factors are not applied (all systems are assumed to have been installed in 2004), and the alloc

the input of specific system characteristics into NREL’s PVWATTS tool. This tool calculates the monthly output of systemlocation, and orientation. A flat-roof PV system will generate 56.1% of its annual electricity during the May to September south-facing system at 45° tilt will generate only 45.5% of its annual electricity in that time period (but about 15% more thover the course of the year). Summer electricity savings were derived on a measure-specific basis, using the allocation factors from the NJ Clean Energy appropriate surrogates (such as the OTC Emission Reduction Workbook or PVWATTS). The combination of energy efficimplemented in 2002 to 2004 have a weighted seasonal allocation factor of 46.38%. This factor is used for energy efficienimplemented in future years, as the exact combination of measures to be implemented is not known at this time. The PV sy

weighted seasonal allocation factor of 50.13%, with generation of 1505.455 MWh per summer (May-September) and 3002NOx emission reductions for 2005 are generated using the allowance allocation factor of 1.5 lbs/MWh. See Appendix 4 for 

establishing that 2005 emission reductions will likely exceed this value. 2. Projection of Future NOx Emission Reductions and Sensitivity Analysis

Step 1: Defining the Scenarios

Base Case EE PVProgram Growth 20% 40%Degradation Factor 15% 5%Grid Improvement 5% 5%

Low Growth EE REProgram Growth 10% 30%Degradation Factor 15% 5%Grid Improvement 5% 5%

Continued Savings EE Program Growth 20% Degradation Factor 5% Grid Improvement 5% 

Full RE EE REProgram Growth 20% 41%Degradation Factor 15% 5%Grid Improvement 5% 5%

Clean Grid EE PVProgram Growth 20% 40%Degradation Factor 15% 5%Grid Improvement 10% 10%

These tables establish the variables for the analysis of future NOx emission reductions. The four scenarios other than the “B

illustrate the sensitivity of the analysis to the various assumptions. “Grid Improvement” refers to decreases in the avoided 

rates through 2008 were identified by NJ DEP. 

1 We consider our results to be accurate to no more than two significant figures. However, in order to reduce rounding errors, we carry 

in our calculations, only rounding off the final result. 

1-2 
Appendix 1: Methodology and Calculations 

Step 2: Identifying Annual Electricity Savings and Generation

EE MWh

Year of Project 2003 2004 2005 2006 2007 2008 2009 2010 2011 

2002 168,796 143,477 121,955 103,662 88,113 74,896 63,661 54,112 45,995 

2003 285,577 242,740 206,329 175,380 149,073 126,712 107,705 91,549 2004 328,512 279,235 237,350 201,747 171,485 145,763 123,898 2005 394,214 335,082 284,820 242,097 205,782 174,915 2006 473,057 402,099 341,784 290,516 246,939 2007 567,669 482,518 410,141 348,620 2008 681,202 579,022 492,169 2009 817,443 694,827 2010 980,932 2011 

168,796 429,054 693,208 983,441 1,308,982 1,680,303 2,109,460 2,610,484 3,199,843 

PV MWh

Year of Project 2003 2004 2005 2006 2007 2008 2009 2010 2011 

2002 0000000002003 000000002004 3,003 2,853 2,710 2,575 2,446 2,324 2,207 2005 4,204 3,994 3,794 3,605 3,424 3,253 2006 5,886 5,592 5,312 5,046 4,794 2007 8,240 7,828 7,437 7,065 2008 11,536 10,960 10,412 2009 16,151 15,343 2010 22,611 2011 

0 0 3,003 7,057 12,590 20,201 30,727 45,342 65,686 

Year of Savings

These tables illustrate the annual electricity savings or generation achieved by the energy efficiency measures and photovoshaded boxes illustrate the most recent measures implemented. The unshaded boxes represent continuing electricity savingpreviously implemented, and reflect the degradation factor (in the base case, 15% for energy efficiency, 5% for photovoltaiThe savings from the most recent projects increase each year according to the growth factor for each scenario. For examplthat the budget for energy efficiency programs will grow by about 10% each year and that the cost-effectiveness of the progabout 10%. Therefore, these tables include energy efficiency program growth of about 20% per year in the base case. The 

savings from the 2005 energy efficiency projects are 20% larger than the 2005 savings from 2004 energy efficiency project

1-3 
Appendix 1: Methodology and Calculations 

Steps 3 and 4: Identifying Summer Electricity Savings or Generation and Identifying Summer NOx Emission Reductions

2003 2004 2005 2006 2007 2008 2009 2010 20

EE Summer Fraction 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.463PV Summer Fraction 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 0.50

Summer Energy 

Savings (MWh) 78,284 198,985 322,999 459,635 613,387 789,413 993,723 1,233,412 1,516

Projected NOx rate 

(lb/MWh) 1.85 1.85 1.65 1.65 1.24 0.97 0.92 0.88 0.

Credited NOx rate 1.50 1.50 1.50 1.50 1.24 0.97 0.92 0.88 0.

Credited NOx 

Emissions (tons) 59 149 242 345 380 383 458 540 6

Treductions. First, the summer season allocation factors are applied. For energy efficiency, we multiply annual energy savin

fraction of 2005 annual electricity savings (from measures implemented in 2002 to 2004). For photovoltaic systems, we mgeneration by the summer fraction identified by the PVWATTS tool for the systems in the CORE database (this accounts f

inclination). Then, the idenlbs/MWh or the projected NOx emissions rate. The projected NOx emissions rate for 2003-2008 is based on data provided 

2012, it is the previous year’s rate decreased by the “grid improvement” factor. For the base case, this factor is 5% per yeato conduct an analysis of the likely impact of CAIR and other regulations to more accurately project the relevant NOx emis

hese tables translate the identified annual electricity savings and generation into summer season savings and generation, a

tified summer energy savings and generation are multiplied by the credited NOx emissions rate. The rate app

1-4 
Appendix 1: Methodology and Calculations 

PVWatts Methodology

Bin

Min Max Assumed Min Max Assumed kWh/kW kW kWh kWh/kW kWh

1 Any Any 0 0 9 0 605 361.38 218,635 1079 389,929 56%

2 80 120 100 10 19 15 598 29.465 17,620 1091 32,146 55%

3 121 160 140 10 19 15 616 35.748 22,021 1176 42,040 52%

4 161 200 180 10 19 15 626 90.218 56,476 1209 109,074 52%

5 201 240 220 10 19 15 618 42.549 26,295 1172 49,867 53%

6 241 280 260 10 19 15 599 11.326 6,784 1084 12,277 55%

7 80 120 100 20 29 25 578 54.572 31,543 1071 58,447 54%

8 121 160 140 20 29 25 606 176.498 106,958 1203 212,327 50%

9 161 200 180 20 29 25 620 309.574 191,936 1253 387,896 49%

10 201 240 220 20 29 25 611 133.653 81,662 1197 159,983 51%11 241 280 260 20 29 25 582 40.28 23,443 1061 42,737 55%12 80 120 100 30 39 35 551 18.562 10,228 1035 19,212 53%13 121 160 140 30 39 35 587 113.235 66,469 1201 135,995 49%14 161 200 180 30 39 35 596 698.99 416,598 1263 882,824 47%15 201 240 220 30 39 35 588 125.419 73,746 1192 149,499 49%16 241 280 260 30 39 35 555 63.552 35,271 1023 65,014 54%17 80 120 100 40 49 45 516 9.135 4,714 983 8,980 52%18 121 160 140 40 49 45 554 52.39 29,024 1173 61,453 47%19 161 200 180 40 49 45 563 82.963 46,708 1241 102,957 45%20 201 240 220 40 49 45 556 56.265 31,283 1162 65,380 48%

21 241 280 260 40 49 45 522 15.404 8,041 970 14,942 54%

Total 2521.178 1,505,455 3,002,979 50.1321%

Summer 

Ratio

Orientation Tilt Summer Generation Annual Generation

This table illustrates the process used to identify the generation from the PV systems in the state’s CORE database. There systems with a total capacity of 2,521.178 kW (see Appendix 2). The project team considered that it would be unnecessarievaluate every single system through PVWATTS, and determined that such a detailed evaluation would provide only limitchange being relegated to non-significant digits in the final analysis). Therefore, the PV projects were grouped according tinclination. The project team evaluated a model 1-kW system for each group in PVWATTS to identify the annual and sumSeptember) kWh/kW ratio. The summer ratio was multiplied by the capacity within that “bin” to give the summer generati

total summer generation was found to be 3,002.979 MWh per year for the projects in the CORE database. The project team evaluated three different locations in New Jersey using PVWATTS. The two outlying locations had 4% h

summer generation than the mid-range location. We therefore modeled all systems as if located at the mid-range location. that all PV systems were flat-roof would have overestimated the summer generation by about 1.3% (but underestimated an

that 1.3% error is only about 30 pounds of NOx for 2005, even this rough breakdown by orientation and inclination is unne

error caused by the assumption of a single representative location is also negligible. 

1-5 
Appendix 1: Methodology and Calculations 

Scenario: Full Renewable Energy Goal Met

2003 2004 2005 2006 2007 2008 2009 2010 

EE Summer Fraction 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 0.4637767 PV Summer Fraction 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 0.501321 

Other RE Summer Fraction 0.434091 0.434091 0.434091 0.434091 0.434091 0.434091 0.434091 0.434091 

Summer Energy Savings (MWh) 78,284 198,985 322,999 477,500 655,576 865,085 1,115,850 1,420,342 

Projected NOx rate (lb/MWh) 1.85 1.85 1.65 1.65 1.24 0.97 0.92 0.88 

Credited NOx rate 1.50 1.50 1.50 1.50 1.24 0.97 0.92 0.88 

Credited NOx Emissions (tons) 59 149 242 358 406 420 514 622 

EE 59 149 241 342 376 378 451 530 

Wind/Biomass 0 0 0 13 26 37 56 82 

PV0 0 1 3 4 5 7 10 

2005 2006 2007 2008 2009 2010 

PV installed in previous year (kW) 2,521 3,555 5,014 7,071 9,972 14,063 Cumulative PV at beginning of year (kW) 2,521 6,076 11,090 18,161 28,133 42,197 Effective PV at beginning of year (kW) 2,521 5,950 10,667 17,204 26,316 39,064 Other RE installed in previous year (kW) 8,535 12,036 16,975 23,939 33,760 Cumulative other RE at beginning of year (kW) 8,535 20,571 37,546 61,484 95,244 Effective other RE at beginning of year (kW) 8,535 20,144 36,112 58,245 89,093 Wind MWh 13,084 30,881 55,359 89,289 136,579 Biomass MWh 28,037 66,174 118,627 191,334 292,669 Summer MWh 17,850 42,131 75,526 121,816 186,333 

The “Full RE” scenario has several differences from other analyses. It assumes that New Jersey meets its goals of 90 MW of PV and 210 MW of other renewable energy by the end of 2011. The “program growth” rate for renewable energy in this case is actually 41%, selected to allow the cumulative installed capacity of PV to grow from 2.521 MW through 2004 (CORE program only) to 90 MW by the end of 2011. New Jersey would need to install 8.535 MW of non-PV renewable energy in 2005 to achieve a cumulative 210 MW installed by the end of 2011 with a 41% growth rate. For purposes of this analysis, we assume that installation and growth rate to occur. 

Fraction of WFraction of BWind CapacWind SummBiomass CaBiomass SuOverall Sum

Assum

“Program growth” applies to the capacity of renewable energy systems as well as their annual generation, assuming that nesame capacity factor as current systems. “Effective PV at beginning of year” is the previous year’s effective capacity times plus the most recent capacity additions. The same methodology is used for other renewable energy. Assumptions about threnewable energy are indicated in the table above. These give an effective summer allocation factor. Emission reductions RE are calculated separately by multiplying together the annual generation, the summer allocation factor, and the credited N

electricity, which in New Jersey typically consists of landfill gas energy projects, was assumed to have no net NOx emissio

were assumed to be equal to the avoided flaring emissions. This is not always the case; however, if NJCEP wishes to achie

possible emission reductions, it will focus its efforts on landfill gas systems that produce fewer NOx emissions, such as mic

1-6 
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Barnegat Light 08006 0.925 Sharp 185 185 5 Sharp Sunvista 3500 0.93 

Barnegat Light 08006 0.925 Sharp 185 185 5 Sharp Sunvista 3500 0.93 Barnegat Light 08006 0.925 Sharp 185 185 5 Sharp Sunvista 3500 0.93 Budd Lake 07828 1.336 Sharp ND-167U1 167 8 SMA SunnyBoy1100U 0.93 Montclair 07042 1.43 Evergreen Solar EC-110 110 13 SMA America Sunny Boy 1800U 0.944 Rumson 07760 1.665 Sharp NT-S5EIU 185 9 SMA America SMA-2500 0.94 

Pennington 08534 1.8 BP Solar BP3150 150 12 Sharp JH-3500U 0.94 

Blairstown 07825 1.84 Evergreen Solar EC-115 115 16 SMA-America SB2500U 0.944 Sea Bright 07760 1.92 BP Solar BP3160 160 12 SMA 1800U 0.94 

Atlantic City 08401 2.1 BP Solar BP3150 150 14 SMA America Sunny Boy 1800U 0.94 1Atlantic City 08401 2.1 BP Solar BP3150 150 14 SMA America Sunny Boy 1800U 0.94 1Atlantic City 08401 2.1 BP Solar BP3150 150 14 SMA America Sunny Boy 1800U 0.94 1Atlantic City 08401 2.1 BP Solar BP-3150 150 14 SMA America Sunny Boy 1800U 0.935 1Atlantic City 08401 2.1 BP Solar BP3150 150 14 SMA America Sunny Boy 1800U 0.94 1Atlantic City 08401 2.1 BP Solar BP3150 150 14 SMA America Sunny Boy 1800U 0.94 1Neptune 07753 2.16 Astropower AP 120 120 18 SMA America SWR 2500U 0.94 

Brielle 08730 2.24 BP Solar BP 3160 160 14 SMA 1800U 0.93 

Farmingdale 07727 2.24 BP Solar BP3160B 160 14 Fronius IG3000 0.94 

Bradley Beach 07720 2.31 GE PV 165 165 14 SMA Sunny Boy2500 0.94 

Blairstown 07825 2.31 Evergreen Solar EC-110 110 21 SMA America SB2500U 0.944 

Bordentown 08505 2.338 Kyocera KC167G 167 14 SMA SunnyBoy2500U 0.94 Southampton 08088 2.4 Astropower AP100 100 24 SMA 2500U 0.94 

Newton 07860 2.4 Astropower AP 110 120 20 Trace 4024 SW 0.95 

Stockton 08559 2.475 GE165 165 15 SMA SB2500 0.94 Chatham 07928 2.56 BP Solar BP3160 160 16 Outback GVFX3648 0.9 

Trenton 2.625 Sharp

Sharp 175-NT-R5eiu 175 15 Sunvista Sharp JH-3500-U 0.924 

Trenton 2.625 Sharp

Sharp 175-NT-R5eiu 175 15 Sunvista Sharp JH-3500-U 0.924 

Newark 07107 2.64 Astropower AP120 120 22 SMA America SMA2500W 0.94 High Bar Harbor 08008 2.64 165 165 16 Fronius IG3000 0.94 Millstone 08844 2.64 Evergreen Solar EC-110 110 24 Sunny Boy SWR 2500U 0.941 Hewitt 07421 2.64 Evergreen Solar EC-110 110 24 SMA America SB2500U 0.944 Peneala Park 08008 2.7 BP Solar SX150S 150 18 SMA America Sunny Boy 2500U 0.941 Wanague 07465 2.75 Evergreen Solar EC-110 110 25 SMA America Sunny Boy 2500U 0.941 West Orange 07052 2.75 Evergreen Solar EC-110 110 24 SMA America Sunny Boy 2500U 0.944 Flemington 08822 2.76 Evergreen Solar EC115 115 24 SMA SB2500U 0.94 

High Bridge 08829 2.76 Evergreen Solar EC-115 115 24 SMA SB2500U 0.94 

Woodcliff Lake 07677 2.88 BP Solar BP3160 160 18 SMA 1800U 0.93 

Page 1
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Oakland 07436 2.88 Astropower AP120 120 24 SMA America 2500U 0.941 

Vineland 08360 2.88 Astropower AP120 120 24 Sunny Boy SMA2500W 0.941 Edgewater 07020 2.88 BP Solar BP3160 160 18 SMA 2500U 0.93 

Hillsborough 08844 2.88 Astropower AP 120 120 24 Trace SW 5548 W/GTI 0.96 Pennsville 08070 2.88 Astropower AP120 120 24 SMA America SMA 2500 0.941 

Cape May 08210 2.88 Astropower AP120 120 24 SMA America SWR2500U 0.941 Brigantine 08203 2.96 Sharp NT-185U 185 16 SMA 2500U 0.94 

Atlantic City 08401 2.96 Sharp NT-185U1 185 16 SMA SWR-2500U 0.94 

Atlantic City 08401 2.96 Sharp NT-185U1 185 16 SMA SWR-2500U 0.94 

Atlantic City 08401 2.96 Sharp NT-185U1 185 16 SMA SWR-2500U 0.94 

Barnegat Light 08006 2.97 Sharp 165 165 18 Sunny Boy SMA2500W 0.941 Manville 08835 3 Shell SP150PC 150 20 SMA America 2500U 0.944 

Avon by the Sea 07717 3 BP Solar BP3150 150 20 SMA Sunny Boy 2500U 0.94 Barnegat Light 08006 3 BP Solar BP SX 150 150 20 Sunny Boy SMA2500W 0.941 Southampton 08088 3 BP Solar BP3150 150 20 Sunny Boy SMA 2500U 0.93 

Howell 07731 3 BP Solar BP150 150 20 SMA SunnyBoy2500U 0.94 Barnegat Light 08006 3 BP Solar BP150 150 20 Sunny Boy SMA 2500 0.941 Beach Haven Terr. 08008 3 BP Solar SMA 2500 150 20 Sunny Boy SMA 2500 0.93 High Bar Harbor 08008 3 BP Solar 150 150 20 Sunny Boy 2500 0.93 High Bar Harbor 08008 3 BP Solar BP5X 150 20 Sunny Boy SMA 2500 0.941 Avon by the Sea 07717 3 BP Solar BP3150 150 20 SMA Sunny Boy 2500U 0.94 Roseland 07068 3 BP Solar BP3150 150 20 SMA SB 2500U 0.941 

Surf City 08008 3 BP Solar BP5x150S 150 20 Sunny Boy SMA 2500 0.94 Lambertville 08530 3.09 Sharp/BP

NEQ5E2U/BP3150 165 6 Trace SW 4048 0.95 

North Beach Haven 08008 3.15 150 150 21 Fronius IG3000 0.94 

Oceanport 07757 3.162 Astropower AP120 120 28 SMA SB1800 0.941 

Morristown 07960 3.2 BP Solar 3160 160 20 SMA America 2500U 0.93 

Red Bank 07701 3.2 BP Solar BP3160 160 20 SMA SB2500U 0.93 

Edison 08837 3.2 BP Solar BP3160 160 20 SMA America SB 2500U 0.941 

Bradley Beach 07720 3.3 Evergreen Solar EC-110 110 30 SMA SB1800U 0.944 

Manahawkin 08050 3.33 Sharp NE-Q5E2U 185 18 Sharp Sunvista JH3500-U 0.93 

Marmora 08223 3.36 BP Solar BP3160 160 21 Fronius IG3000 0.94 Hardwick 07825 3.45 Evergreen Solar EC-115 115 30 SMA SB1800U 0.94 

Page 2
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

West Orange 07052 3.45 Evergreen Solar EC-115 115 30 SMA SB1800U 0.944 

Lakewood 08701 3.6 BP Solar BP 3150 150 24 SMA SWR 1800 U SBD 0.94 Egg Harbor Township 08234 3.675 Sharp NT-175U1 175 21 Sharp Sunvista JH-3500U 0.94 Hardwick 07825 3.68 Evergreen Solar EC-115 115 32 SMA SB1800U 0.944 

Newton 07860 3.68 Evergreen Solar EC-115 115 32 SMA SB1800U 0.944 

Asbury 08802 3.68 Evergreen Solar EC-115 115 32 SMA-America SB1800U 0.944 North Beach Long Island 07901 3.7 Sharp NT- 185U1 185 20 Sharp JH-3500U 0.94 

Hamilton 08610 3.7 Sharp NT-185U1 185 20 Sharp JH3500U 0.94 

Hope 07844 3.795 Evergreen Solar EC-115 115 33 SMA SB1800U 0.944 Marlboro 07751 3.84 BP Solar BP-3160 160 24 SMA SB1800U 0.93 

Egg Harbor City 08215 3.84 Astropower AP-120 120 32 SMA 1800U 0.94 

Brick 08723 3.84 BP Solar BP3160 160 24 SMA Sunny Boy 1800 0.941 Pennington 08534 3.885 Sharp NT-S5EIU 185 21 Sharp JH-3500U 0.93 

Atlantic City 08401 3.885 Sharp NT-185U1 185 21 Sharp SunVista 3500 0.93 

Atlantic City 08401 3.885 Sharp NT-185U1 185 21 Sharp SunVista 3500 0.93 

Hasbrouck Heights 07604 3.885 Sharp NT-5E1U 185 21 Sharp JH3500U 0.924 

Allentown 08501 3.885 Sharp NT-S5E1U 185 21 Sharp JH-3500 0.93 

Palermo 08223 3.885 Sharp NT-185-U1 185 21 Sharp JH 3500 U 0.924 

Villas 08251 3.885 Sharp NT-185U 185 21 Sharp Sunvista JH3500U 0.92 Clifton 07004 3.9 Sharp ND-NOECU 140 28 Sharp Sunvista JH3500u 0.92 Titusville 08560 3.91 Evergreen Solar EC-115 115 34 SMA SB1800U 0.994 

Cherry Hill 08003 3.936 Sharp NT-123U1 123 32 SMA SWR1800U 0.94 

Bridgeton 08302 3.95 Sharp 10-185w 185 22 SMA SWR1800U 0.93 

Mt. Ephraim 08059 3.96 Sharp 165 165 24 SMA 2500U 0.935 

Mickleton 08056 3.96 Astropower AP 120 120 33 SMA Sunnyboy 1800U 0.935 Morristown 07960 3.96 Evergreen Solar EC-110 110 36 SMA America 1800U 0.941 Morristown 07960 3.96 Evergreen Solar EC-110 110 36 SMA America 1800U 0.941 Morristown 07960 3.96 Evergreen Solar EC-110 110 36 SMA America 1800U 0.941 Ringoes 08551 4 Sharp ND167U1 167 24 Sharp JH 3500U 0.924 Short Hills 07078 4 BP Solar 3160 160 25 SMA SB2500 0.94 

Lambertville 08530 4.008 Sharp ND 16701 167 24 Sharp JH 3500U 0.93 

Titusville 08560 4.008 Sharp ND-167U1 167 24 Sharp JH-3500U 0.93 

SeaBright 07760 4.008 BP Solar 3160B 167 24 SMA 1800U 0.94 

Howell 07731 4.05 BP Solar BP3150 150 27 SMA SB 2500 0.94 

Stone Harbor 08247 4.08 Astropower AP-120 120 36 Sunny Boy 1800U 0.942 

Crosswicks 08515 4.2 Powerlight Powerguard 175 24 SMA America SWR 1800 0.94 

Page 3
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Atco 08004 4.2 RWESchott ASE-300 300 14 SMA SunnyBoy2500U 0.96 Hewitt 07421 4.2 RWESchott

ASM300/D6F150 300 14 SMA 2500U 0.91 

Basking Ridge 07920 4.2 BP Solar 3150 150 28 SMA America SWR 1800 0.94 

Tuckerton 08087 4.2 BP Solar BP2150 150 28 SMA America Sunny boy 1800 0.943 Williamstown 08094 4.32 Astropower AP120 120 36 SMA

SMA 2500 &SMA 1300 0.941 

Egg Harbor 08234 4.32 Astropower AP120 120 36 Sharp JH3500U 0.941 

Sewell 08080 4.32 Astropower AP120 120 36 SMA America

SMA 1800 &SMA 2500 0.941 

Port Murray 07865 4.4 Evergreen Solar EC-110 110 40 SMA America Sunny Boy 2500U 0.944 Cherry Hill 08003 4.4 Sharp 185 185 24 Trace Xantrex SW5548 w/Gti 0.96 

Hillsborough 08844 4.44 Sharp NT S5E1U 185 24 Sharp 1800U 0.93 Titusville 08560 4.44 Sharp NT-185U1 185 24

Trace Engineering SW 5548 w/ GTI 0.96 

Andover 07821 4.45 Sharp

Isofoton 165w 165 27 Fronius IG2000 0.93 

Villas 08251 4.46 Astropower GEPV165 165 27 SMA SMA2500U 0.94 West Orange 07052 4.48 BP Solar BP 3160B 160 28 SMA Sunny Boy 2500U 0.944 East Brunswick 08816 4.48 BP Solar BP 3160 160 28 SMA SWR-1800D 0.94 

Holmdel 07733 4.48 BP Solar 3160 160 28 SMA 2500U 0.93 

Port Murray 07865 4.485 Evergreen Solar EC-115 115 39 SMA SB1800U 0.944 

East Brunswick 08816 4.495

Matrix Solar/Photowatt PW1650-175 155 29 SMA SWR-2500-SBD 0.941 

Howell 07731 4.5 BP Solar BP3150 150 30 SMA SunnyBoy2500 0.94 

Marlboro 07746 4.62 Evergreen Solar EC-110 110 42 SMA America Sunny Boy 2500U 0.941 Maplewood 07040 4.625 Sharp NT-185U1 185 25 SMA SWR1800U 0.941 

Hillsborough 08844 4.671 GE 173 173 27 SMA SB2500U 0.94 

Blairstown 07825 4.7 Evergreen Solar EC-115 115 41 SMA SB2500U 0.94 

Roseland 07068 4.8 BP Solar BP3160 160 30 SMA

Sunny Boy 1800/2500 0.941 

Pennington 08534 4.8 Astropower AP 120 120 40 Trace Xantrex SW 5548 0.96 

Marlboro 07746 4.8 BP Solar BP3160 160 30 SMA SunnyBoy2500 0.94 

North Haledon 07508 4.8 BP Solar SX120 120 39 SMA America

SWR1800, SWR 2500 0.94 

S.Toms River 08753 4.8 BP Solar BP SX 160 160 30 SMA

Sunny Boy 1800(1) 2500(1) 0.94 

Lumberton 08048 4.8 Astropower AP120 120 40 SMA 2500U 0.94 

Farmingdale 07727 4.8 BP Solar BP3150 150 32 SMA SB2500 0.94 

Page 4
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Long Branch 07740 4.8 BP Solar BP3160B 160 30 SMA SMA2500 0.94 

Martinsville 08836 4.8 BP Solar 3160 160 30 SMA SunnyBoy2500U 0.94 Livingston 07039 4.8 Astropower AP120 120 40 Trace Xantrex SW5548 w/Gti 0.96 Highstown 08520 4.8 BP Solar 3160 160 30 SMA 1800U 0.93 

Avon By The Sea 07717 4.8 BP Solar BP3160 160 30 SMA SB2500U 0.94 Hampton 08827 4.8 Astropower AP-120 120 40 Trace Xantrex SW5548 w/Gti 0.96 

High Bridge 08829 4.83 Evergreen Solar EC-115 115 42 SMA 2500U 0.944 

East Brunswick 08816 4.95

Matrix Solar/Photowatt PW1650-165 165 30 SMA America SWR2500U 0.941 

Lincoln Park 07035 4.95 GEPV165 165 30 SMA SWR2500U 0.94 

Edison 08820 4.96 BP Solar BP3160B 160 31 Fronius 3000 0.94 

Turnersville 08012 4.99 Sharp NT185U1 185 27 Sharp JH 3500U 0.93 Bayville 08721 5.01 Kyocera KC167G 167 30 SMA

Sunny Boy 1800U;2500U 0.94 

Rahway 07065 5.01 I165 165 24 Sharp JH3500HPVP1800 0.924 Freehold 07728 5.1 BP Solar BP3150 150 34 SMA SB1800 0.94 

Northfield 08225 5.12 BP Solar 3160 160 32 SMA SB2500U 0.941 

Seaside Park 08752 5.177 Kyocera KC 167G 167 36 SMA Sunny Boy 2500U 0.94 Hillsborough 08844 5.28 BP Solar BP3160 160 33 SMA America

SWR 2500 & SWR 1800 0.96 

North Beach Haven 08008 5.28 165 165 32 Fronius IG3000 0.94 

Newton 07860 5.28 Evergreen Solar EC-110 110 48 SMA America SB2500U 0.944 Cinnaminson

08077-4513 5.344 Kyocera Kyocera 167 32 SMA SunnyBoy2500U 0.94 

Wyckoff 07481 5.365 Sharp NT-S5E1U 185 29 Sharp/SMA

Sun Vista JH3500U/Sunny Boy 2500U 0.93 

Bradley Beach 07720 5.4 BP Solar SX150 150 36 SMA 2500 U 0.941 

Shrewsbury 07702 5.4 BP Solar BP SX 150 150 36 SMA America 2500U 0.94 Jackson 08527 5.4 BP Solar BP3150 150 36 SMA SB2500 0.94 

West Long Branch 07764 5.4 BP Solar SX150S 150 36 Sunny Boy SMA 2500 0.94 

Cherry Hill 08003 5.4 BP Solar 167W 150 36 Fronius IG3000 0.93 Hoboken 07030 5.4 RWESchott

ASE 300-DGF 300 18 SMA 2500U 0.94 

Delran

08075-2035 5.4 BP Solar SX 150B 150 36 SMA America

Sunny Boy SWR 2500 0.941 

Page 5
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

West Orange 07052 5.4 BP Solar BP3150 150 36 SMA SB1800U 0.941 

Hope 07844 5.52 Evergreen Solar EC-115 115 48 SMA SB2500U 0.94 Washington 07882 5.52 Evergreen Solar EC-115 115 48 SMA-America SB2500U 0.944 Blairstown 07825 5.52 Evergreen Solar EC-115 115 48 SMA SB2500U 0.944 

Rosemount 08559 5.52 Evergreen Solar EC-115 115 48 SMA SB2500U 0.944 

Hardwick 07825 5.52 Evergreen Solar EC-115 115 48 SMA 2500U 0.944 

Blairstown 07825 5.528 Evergreen Solar EC-115 115 48 SMA-America SB2500U 0.94 

Glen Rock 07452 5.55 Sharp NT185U1 185 30 Sharp JH3500U 0.93 Monroeville 08343 5.55 Sharp 185W 185 30 SMA America 1800U 0.941 Franklinville 08322 5.61 I165 165 34 Fronius IG3000 0.93 Brick 08724 5.678 Kyocera KC167G 167 34 SMA SB2500U 0.94 Edison 08817 5.678 Kyocera KC167G 167 34 SMA SB2500U 0.94 

East Windsor 08520 5.678 Kyocera KC167G 167 34 SMA SB2500U 0.94 

Union Beach 07735 5.68 Kyocera KC167G 167 34 SMA SunnyBoy2500U 0.94 Kendall Park

08824-1145 5.76 BP Solar BP 3160 160 36 SMA Sunnyboy 2500 0.94 

Toms River 08753 5.76 BP Solar BP3160 160 36 SMA Sunny Boy 2500U 0.941 Manalapan 07726 5.76 BP Solar BP3160 160 36 SMA Sunnyboy2500U 0.94 Forked River 08731 5.76 BP Solar BP3160 160 36 SMA Sunny Boy 2500U 0.94 Springfeild 07051 5.76 Sharp NT-185U1 160 36 SMA SWR 2500U 0.941 

Perrineville 08535 5.76 BP Solar BP 3150B 160 36 SMA America

Sunny boy 1800 & Sunny boy 2500 0.94 

Wayne 07470 5.76 BP Solar 3160 160 36 SMA 2500U 0.94 West Orange 07052 5.76 BP Solar BP3160B 160 36 SMA Sunny Boy 2500 0.944 Farmingdale 07727 5.76 BP Solar BP3160 160 36 SMA SB2500U 0.94 

New Egypt 08533 5.76 BP Solar BP3160 160 36 SMA Sunny Boy 2500 0.941 Old Bridge 08857 5.76 BP Solar BP3160 160 36 SMA Sunny Boy 2500U 0.94 Somerset 08873 5.9 Sharp NT185U1 185 32 SMA SMA2500U 0.94 

Lawrence 08759 5.92 Sharp NT-S5EIU 185 32 Sunny Boy 2500 U 0.941 

Rutherford 07070 5.92 Sharp NT-S5E1U 185 32 SMA SWR2500U SBD 0.94 Princeton 08540 5.94 Sharp NE-165U1 165 36 SMA SWR2500U 0.944 

Flemington 08822 5.94 GEsunline36 165 36 SMA SB2500U 0.94 Oldwick 08858 5.94 Sharp NE-Q5E2U 165 36 SMA SWR2500U 0.94 

Redbank 07701 6 BP Solar BP-3150B 150 40 SMA SB2500 0.94 Sewell 08080 6 BP Solar 3150 PU 150 40 SMA 2500U 0.92 

Brick 08723 6 BP Solar BP3150 150 40 SMA Sunny Boy 2500U 0.94 Howell 07731 6 BP Solar BP150 150 40 Sunny Boy SMA 2500 0.941 Surf City 08008 6 BP Solar BP5X 150 40 Sunny Boy SMA 2500 0.94 

Wrightstown 08562 6 BP Solar BP3150 150 40 SMA SunnyBoy2500 0.94 

Page 6
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Wall 07719 6 BP Solar BP3150 150 40 SMA SB2500 0.94 Bargenfield 07621 6.012 Sharp NT-167U1 167 36 SMA SMA2500U 0.94 

Bayville 08721 6.012 Kyocera KC-167G 167 36 SMA America

Sunny Boy SWR 2500U 0.94 

Montville 07045 6.012 Kyocera KC-167G 167 36 SMA America SMA 2500 UL-SBD 0.944 Brigantine 08203 6.105 Sharp NT-185-U1 185 33 Sharp JH 3500 U 0.924 

Springfield 07081 6.29 Sharp NT-185U1 185 34 SMA America Sunny Boy 2500U 0.941 Edison 08820 6.3

Matrix Solar/Photowatt PW1650-165 175 36 SMA America SWR2500U 0.94 

Kendall Park 08824 6.358 Kyocera KC187 187 34 SMA SB1800 0.94 

Wall 07719 6.4 Uni-solar PVL-128 128 50 SMA PV10208 0.94 

Pennington 08534 6.66 Sharp NT-185U1 185 36 Sharp JH 3500U 0.93 

Glassboro 08028 6.66 ND 16701 165 24 Fronius IG3000 0.93 

Hillsborough 08844 6.66 Sharp NT-S5EIU 185 36 SMA SWR 1800U 0.94 Holmdel 07731 6.66 Sharp NT188U1 185 36 SMA America SWR2500U 0.941 Cedar Grove 07009 6.68 Kyocera KC167G 167 40 SMA Sunnyboy 2500U 0.94 Tuckerton 08087 6.72 BP Solar BP3160 160 42 SMA 2500U 0.94 Allenhurst 07711 6.72 BP Solar BP3160 160 42 SMA

Sunny Boy 1800/2500 0.941 

Freehold 07728 6.8 BP Solar BP 5170 170 40 SMA 2500U 0.941 

Hamilton 08610 6.825 Sharp NT-S5E1U 175 39 Sharp JH3500 0.93 

Franklinville 08322 6.89 Sharp 185W 185 21 Sharp JH-3500U 0.93 Hackettstown 07840 6.9 Evergreen Solar EC-115 115 60 SMA SB2500U 0.944 

Hamilton Square 08690 7 Sanyo HIP-J54BA2 180 40 Sharp JH-3500U 0.94 

Lebanon 7 Astropower AP 100 100 70 SMA America SWR2500U 0.96 Belle Mead/ Griggstown 08502 7.014 Sharp ND-167U1 167 42 Sharp JH-3500U 0.924 Corbin City 08270 7.175 Sharp NT-175U1 175 41 Sunvista JH3500 0.94 

Palmyra 08065 7.2 RWESchott ASE300 300 24 SMA Sunny Boy 2500 0.93 White house Station 08889 7.2 BP Solar SX150S 150 48 SMA America

Sunny Boy 1800 and 2500 0.94 

Frenchtown 08825 7.2 Astropower AP-120 120 60 SMA America 2,500U SBD 0.94 

Lebanon 7.2 Astropower AP 100 100 72 SMA America

SWR 1800, SWR 2500 0.96 

Jackson 08527 7.2 Astropower AP120 120 60 SMA 2500U 0.94 

Iselin 08830 7.2 RWESchott 300ASE 300 24 SMA SunnyBoy2500 0.96 

Page 7
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Long Valley 07853 7.35

Matrix Solar/Photowatt

PW 1650-175 175 42 Xantrex SW 4080 0.94 

West Milford 07840 7.36 Evergreen Solar EC-115 115 64 SMA SB2500U 0.94 

Whitehouse Statiion 08889 7.4 GEPV 165 165 45 SMA America

Sunnyboy 2500-SBD(3) 0.94 

Kendall Park 08824 7.425 GE165 165 45 SMA SB 2500 0.94 

West Atlantic City 08234 7.425 I165 165 45 Fronius IG2000 0.94 

Bridgewater 08807 7.49 Sharp SH140 140 42 Xantrex SW5548 0.94 

Elm 08037 7.56 Sharp NE-Q5E2U 180 42 SMA America SWR2500U 0.94 

Flemington 08822 7.59 Evergreen Solar EC-115 115 66 SMA SB2500U 0.94 

Clarksburg 08510 7.63 Sunslates 14 545 Trace&Sharp Sw5548&JH3500 0.95 Little Silver 07739 7.65 BP Solar SX150 150 51 Xantrex SW4048 0.96 Old Bridge 08857 7.68 BP Solar BP3160 160 48 SMA Sunny Boy 2500U 0.94 Hamilton Square 08690 7.68 BP Solar 3160 160 48 SMA 2500 0.94 

Chatsworth 08019 7.77 Sharp 185W 185 42 Sharp

Sun Vista JH3500U/ Sunny Boy 2500U 0.93 

Hampton 08827 7.77 Sharp NT-S5EIU 185 42 Sharp JH-3500U 0.92 

Galloway 08205 7.77 Sharp NT-185U1 185 42 Sharp JH-3500U 0.924 

Warren 07059 7.77 Sharp NT-185U1 185 42 SMA America

Sunnyboy 2500 and 1800 0.944 

Northfield 08225 7.8 BP Solar 3150B 150 52 SMA America Sunny Boy 2500 0.941 Little Egg Harbor 08087 7.84 BP Solar BP3160 160 49 SMA

Sunny Boy 1800/2500 0.941 

Union 07083 7.84 Sharp

NDNU ECU&NDO70ERU 140 46 Sharp Sharp JH-3500U 0.924 

Hopewell 08525 7.92 AP110 110 72 SMA SWR2500U 0.94 

Glen Gardner 08828 7.92 Evergreen Solar EC-115 115 72 SMA-America SB2500U 0.944 

Blairstown 07825 7.94 Evergreen Solar EC-115 115 61 SMA SB2500U 0.944 

Lawrence Township

08648-1431 8.016 Kyocera KC167G 167 48 SMA 2500U 0.94 

Wall twp 07753 8.1 BP Solar SX150B 150 54 SMA America Sunny Boy 2500U 0.941 Long Valley 07853 8.28 Evergreen Solar EC-115 115 72 SMA SB2500U 0.944 

Mendham 07945 8.36 Evergreen Solar EC-110 110 76 SMA America SB2500U 0.944 

Newton 07860 8.4 Sharp NT175U1 175 48 SMA

Sunny Boy SWR 2500U 0.93 

Page 8
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Monmouth Beach 07750 8.4 BP Solar BP3160 150 56 SMA SB2500U 0.94 

Lawrence Twp. 08648 8.4 Sharp NT175U1 175 48 SMA SWR2500U 0.94 

Freehold 07728 8.64 BP Solar BP3160 160 54 SMA SB2500U 0.94 

Holmdel 07733 8.64 BP Solar 3160 160 54 Outback GUFX7248 0.94 Flemington 08822 8.64 Astropower AP120 120 72 Trace SW4048 0.96 Mays Landing 08330 8.684 Sharp ND-167U1 167 52 Sharp Sunvista JH 3500U 0.92 Pitman 08071 8.695 Sharp NT-S5EIU 185 47 SMA America Sunny Boy 2500U 0.941 Blairstown 07825 8.8 Evergreen Solar EC-115 115 80 SMA America Sunny Boy 2500U 0.944 Ringoes 08551 8.85 Sharp ND-167U1 167 53 Sharp JH-3500U 0.93 

Chester 07930 8.88 Sharp NE-S5E1U 185 48 SMA America SMA2500 UL-SBD 0.944 Califon 07830 8.888 Sharp NT-SSEIV 185 48 SMA America

SWR 1800 V w/display 0.94 

Voorhees 08043 8.91 Sharp NE-Q5E2U 165 54 SMA America SMA 2500 0.94 

Hackettstown 07840 9.018 Kyocera KC167G 167 54 SMA SunnyBoy2500U 0.94 Marlboro 07746 9.018 Kyocera KC167G 167 54 SMA Sunny Boy 2500U 0.941 Randolph 07869 9.018 Kyocera KC167G 167 54 SMA SB2500U 0.94 

Allentown 08514 9.24 GEPV-110 110 84 SMA Sunny Boy 2500U 0.94 Mansfield 08022 9.28 BP Solar 3160 160 58 SMA 2500U 0.94 

Edison 08820 9.45 Sharp NT175U1 175 54 Sharp JH-3500U 0.941 Edison 08820 9.45 Sharp NT185U1 175 54 Sharp JH3500U 0.941 Pennington 08534 9.45 Sharp NT-S5E1N 175 54 Sharp JH3500 0.93 

Bridgeton 08302 9.45 Sharp NT-175U1 175 54 Fronius IG3000 0.94 

Neshanic Station 08853 9.45 I150S 150 63 SMA SB2500U 0.93 

Andover Township 07860 9.6 BP Solar 3160 160 60 Fronius IG2000 0.94 

Red Bank 07701 9.6 BP Solar 3160 160 60 SMA 1800 0.94 

Belle Meade 08502 9.72 Terra Solar TS 40 243 40 SMA America 2500U 0.94 

Brick 08723 9.76 BP Solar BP3160 160 61 SMA Sunny Boy 2500 0.941 Manalapan 07726 9.76 BP Solar BP3160 160 61 SMA SB2500U 0.94 

Lebanon 08833 9.795 RWESchott

ASE300DGF150 32 SMA SWR2500U 0.96 

Hopewell 08525 9.8 Sharp NT175U1 175 56 SMA JH3500U 0.93 

Holmdel 07733 9.8 BP Solar SX140S 140 70 SMA America SMA-2500 0.94 Holmdel 07733 9.8 BP Solar SX140S 140 70 SMA America SMA-2500 0.94 Mahwah 07430 9.8 Sanyo

HIPG751BA2 167 59 SMA America Sunny Boy 2500U 0.941 

Page 9
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Ringoes 08551 9.8 Sharp NT-175U1 175 56

Sharp Electronics J3500U 0.93 

Morristown 07960 9.8 Sharp NT-175U1 175 56 Sharp JH3500 0.93 

Long Branch 07740 9.805 Sharp NT-185U1 185 53 Sunny Boy 2500u 0.941 

Morganville 07751 9.81 BP Solar BP3160 150 66 SMA SB2500 0.94 

Titusville 08560 9.84 Astropower AP120 120 82 SMA America SWR2500U 0.94 Princeton 08540 9.853 Kyocera KC167G 167 59 SMA SB1100U 0.94 

Glenwood

07418-2015 9.853 Kyrocera KC161G 167 59 SMA

Sunny Boy 2500U(3)Sunny Boy 1800U (1) 0.94 

Clifton 07013 9.9 Sharp NT-185U1 185 54 Sharp Sunvista 3500 0.924 

Cedarbrook 08009 9.9 Sharp NT-185W 185 54 SMA 2500U 0.93 

Ringoes 08551 9.9 Sharp NT-185U1 185 54 Sharp JH-3500U 0.93 

Milford 08848 9.9 GE-165 165 60 SMA SB2500U 0.94 

Elwood 08217 9.9 Sharp NE-165U1 165 60 SMA Sunny Boy 2500U 0.941 

Manalapan 07726 9.9 GEPV165 165 60 SMA SB2500 0.94 

Pittstown 08867 9.9 GEPV165 165 60 SMA-America Sunny Boy 2500 0.94 Manalapan 07726 9.9 GEPV165 165 60 SMA SB2500 0.94 

Southampton/Tabernacle 08088 9.9 Sharp NT-S5E1U 185 54 Sharp JH 3500U 0.924 

Pennington 08534 9.9 Sharp NE-Q5E2U 165 60 SMA America SWR2500U 0.94 

Trenton 08619 9.92 BP Solar 3160 160 62 SMA 2500 0.94 East Brunswick 08816 9.92 Sharp NT-185U1 160 62 SMA Sunny Boy 2500U 0.941 Belmar 07719 9.96 Astropower AP120 120 83 SMA America 2500 U 0.94 

Brick 08723 9.96 Astropower AP120 120 83 SMA America SW4048 1 Lambertville 08530 9.96 Astropower AP120 120 83 SMA SWR2500U 0.94 

Whitehouse Station 08889 9.967 GEPV 165 165 69 SMA America SunnyBoy 2500 0.94 

Atco 08004 9.975 Sharp NE-175 175 57 Sharp JH3500u 0.93 

Waterford 08089 9.98 Sharp NT-175U1 175 57 Sharp JH3500U 0.93 

Southampton 08088 9.99 Sharp NT-185U1 185 54 SMA 2500U 0.93 

Brown's Mill 08015 9.99 Sharp NT-185U1 185 54 Sharp JH-3500U 0.92 

Lebanon 07753 9.99 Sharp NT 185U1 185 54 SMA

Sunny Boy SWR 2500U 0.941 

Burlington 08016 9.99 Sharp NT S5E1U 185 54 Sharp Sunvista JH 3500U 0.93 Little Egg Harbor 08087 9.99 Sharp NT-185U1 185 54 Sharp JH-3500U 0.94 

Long Valley 07853 9.99 Sharp NT-55EIU 185 54 Sharp Sunvista 0.92 

Westampton 08060 9.99 Sharp NT185U1 185 54 Sharp JH3500U 0.93 

Page 10
Appendix 2

NJCEP CORE Program 

PV Systems Installed 2003-2004

City Zip Code

System 

Size (DC 

kW)

Module 

Manufacturer Model #.

Power 

Rating / 

Output

# of 

Modules

Inverter 

Manufacturer Inverter Model #

Inverters 

Peak 

Efficiency

Sola

Elect

Modu

Orienta

Freehold 07728 10 BP Solar BP3150 160 63 SMA SB2500U 0.94 Colts Neck 07722 10.02 Kyocera KC167G 167 60 SMA SB1100U 0.94 

Neptune 07753 10.8 BP Solar BP-110 150 72 SMA America Sunny Boy 2500U 0.941 Magnolia 08049 11.04 Evergreen Solar EC-115 115 96 SMA-America Sunny Boy 2500U 0.944 Cream Ridge 08514 11.04 Evergreen Solar EC-115 115 96 SMA America SB2500U 0.944 Farmingdale 07727 11.04 Evergreen Solar EC-115 115 96 SMA-America SB2500U 0.944 Colts Neck 07722 11.1 Sharp NT-185U1 185 60 Sharp JH-3500U 0.924 

Alloway 08001 11.84 Sharp NT-185U1 185 64 SMA America Sunny Boy 2500U 0.941 Somerset

08873-2861 12.3 BP Solar SX150B 150 82 SMA

Sunny Boy 2500(2) 1800(3) 0.94 

Gladstone 07934 12.6 Astropower AP75 75 168 SMA SB1800U 0.944 

Springfiled 07081 14.4 RWESchott

ASE300 DGF50 300 48 SMA SU2500 0.94 

Lafayette 07848 16.56 Evergreen Solar EC-115 115 144 SMA SB2500U 0.94 

Newton 07860 16.56 Evergreen Solar EC-115 115 144 SMA SB2500U 0.944 

Rockleigh 07647 19.2 RWESchott ASE 300DG 300 64 SMA America SWR2500U 0.94 Wall 07719 21.84 Astropower AP 130 130 168 Trace Xantrex PV 20 0.96 Mandham 07945 22.08 Evergreen Solar EC-115 115 192 SMA-America Sunny Boy 2500U 0.944 Long Valley 07853 22.08 Evergreen Solar EC-115 115 192 SMA SB2500U 0.944 

Cream Ridge 08514 38.64 Evergreen Solar EC-115 115 336 SMA America SB2500U 0.944 

East Windsor 08520 41.4 Evergreen Solar EC-115 115 360 SMA SB2500U 0.94 

Howell 07731 50.4 BP Solar MSX 120 120 420 Trace Xantrex PV 45 0.96 

Howell 07731 50.4 BP Solar MSX 120 120 420 Trace Xantrex PV 45 0.96 Newark 07102 50.4 RWESchott

ASE3005OV315W 315 160 PVPowered PVP2800 PVP1800 0.944 

Sewell 08080 88.8 Sharp NT-S5EIU 185 480 SMA America Sunny Boy 2500U 0.941 EdgeWater 07020 120.96 BP Solar 3140 140 864 Xantrex PV100208 0.96 

Total 2521.178

Page 11
New Jersey Clean Energy Program 

Protocols to Measure Resource Savings 

New Jersey Clean Energy Protocols 

September 2004 

DOE/GO-102006-2354 

August 2006 

Appendix 3 
New Jersey Clean Energy Program Page i 

Protocols to Measure Resource Savings 

September 2004 

Table of Contents

Introduction...........................................................................................................1

Purpose.......................................................................................................................................1

Types of Protocols .....................................................................................................................2

Algorithms .................................................................................................................................4

Data and Input Values................................................................................................................4

Baseline Estimates .....................................................................................................................5

Resource Savings in Current and Future Program Years...........................................................5

Prospective Application of the Protocols...................................................................................6

Resource Savings .......................................................................................................................6

Electric...................................................................................................................................6

Natural Gas ...........................................................................................................................7

Other Resources.....................................................................................................................7

Post-Implementation Review .....................................................................................................7

Adjustments to Energy and Resource Savings...........................................................................8

Coincidence with Electric System Peak.................................................................................8

Measure Retention and Persistence of Savings .....................................................................8

Interaction of Energy Savings ...............................................................................................8

Calculation of the Value of Resource Savings...........................................................................8

Transmission and Distribution System Losses...........................................................................9

Electric Loss Factor ..............................................................................................................9

Gas Loss Factor.....................................................................................................................9

Calculation of Clean Air Impacts...............................................................................................9

Measure Lives..........................................................................................................................10

Protocols for Program Measures..............................................................................................10

Residential Electric HVAC ................................................................................11

Protocols ..................................................................................................................................11

Central Air Conditioner (A/C) & Air Source Heat Pump (ASHP) ......................................11

Ground Source Heat Pumps (GSHP) ..................................................................................11

GSHP Desuperheater ..........................................................................................................12

Residential Gas HVAC.......................................................................................15

Protocols ..................................................................................................................................15

Space Heaters ......................................................................................................................15

Water Heaters......................................................................................................................16

Residential Energy Star Windows.....................................................................17

Protocols ..................................................................................................................................17

ENERGY STAR Windows ....................................................................................................17

Residential Low Income Program.....................................................................21

Protocols ..................................................................................................................................21

Efficient Lighting .................................................................................................................21

Hot Water Conservation Measures......................................................................................21

Efficient Refrigerators .........................................................................................................22

Air Sealing ...........................................................................................................................22

Duct Sealing and Repair......................................................................................................23

Insulation Up-Grades ..........................................................................................................23

Thermostat Replacement .....................................................................................................23

Heating and Cooling Equipment Maintenance Repair/Replacement..................................24

Total Space Conditioning Savings.......................................................................................24
New Jersey Clean Energy Program Page ii 

Protocols to Measure Resource Savings 

September 2004 

Other “Custom” Measures..................................................................................................24

Residential New Construction Program...........................................................28

Protocols ..................................................................................................................................28

Insulation Up-Grades, Efficient Windows, Air Sealing, Efficient HVAC Equipment, and 

Duct Sealing ........................................................................................................................28

Lighting and Appliances......................................................................................................30

Ventilation Equipment .........................................................................................................30

Residential Retrofit Program.............................................................................35

Protocols ..................................................................................................................................35

Residential ENERGY STAR Lighting..............................................................36

Protocols ..................................................................................................................................36

Residential ENERGY STAR Appliances..........................................................37

Protocols ..................................................................................................................................37

ENERGY STAR Refrigerators..............................................................................................37

ENERGY STAR Clothes Washers ........................................................................................37

ENERGY STAR Dishwashers...............................................................................................37

ENERGY STAR Room Air Conditioners..............................................................................38

Commercial and Industrial Energy Efficient Construction ...........................41

C&I Electric Protocols .............................................................................................................41

Baselines and Code Changes...............................................................................................41

Lighting Equipment .............................................................................................................41

Prescriptive Lighting for Small Commercial Customers.....................................................45

Prescriptive Lighting Savings Table....................................................................................46

Lighting Controls.................................................................................................................47

Motors..................................................................................................................................48

HVAC Systems .....................................................................................................................49

Electric Chillers...................................................................................................................51

Variable Frequency Drives..................................................................................................54

C&I Construction Gas Protocols..............................................................................................55

Gas Chillers.........................................................................................................................55

Gas Fired Desiccants ..........................................................................................................58

Gas Booster Water Heaters .................................................................................................58

Water Heaters......................................................................................................................59

Furnaces and Boilers...........................................................................................................60

Commercial and Industrial Building Operation & Maintenance Program .61

Protocols ..................................................................................................................................61

Electric Savings ...................................................................................................................61

Gas Savings .........................................................................................................................61

Compressed Air System Optimization..............................................................63

Protocols ..................................................................................................................................63

Compressed Air Systems......................................................................................................63

Residential Air Conditioning Cycling Load Control Program ......................64

Protocols ..................................................................................................................................64

School Energy Efficiency and Renewable Energy Education Program........65

Protocols ..................................................................................................................................65

Customer-Sited Generation ...............................................................................66

Protocols ..................................................................................................................................66

Photovoltaic Systems ...........................................................................................................66

Wind Systems .......................................................................................................................68
New Jersey Clean Energy Program Page iii 

Protocols to Measure Resource Savings 

September 2004 

Fuel Cells.............................................................................................................................69

Sustainable Biomass ............................................................................................................69

Appendix A Measure Lives..............................................................................70
New Jersey Clean Energy Program Page 1 

Protocols to Measure Resource Savings 

September 2004 

New Jersey Clean Energy Program 

Protocols to Measure Resource Savings 

Introduction 

These protocols have been developed to measure resource savings, including energy, 

capacity, and other resource savings. Specific protocols for determination of the resource 

savings from each program are presented for each eligible measure and technology. 

These protocols use measured and customer data as input values in industry-accepted 

algorithms. The data and input values for the algorithms come from the program 

application forms or from standard values. The standard input values are based on the 

best available measured or industry data applicable for the New Jersey programs. The 

standard values for most commercial and industrial (C&I) measures are supported by end 

use metering for key parameters for a sample of facilities and circuits, based on the 

metered data from the JCP&L Shared Savings Program. These C&I standard values are 

based on five years of data for most measures and two years of data for lighting. Some 

electric and gas input values were derived from a review of literature from various 

industry organizations, equipment manufacturers, and suppliers. 

Purpose 

These protocols were developed for the purpose of determining energy and resource 

savings for Clean Energy technologies and measures supported by the programs 

approved by Board Order dated March 9, 2001 and subsequently described in a Program 

Compliance Filing made on April 9, 2001 in the Comprehensive Resources Analysis 

(CRA) of Energy Programs proceeding, Docket Nos. EX99050347, EO99050348, 

EO99050349, EO99050350, EO99050351, GO99050352, GO99050353, and 

GO99050354. These protocols will be used consistently statewide to assess program 

impacts and calculate energy and resource savings to: 

1. Report to the Board on program performance 

2. Provide inputs for planning and cost-effectiveness calculations 

3. Calculate lost margin revenue recovery 

4. Provide information to regulators and program administrators for determining 

eligibility for administrative performance incentives (to the extent that such 

incentives are approved by the BPU) 

5. Assess the environmental benefits of program implementation 

Resource savings to be measured include electric energy (kWh) and capacity (kW) 

savings, natural gas savings (therms), and savings of other resources (oil, propane, water, 

and maintenance), where applicable. In turn, these resource savings will be used to 

determine avoided environmental emissions. 

The protocols in this document focus on the determination of the per unit savings for the 

energy efficiency measures included in the programs in the April 9, 2001 Program 
New Jersey Clean Energy Program Page 2 

Protocols to Measure Resource Savings 

September 2004 

Compliance Filing. The number of adopted units to which these per unit savings apply 

are captured in the program tracking and reporting process, supported by market 

assessments for some programs. The unit count will reflect the direct participation and, 

through market assessments, the number of units due to market effects in comparison to a 

baseline level of adoptions. Free riders and free drivers will be captured implicitly on a 

net basis through this approach to counting adoption of units. Further, the net of free 

riders and free drivers are assumed to be zero in the counting of units from direct 

program participation. 

The following four attachments to Supplement 1 to the April 9, 2001 Program 

Compliance Filing present inter-related plans and analyses to support regulatory 

reporting, measure energy and resource savings, assess program cost effectiveness and 

environmental benefits, and track and evaluate program implementation: 

• Attachment 1 - Energy and Economic Assessment of Energy Efficiency Programs 

(Cost Effectiveness) 

• Attachment 2 - Protocols to Measure Resource Savings 

• Attachment 3 - Program Evaluation Plan 

• Attachment 4 - Regulatory Reporting 

• Attachment 5 - Performance Incentives 

The protocols (Attachment 2) provide the methods to measure per unit savings for 

program tracking and reporting. The Evaluation Plan (Attachment 3) outlines the plans 

for assessing markets and program progress in transforming markets, and to update key 

assumptions used in the protocols to assess program energy savings. Reporting 

(Attachment 4) provides formats and definitions to be used to document program 

expenditures, participation rates, and program impacts, including energy and resource 

savings. The program tracking systems, that support program evaluation and reporting, 

will track and record the number of units adopted due to the program, and assist in 

documenting the resource savings using the per unit savings values in the protocols. The 

Energy and Economic Assessment of Energy Efficiency Programs (Cost Effectiveness) 

(Attachment 1) presents the projected impacts of programs, including market effects, and 

their relationship to costs in a multi-year analysis. The assumptions and methods used in 

these statewide analyses are consistent and integrated (e.g., the same per unit savings 

were used to project program savings, to assess program cost-effectiveness and 

environmental benefits, and to set savings goals for program performance incentives). 

Types of Protocols 

In general, energy and demand savings will be measured using measured and customer 

data as input values in algorithms in the protocols, tracking systems, and information 

from the program application forms, worksheets, and field tools. 
New Jersey Clean Energy Program Page 3 

Protocols to Measure Resource Savings 

September 2004 

The following table summarizes the spectrum of protocols and approaches to be used for 

measuring energy and resource savings. No one protocol approach will serve all 

programs and measures. 

Summary of Protocols and Approaches 

Type of 

Measure 

Type of 

Protocol 

General Approach Examples 

1. Standard 

prescriptive 

measures 

Standard formula 

and standard 

input values 

Number of installed 

units times standard 

savings/unit 

Residential lighting 

(number of units 

installed times 

standard savings/unit) 

2. Measures 

with important 

variations in one 

or more input 

values (e.g., delta 

watts, efficiency 

level, capacity, 

load, etc.) 

Standard formula 

with one or more 

site-specific 

input values 

Standard formula in 

the protocols with one 

or more input values 

coming from the 

application form, 

worksheet, or field 

tool (e.g., delta watts, 

efficiency levels, unit 

capacity, site-specific 

load) 

Some prescriptive 

lighting measures 

(delta watts on the 

application form times 

standard operating 

hours in the protocols) 

Residential Electric 

HVAC (change in 

efficiency level times 

site-specific capacity 

times standard 

operating hours) 

Field screening tools 

that use site-specific 

input values 

3. Custom or 

site-specific 

measures, 

or measures 

in complex 

comprehensive 

jobs 

Site-specific 

analysis 

Greater degree of sitespecific analysis, 

either in the number of 

site-specific input 

values, or in the use of 

special engineering 

algorithms 

Custom 

Industrial process 

Complex 

comprehensive jobs 

Three or four systems will work together to ensure accurate data on a given measure: 

1. The application form that the customer or customer’s agent submits with basic 

information. 

2. Application worksheets and field tools with more detailed site-specific data, input 

values, and calculations (for some programs). 
New Jersey Clean Energy Program Page 4 

Protocols to Measure Resource Savings 

September 2004 

3. Program tracking systems that compile data and may do some calculations. 

4. Protocols that contain algorithms and rely on standard or site-specific input values 

based on measured data. Parts or all of the protocols may ultimately be 

implemented within the tracking system, the application forms and worksheets, 

and the field tools. 

Algorithms 

The algorithms that have been developed to calculate the energy and or demand savings 

are driven by a change in efficiency level for the installed measure compared to a 

baseline level of efficiency. This change in efficiency is reflected in both demand and 

energy savings for electric measures and energy savings for gas. Following are the basic 

algorithms. 

Electric Demand Savings = ∆kW = kWbaseline - kWenergy efficient measure

Electric Energy Savings = ∆kW X EFLH 

Electric Peak Coincident Demand Savings = ∆kW X Coincidence Factor 

Gas Energy Savings = ∆Btuh X EFLH 

Where: 

EFLH = Equivalent Full Load Hours of operation for the installed measure. 

∆Btuh = Btuhbaseline input – Btuhenergy efficient measure input 

Other resource savings will be calculated as appropriate. 

Specific algorithms for each of the program measures may incorporate additional factors 

to reflect specific conditions associated with a program or measure. This may include 

factors to account for coincidence of multiple installations, or interaction between 

different measures. 

Data and Input Values 

The input values and algorithms in the protocols and on the program application forms 

are based on the best available and applicable data for the New Jersey programs. The 

input values for the algorithms come from the program application forms or from 

standard values based on measured or industry data. 

Many input values, including site-specific data, come directly from the program 

application forms, worksheets, and field tools. Site-specific data on the application forms 

are used for measures with important variations in one or more input values (e.g., delta 

watts, efficiency level, capacity, etc.). 
New Jersey Clean Energy Program Page 5 

Protocols to Measure Resource Savings 

September 2004 

Standard input values are based on the best available measured or industry data, 

including metered data, measured data from prior evaluations (applied prospectively), 

field data and program results, and standards from industry associations. The standard 

values for most commercial and industrial measures are supported by end use metering 

for key parameters for a sample of facilities and circuits. These standard values are based 

on five years of metered data for most measures1

. Data that were metered over that time 

period are from measures that were installed over an eight-year period. Many input 

values are based on program evaluations of prior New Jersey programs or similar 

programs in other regions. 

For the standard input assumptions for which metered or measured data were not 

available, the input values (e.g., delta watts, delta efficiency, equipment capacity, 

operating hours, coincidence factors) were based on the best available industry data or 

standards. These input values were based on a review of literature from various industry 

organizations, equipment manufacturers, and suppliers. 

Program evaluation will be used to assess key data and input values to either confirm that 

current values should continue to be used or update the values going forward. 

Baseline Estimates 

For most programs the ∆ kW and ∆ kWh values are based on the energy use of standard 

new products vs. the high efficiency products promoted through the programs. This 

baseline may be different than the baseline estimates used in previous programs such as 

the Standard Offer in which the baseline assumptions were based on either the existing 

equipment for retrofits or current code or practice for new construction. The approach 

used for the new programs encourages residential and business consumers to purchase 

and install high efficiency equipment vs. new standard efficiency equipment. The 

baseline estimates used in the protocols are documented in the baseline studies or other 

market information. Baselines will be updated to reflect changing codes, practices and 

market transformation effects. 

Resource Savings in Current and Future Program Years 

The Protocols support tracking and reporting the following categories of energy and 

resource savings: 

1. Savings from installations that were completed in the program year and prior 

program years due to the program’s direct participation and documented market 

effects. 

2. Savings from program participant future adoptions due to program commitments. 

3. Savings from future adoptions due to market effects. 

 

1

 Values for lighting, air conditioners, chillers, and motors are based on measured usage from a large 

sample of participants from 1995 through 1999. Values for heat pumps reflect metered usage from 1996 

through 1998, and variable speed drives reflect metered usage from 1995 through 1998. 
New Jersey Clean Energy Program Page 6 

Protocols to Measure Resource Savings 

September 2004 

Prospective Application of the Protocols 

The protocols will be applied prospectively. The input values are from the program 

application forms and standard input values (based on measured data including metered 

data and evaluation results). The protocols will be updated periodically based on 

evaluation results and available data, and then applied prospectively for future program 

years. 

The only exceptions to prospective application of the protocols are (1) utility review of 

tracking systems and any necessary adjustments after the end of the program year and 

prior to the completion of the annual report for that year, and (2) adjustments due to 

review and on-site verification of custom measures and large comprehensive jobs, also to 

be completed before the submission of the annual report for that year. 

Resource Savings 

Electric 

Protocols have been developed to determine the electric energy and coincident peak 

demand savings. 

Annual Electric energy savings are calculated and then allocated separately by season 

(summer and winter) and time of day (on-peak and off-peak). Summer coincident peak 

demand savings are calculated using a demand savings protocol for each measure that 

includes a coincidence factor. Application of this coincidence factor converts the 

demand savings of the measure, which may not occur at time of system peak, to demand 

savings that is expected to occur during the Summer On-Peak period. These periods for 

energy savings and coincident peak demand savings are defined as: 

Energy Savings Coincident Peak 

Demand Savings 

Summer May through 

September 

June through 

August 

Winter October through 

April 

NA 

On Peak (Monday - 

Friday) 

8:00 a.m. to 8:00 

p.m. 

12:00 p.m. to 8:00 

p.m. 

Off Peak (Weekends 

and Holidays) 

8:00 p.m. to 8:00 

a.m. 

NA 

The time periods for energy savings and coincident peak demand savings were chosen to 

best fit the seasonal avoided cost patterns for electric energy and capacity that were used 

for the energy efficiency program cost effectiveness purposes. For energy, the summer 

period May through September was selected based on the pattern of avoided costs for 

energy at the PJM level. In order to keep the complexity of the process for calculating 

energy savings benefits to a reasonable level by using two time periods, the knee periods 

for spring and fall were split approximately evenly between the summer and winter 
New Jersey Clean Energy Program Page 7 

Protocols to Measure Resource Savings 

September 2004 

periods. For capacity, the summer period June through August was selected to match the 

highest avoided costs time period for capacity. The experience in PJM and New Jersey 

has been that nearly all system peak events occur during these three months. 

The electric energy savings are tracked by rate schedules. 

Natural Gas 

Protocols have been developed to determine the natural gas energy savings on a seasonal 

basis. The gas energy savings are tracked by rate schedule. The seasonal periods are 

defined as: 

Summer - April through September 

Winter - October through March 

The time periods for gas savings were chosen to best fit the seasonal avoided gas cost 

pattern that was used for calculating energy efficiency program benefits for cost 

effectiveness purposes. However, given the changing seasonal cost patterns for gas 

supply, different time periods may be more appropriate to reflect a current outlook for the 

seasonal pattern, if any, at the time that the avoided cost benefits are calculated. The 

seasonal factors used in the following protocols that correspond to the above time periods 

reflect either base load or heating load usage. In the case of base load, one twelfth of the 

annual use is allocated to each month. In the case of heating load, the usage is prorated 

to each month based on the number of normal degree-days in each month. This approach 

makes it relatively easy to calculate new seasonal factors to best match different avoided 

cost patterns. 

Other Resources 

Some of the energy savings measures also result in environmental benefits and the saving 

of other resources. Environmental impacts are quantified based on statewide conversion 

factors supplied by the NJDEP for electric, gas and oil energy savings. Where 

identifiable and quantifiable these other key resource savings, such as water, will be 

estimated. Water, oil, propane and maintenance savings are the major resources that 

have been identified. If other resources are significantly impacted, they will be included 

in the resource savings estimates. 

Post-Implementation Review 

Program administrators will review application forms and tracking systems for all 

measures and conduct field inspections on a sample of installations. For some programs 

and jobs (e.g., custom, large process, large and complex comprehensive design), postinstallation review and on-site verification of a sample of application forms and 

installations will be used to ensure the reliability of site-specific savings estimates. 
New Jersey Clean Energy Program Page 8 

Protocols to Measure Resource Savings 

September 2004 

Adjustments to Energy and Resource Savings 

Coincidence with Electric System Peak 

Coincidence factors are used to reflect the portion of the connected load savings that is 

coincident with the electric system peak. 

Measure Retention and Persistence of Savings 

The combined effect of measure retention and persistence is the ability of installed 

measures to maintain the initial level of energy savings over the measure life. Measure 

retention and persistence effects were accounted for in the metered data that were based 

on C&I installations over an eight-year period. As a result, some protocols incorporate 

retention and persistence effects in the other input values. For other measures, if the 

measure is subject to a reduction in savings over time, the reduction in retention or 

persistence is accounted for using factors in the calculation of resource savings (e.g., inservice rates for residential lighting measures). 

Interaction of Energy Savings 

Interaction of energy savings is accounted for in certain programs as appropriate. For all 

other programs and measures, interaction of energy savings is zero. 

For the Residential New Construction program, the interaction of energy savings is 

accounted for in the home energy rating tool that compares the efficient building to the 

baseline or reference building and calculates savings. 

For the Commercial and Industrial Efficient Construction program, the energy savings 

for lighting is increased by an amount specified in the protocol to account for HVAC 

interaction. 

For commercial and industrial custom measures, interaction where relevant is accounted 

for in the site-specific analysis. 

Calculation of the Value of Resource Savings 

The calculation of the value of the resources saved is not part of the protocols. The 

protocols are limited to the determination of the per unit resource savings in physical 

terms. 

In order to calculate the value of the energy savings for reporting and other purposes, the 

energy savings are determined at the customer level and then increased by the amount of 

the transmission and distribution losses to reflect the energy savings at the system level. 

The energy savings at the system level are then multiplied by the appropriate avoided 

costs to calculate the value of the benefits. 

System Savings = (Savings at Customer) X (T&D Loss Factor) 
New Jersey Clean Energy Program Page 9 

Protocols to Measure Resource Savings 

September 2004 

Value of Resource Savings = (System Savings) X (System Avoided Costs + 

Environmental Adder) + (Value of Other Resource Savings) 

The value of the benefits for a particular measure will also include the value of the water, 

oil, maintenance and other resource savings where appropriate. Maintenance savings 

will be estimated in annual dollars levelized over the life of the measure. 

Transmission and Distribution System Losses 

The protocols calculate the energy savings at the customer level. These savings need to 

be increased by the amount of transmission and distribution system losses in order to 

determine the energy savings at the system level. The following loss factors multiplied 

by the savings calculated from the protocols will result in savings at the supply level. 

Electric Loss Factor 

The electric loss factor applied to savings at the customer meter is 1.11 for both energy 

and demand. The electric system loss factor was developed to be applicable to statewide 

programs. Therefore, average system losses at the margin based on PJM data were 

utilized. This reflects a mix of different losses that occur related to delivery at different 

voltage levels. The 1.11 factor used for both energy and capacity is a weighted average 

loss factor and was adopted by consensus. 

Gas Loss Factor 

The gas loss factor is 1.0. The gas system does not have losses in the same sense that the 

electric system does. All of the gas gets from the “city gate” (delivery point to the 

distribution system) to the point of use except for unaccounted for gas (such as theft), gas 

lost due to system leakage or loss of gas that is purged when necessary to make system 

repairs. Since none of these types of “losses” is affected by a decrease in gas use due to 

energy efficiency at the customer, there are no losses for which to make any adjustment. 

Therefore, a system loss factor of 1.0 is appropriate for gas energy efficiency savings. 

These electric and gas loss factors reflect losses at the margin and are a consensus of the 

electric and gas utilities. 

Calculation of Clean Air Impacts 

The amount of air emission reductions resulting from the energy savings are calculated 

using the energy savings at the system level and multiplying them by factors developed 

by the New Jersey Department of Environmental Protection (NJDEP). 

System average air emissions reduction factors provided by the NJDEP are: 

Electric Emissions Factors 

Emissions 

Product 

Jan 2001-June 2002 July 2003-Present 

CO2 1.1 lbs per kWh 

saved 

1,520 lbs per MWh 

saved 
New Jersey Clean Energy Program Page 10 

Protocols to Measure Resource Savings 

September 2004 

NOX 6.42 lbs per metric 

ton of CO2 saved 

2.8 lbs per MWh 

saved 

SO2 10.26 lbs per metric 

ton of CO2 saved 

6.5 lbs per MWh 

saved 

Hg 0.00005 lbs per 

metric ton of CO2

saved 

0.0000356 lbs per 

MWh saved 

Gas Emissions Factors 

Emissions 

Product 

Jan 2001-June 2002 July 2003-Present 

CO2 NA 11.7 lbs per therm 

saved 

NOX NA 0.0092 lbs per 

therm saved 

All factors are provided by the NJ Department of Environmental Protection and 

are on an average system basis. They will be updated as new factors become 

available. 

Measure Lives 

Measure lives are provided in Appendix A for informational purposes and for use in 

other applications such as reporting lifetime savings or in benefit cost studies that span 

more than one year. For regulatory reporting, the following are the average lives that 

relate lifetime savings to annual savings for each program reporting savings. 

 Measure Life 

(Years) 

Program Electric Gas 

 

Residential HVAC 15 20 

Residential Low Income 16 20 

Energy Star Homes 20 20 

C&I Construction 15 15 

Customer Sited Generation 

 PV 

 Wind 

 Fuel Cell 

20 

15 

10 

Protocols for Program Measures 

The following pages present measure-specific protocols. 
New Jersey Clean Energy Program Page 11 

Protocols to Measure Resource Savings 

September 2004 

Residential Electric HVAC 

Protocols 

The measurement plan for residential high efficiency cooling and heating equipment is 

based on algorithms that determine a central air conditioner’s or heat pump’s 

cooling/heating energy use and peak demand. Input data is based both on fixed 

assumptions and data supplied from the high efficiency equipment rebate application 

form. The algorithms also include the calculation of additional energy and demand 

savings due to the required proper sizing and installation of high efficiency units. 

The savings will be allocated to summer/winter and on-peak/off-peak time periods based 

on load shapes from measured data and industry sources. The allocation factors are 

documented below in the input value table. 

The protocols applicable for this program measure the energy savings directly related to 

the more efficient hardware installation. Estimates of energy savings due to the proper 

sizing of the equipment and improved installation practices are also included. 

The following is an explanation of the algorithms used and the nature and source of all 

required input data. 

Algorithms

Central Air Conditioner (A/C) & Air Source Heat Pump (ASHP) 

Cooling Energy Consumption and Peak Demand Savings – Central A/C & ASHP 

Energy Impact (kWh) = CAPY/1000 X (1/SEERb – (1/SEERq X (1-ESF))) X 

EFLH 

Peak Demand Impact (kW) = CAPY/1000 X (1/EERb – (1/EERq X (1-DSF))) X 

CF 

Heating Energy Savings – ASHP 

Energy Impact (kWh) = CAPY/1000 X (1/HSPFb – (1/HSPFq X (1-ESF))) X 

EFLH 

Ground Source Heat Pumps (GSHP) 

Cooling Energy (kWh) Savings = CAPY/1000 X (1/SEERb – (1/EERg X GSER)) 

X EFLH 

Heating Energy (kWh) Savings = CAPY/1000 X (1/HSPFb – (1/COPg X GSOP)) 

X EFLH 
New Jersey Clean Energy Program Page 12 

Protocols to Measure Resource Savings 

September 2004 

Peak Demand Impact (kW) = CAPY/1000 X (1/EERb – (1/EERg X GSPK)) X CF

GSHP Desuperheater 

Energy (kWh) Savings = EDSH 

Peak Demand Impact (kW) = PDSH 

Definition of Terms

CAPY = The cooling capacity (output) of the central air conditioner or heat pump being 

installed. This data is obtained from the Application Form based on the model number. 

SEERb = The Seasonal Energy Efficiency Ratio of the Baseline Unit. 

SEERq = The Seasonal Energy Efficiency Ratio of the qualifying unit being installed. 

This data is obtained from the Application Form based on the model number. 

EERb = The Energy Efficiency Ratio of the Baseline Unit. 

EERq = The Energy Efficiency Ratio of the unit being installed. This data is obtained 

from the Application Form based on the model number. 

EERg = The EER of the ground source heat pump being installed. Note that EERs of 

GSHPs are measured differently than EERs of air source heat pumps (focusing on 

entering water temperatures rather than ambient air temperatures). The equivalent SEER 

of a GSHP can be estimated by multiplying EERg by 1.02. 

GSER = The factor to determine the SEER of a GSHP based on its EERg. 

EFLH = The Equivalent Full Load Hours of operation for the average unit. 

ESF = The Energy Sizing Factor or the assumed saving due to proper sizing and proper 

installation. 

CF = The coincidence factor which equates the installed unit’s connected load to its 

demand at time of system peak. 

DSF = The Demand Sizing Factor or the assumed peak demand capacity saved due to 

proper sizing and proper installation. 

HSPFb = The Heating Seasonal Performance Factor of the Baseline Unit. 

HSPFq = The Heating Seasonal Performance Factor of the unit being installed. This data 

is obtained from the Application Form. 

COPg = Coefficient of Performance. This is a measure of the efficiency of a heat pump. 
New Jersey Clean Energy Program Page 13 

Protocols to Measure Resource Savings 

September 2004 

GSOP = The factor to determine the HSPF of a GSHP based on its COPg. 

GSPK = The factor to convert EERg to the equivalent EER of an air conditioner to enable 

comparisons to the baseline unit. 

EDSH = Assumed savings per desuperheater. 

PDSH = Assumed peak demand savings per desuperheater. 

The 1000 used in the denominator is used to convert watts to kilowatts. 

A summary of the input values and their data sources follows: 

Residential Electric HVAC 

Component Type Value Sources 

CAPY Variable Rebate 

Application 

SEERb Fixed Baseline = 10 1 

SEERq Variable Rebate 

Application 

EERb Fixed Baseline = 9.2 2 

EERq Variable Rebate 

Application 

EERg Variable Rebate 

Application 

GSER Fixed 1.02 3 

EFLH Fixed Cooling = 600 Hours 

Heating = 2250 Hours 

4 

ESF Fixed 17% 5 

CF Fixed 70% 6 

DSF Fixed 7% 7 

HSPFb Fixed Baseline = 6.8 8 

HSPFq Variable Rebate 

Application 

COPg Variable Rebate 

Application 

GSOP Fixed 3.413 9 

GSPK Fixed 0.8416 10 

EDSH Fixed 1842 kWh 11 

PDSH Fixed 0.34 kW 12 
New Jersey Clean Energy Program Page 14 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

Cooling - CAC 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 64.9% 

Summer/Off-Peak 35.1% 

Winter/On-Peak 0% 

Winter/Off-Peak 0% 

13 

Cooling – ASHP 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 59.8% 

Summer/Off-Peak 40.2% 

Winter/On-Peak 0% 

Winter/Off-Peak 0% 

13 

Cooling – GSHP 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 51.7% 

Summer/Off-Peak 48.3% 

Winter/On-Peak 0% 

Winter/Off-Peak 0% 

13 

Heating – ASHP & 

GSHP 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 0.0% 

Summer/Off-Peak 0.0% 

Winter/On-Peak 47.9% 

Winter/Off-Peak 52.1% 

13 

GSHP 

Desuperheater Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 4.5% 

Summer/Off-Peak 4.2% 

Winter/On-Peak 43.7% 

Winter/Off-Peak 47.6% 

13 

Sources: 

1. Federal minimum SEER is 10.0 and national data confirms that this is 

predominately the unit installed without intervention. 

2. Analysis of ARI data. 

3. VEIC estimate. Extrapolation of manufacturer data. 

4. VEIC estimate. Consistent with analysis of PEPCo and LIPA, and conservative 

relative to ARI. 

5. From Neme, Proctor and Nadel, 1999. This value is identified as a priority for 

future evaluation. 

6. Based on an analysis of 6 different utilities by Proctor Engineering. 

7. From Neme, Proctor and Nadel, 1999. 

8. Federal minimum HSPF is 6.8. 

9. Engineering calculation, HSPF/COP=3.413 

10. VEIC Estimate. Extrapolation of manufacturer data. 

11. VEIC estimate, based on PEPCo assumptions. 

12. VEIC estimate, based on PEPCo assumptions. 

13. Time period allocation factors used in cost-effectiveness analysis. 
New Jersey Clean Energy Program Page 15 

Protocols to Measure Resource Savings 

September 2004 

Residential Gas HVAC 

Protocols 

The following two algorithms detail savings for gas heating and water heating equipment. 

They are to be used to determine gas energy savings between baseline standard units and 

the high efficiency units promoted in the program. The input values are based on data on 

typical customers supplied by the gas utilities, an analysis by the Federal Energy 

Management Program (FEMP), and customer information on the application form, 

confirmed with manufacturer data. The energy values are in therms. 

Space Heaters 

Algorithms

Gas Savings = Capyq/Capyt X ((AFUEq – AFUEb)/AFUEq) X Baseline Heating Usage 

Definition of Variables

Capyq = Actual output capacity of the qualifying heating system in Btus/hour 

Capyt = Output capacity of the typical heating unit output in Btus/hour 

AFUEq = Annual Fuel Utilization Efficiency of the qualifying energy efficient furnace or 

boiler 

AFUEb = Annual Fuel Utilization Efficiency of the baseline furnace or boiler 

Baseline Heating Usage = The weighted average annual heating usage (therms) of typical 

New Jersey heating customers 

Space Heating 

Component Type Value Source 

Capyq Variable Application Form, 

confirmed with 

Manufacturer Data 

Capyt Fixed 80,000 1 

AFUEq Variable Application Form, 

confirmed with 

Manufacturer Data 

AFUEb Fixed Furnaces: 80% 

Boilers: 83% 

2 

Baseline Heating 

Usage 

Fixed 965 therms 3 
New Jersey Clean Energy Program Page 16 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Source 

Time Period 

Allocation Factors 

Fixed Summer = 12% 

Winter = 88% 

4 

Sources: 

1. NJ utility analysis of heating customers, typical output capacity. 

2. Based on the quantity of models available by efficiency ratings as listed in the 

April 2003 Gamma Consumers Directory of Certified Efficiency Ratings. 

3. NJ utility analysis of heating customers, annual gas heating usage. 

4. Prorated based on 12% of the annual degree days falling in the summer period 

and 88% of the annual degree days falling in the winter period. 

Water Heaters 

Algorithms

Gas Savings = ((EFq – EFb)/EFq) X Baseline Water Heater Usage 

Definition of Variables

EFq = Energy factor of the qualifying energy efficient water heater. 

EFb = Energy factor of the baseline water heater. 

Baseline Water Heater Usage = Annual usage of the baseline water heater, in therms. 

Water Heaters 

Component Type Value Source 

Efq Variable Application Form, 

confirmed with 

Manufacturer Data 

Efb Fixed 0.544 1 

Baseline Water 

Heater Usage 

Fixed 277 2 

Time Period 

Allocation Factors 

Fixed Summer = 50% 

Winter = 50% 

3 

Sources: 

1. Federal EPACT Standard for a 40 gallon gas water heater. Calculated as 0.62 – 

(0.0019 X gallons of capacity). 

2. DOE/FEMP website. http://www.eren.doe.gov/femp/pro

3. Prorated based on 6 months in the summer period and 6 months in the winter 

period. 
New Jersey Clean Energy Program Page 17 

Protocols to Measure Resource Savings 

September 2004 

Residential Energy Star Windows 

Protocols 

The general form of the equation for the ENERGY STAR or other high efficiency 

windows energy savings algorithms is: 

Square Feet of Window Area X Savings per Square Foot 

To determine resource savings, the per square foot estimates in the protocols will be 

multiplied by the number of square feet of window area. The number of square feet of 

window area will be determined using market assessments and market tracking. Some of 

these market tracking mechanisms are under development. The per unit energy and 

demand savings estimates are based on prior building simulations of windows. 

ENERGY STAR Windows 

Savings estimates for ENERGY STAR Windows are based on modeling a typical 2,500 

square foot home using REM Rate, the home energy rating tool. Savings are per square 

foot of qualifying window area. Savings will vary based on heating and cooling system 

type and fuel. These fuel and HVAC system market shares will need to be estimated 

from prior market research efforts or from future program evaluation results. 

Heat Pump

Electricity Impact (kWh) = ESavHP 

Demand Impact (kW) = DSavHP x CF 

Gas Heat/CAC

Electricity Impact (kWh) = ESavGAS/CAC

Demand Impact (kW) = DSavCAC x CF 

Gas Impact (therms) = GSavGAS

Gas Heat/No CAC

Electricity Impact (kWh) = ESavGAS/NOCAC 

Demand Impact (kW) = DSavNOCAC x CF 

Gas Impact (therms) = GSavGAS
New Jersey Clean Energy Program Page 18 

Protocols to Measure Resource Savings 

September 2004 

Oil Heat/CAC

Electricity Impact (kWh) = ESavOIL/CAC

Demand Impact (kW) = DSavCAC x CF 

Oil Impact (MMBtu) = OSavOIL

Oil Heat/No CAC

Electricity Impact (kWh) = ESavOIL/NOCAC

Demand Impact (kW) = DSavNOCAC x CF 

Oil Impact (MMBtu) = OSavOIL

Electric Heat/CAC

Electricity Impact (kWh) = ESavRES/CAC

Demand Impact (kW) = DSavCAC x CF

Electric Heat/No CAC

Electricity Impact (kWh) = ESavRES/NOCAC

Demand Impact (kW) = DSavNOCAC x CF

Definition of Terms

ESavHP = Electricity savings (heating and cooling) with heat pump installed. 

ESavGAS/CAC = Electricity savings with gas heating and central AC installed. 

ESavGAS/NOCAC = Electricity savings with gas heating and no central AC installed. 

ESavOIL/CAC = Electricity savings with oil heating and central AC installed. 

ESavOIL/NOCAC = Electricity savings with oil heating and no central AC installed. 

ESavRES/CAC = Electricity savings with electric resistance heating and central AC 

installed. 

ESavRES/NOCAC = Electricity savings with electric resistance heating and no central AC 

installed. 
New Jersey Clean Energy Program Page 19 

Protocols to Measure Resource Savings 

September 2004 

DSavHP = Summer demand savings with heat pump installed. 

DSavCAC = Summer demand savings with central AC installed. 

DSavNOCAC = Summer demand savings with no central AC installed. 

CF = System peak demand coincidence factor. Coincidence of building cooling demand 

to summer system peak. 

GSavGAS = Gas savings with gas heating installed. 

OSavOIL = Oil savings with oil heating installed. 

ENERGY STAR Windows 

Component Type Value Sources 

ESavHP Fixed 2.2395 kWh 1 

HP Time Period 

Allocation Factors 

Fixed Summer/On-Peak 10% 

Summer/Off-Peak 7% 

Winter/On-Peak 40% 

Winter/Off-Peak 44% 

2 

ESavGAS/CAC Fixed 0.2462 kWh 1 

Gas/CAC Electricity 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 65% 

Summer/Off-Peak 35% 

Winter/On-Peak 0% 

Winter/Off-Peak 0% 

2 

ESavGAS/NOCAC Fixed 0.00 kWh 1 

Gas/No CAC 

Electricity Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 3% 

Summer/Off-Peak 3% 

Winter/On-Peak 45% 

Winter/Off-Peak 49% 

2 

Gas Heating Gas 

Time Period 

Allocation Factors 

Fixed Summer = 12% 

Winter = 88% 

4 

ESavOIL/CAC Fixed 0.2462 kWh 1 

Oil/CAC Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 65% 

Summer/Off-Peak 35% 

Winter/On-Peak 0% 

Winter/Off-Peak 0% 

2 

ESavOIL/NOCAC Fixed 0.00 kWh 1 

Oil/No CAC Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 3% 

Summer/Off-Peak 3% 

Winter/On-Peak 45% 

Winter/Off-Peak 49% 

2 
New Jersey Clean Energy Program Page 20 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

ESavRES/CAC Fixed 4.0 kWh 1 

Res/CAC Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 10% 

Summer/Off-Peak 7% 

Winter/On-Peak 40% 

Winter/Off-Peak 44% 

2 

ESavRES/NOCAC Fixed 3.97 kWh 1 

Res/No CAC Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 3% 

Summer/Off-Peak 3% 

Winter/On-Peak 45% 

Winter/Off-Peak 49% 

2 

DSavHP Fixed 0.000602 kW 1 

DSavCAC Fixed 0.000602 kW 1 

DSavNOCAC Fixed 0.00 kW 1 

GSavGAS Fixed 0.169 therms 1 

OSavOIL Fixed 0.0169 MMBtu 1 

CF Fixed 0.75 3 

Sources: 

1. From REMRATE Modeling of a typical 2,500 sq. ft. NJ home. Savings 

expressed on a per sq. ft. of window area basis. New Brunswick climate data. 

2. Time period allocation factors used in cost-effectiveness analysis. 

3. Based on reduction in peak cooling load. 

4. Prorated based on 12% of the annual degree days falling in the summer period 

and 88% of the annual degree days falling in the winter period. 
New Jersey Clean Energy Program Page 21 

Protocols to Measure Resource Savings 

September 2004 

Residential Low Income Program 

Protocols 

The savings protocols for the low-income program are based upon estimated per unit 

installed savings. In some cases, such as lighting and refrigerators, the savings per unit 

estimate is based on direct observation or monitoring of the existing equipment being 

replaced. For other measures, for example air sealing and insulation, the protocols 

calculation is based on an average % savings of pre-treatment consumption. The 

protocols for space heating measures were established considering the non-additive 

nature of individual measures. Further, (for protocol reporting only) the cumulative 

savings from space conditioning measures is capped at 10% of pre-treatment electric 

space conditioning consumption and 15% of pre-treatment natural gas space conditioning 

consumption. 

Base Load Measures 

Efficient Lighting 

Savings from installation of screw-in CFLs, high performance fixtures and fluorescent 

torchieres are based on a straightforward algorithm that calculates the difference between 

existing and new wattage, and the average daily hours of usage for the lighting unit being 

replaced. 

Algorithm

Compact Fluorescent Screw In Lamp 

Electricity Impact (kWh) = ((CFLwatts) X (CFLhours X 365))/1000 

Peak Demand Impact (kW) = (CFLwatts) X Light CF 

Efficient Fixtures 

Electricity Impact (kWh) = ((Fixtwatts) X (Fixthours X 365))/1000 

Peak Demand Impact (kW) = (Fixtwatts) X Light CF 

Efficient Torchieres 

Electricity Impact (kWh) = ((Torchwatts) X (Torchhours X 365))/1000 

Peak Demand Impact (kW) = (Torchwatts) X Light CF 

Hot Water Conservation Measures 

The protocols savings estimates are based on an average package of domestic hot water 

measures typically installed by low-income programs. 
New Jersey Clean Energy Program Page 22 

Protocols to Measure Resource Savings 

September 2004 

Algorithm

Electricity Impact (kWh) = HWeavg 

Gas Savings (MMBtu) = HWgavg 

Peak Demand Impact (kW) = HWwatts X HW CF 

Water Savings (gallons) = WS 

Efficient Refrigerators 

The eligibility for refrigerator replacement is determined by comparing monitored 

consumption for the existing refrigerator with the rated consumption of the eligible 

replacement. Estimated savings are directly calculated based on the difference between 

these two values. Note that in the case where an under-utilized or unneeded refrigerator 

unit is removed, and no replacement is installed, the Refnew term of the equation will be 

zero. 

Algorithm

Electricity Impact (kWh) = Refold – Refnew 

 

Peak Demand Impact (kW) = (Refold – Refnew) *(Ref DF)

Space Conditioning Measures 

Savings from individual space conditioning measures are affected by any other measures 

that also are being installed; i.e., such savings are not cumulative. Further, technical 

reasons dictate prioritizing certain measures over others. The savings algorithms for all 

space conditioning measures accommodate these considerations by presuming a fixed 

sequence of measure installation for the purpose of projecting savings and by limiting 

total estimated electric space conditioning savings to 10% of electric space conditioning 

pre-treatment usage. Fossil fuel heated houses typically have more substantial 

opportunities for space conditioning savings than electrically heated houses. Further, 

there are greater opportunities for interaction between measure types. For protocol 

reporting, these savings estimates will be capped at 15% of pre-treatment space heating 

consumption. When available, gas heat measure savings will be based on heating use. If 

only total gas use is known, heating use will be estimated as total use less 300 therms. 

Air Sealing 

It is assumed that air sealing is the first priority among candidate space conditioning 

measures. Expected percentage savings is based on previous experiences with measured 

savings from similar programs. Note there are no summer coincident electric peak 

demand savings estimated at this time. 
New Jersey Clean Energy Program Page 23 

Protocols to Measure Resource Savings 

September 2004 

Algorithm

Electricity Impact (kWh) = ESCpre X 0.05 

MMBtu savings = (GHpre X 0.05) 

Duct Sealing and Repair 

The second priority for homes with either Central Air Conditioning (CAC) or some other 

form of ducted distribution of electric space conditioning (electric furnace, gas furnace or 

heat pump) is ensuring integrity and effectiveness of the ducted distribution system. 

Algorithm

With CAC 

Electricity Impact (kWh) = (ECoolpre) X 0.10 

Peak Demand Impact (kW) = (Ecoolpre X 0.10) / EFLH X AC CF 

MMBtu savings = (GHpre X 0.02) 

No CAC 

Electricity Impact (kWh) = (ESCpre X 0.95) X 0.02 

MMBtu savings = (GHpre X 0.02) 

Insulation Up-Grades 

For savings calculations, it is assumed that any applicable air sealing and duct 

sealing/repair have been done, thereby reducing the space conditioning load, before 

consideration of upgrading insulation. Attic insulation savings are then projected on the 

basis of the “new” load. Gas savings are somewhat greater, as homes with gas heat 

generally have less insulation. 

Algorithm

Electricity Impact (kWh) = (ESCpre X 0.93) X 0.08 

MMBtu savings = GHpre X 0.13 

Thermostat Replacement 

Thermostats are eligible for consideration as an electric space conditioning measure only 

after the first three priority items. Savings projections are based on a conservative 3% of 

the “new” load after installation of any of the top three priority measures. 
New Jersey Clean Energy Program Page 24 

Protocols to Measure Resource Savings 

September 2004 

Algorithm

Electricity Impact (kWh) = (ESCpre X 0.85) X 0.03 

MMBtu savings = (GHpre X 0.03) 

Heating and Cooling Equipment Maintenance Repair/Replacement 

Savings projections for heat pump charge and air flow correction. Protocol savings 

account for shell measures having been installed that reduce the pre-existing load. 

Algorithm

Electricity Impact (kWh) = (ESCpre X 0.93) X 0.17 

Peak Demand Impact (kW) = (Capy/EER X 1000) X HP CF X DSF 

Total Space Conditioning Savings 

As noted, for protocol reporting the total electric savings from all space conditioning 

measures are presumed to not exceed 10% of the pre-treatment consumption, and gas 

savings are presumed to not exceed 15% of pre-treatment space heating consumption. 

Algorithm

Maximum Electricity Impact (kWh) ≤ (ESCpre X 0.10) 

Maximum MMBtu savings = (GHpre X 0.15) 

Other “Custom” Measures 

In addition to the typical measures for which savings algorithms have been developed, it 

is assumed that there will be niche opportunities that should be identified and addressed. 

The savings for these custom measures will be reported based on the individual 

calculations supplied with the reporting. As necessary the program working group will 

develop specific guidelines for frequent custom measures for use in reporting and 

contractor tracking. 

Definition of Terms

CFLwatts = Average watts replaced for a CFL installation. 

CFLhours = Average daily burn time for CFL replacements. 

Fixtwatts = Average watts replaced for an efficient fixture installation. 
New Jersey Clean Energy Program Page 25 

Protocols to Measure Resource Savings 

September 2004 

Fixthours = Average daily burn time for CFL replacements. 

Torchwatts = Average watts replaced for a Torchiere replacement. 

Torchhours = Average daily burn time for a Torchiere replacements. 

Light CF = Summer demand coincidence factor for all lighting measures. Currently fixed 

at 5%. 

HWeavg = Average electricity savings from typical electric hot water measure package. 

HWgavg = Average natural gas savings from typical electric hot water measure package. 

HWwatts = Connected load reduction for typical hot water efficiency measures 

HW CF = Summer demand coincidence factor for electric hot water measure package. 

Currently fixed at 75%. 

Refold = Annual energy consumption of existing refrigerator based on on-site monitoring. 

Refnew = Rated annual energy consumption of the new refrigerator. 

Ref DF = kW /kWh of savings. Refrigerator demand savings factor. 

Ref CF = Summer demand coincidence factor for refrigeration. Currently 100%, diversity 

accounted for in the Ref DF factor. 

ESCpre = Pre-treatment electric space conditioning consumption. 

ECoolpre = Pre-treatment electric cooling consumption. 

EFLH = Equivalent full load hours of operation for the average unit. This value is 

currently fixed at 650 hours. 

AC CF = Summer demand coincidence factor for air conditioning. Currently 85%. 

Capy = Capacity of Heat Pump in Btuh 

EER = Energy Efficiency Ratio of average heat pump receiving charge and air flow 

service. Fixed at 9.2 

HP CF = Summer demand coincidence factor for heat pump. Currently fixed at 70%. 

DSF = Demand savings factor for charge and air flow correction. Currently fixed at 7%. 

GCpre = Pre treatment gas consumption. 
New Jersey Clean Energy Program Page 26 

Protocols to Measure Resource Savings 

September 2004 

GHpre = Pre treatment gas space heat consumption (=.GCpre less 300 therms if only total 

gas use is known.

WS = Water Savings associated with water conservation measures. Currently fixed at 

3,640 gallons per year per home receiving low flow showerheads, plus 1,460 

gallons saved per year per home receiving aerators. 

Residential Low Income 

Component Type Value Sources 

CFLWatts Fixed 42 Watts 1 

CFLHours Fixed 2.5 hours 1 

FixtWatts Fixed 90 Watts 1 

FixtHours Fixed 3.5 hours 1 

TorchWatts Fixed 245 Watts 1 

TorchHours Fixed 3.5 hours 1 

Light CF Fixed 5% 2 

Elec. Water Heating 

Savings 

Fixed 178 kWh 3 

Gas Water Heating 

Savings 

Fixed 1.01 MMBTU 3 

WS Water Savings Fixed 3,640 gal/year per home 

receiving low flow 

shower heads, plus 1,460 

gal/year per home 

receiving aerators. 

12 

HWwatts Fixed 0.022 kW 4 

HW CF Fixed 75% 4 

Refold Variable Contractor 

Tracking 

Refnew Variable Contractor 

Tracking and 

Manufacturer 

data 

Ref DF Fixed 0.000139 kW/kWh 

savings 

5 

RefCF Fixed 100% 6 

ESCpre Variable 7 

Ecoolpre Variable 7 

ELFH Fixed 650 hours 8 

AC CF Fixed 85% 4 

Capy Fixed 33,000 Btu/hr 1 

EER Fixed 9.2 8 

HP CF Fixed 70% 9 
New Jersey Clean Energy Program Page 27 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

DSF Fixed 7% 10 

GCpre Variable 7 

GHpre Variable 7 

Time Period 

Allocation Factors - 

Electric 

Fixed Summer/On-Peak 21% 

Summer/Off-Peak 22% 

Winter/On-Peak 28% 

Winter/Off-Peak 29% 

11 

Time Period 

Allocation Factors - 

Gas 

Fixed Heating: 

Summer 12% 

Winter 88% 

Non-Heating: 

Summer 50% 

Winter 50% 

13 

Sources/Notes: 

1. Working group expected averages for product specific measures. 

2. Efficiency Vermont Reference Manual – average for lighting products. 

3. Experience with average hot water measure savings from low income and direct 

install programs. 

4. VEIC estimate. 

5. UI Refrigerator Load Data profile, .16 kW (5pm July) and 1,147 kWh annual 

consumption. 

6. Diversity accounted for by Ref DF. 

7. Billing histories and (for electricity) contractor calculations based on program 

procedures for estimating space conditioning and cooling consumption. 

8. Analysis of ARI data 

9. Analysis of data from 6 utilities by Proctor Engineering 

10. From Neme, Proctor and Nadel, 1999. 

11. These allocations may change with actual penetration numbers are available. 

12. VEIC estimate, assuming 1 GPM reduction for 14 five minute showers per week 

for shower heads, and 4 gallons saved per day for aerators. 

13. Heating: Prorated based on 12% of the annual degree days falling in the summer 

period and 88% of the annual degree days falling in the winter period. 

Non-Heating: Prorated based on 6 months in the summer period and 6 months in 

the winter period. 
New Jersey Clean Energy Program Page 28 

Protocols to Measure Resource Savings 

September 2004 

Residential New Construction Program 

Protocols 

Insulation Up-Grades, Efficient Windows, Air Sealing, Efficient HVAC Equipment, 

and Duct Sealing 

The energy savings due to the Residential New Construction Program will be a direct 

output of the home energy rating software. This software has a module that compares the 

energy characteristics of the energy efficient home to the baseline/reference home and 

calculates savings. 

The system peak electric demand savings will be calculated from the software output 

with the following algorithms then applied: 

Peak demand of the baseline home = (PLb X OFb) / (SEERb X BLEER X 1,000) 

Peak demand of the qualifying home = (PLq X OFq) / (EERq X 1,000) 

Coincident system peak electric demand savings = (Peak demand of the baseline home – 

Peak demand of the qualifying home) X CF 

Definition of Terms

PLb = Peak load of the baseline home in Btuh. 

OFb = The oversizing factor for the HVAC unit in the baseline home. 

SEERb = The Seasonal Energy Efficiency Ratio of the baseline unit. 

BLEER = Factor to convert baseline SEERb to EERb.

PLq = The actual predicted peak load for the program qualifying home constructed, in 

Btuh. 

OFq = The oversizing factor for the HVAC unit in the program qualifying home. 

EERq = The EER associated with the HVAC system in the qualifying home. 

CF = The coincidence factor which equates the installed HVAC system’s demand to its 

demand at time of system peak. 

In July 2002 energy code changes took place with the adoption of MEC 95. This code 

change affects baselines for variables used in the protocols. Therefore, to reflect these 

changes, tables and or values are identified as needed for installations completed during 
New Jersey Clean Energy Program Page 29 

Protocols to Measure Resource Savings 

September 2004 

2001 through March 2003 and for installations completed in April 2003 through the 

present. The application of the code changes to completions starting in April allows for 

the time lag between when the permits are issued and a when a home would reasonably 

be expected to be completed. 

A summary of the input values and their data sources follows: 

Applicable to building completions from January 2001 through March 2003

Component Type Value Sources 

PLb Variable 1 

OFb Fixed 1.6 2 

SEERb Fixed 10 3 

BLEER Fixed 0.92 4 

PLq Variable REM Output 

OFq Fixed 1.15 5 

EERq Variable Program 

Application 

CF Fixed 0.70 6 

Sources: 

1. Calculation of peak load of baseline home from the home energy rating tool, 

based on the reference home energy characteristics. 

2. PSE&G 1997 Residential New Construction baseline study. 

3. Federal minimum SEER is 10.0 and national data suggests that this is 

predominately the unit installed without intervention. 

4. Engineering calculation. 

5. Program guideline for qualifying home. 

6. Based on an analysis of six different utilities by Proctor Engineering. 

Applicable to building completions from April 2003 to present

Component Type Value Sources 

PLb Variable 1 

OFb Fixed 1.6 2 

SEERb Fixed 10 3 

BLEER Fixed 0.92 4 

PLq Variable REM Output 

OFq Fixed 1.15 5 

EERq Variable Program 

Application 

CF Fixed 0.70 6 

Sources: 

1. Calculation of peak load of baseline home from the home energy rating tool, 

based on the reference home energy characteristics. 

2. PSE&G 1997 Residential New Construction baseline study. 

3. Federal minimum SEER is 10.0 and national data suggests that this is 

predominately the unit installed without intervention. 
New Jersey Clean Energy Program Page 30 

Protocols to Measure Resource Savings 

September 2004 

4. Engineering calculation. 

5. Program guideline for qualifying home. 

6. Based on an analysis of six different utilities by Proctor Engineering. 

Lighting and Appliances 

Quantification of additional saving due to the addition of high efficiency light fixtures 

and clothes washers will be based on the algorithms presented for these appliances in the 

Energy Star Lighting Protocols and the Energy Star Appliances Protocols, respectively. 

Ventilation Equipment 

Additional energy savings of 175 kWh and peak demand saving of 60 Watts will be 

added to the output of the home energy rating software to account for the installation of 

high efficiency ventilation equipment. These values are based on a baseline fan of 80 

Watts and an efficient fan of 20 Watts running for 8 hours per day. 

The following table describes the characteristics of the three reference homes. 
New Jersey Clean Energy Program Page 31 

Protocols to Measure Resource Savings September 2004 New Jersey ENERGY STAR Homes 

REMRate User Defined Reference Homes -- Applicable to building completions from January 2001 through March 

Data Point Single Family Multiple Single Family Multifamily 

Active Solar None None None Ceiling Insulation R-30 R-30 R-30 Radiant Barrier None None None Rim/Band Joist R-13 R-13 R-13 Exterior Walls - Wood R-13 R-13 R-13 Exterior Walls - Steel R-7 effective R-7 effective R-7 effective Foundation Walls R-0 R-0 R-0 Doors R-2.6 R-2.6 R-2.6 Windows U=0.50, SHGC=0.60 U=0.50, SHGC=0.60 U=0.50, SHGlass Doors U=0.50, SHGC=0.60 U=0.50, SHGC=0.60 U=0.50, SHSkylights U=0.50, SHGC=0.60 U=0.50, SHGC=0.60 U=0.50, SHFloor over Garage R-19 R-19 R-19 Floor over Unheated Basement R-0 R-0 R-0 Floor over Crawlspace R-19 R-19 R-19 Floor over Outdoor Air R-19 R-19 R-19 Unheated Slab on Grade R-0 edge/R-5 under R-0 edge/R-5 under R-0 edge/R-Heated Slab on Grade R-0 edge/R-7 under R-0 edge/R-7 under R-0 edge/R-Air Infiltration Rate 0.56 ACH winter/0.28 ACH summer 0.56 ACH winter/0.28 ACH summer 0.56 ACH wDuct Leakage Observable Duct Leakage Observable Duct Leakage Observable Mechanical Ventilation None None None Lights and Appliances Use Default Use Default Use Default Setback Thermostat Yes No No Heating Efficiency  Furnace 80% AFUE 80% AFUE 80% AFUE  Boiler 80% AFUE 80% AFUE 80% AFUE  Combo Water Heater 76% AFUE (recovery efficiency) 76% AFUE (recovery efficiency) 76% AFUE  Air Source Heat Pump 5.4 HSPF 5.4 HSPF 5.4 HSPF  Geothermal Heat Pump 2.8 COP open/3.0 COP closed 2.8 COP open/3.0 COP closed 2.8 COP op
New Jersey Clean Energy Program Page 32 

Protocols to Measure Resource Savings September 2004 

Data Point Single Family Multiple Single Family Multifamily 

 PTAC / PTHP 3.0 COP 3.0 COP 3.0 COP Cooling Efficiency  Central Air Conditioning 8.0 SEER 8.0 SEER 8.0 SEER  Air Source Heat Pump 8.0 SEER 8.0 SEER 8.0 SEER  Geothermal Heat Pump 11.0 EER open/12.0 EER closed 11.0 EER open/12.0 EER closed 11.0 EER o PTAC / PTHP 9.5 EER 9.5 EER 9.5 EER  Window Air Conditioners 8.5 EER 8.5 EER 8.5 EER 

Domestic WH Efficiency 

 Electric 0.88 EF 0.88 EF 0.88 EF  Natural Gas 0.53 EF 0.53 EF 0.53 EF Water Heater Tank Insulation None None None Duct Insulation R-4.8 R-4.8 R-4.8 

Data points listed in normal type have been obtained from the Incentive Analysis Assumptions for the associated building tData points listed in bold have been obtained from the New Jersey Energy Star Homes Operations Manual. Data points listed in italics were not identified in the Incentive Analysis or the Operations Manual. Values were assigned by 

An asterisk (*) indicates the value is more stringent than code. 
New Jersey Clean Energy Program Page 33 

Protocols to Measure Resource Savings September 2004 New Jersey ENERGY STAR Homes 

REMRate User Defined Reference Homes -- Applicable to building completions from April 2003 to present -- Reflec

Data Point Single and Multiple Family Except as Noted. 

Active Solar None Ceiling Insulation U=0.031 (1) Radiant Barrier None Rim/Band Joist U=0.141 Type A-1, U=0.215 Type A-2 (1) Exterior Walls - Wood U=0.141 Type A-1, U=0.215 Type A-2 (1) Exterior Walls - Steel U=0.141 Type A-1, U=0.215 Type A-2 (1) Foundation Walls U=0.99 Doors U=0.141 Type A-1, U=0.215 Type A-2 (1) Windows 

U=0.141 Type A-1, U=0.215 Type A-2 (1), No SHGC req. 

Glass Doors 

U=0.141 Type A-1, U=0.215 Type A-2 (1), No SHGC req. 

Skylights U=0.031 (1), No SHGC req. Floor over Garage U=0.050 (1)Floor over Unheated Basement U=0.050 (1)Floor over Crawlspace U=0.050 (1)Floor over Outdoor Air U=0.031 (1)Unheated Slab on Grade R-0 edge/R-4.3 underHeated Slab on Grade R-0 edge/R-6.4 underAir Infiltration Rate 0.51 ACH winter/0.51 ACH summer Duct Leakage No Observable Duct Leakage Mechanical Ventilation None Lights and Appliances Use Default Setback Thermostat Yes for heating, no for cooling Heating Efficiency  Furnace 80% AFUE (3)  Boiler 80% AFUE  Combo Water Heater 76% AFUE (recovery efficiency)  Air Source Heat Pump 6.8 HSPF 
New Jersey Clean Energy Program Page 34 

Protocols to Measure Resource Savings September 2004 

Data Point Single and Multiple Family Except as Noted. 

 Geothermal Heat Pump Open not modeled, 3.0 COP closed PTAC / PTHP Not differentiated from air source HPCooling Efficiency  Central Air Conditioning 10.0 SEER  Air Source Heat Pump 10.0 SEER  Geothermal Heat Pump 3.4 COP (11.6 EER)  PTAC / PTHP Not differentiated from central AC Window Air Conditioners Not differentiated from central AC 

Domestic WH Efficiency 

 Electric 0.86 EF (4) Natural Gas 0.53 EF (4) Water Heater Tank Insulation None Duct Insulation N/A 

Notes: (1) Varies with heating degree-days (“HHD”). Above value reflects 5000 HDD average for New Jersey. 

U values represent total wall system U value, including all components (i.e., clear wall, windows, doors). 

Type A-1 - Detached one and two family dwellings. Type A-2 - All other residential buildings, three stories in height or less. 

(2) Closest approximation to MEC 95 requirements given the limitations of REM/Rate UDRH scripting language. 

(3) MEC 95 minimum requirement is 78 AFUE. However, 80 AFUE is adopted for New Jersey based on typical minimum a

(4) Size dependent. 50 gallon assumed. 
New Jersey Clean Energy Program Page 35 

Protocols to Measure Resource Savings 

September 2004 

Residential Retrofit Program 

Protocols 

No protocol was developed to measure energy savings for this program. The purpose of 

the program is to provide information and tools that residential customers can use to 

make decisions about what actions to take to improve energy efficiency in their homes. 

Many measure installations that are likely to produce significant energy savings are 

covered in other CRA programs. These savings are captured in the measured savings for 

those programs. The savings produced by this program that are not captured in other 

CRA programs would be difficult to isolate and relatively expensive to measure. 
New Jersey Clean Energy Program Page 36 

Protocols to Measure Resource Savings 

September 2004 

Residential ENERGY STAR Lighting 

Protocols 

See the protocols for efficient lighting savings under the Residential Low Income 

program. 

ENERGY STAR CFL Bulbs

 Same as Compact Flourescent Screw In Lamp. 

ENERGY STAR Torchieres

 Same as Efficient Torchieres 

ENERGY STAR Recessed Cans

 Same as Efficient Fixtures. 

ENERGY STAR Fixtures(Other)

 Same as Efficient Fixtures. 
New Jersey Clean Energy Program Page 37 

Protocols to Measure Resource Savings 

September 2004 

Residential ENERGY STAR Appliances 

Protocols 

The general form of the equation for the ENERGY STAR Appliance Program measure 

savings algorithms is: 

Number of Units X Savings per Unit 

To determine resource savings, the per unit estimates in the protocols will be multiplied 

by the number of appliance units. The number of units will be determined using market 

assessments and market tracking. Some of these market tracking mechanisms are under 

development. Per unit savings estimates are derived primarily from a 2000 Market 

Update Report by RLW for National Grid’s appliance program and from previous NEEP 

screening tool assumptions (clothes washers). 

Note that the pre-July 2001 refrigerator measure has been deleted given the timing of 

program implementation. As no field results are expected until July 2001, there was no 

need to quantify savings relative to the pre-July 2001 efficiency standards improvement 

for refrigerators. 

ENERGY STAR Refrigerators 

Electricity Impact (kWh) = ESavREF 

Demand Impact (kW) = DSavREF x CFREF

ENERGY STAR Clothes Washers 

Electricity Impact (kWh) = ESavCW 

Demand Impact (kW) = DSavCW x CFCW

Gas Impact (MMBtu) = EGSavCW

Oil Impact (MMBtu) = OSavCW

Water Impact (gallons) = WSavCW

ENERGY STAR Dishwashers 

Electricity Impact (kWh) = ESavDW 

Demand Impact (kW) = DSavREF x CFDW

Gas Impact (MMBtu) = EGSavDW

Oil Impact (MMBtu) = OsavDW 
New Jersey Clean Energy Program Page 38 

Protocols to Measure Resource Savings 

September 2004 

Water Impact (gallons) = WSavDW

ENERGY STAR Room Air Conditioners 

Electricity Impact (kWh) = ESavRAC 

Demand Impact (kW) = DSavRAC x CFRAC

Definition of Terms

ESavREF = Electricity savings per purchased ENERGY STAR refrigerator. 

DSavREF = Summer demand savings per purchased ENERGY STAR refrigerator. 

ESavCW = Electricity savings per purchased ENERGY STAR clothes washer. 

DSavCW = Summer demand savings per purchased ENERGY STAR clothes washer. 

WSavCW = Water savings per purchased clothes washer. 

ESavDW = Electricity savings per purchased ENERGY STAR dishwasher. 

DSavDW = Summer demand savings per purchased ENERGY STAR dishwasher. 

WsavDW = Water savings per purchased dishwasher. 

ESavRAC = Electricity savings per purchased ENERGY STAR room AC. 

DSavRAC = Summer demand savings per purchased ENERGY STAR room AC. 

CFREF, CFCW, CFDW, CFRAC = Summer demand coincidence factor. The coincidence of 

average appliance demand to summer system peak equals 1 for demand impacts for all 

appliances reflecting embedded coincidence in the DSav factor except for room air 

conditioners where the CF is 58%. 

ENERGY STAR Appliances 

Component Type Value Sources 

ESavREF Fixed 48 kWh 1 

DSavREF Fixed 0.0066 kW 1 

REF Time Period 

Allocation Factors 

Fixed Summer/On-Peak 20.9% 

Summer/Off-Peak 21.7% 

Winter/On-Peak 28.0% 

Winter/Off-Peak 29.4% 

2 

ESavCW Fixed 201 kWh 3 

GsavCW Fixed 10.6 therms 3 
New Jersey Clean Energy Program Page 39 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

OsavCW Fixed 1.06 MMBtu 3 

DSavCW Fixed 0.0267 kW 3 

WSavCW Fixed 4,915 gallons 4 

CW Electricity Time 

Period Allocation 

Factors 

Fixed Summer/On-Peak 24.5% 

Summer/Off-Peak 12.8% 

Winter/On-Peak 41.7% 

Winter/Off-Peak 21.0% 

2 

CW Gas Time 

Period Allocation 

Factors 

Fixed Summer 50% 

Winter 50% 

ESavDW Fixed 82 kWh 5 

GsavDW Fixed 0.0754 kW 5 

OsavDW Fixed 1.0 5 

DSavDW Fixed 0.0225 5 

WsavDW Fixed 159 gallons 5 

DW Electricity 

Time Period 

Allocation Factors 

Fixed 19.8%, 21.8%, 27.8%, 

30.6% 

2 

DW Gas Time 

Period Allocation 

Factors 

Fixed Summer 50% 

Winter 50% 

9 

ESavRAC Fixed 56.4 kWh 6 

DSavRAC Fixed 0.1018 kW 7 

CFREF, CFCW, CFDW, 

CFRAC

Fixed 1.0, 1.0, 1.0, 0.58 8 

RAC Time Period 

Allocation Factors 

Fixed 65.1%, 34.9%, 0.0%, 0.0% 2 

Sources: 

1. Electricity savings from RLW ENERGY STAR Market Update for National Grid. 

June 2000. Difference is for a post-7/1/2001 fed standards unit. Demand savings 

derived using refrigerator load shape. 

2. Time period allocation factors used in cost-effectiveness analysis. From 

residential appliance load shapes. 

3. Energy savings estimates consistent with prior NEEP screening. Demand savings 

derived using clothes washer load shape. 

4. Clothes washer water savings from RLW Market Update. 

5. Energy and water savings from RLW Market Update. Assumes 37% electric hot 

water market share and 63% gas hot water market share. Demand savings 

derived using dishwasher load shape. 

6. Energy and demand savings from engineering estimate based on 600 hours of use. 

Based on delta watts for ENERGY STAR and non-ENERGY STAR units in five 

different size (cooling capacity) categories. Category weights from LBNL 

Technical Support Document for ENERGY STAR Conservation Standards for 

Room Air Conditioners. 
New Jersey Clean Energy Program Page 40 

Protocols to Measure Resource Savings 

September 2004 

7. Average demand savings based on engineering estimate. 

8. Coincidence factors already embedded in summer peak demand reduction 

estimates with the exception of RAC. RAC CF is based on data from PEPCO. 

9. Prorated based on 6 months in the summer period and 6 months in the winter 

period. 
New Jersey Clean Energy Program Page 41 

Protocols to Measure Resource Savings 

September 2004 

Commercial and Industrial Energy Efficient Construction 

C&I Electric Protocols 

Baselines and Code Changes 

All baselines are designed to reflect an improvement over market practice defined by 

baselines, which are generally the higher of code or available equipment, that are updated 

periodically to reflect upgrades in code, or information from evaluation results. 

Baseline data reflect ASHRAE 90.1 1989 for program commitments made prior to July 

16, 2002 and ASHRAE 90.1 1999 for commitments starting on July 16, 2002. 

Lighting Equipment 

With the exception of small commercial lighting, savings are calculated using marketdriven assumptions for new construction, renovation, remodeling, or equipment 

replacement that presume a decision to upgrade the lighting system. For small 

commercial lighting, the most efficient T-12 lamp and magnetic ballast fixture serves as 

the baseline. This approach is different from earlier protocols that referenced preexisting lighting connected load. 

Lighting equipment includes fluorescent fixtures, ballasts, compact fluorescent fixtures, 

exit signs, and metal halide lamps. The measurement of energy savings is based on 

algorithms with measurement of key variables (i.e., Coincidence Factor and Operating 

Hours) through end-use metering data accumulated from a large sample of participating 

facilities from 1995 through 1999. 

Algorithms

Demand Savings = ∆kW X CF X (1+IF) 

Energy Savings = ∆kW X EFLH X (1+IF) 

∆kW is calculated from example worksheet below: 
New Jersey Clean Energy Program Page 42 

Protocols to Measure Resource Savings 

September 2004 

This worksheet is an example and does not represent that present stage of improvement 

to the worksheets presently being used and updated in the field. 

Code and Program Limits 

A B C D E F G 

Building Type or Gross Lighted Unit Lighting Lighting Power Program Limit Lighting Power Composite 

Space Activity Area (sf) Power Allowance Allowance (W) ( Watts/sf ) Limit (W) Program Limit 

 ( Watts/sf ) [ B x C ] [ C x .07 ] [ B x E ] [ sum F / sum B ]

#1Dorm Bed/Study 42,752 1.40 59,853 0.98 41,897 

#2Dorm Bath 7,936 1.20 9,523 0.84 6,666 

#3Stairs 9,216 0.60 5,530 0.42 3,871 

 59,904 74,906 52,434 0.875299145

Installed Lighting Levels 

H I J K L M 

Space ID Luminaire Tag # Luminaire Number of Watts per Connected Watts

 if applicable Description Luminaires Luminaire [ K x L ] 

 

#1 32w T8 384 27 10,368

#1&2 26W plt 128 61 7,808

#1 26w Quad 192 27 5,184

#3 26w plt 24 27 648

#3 13w plc 16 30 480

 

Other Wattage 

not applicable 

listed below 9,600

 744 34,088 

N. Composite Connected Watts/Square Foot [ sum M / sum B ] 0.57 

Definition of Variables

∆kW = Change in connected load from baseline to efficient lighting level. The baseline 

value is expressed in watts/square foot calculated as: (Watts/Sq.Ft. - Watts/Sq.Ft. 

(qualified equipment by same area))*Area Sq.Ft./1000 (see table above). 

There is a lighting table used that is to be periodically updated by the program 

administrator(s) in the State that shows standardized values of fixture wattages for 

common lighting systems. These tables are based on evaluations of several 

manufacturers’ wattage ratings for a given fixture type, and have been used in measuring 

energy and demand savings. The program administrator(s), in a cooperative effort will 

be responsible for the lighting tables. 

CF = Coincidence Factor – This value represents the percentage of the total lighting 

connected load which is on during electric system’s Peak Window. The Peak Window 

covers the time period from 12 noon to 8 p.m. These values are based on measured usage 

in the JCP&L service territory. 
New Jersey Clean Energy Program Page 43 

Protocols to Measure Resource Savings 

September 2004 

IF = Interactive Factor – This applies to C&I interior lighting only. This represents the 

secondary demand and energy savings in reduced HVAC consumption resulting from 

decreased indoor lighting wattage. This value will be fixed at 5%. 

EFLH = Equivalent Full Load Hours – This represents the annual operating hours and is 

computed based on JCP&L metered data and divided into Large (facilities with over 50 

kW of reduced load) and other size and building types. 

Lighting Verification Summary 

Component Type Value Source 

∆kW Fixed Change in connected load from 

baseline. 

• Installed load is 

based on standard 

wattage tables and 

verified watts/sq.ft. 

• For commitments 

prior to 7/16/2002, 

baseline is 30% better 

than ASHRAE 90.1 

1989 by space. 

• For commitments 

after 7/16/2002, 

baseline is 5 percent 

better than ASHRAE 

90.1-1999 by space. 

CF Fixed Large Office* 65% 

 Large Retail 81% 

 Large Schools 41% 

 Large All Other 63% 

 All Hospitals 67% 

 All Other Office 71% 

 All Other Retail 84% 

 Other Schools 40% 

 All Other 69% 

 Industrial 71% 

 Continuous 90% 

 JCP&L metered data2

Cost effectiveness study 

Estimate 

 

2

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 44 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Source 

IF Fixed 5% Impact of lighting watt 

reduction on airconditioning load used in 

previous lighting savings.

EFLH Fixed Large Office 3309 

 Large Retail 5291 

 Large Schools 2289 

 Large All Other 3677 

 All Hospitals 4439 

 All Other Office 2864 

 All Other Retail 4490 

 Other Schools 2628 

 All Other 2864 

 Industrial 4818 

 Continuous 7000 

JCP&L metered data3

Cost effectiveness study 

Estimate 

Time 

Period 

Allocation 

Factors 

Fixed Summer/On-Peak 26% 

Summer/Off-Peak 16% 

Winter/On-Peak 36% 

Winter/Off-Peak 22%

* For facility with greater than 50kW reduction in load. 

** For facilities that operate at or near 24 hours, 7 days per week. 

Traffic Signals (data from NJDOT)

Traffic Signals 

Type of 

Fixture 

kW 

Reduced 

EFLH 

Total 

Summer

on-peak 

Summer 

off-peak 

Winter 

on-peak 

Winter 

off-peak 

8'' red 0.052 5257 636 1125 1246 2250 

12" red 0.120 5257 636 1125 1246 2250 

8'' green 0.051 3066 371 656 727 1312 

12"green 0.117 3066 371 656 727 1312 

 

 Pedestrian Walk Sign 8” or 12”, kW reduced = 0.068, kWh per year = 550. 

 

3

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 45 

Protocols to Measure Resource Savings 

September 2004 

Coincidence factor for demand savings = 60% for red and 35% for green. 

Prescriptive Lighting for Small Commercial Customers 

This is a fixture replacement program for new and existing small commercial customers 

which is targeted at facilities the following facilities: 

• Existing small commercial and industrial (up to 50 kW average twelve month 

metered demand through 2001, up to 75 kW average twelve month metered demand 

beginning 1/1/2002) 

• New/renovated/change-of-use small commercial and industrial <= 10,000 s.f. of 

conditioned space 

The baseline is existing T-12 fixtures with energy efficient lamps and magnetic ballast. 

The baseline for compact fluorescent is that the fixture replaced was 4 times the wattage 

of the replacement compact fluorescent. 

Algorithms

Demand Savings = ∆kW X CF 

Energy Savings = ∆kW X EFLH 

∆kW=Number of fixtures installed X (baseline wattage for fixture type(from 

above baseline))-number of replaced fixtures X (wattage from table) 

Prescriptive Lighting for Small Commercial Customers 

Component Type Value Source 

∆kW Fixed See Prescriptive Lighting Savings 

Table (below) 

From NJ lighting 

tables 

CF Fixed Average of the small retail and office 

from lighting verification summary 

table, 77.5%. 

JCP&L metered data4

EFLH Fixed Average of small retail and office 

from lighting verification summary 

3,677. 

JCP&L metered data 

 

4

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 46 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Source 

Time Period 

Allocation 

Factors 

Fixed Summer/On-Peak 21% 

Summer/Off-Peak 22% 

Winter/On-Peak 28% 

Winter/Off-Peak 29% 

Prescriptive Lighting Savings Table 

Fixture Type New Watts 

(w/ fixture) 

Baseline 

(w/ fixture) 

Savings 

(w/ fixture) 

COMPACT FLUORESCENT (2) 18W CF/HW 36 144 108 

COMPACT FLUORESCENT (2) 11W CF/HW 26 104 78 

COMPACT FLUORESCENT (2) 13W CF/HW 30 120 90 

COMPACT FLUORESCENT (2) 18W QD/ELEC 38 152 114 

COMPACT FLUORESCENT (2) 26W CF/HW 53 212 159 

COMPACT FLUORESCENT (2) 26W QD/ELEC 54 216 162 

COMPACT FLUORESCENT (2) 5W CF/HW 14 56 42 

COMPACT FLUORESCENT (2) 7W CF/HW 18 72 54 

COMPACT FLUORESCENT (2) 9W CF/HW 22 88 66 

COMPACT FLUORESCENT 11W CF/HW 13 52 39 

COMPACT FLUORESCENT 13W CF/HW 15 60 45 

COMPACT FLUORESCENT 18W CF/HW 19 76 57 

COMPACT FLUORESCENT 18W QD/ELEC 22 88 66 

COMPACT FLUORESCENT 20W CF/HW 22 88 66 

COMPACT FLUORESCENT 22W QD/ELEC 26 104 78 

COMPACT FLUORESCENT 26W CF/HW 28 112 84 

COMPACT FLUORESCENT 26W QD/ELEC 27 108 81 

COMPACT FLUORESCENT 28W CF/HW 30 120 90 

COMPACT FLUORESCENT 32W CF/HW 34 136 102 

COMPACT FLUORESCENT 36W CF/HW 41 164 123 

COMPACT FLUORESCENT 40W CF/HW 45 180 135 

COMPACT FLUORESCENT 5W CF/HW 7 28 21 

COMPACT FLUORESCENT 7W CF/HW 10 40 30 

COMPACT FLUORESCENT 9W CF/HW 11 44 33 

High Efficiency Fluorescent 1L2 (1) FO17T8/Elec 18 32 14 

High Efficiency Fluorescent 1L2 (2) FO17T8/Elec 34 56 22 

High Efficiency Fluorescent 1L2 (3) FO17T8/Elec 50 78 28 

High Efficiency Fluorescent 1L2 (4) FO17T8/Elec 62 112 50 

High Efficiency Fluorescent 1L3 (1) FO25T8/Elec 30 46 16 

High Efficiency Fluorescent 1L3 (2) FO25T8/Elec 48 80 32 

High Efficiency Fluorescent 1L3 (3) FO25T8/Elec 68 126 58 

High Efficiency Fluorescent 1L3 (4) FO25T8/Elec 90 160 70 

High Efficiency Fluorescent T-8 1L4 28 42 14 

High Efficiency Fluorescent T-8 1L8 67 78 11 

High Efficiency Fluorescent T-8 2L2 62 94 32 

High Efficiency Fluorescent T-8 2L4 55 73 18 

High Efficiency Fluorescent T-8 2L8 118 158 40 

High Efficiency Fluorescent T-8 3L4 79 105 26 

High Efficiency Fluorescent T-8 4L4 110 146 36 

High Efficiency Fluorescent T-8 4L8 233 316 83 

LED Exit Sign 20 18 2 

PULSE START METAL HALIDE 50 W 68 95 27 

PULSE START METAL HALIDE 70 W 90 95 5 

PULSE START METAL HALIDE 100 W 120 120 0 

PULSE START METAL HALIDE 125 W 150 205 55 

PULSE START METAL HALIDE 150 W 190 205 15 

PULSE START METAL HALIDE 175 W 205 205 0 

PULSE START METAL HALIDE 200 W 235 290 55 

PULSE START METAL HALIDE 250 W 288 290 2 

PULSE START METAL HALIDE 300 W 342 450 108 

PULSE START METAL HALIDE 320 W 368 450 82 
New Jersey Clean Energy Program Page 47 

Protocols to Measure Resource Savings 

September 2004 

Fixture Type New Watts 

(w/ fixture) 

Baseline 

(w/ fixture) 

Savings 

(w/ fixture) 

PULSE START METAL HALIDE 350 W 400 450 50 

PULSE START METAL HALIDE 400 W 450 450 0 

PULSE START METAL HALIDE 750 W 815 1075 260 

PULSE START METAL HALIDE 875 W 940 1075 135 

PULSE START METAL HALIDE 1000 W 1075 1075 0 

Lighting Controls 

Lighting controls include occupancy sensors, daylight dimmer systems, and occupancy 

controlled hi-low controls for fluorescent, and HID controls. The measurement of energy 

savings is based on algorithms with key variables (i.e., coincidence factor, equivalent full 

load hours) provided through existing end-use metering of a sample of facilities or from 

other utility programs with experience with these measures (i.e., % of annual lighting 

energy saved by lighting control). For lighting controls, the baseline is a manual switch, 

based on the findings of the New Jersey Commercial Energy Efficient Construction 

Baseline Study. 

Algorithms

Demand Savings = kWc X SVG X CF 

Energy Savings = kWc X SVG X EFLH X (1+IF) 

Definition of Variables

SVG = % of annual lighting energy saved by lighting control; refer to table by control 

type 

kWc = kW lighting load connected to control 

IF = Interactive Factor – This applies to C&I interior lighting only. This represents the 

secondary demand and energy savings in reduced HVAC consumption resulting from 

decreased indoor lighting wattage. This value will be fixed at 5%. 

CF = Coincidence Factor – This value represents the percentage of the total load which is 

on during electric system’s peak window. 

EFLH = Equivalent full load hours. 
New Jersey Clean Energy Program Page 48 

Protocols to Measure Resource Savings 

September 2004 

Lighting Controls 

Component Type Value Source 

kWc Variable Load connected to control Application 

SVG Fixed Occupancy Sensor, Controlled HiLow Fluorescent Control and 

controlled HID = 30% 

Daylight Dimmer System=50% 

See sources below 

CF Fixed By building type and size see lighting 

verification summary table 

Assumes same as 

JCP&L metered data 

EFLH Fixed By building type and size see lighting

verification summary table 

JCP&L metered data 

Time Period 

Allocation 

Factors 

Fixed Summer/On-Peak 26% 

Summer/Off-Peak 16% 

Winter/On-Peak 36% 

Winter/Off-Peak 22% 

Sources: 

• Northeast Utilities, Determination of Energy Savings Document, 1992 

• Levine, M., Geller, H., Koomey, J., Nadel S., Price, L., "Electricity Energy Use 

Efficiency: Experience with Technologies, Markets and Policies” ACEEE, 1992 

• Lighting control savings fractions consistent with current programs offered by 

National Grid, Northeast Utilities, Long Island Power Authority, NYSERDA, and 

Energy Efficient Vermont. 

Motors 

Algorithms

From application form calculate ∆kW where: 

∆kW = HP*0.7456 X (1/EFFb – 1/ EFFq) 

Demand Savings = (∆kW) X CF 

Energy Savings = (∆kW)*EFLH 

Motors 

Component Type Value Source 

Motor kW Variable Based on horsepower and 

efficiency 

Application 

EFLH Fixed Commercial 2,502 

Industrial 4,599 

JCP&L metered data5

and PSEG audit data 

for industrial 

 

5

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 49 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Source 

Efficiency – EFF b Fixed Comparable EPACT Motor From EPACT 

directory. 

Efficiency - EFF q Variable Nameplate Application 

CF Fixed 35% JCP&L metered data 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 25% 

Summer/Off-Peak 16% 

Winter/On-Peak 36% 

Winter/Off-Peak 23% 

HVAC Systems 

The measurement of energy and demand savings for C/I Efficient HVAC program for 

Room AC, Central AC, and air cooled DX is based on algorithms. (Includes split 

systems, air to air heat pumps, packaged terminal systems, water source heat pumps, 

central DX AC systems, ground water or ground source heat pumps) 

Algorithms

Air Conditioning Algorithms: 

Demand Savings = (BtuH/1000) X (1/EERb-1/EERq) X CF 

Energy Savings = (BtuH/1000) X (1/EERb-1/EERq) X EFLH 

Heat Pump Algorithms 

Energy Savings-Cooling = (BtuHc/1000) X (1/EERb-1/EERq) X EFLHc 

Energy Savings-Heating = BtuHh/1000 X (1/EERb-1/EERq ) X EFLHh 

Where c is for cooling and h is for heating. 

Definition of Variables

BtuH = Cooling capacity in Btu/Hour – This value comes from ARI or AHAM rating or 

manufacturer data. 

EERb = Efficiency rating of the baseline unit. This data is found in the HVAC and Heat 

Pump verification summary table. For units < 65,000, SEER and HSPF should be used for 

cooling and heating savings, respectively. 

EERq = Efficiency rating of the High Efficiency unit – This value comes from the ARI or 

AHAM directories or manufacturer data. For units < 65,000, SEER and HSPF should be 

used for cooling and heating savings, respectively. 
New Jersey Clean Energy Program Page 50 

Protocols to Measure Resource Savings 

September 2004 

CF = Coincidence Factor – This value represents the percentage of the total load which is 

on during electric system’s Peak Window. This value will be based on existing measured 

usage and determined as the average number of operating hours during the peak window 

period. 

EFLH = Equivalent Full Load Hours – This represents a measure of energy use by season 

during the on-peak and off peak periods. This value will be determined by existing 

measured data of kWh during the period divided by kW at design conditions. 

HVAC and Heat Pumps 

Component Type Value Source 

BtuH Variable ARI or AHAM or Manufacturer Data Application 

EERb Variable See Table below Collaborative 

agreement and C/I 

baseline study 

EERq Variable ARI or AHAM Values Application 

CF Fixed 67% Engineering 

estimate 

EFLH Fixed HVAC 1,131 

HP cooling 381 

HP heating 800 

JCP&L metered 

data6

Cooling 

Time 

Period 

Allocation 

Factors 

Fixed Summer/On-Peak 45% 

Summer/Off-Peak 39% 

Winter/On-Peak 7% 

Winter/Off-Peak 9% 

Heating 

Time 

Period 

Allocation 

Factors 

Fixed Summer/On-Peak 0% 

Summer/Off-Peak 0% 

Winter/On-Peak 41% 

Winter/Off-Peak 58% 

HVAC Baseline Table 

Equipment Type Baseline ASHRAE Std. 90.1 – 1989 ASHRAE Std. 90.1 – 1999 

Unitary HVAC/Split 

Systems 

· <=5.4 tons: 

· >5.4 to 11.25 tons 

· >11.25 to 30 tons 

10 SEER 

8.9 EER 

8.5 EER 

10 SEER 

8.9 EER 

8.5 EER up to 20 tons 

8.2 EER above 30 tons 

10 SEER 

10.3 EER 

9.7 EER up to 20 tons 

9.7 EER above 30 tons 

 

6

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 51 

Protocols to Measure Resource Savings 

September 2004 

Equipment Type Baseline ASHRAE Std. 90.1 – 1989 ASHRAE Std. 90.1 – 1999 

Air-Air Heat Pump Systems 

· <=5.4 tons: 

· >5.4 to 11.25 tons 

· >11.25 to 30 tons 

6.8 HSPF & 

10.0 SEER 

8.9 EER 

8.5 EER 

10 SEER 

8.9 EER 

8.5 EER up to 20 tons 

8.2 EER above 30 tons 

10 SEER 

10.1 EER 

9.3 EER up to 20 tons 

9.0 EER above 30 tons 

Package Terminal Systems 9 EER 10 – [0.91 * cap/1000] 10.9 – [0.213 * cap/1000] 

EER 

Water Source Heat Pumps 

<=30 tons 

>30 tons 

10.5 EER 

10.5 EER 

 up to 5.4 tons– 9.3 EER 

>5.4 Tons 10.5 

10.5 EER 

 up to 5.4 tons– 12.0 EER 

>5.4 Tons 12.0 EER 

12.0 EER 

Central DX AC Systems 

· >30 to 63 tons 

· > 63 tons 

8.5 EER 

8.5 EER 

8.5 EER 

8.2 EER 

9.5 EER 

9.5 EER 

GWSHPs 11 EER 3.1 COP 

Electric Chillers 

The measurement of energy and demand savings for C/I Chillers program is based on 

algorithms with key variables (i.e., kW/ton, Coincidence Factor, Equivalent Full Load 

Hours) measured through existing end-use metering of a sample of facilities. 

Algorithms

Demand Savings = Tons X (kW/tonb – kW/tonq) X CF 

Energy Savings = Tons X (kW/tonb – kW/tonq) X EFLH 

Definition of Variables

Tons = The capacity of the chiller (in tons) at site design conditions accepted by the 

program. 

kW/tonb = This data is the baseline and is found in the Chiller verification summary table. 

kW/tonq = This is the manufacturer data and equipment ratings in accordance with ARI 

Standard 550/590 latest edition. 

CF = Coincidence Factor – This value represents the percentage of the total load which is 

on during electric system’s Peak Window derived from JCP&L metered data. 

EFLH = Equivalent Full Load Hours – This represents a measure of chiller use by season 

determined by measured kWh during the period divided by kW at design conditions from 

JCP&L measurement data. 
New Jersey Clean Energy Program Page 52 

Protocols to Measure Resource Savings 

September 2004 

Electric Chillers 

(Applicable to project commitments before 7/16/2002) 

Component Type Value Source 

Tons Variable From Rebate Application 

kW/tonb Fixed Water Cooled Chillers (<70 tons) 

Baseline:............... 0.93 kW/Ton 

ASHRAE Std 90.1-1989.....0.93 kW/Ton

Water Cooled Chillers (70 to <150 

tons) 

Baseline:............... 0.86 kW/Ton 

ASHRAE Std 90.1-1989.....0.93 kW/Ton 

Water Cooled Chillers (150 to <300 

tons) 

Baseline:............... 0.72 kW/Ton 

ASHRAE Std 90.1-1989.....0.84 kW/Ton 

Water Cooled Chillers (=>300 tons)

Baseline:............... 0.64 kW/Ton 

ASHRAE Std 90.1-1989.....0.68 kW/Ton 

Air Cooled Chillers (<150 tons) 

Baseline:............... 1.30 kW/Ton 

ASHRAE Std 90.1-1989.....1.41 kW/Ton 

Air Cooled Chillers (+150 tons) 

Baseline:............... 1.30 kW/Ton 

ASHRAE Std 90.1-1989.....1.41 kW/Ton 

Collaborative 

agreement and C/I 

baseline study 

kW/tonq Variable ARI Standards 550/590-Latest edition Application 

CF Fixed 67% Engineering estimate 

EFLH Fixed 1,360 JCP&L metered data7

Time Period 

Allocation 

Factors 

Fixed Summer/On-Peak 45% 

Summer/Off-Peak 39% 

Winter/On-Peak 7% 

Winter/Off-Peak 9% 

 

7

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 53 

Protocols to Measure Resource Savings 

September 2004 

Electric Chillers 

(Applicable to project commitments on or after 7/16/2002) 

Component Type Value Source 

Tons Variable From Rebate Application 

kW/tonb Fixed Water Cooled Chillers (<70 tons) 

Baseline:............... 0.79 kW/Ton 

ASHRAE Std 90.1-1999.....0.84 kW/Ton 

Water Cooled Chillers (70 to <150 

tons) 

Baseline:............... 0.79 kW/Ton 

ASHRAE Std 90.1-1999.....0.84 kW/Ton 

Water Cooled Chillers (150 to <300 

tons) 

Baseline:............... 0.718 kW/Ton 

ASHRAE Std 90.1-1999.....0.718 kW/Ton 

Water Cooled Chillers (=>300 tons)

Baseline:............... 0.639 kW/Ton 

ASHRAE Std 90.1-1999.....0.639 kW/Ton 

Air Cooled Chillers (<150 tons) 

Baseline:............... 1.256 kW/Ton 

ASHRAE Std 90.1-1999....1.256 kW/Ton 

Air Cooled Chillers (+150 tons) 

Baseline:............... 1.256 kW/Ton 

ASHRAE Std 90.1-1999.....1.256 kW/Ton 

Collaborative 

agreement, C/I 

baseline study, ECube Inc. Study, May 

2003 

kW/tonq Variable ARI Standards 550/590-Latest edition Application 

CF Fixed 67% Engineering estimate 

EFLH Fixed 1,360 JCP&L metered data8

Time Period 

Allocation 

Factors 

Fixed Summer/On-Peak 45% 

Summer/Off-Peak 39% 

Winter/On-Peak 7% 

Winter/Off-Peak 9% 

For certain fixed components, studies and surveys developed by the utilities in the State 

or based on a review of manufacturer’s data, other utilities, regulatory commissions or 

consultant’s reports will be used to update the values for future filings. 

 

8

 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 54 

Protocols to Measure Resource Savings 

September 2004 

Variable Frequency Drives 

The the measurement of energy and demand savings for C/I Variable Frequency Drive for 

VFD applications is for HVAC fans and water pumps only. VFD applications for other 

than this use should follow the custom path. 

Algorithms

Energy Savings = Motor HP X kWh/motor HP 

There are no Demand Savings for VFD’s 

Definitions of Variables

Motor HP – This value comes from the nameplate of the motor. 

Variable Frequency Drives 

Component Type Value Source 

Motor HP Variable Nameplate Application 

kWh/motor HP Fixed 1,653 for VAV air handler 

systems. 1,360 for chilled 

water pumps. 

JCP&L metered data 

for VFD’s9 and 

chillers10. 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 22% 

Summer/Off-Peak 10% 

Winter/On-Peak 47% 

Winter/Off-Peak 21% 

 

9

 Results reflect metered use from 1995 – 1998. 

10 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 55 

Protocols to Measure Resource Savings 

September 2004 

C&I Construction Gas Protocols 

Gas Chillers 

The the measurement of energy savings for C&I gas fired chillers and chiller heaters is 

based on algorithms with key variables (i.e., Equivalent Full Load Hours, Vacuum Boiler 

Efficiency, Input Rating, Coincidence Factor) provided by manufacturer data or 

measured through existing end-use metering of a sample of facilities. 

Algorithms

Winter Gas Savings = (VBEq – BEb)/VBEq X IR X EFLH 

Electric Demand Savings = Tons X (kW/Tonb – kW/Tongc) X CF 

Electric Energy Savings = Tons X (kW/Tonb – kW/Tongc) X EFLH 

Summer Gas Usage (MMBtu) = MMBtu Output Capacity / COP X EFLH 

Net Energy Savings = Electric Energy Savings + Winter Gas Savings – Summer Gas 

Usage 

Definition of Terms 

VBEq = Vacuum Boiler Efficiency 

BEb = Efficiency of the baseline gas boiler 

IR = Input Rating = Therms/hour 

Tons = The capacity of the chiller (in tons) at site design conditions accepted by the 

program. 

kW/Tonb = The baseline efficiency for electric chillers, as shown in the Gas Chiller 

Verification Summary table below. 

kW/Tongc = Parasitic electrical requirement for gas chiller. 

COP = Efficiency of the gas chiller 

MMBtu Output Capacity = Cooling Capacity of gas chiller in MMBtu. 

CF = Coincidence Factor. This value represents the percentage of the total load that is on 

during electric system peak. 

EFLH = Equivalent Full Load Hours. This represents a measure of chiller use by season. 
New Jersey Clean Energy Program Page 56 

Protocols to Measure Resource Savings 

September 2004 

Gas Chillers 

(Applicable to project commitments before 7/16/2002) 

Component Type Value Source 

VBEq Variable Rebate Application 

or Manufacturer 

Data 

BEb Fixed 75% ASHRAE 90.1 

IR Variable Rebate Application 

or Manufacturer 

Data 

Tons Variable Rebate Application

MMBtu Variable Rebate Application

kW/Tonb Fixed <100 tons 

1.30 kW/Ton 

100 to 150 tons 

0.86 kW/ton 

150 to <300 tons: 

0.72 kW/Ton 

300 tons or more: 

0.64 kW/ton 

Collaborative 

agreement and C/I 

baseline study 

Assumes new 

electric chiller 

baseline using air 

cooled unit for 

chillers less than 

100 tons;water 

cooled for chillers 

greater than 100 

tons 

kW/Tongc Variable Manufacturer Data 

COP Variable Manufacturer Data 

CF Fixed 67% Engineering 

estimate 

EFLH Fixed 1,360 JCP&L Measured 

data11

Electric 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 45% 

Summer/Off-Peak 39% 

Winter/On-Peak 7% 

Winter/Off-Peak 9% 

 

11 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 57 

Protocols to Measure Resource Savings 

September 2004 

Gas Chillers 

(Applicable to project commitments on or after 7/16/2002) 

Component Type Value Source 

VBEq Variable Rebate Application 

or Manufacturer 

Data 

BEb Fixed 75% ASHRAE 90.1 

IR Variable Rebate Application 

or Manufacturer 

Data 

Tons Variable Rebate Application

MMBtu Variable Rebate Application

kW/Tonb Fixed <100 tons 

0.79kW/Ton 

100 to 150 tons 

0.79 kW/ton 

150 to <300 tons: 

0.718 kW/Ton 

300 tons or more: 

0.639 kW/ton 

Collaborative 

agreement and C/I 

baseline study. 

Assumes new 

electric chiller 

baseline using air 

cooled unit for 

chillers less than 

100 tons; water 

cooled for chillers 

greater than 100 

tons 

kW/Tongc Variable Manufacturer Data 

COP Variable Manufacturer Data 

CF Fixed 67% Engineering 

estimate 

EFLH Fixed 1,360 JCP&L Measured 

data12

Electric 

Time Period 

Allocation Factors 

Fixed Summer/On-Peak 45% 

Summer/Off-Peak 39% 

Winter/On-Peak 7% 

Winter/Off-Peak 9% 

Variable data will be captured on the application form or from manufacturer’s data sheets 

and collaborative/utility studies. 

For certain fixed components, studies and surveys developed by the utilities in the State 

or based on a review of manufacturer’s data, other utilities, regulatory commissions or 

consultants’ reports will be used to update the values for future filings. 

 

12 Results reflect metered use from 1995 – 1999. 
New Jersey Clean Energy Program Page 58 

Protocols to Measure Resource Savings 

September 2004 

Gas Fired Desiccants 

Protocols to be developed. 

Gas Booster Water Heaters 

C&I gas booster water heaters are substitutes for electric water heaters. The 

measurement of energy savings is based on engineering algorithms with key variables 

(i.e., Input Rating Coincidence Factor, Equivalent Full Load Hours) provided by 

manufacturer data or measured through existing end-use metering of a sample of 

facilities. 

Algorithms

Demand Savings (kW) = IR X EFF/3412 X CF 

Energy Savings (kWh) = IR X EFF/3412 X EFLH 

Gas Usage Increase = IR X EFLH 

Net Energy Savings = Electric Energy Savings – Gas Usage Increase 

(Calculated in MMBtu) 

Definition of Variables

IR = Input Rating in Btuh 

EFF = Efficiency 

CF = Coincidence Factor 

EFLH = Equivalent Full Load Hours 

The 3412 used in the denominator is used to convert Btus to kWh. 

Gas Booster Water Heaters 

Component Type Value Source 

IR Variable Application Form or 

Manufacturer Data 

CF Fixed 50% PSE&G 

EFLH Fixed 1,000 PSE&G 

EF Variable Application Form or 

Manufacturer Data 
New Jersey Clean Energy Program Page 59 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Source 

Electric Time 

Period Allocation 

Factors 

Fixed Requires additional 

research

Water Heaters 

This prescriptive measure targets solely the use of smaller-scale domestic water heaters 

(50 gallons or less per unit) in all commercial facilities. Larger gas water heaters are 

treated under the custom measure path. The measurement of energy savings for C&I gas 

water heaters is based on algorithms with key variables (i.e., energy factor) provided by 

manufacturer data. 

Algorithms

Gas Savings = ((EFq – EFb)/EFq) X Baseline Usage 

Definition of Variables

EFq = Energy factor of the qualifying energy efficient water heater. 

EFb = Energy factor of the baseline water heater. Calculated as 0.62 – (0.0019 X gallons 

of capacity). Based on a 40 gallon water heater. 

Baseline Usage = Annual usage of the baseline water heater, in therms. 

Water Heaters 

Component Type Value Source 

EFq Variable Application Form or 

Manufacturer Data 

EFb Fixed 0.544 Federal EPACT 

Standard 

Baseline Usage Fixed 277 DOE/FEMP website 

http://www.eren.doe

.gov/femp/pro

Time Period 

Allocation Factors 

Fixed Summer 50% 

Winter 50% 

1 

1. Prorated based on 6 months in the summer period and 6 months in the winter 

period. 
New Jersey Clean Energy Program Page 60 

Protocols to Measure Resource Savings 

September 2004 

Furnaces and Boilers 

This prescriptive measure targets the use of smaller-scale boilers (less than or equal to 

1500 MBH) and furnaces (no size limitation) in all commercial facilities. Larger sized 

boilers are treated under the custom measure path. The measurement of energy savings 

for C&I gas fired furnaces and boilers is based on algorithms with key variables (i.e. 

Annual Fuel Utilization Efficiency, capacity of the furnace, EFLH) provided by 

manufacturer data or utility data. 

Algorithms

Gas Savings = ((AFUEq – AFUEb)/AFUEq) X CAPY X EFLH 

Definition of Variables

AFUEq = Annual Fuel Utilization Efficiency of the qualifying energy efficient furnace or 

boiler 

AFUEb = Annual Fuel Utilization Efficiency of the baseline furnace or boiler 

CAPY = Capacity of the furnace or boiler in therms/hour 

EFLH = Equivalent full load heating hours 

Furnaces and Boilers 

Component Type Value Source 

AFUEq Variable Application Form or 

Manufacturer Data 

AFUEb Fixed Furnaces: 78% 

Boilers: 80% 

EPACT Standard 

for furnaces and 

boilers 

CAPY Variable Application Form or 

Manufacturer Data 

EFLH Fixed 900 PSE&G 

Time Period 

Allocation Factors 

Fixed Summer 12% 

Winter 88% 

1 

1. Prorated based on 12% of the annual degree days falling in the summer period 

and 88% of the annual degree days falling in the winter period. 
New Jersey Clean Energy Program Page 61 

Protocols to Measure Resource Savings 

September 2004 

Commercial and Industrial Building Operation & 

Maintenance Program 

Protocols 

The measurement of energy and demand savings for the building O&M program is based 

on saving a fixed percent of a building electric and gas load through the performance of 

various O&M improvement activities. It will be necessary to collect a facilities prior 

year electric and gas usage for input to the equations. 

The following is an explanation of the algorithms used and the nature and source of all 

required input data. 

Algorithms

Electric Savings 

Energy Impact (kWh) = PYEL X ESF 

Peak Demand Impact (kW) = (Energy Impact / EFLH) X CF 

Gas Savings 

Energy Savings (Therms) = PYGL X GSF 

Definition of Variables

PYEL = Participants previous years electric energy use. 

PYGL = Participants previous years gas energy use. 

EFLH = The equivalent full load hours of operation for the average commercial or 

industrial establishment in New Jersey. 

CF = The coincidence factor for the average commercial or industrial establishment in 

New Jersey. 

ESF = Electric savings factor as a % of facility load prior to program participation. 

GSF = Gas savings factor as a % of facility load prior to program participation. 

A summary of the data sources and fixed values follows: 
New Jersey Clean Energy Program Page 62 

Protocols to Measure Resource Savings 

September 2004 

C&I Building O&M 

Component Type Value Sources 

PYEL Variable Customer 

Application 

PYGL Variable Customer 

Application 

EFLH Fixed 3900 1 

CF Fixed 0.875 2 

ESF Fixed 10% 3 

GSF Fixed 7% 4 

Source Notes: 

1. EFLH: Equivalent Full Load Hours of 3900 is based on a typical NJ load profile 

from the NJ 2000 Forecast. 

2. CF: Coincidence Factor of 0.875 is based on the average of 85% for commercial 

customers and 90% for industrial customers. 

3. ESF: Electric Savings Factor of 10% of pre-participation facility load is based on 

a review of multiple O&M improvement programs. 

4. GSF: Gas Savings Factor of 7% of pre-participation facility load is based on a 

review of multiple O&M improvement programs. 
New Jersey Clean Energy Program Page 63 

Protocols to Measure Resource Savings 

September 2004 

Compressed Air System Optimization 

Protocols 

Compressed Air Systems 

The energy and peak demand savings due to Compressed Air Optimization measures will 

be based on an a site-specific engineering analysis completed for each participating site. 

The engineering analysis will determine what increase in efficiency will be realized 

through program participation. This will be compared to the current baseline condition to 

estimate savings. 
New Jersey Clean Energy Program Page 64 

Protocols to Measure Resource Savings 

September 2004 

Residential Air Conditioning Cycling Load Control Program 

Protocols 

Each company has individually assessed the peak reductions of this program utilizing 

methodologies acceptable for PJM contractual and reporting purposes. Those same 

impacts will be used to report the peak savings for this program. 
New Jersey Clean Energy Program Page 65 

Protocols to Measure Resource Savings 

September 2004 

School Energy Efficiency and Renewable Energy Education 

Program 

Protocols 

No protocol was developed to measure energy savings for this program, because the 

program purpose is to instill values and awareness as students that will inform their 

decisions about energy use as the next generation of consumers and buyers. A secondary 

purpose is to increase their families’ awareness through homework and family 

involvement in helping the children. Isolating the energy savings as a result of this 

program would require tracking behavior of these young participants ten or more years in 

the future. Actions that children’s parents take as a result of this program are likely to be 

reflected in measure installations or market effects that are covered in other CRA 

programs. These savings are captured in the measured savings for those programs. The 

savings produced by this program that are not captured in other CRA programs would be 

difficult to isolate and relatively expensive to measure. 
New Jersey Clean Energy Program Page 66 

Protocols to Measure Resource Savings 

September 2004 

Customer-Sited Generation 

Protocols 

The measurement of energy and demand impacts for customer sited generation systems is 

based on algorithms that estimate each systems annual energy production and coincident 

peak capacity production. Input data is based on fixed assumptions, engineering 

estimates and data supplied from the program’s technical worksheets and rebate 

application forms. An industry standard calculation tool (PVWATTS from the National 

Renewable Energy Laboratory) will be used for estimating PV system annual outputs. 

For wind installations estimated annual energy output is calculated using industry data 

table and inputs on average wind speed at hub height, rotor diameter and typical system 

efficiencies for wind speed/rotor diameter combinations. 

For fuel cell and sustainable biomass projects the protocols include recommended 

formats but the energy and peak capacity for each project will be estimated on a case by 

case basis. This level of flexibility allows for the use of more detailed case specific 

engineering data in the protocol reporting. 

All of the customer sited generation protocols report the gross energy production from 

the generation system. The protocols for fuel cell installations account for estimated 

natural gas consumption. Sustainable biomass projects account for estimated 

consumption of the applicable biomass fuel. 

In support of the protocol estimates, sub-metering must be installed to measure the gross 

output of the generating systems capable of recording at 15 minute intervals for a 

minimum of 12 months. 

Sub-Metering Samples Size by technology:

• 50% of first 30 installations 

• 10% above 30 Installations 

• Not to exceed 100 

The following is an explanation of the algorithms used and the nature and source of all 

required input data. 

Algorithms

Photovoltaic Systems 

PVWATTS will be used to estimate the energy generated by photovoltaic systems. 

PVWATTS was developed and is available through the Renewable Resource Data Center 

(RReDC). The RReDC is supported by the National Center for Photovoltaics (NCPV) 

and managed by the Department of Energy's Office of Energy Efficiency and Renewable 
New Jersey Clean Energy Program Page 67 

Protocols to Measure Resource Savings 

September 2004 

Energy. The RReDC is maintained by the Distributed Energy Resources Center of the 

National Renewable Energy Laboratory. The subroutines used to calculate the energy 

generation are based on information developed by Sandia National Laboratories. 

PVWATTS is available through the RReDC website, 

http://rredc.nrel.gov/solar/codes_algs/PVWATTS/. 

The following input values are used by PVWATTS to estimate average annual energy 

production, and are collected for each PV project on the PV technical worksheet and 

rebate application. 

Annual Energy Production (kWh) calculated by PVWATTS is a function of: 

• System Rated Output (AC output at Standard Rating Conditions) 

• Fixed, Single or Double Axis Tracking 

• Array Tilt angle (for fixed axis only) 

• Array Azimuth (for fixed axis only) 

• Weather data (based on City and State) 

The Peak demand impact for photovoltaic systems is estimated separately from the 

annual energy output. Summer and winter peak impacts are based on research conducted 

by Richard Perez, of SUNY Albany, (http://www.nrel.gov/ncpv/documents/pv_util.html). 

The estimated summer effective load carrying capacity (ELCC) for New Jersey is 60% to 

70%. A value of 65% is adopted for these protocols. 

Summer Peak Impact (kW) = System Rated Output * Summer Effective Load Carrying 

Capacity (ELCC). 

Winter Peak Impact (kW) = System Rated Output * Winter Effective Load Carrying 

Capacity (WELCC). 

A summary of the input values and their data sources follows: 

Photovoltaic Systems 

Component Type Value Sources 

System Rated 

Output (SRO) 

Variable Application Technical 

Worksheet 

Fixed, Single, 

Double Axis 

tracking 

Variable Application Technical 

Worksheet 

Array Tilt Variable Application Technical 

Worksheet 

Azimuth Angle Variable Application Technical 

Worksheet 

Weather Data Variable City, State – four 

sites will be used 

Application Technical 

Worksheet 
New Jersey Clean Energy Program Page 68 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

(Wilkes Barre PA, 

Newark NJ, 

Philadelphia PA, 

and Atlantic City, 

NJ 

ELCC Fixed 65% (http://www.nrel.gov/ncpv

/documents/pv_util.html) 

WELCC Fixed 8% Monitored system data 

from White Plains NY 

Wind Systems 

Estimated annual energy output for wind systems will be based on an industry data table. 

Currently there is a lack of data on the peak impact of small wind systems in New Jersey 

and an estimate of 0% will be used. This value will be updated if supporting data are 

identified. 

Annual Energy Output (kWh) is a function of: 

• Average annual wind speed at hub height 

• Rotor diameter 

• Total system efficiency 

The Estimated Annual Energy Output data table is drawn from Gipe, Paul (1993), Wind 

Power for Home and Business, Chelsea Green Publishing Company. A spreadsheet with 

the values in this table is attached. 

Data summary of the input values and their data sources follows: 

Wind Systems 

Component Type Value Sources 

Average annual 

wind speed at hub 

height (m/s) or 

(mph) 

Variable Application Technical 

Worksheet 

Rotor diameter in 

meters or feet 

Variable Application Technical 

Worksheet 

Typical System 

Efficiency 

Fixed for 

each wind 

speed / rotor 

diameter 

combination 

Ranges from 12% 

to 30% 

Gipe, (1993). Appendix E1 Table on Estimate 

Annual Energy Output. 

Efficiencies based on 

published data. 

Summer Peak 

Impact 

Fixed 0% Data on peak impact not 

available at this time 
New Jersey Clean Energy Program Page 69 

Protocols to Measure Resource Savings 

September 2004 

Component Type Value Sources 

Winter Peak Impact Fixed 0% Data on peak impact not 

available at this time 

Fuel Cells 

Estimated annual energy output and peak impacts for fuel cell systems will be based on 

case specific engineering estimates and manufacturer data. 

Total Annual Energy = Average Electric Output + Average Thermal Energy Recovered 

Data collected for the protocol estimation for each fuel cell project will include the 

following. 

Fuel Cells 

Component Type Value Sources 

Rated Continuous 

Peak Output (AC) 

Variable Manufacturer 

Specifications – 

Application Technical 

Worksheet 

Rated Fuel Input at 

Peak Output 

(MMBTU/hr) 

Variable Manufacturer 

Specifications – 

Application Technical 

Worksheet 

Average annual 

Electric Output 

Variable Project specific based on 

estimated duty cycle 

Average annual 

thermal energy 

recovery 

Variable Project specific based on 

estimated duty cycle 

Annual fuel 

consumption 

(MMBTU) 

Variable Project specific based on 

estimated duty cycle 

Average total 

system efficiency 

Variable Project specific based on 

manufacturer 

specifications and 

estimated operating 

parameters. 

Summer Peak 

Impact 

Variable Project specific based on 

estimated duty cycle. 

Winter Peak Impact Variable Project specific based on 

estimated duty cycle. 

Sustainable Biomass 

Estimated annual energy output and peak impacts for sustainable biomass systems will be 

based on case specific engineering estimates and manufacturer data. 
New Jersey Clean Energy Program Page 70 

Protocols to Measure Resource Savings 

September 2004 

Appendix A 

Measure Lives 

NEW JERSEY STATEWIDE ENERGY-EFFICIENCY PROGRAMS 

Measure Lives Used in Cost-Effectiveness Screening 

July 2001 

PROGRAM/Measure Measure Life 

Residential Programs 

Energy Star Appliances 

ES Refrigerator post 2001 17 

ES Refrigerator 2001 17 

ES Dishwasher 13 

ES Clotheswasher 20 

ES RAC 10 

Energy Star Lighting 

CFL 6.4

Recessed Can Fluourescent Fixture 20 

torchiere residential 10 

Fixtures Other 20 

Energy Star Windows 

WIN-heat pump 20 

WIN-gas heat/CAC 20

WIN-gas No CAC 20 

Win-elec No AC 20

Win-elec AC 20 

Residential New Construction 

SF gas w/CAC 20 

SF gas w/o CAC 20 

SF oil w/CAC 20 

SF all electric 20 

TH gas w/CAC 20 

TH gas w/o CAC 20 

TH oil w/CAC 20 

TH all electric 20 

MF gas w/AC 20 

MF gas w/o AC 20 

MF oil w/CAC 20 

MF all electric 20 

ES Clotheswasher 20 

Recessed Can Fluor Fixture 20 

Fixtures Other 20 

Efficient Ventilation Fans w/Timer 10 

Residential Electric HVAC 

CAC 13 15 

CAC 14 15 
New Jersey Clean Energy Program Page 71 

Protocols to Measure Resource Savings 

September 2004 

PROGRAM/Measure Measure Life 

ASHP 13 15 

ASHP 14 15 

CAC proper sizing/install 15 

ASHP proper sizing/install 15 

E-Star T-stat (CAC) 15 

E-star T-stat (HP) 15 

GSHP 30 

CAC 15 15 

ASHP 15 15 

Residential Gas HVAC 

High Efficiency Furnace 20 

High Efficiency Boiler 20 

High Efficiency Gas DHW 10 

E-Star T-stat 15 

Low-Income Program 

Air sealing electric heat 17 

Duct Leak Fossil Heat & CAC 15 

typical fossil fuel heat 17 

typical electric DHW pkg 10 

typical fossil fuel DHW pkg 10 

screw-in CFLs 6.4 

high-performance fixtures 20 

fluorescent torchieres 10 

TF 14 20 

TF 16 20 

TF 18 20 

SS 20 20 

TF 21 20 

SS 22 20 

TF 25 20 

audit fees 20 

Attic Insulation- ESH 17 

Duct Leak - ESH 15 

T-Stat- ESH 5 

HP charge air flow 8 

electric arrears reduction 1 

gas arrears reduction 1 

 

 

Non-Residential Programs 

C&I Construction 

Commercial Lighting — New 15 

Commercial Lighting — Remodel/Replacement 15 

Commercial Custom — New 18 

Commercial Chiller Optimization 18 

Commercial Unitary HVAC — New - Tier 1 15 

Commercial Unitary HVAC — Replacement - Tier 1 15 

Commercial Unitary HVAC — New - Tier 2 15 

Commercial Unitary HVAC — Replacement Tier 2 15 
New Jersey Clean Energy Program Page 72 

Protocols to Measure Resource Savings 

September 2004 

PROGRAM/Measure Measure Life 

Commercial Chillers — New 25 

Commercial Chillers — Replacement 25 

Commercial Small Motors (1-10 HP) — New or Replacement 20 

Commercial Medium Motors (11-75 HP) — New or Replacement 20 

Commercial Large Motors (76-200 HP) — New or Replacement 20 

Commercial VSDs — New 15 

Commercial VSDs — Retrofit 15 

Commercial Comprehensive New Construction Design 18 

Commercial Custom — Replacement 18 

Industrial Lighting — New 15 

Industrial Lighting — Remodel/Replacement 15 

Industrial Unitary HVAC — New - Tier 1 15 

Industrial Unitary HVAC — Replacement - Tier 1 15 

Industrial Unitary HVAC — New - Tier 2 15 

Industrial Unitary HVAC — Replacement Tier 2 15 

Industrial Chillers — New 25 

Industrial Chillers — Replacement 25 

Industrial Small Motors (1-10 HP) — New or Replacement 20 

Industrial Medium Motors (11-75 HP) — New or Replacement 20 

Industrial Large Motors (76-200 HP) — New or Replacement 20 

Industrial VSDs — New 15 

Industrial VSDs — Retrofit 15 

Industrial Custom — Non-Process 18 

Industrial Custom — Process 10 

Small Commercial Gas Furnace — New or Replacement 20 

Small Commercial Gas Boiler — New or Replacement 20 

Small Commercial Gas DHW — New or Replacement 10 

C&I Gas Absorption Chiller — New or Replacement 25 

C&I Gas Custom — New or Replacement (Engine Driven Chiller) 25 

C&I Gas Custom — New or Replacement (Gas Efficiency Measures) 18 

Building O&M 

O&M savings 3 

Compressed Air 

Compressed Air (GWh participant) 8 
Appendix 4: Comparison of Alternative Methodologies to Calculate Avoided NOx

Emissions 

The team completed an analysis of avoided emissions to determine if reductions from 

energy efficiency and renewable energy sources would offset at least 1.5 lbs/MWh of NOx 

 emissions. The methodology used in our analysis focused on the generation-weighted 

average of the emissions of fossil fuel fired plants. This is the fourth methodology listed 

below. We present alternative methodologies for informational purposes. 

Several methods may be employed to model the avoided emissions resulting from energy 

efficiency or renewable energy measures. These include: 

1) A complete grid-system dispatch analysis; 

2) A system mix analysis; 

3) A surrogate plant analysis; or 

4) A generation-weighted average of the emissions of fossil fuel fired plants. 

A complete grid-system dispatch analysis considers the dispatch order and scheduling of 

specific fossil fuel-fired units (coal, oil, or natural gas) at each facility on the regional 

grid, providing the most comprehensive estimate of the avoided emissions. An analysis 

of this type may be based on historical data and/or on a unit dispatch model. The 

principal unit dispatch models are proprietary.1

 

The dispatch analysis methodology allows the analyst to compare the EERE measures 

with the actual generation of variably dispatched fossil fuel units for specific time 

periods. This methodology is very time consuming and resource intensive and is hard to 

justify for validating an avoided emissions rate already stipulated in a State NOx trading 

regulation. However, the additional expense of this detailed approach can be justified to 

provide more precise estimates of displaced NOx emissions resulting from a large 

renewable energy project, such as a large wind farm.2 In such as case, a large premium 

obtained for Renewable Energy Certificates may justify the additional expense. 

The system mix analysis uses the generation weighted average of all the plants in the 

electric generating system. In other words, the universe of plants covered by this 

methodology is not limited to fossil fuel generating plants but also includes nuclear and 

hydroelectric plants. 

A major weakness of the system mix methodology is that it significantly underestimates 

the emission reductions resulting from EERE projects because it includes nuclear and 

1

 However, some utilities will enter into agreements to share such data for analysis purposes on a 

confidential basis. In addition, some of the new generation tracking systems may provide sufficient data to 

conduct this analysis. 

2

 See National Renewable Energy Laboratory, “Model State Implementation Plan (SIP) Documentation for 

Wind Energy Purchase in a State with Renewable Energy Set-Aside,” 

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

 

 4-1
hydroelectric generating plants, which do not produce emissions, in calculating the 

average displaced emissions. In reality, EERE almost always displaces fossil fuel 

generation because EERE generally has zero or very low fuel and operating costs, 

whereas fossil fueled generation has relatively high operating costs. Fossil fueled units 

also have the ability to vary their output relatively quickly. 

In comparison, nuclear power and hydroelectric generation is almost never displaced by 

EERE measures. Nuclear plants cannot vary their output quickly and have relatively low 

marginal operating costs. Hydroelectric plants also have low marginal operating costs, 

and therefore, generation from other renewable energy rarely displaces that from 

hydroelectric power. In addition, hydroelectric plants have externally imposed storage 

limits and flow constraints that restrict the ability to meet unpredicted load changes. For 

these reasons, the generation at renewable energy plants and reductions in demand from 

energy efficiency programs will displace generation almost entirely at fossil fueled plants 

in the period from now through 2012. 

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 New Jersey, under prevailing fuel prices and air quality 

regulations, the most likely new plant would be a combined cycle natural gas plant with 

best available NOx control technology. With this approach, the calculated NOx

reductions would be below the 1.5 lbs/MWh stipulated in the NJ NOx trading regulations. 

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

efficiency displacement is spread across a wide range of fossil fueled generating units, 

some of which have relatively high NOx emission rates. The surrogate plant 

methodology 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. 

A fourth methodology – and the one relied upon by the project team – is an analysis 

based on the generation-weighted average of the emissions of fossil fuel fired plants. 

This methodology is a reasonable approximation of the marginal emission rate, without 

the time and cost of a complete grid-system dispatch analysis. 

Two independent analyses were conducted based on the generation-weighted average 

emissions of fossil fueled plants. Resource Systems Group, Inc. (RSG) performed the 

first analysis and relied on emissions and generation data from the EPA’s eGRID 2002 

database.3

 The emissions data in eGRID 2002 is based on emissions data collected in 

2000. The list of facilities used for this assessment and their associated NOx emission 

rates, generation, and primary fuel are included in Table 1. 

The team included small facilities in the analysis even though such facilities do not 

influence the estimate significantly. In addition, it should be noted that although the 

3

 Emissions & Generation Resource Integrated Database (eGRID) 2002. 

 

 4-2
primary fuel is listed for each facility, many facilities operate subordinate units that burn 

other fuels, often contributing to varying emission rates among a fuel group. 

Based on the eGRID 2002 data, the team estimated the generation-weighted NOx

emissions for both annual operation and ozone-season operations. The annual avoided 

NOx emission rates are 2.7 lbs/MWh, and the ozone season rates are 2.2 lbs/MWh. Both 

of these rates are well above the 1.5 lbs/MWh avoided emissions rate that is currently 

stipulated in the NJ NOx trading regulations. Even removing several of the largest and 

most inefficient coal burning facilities from the analysis is not enough to drop the 

estimate below 1.5 lbs/MWh. Therefore, the project team believes that 1.5 lbs/MWh is a 

reasonable (and probably conservative) value for avoided emissions credit in 2005. 

However, it should be noted that this emission rate is expected to decline in the future as 

older, dirtier generating plants are replaced by more efficient plants with superior NOx

control technology. 

New Jersey DEP provided the second analysis,4

 and the project team ultimately relied on 

this analysis for the calculations of emission reductions contained in the body of this 

report. The NJ DEP analysis was similar to the RSG analysis because both 

methodologies were based on the generation weighted average of emissions from fossil 

fueled plants in New Jersey. The two major differences were that the DEP analysis relied 

on: (1) plants operating in 2004 (compared to the 2000 data in the eGRID/RSG 

analysis); and (2) plants with a capacity of more than 15 MW (compared to all fossil fuel 

plants in the RSG analysis). 

The NJ DEP data provided an estimate of 1.85 lbs/MWh for the average avoided 

emissions rate in 2004. DEP also projected future average avoided ozone season NOx

emission rates based on data about new plants completed, under construction, or expected 

to be retired. In addition, the NJ data included information projecting the installation of 

NOx control systems. Based on this information, DEP estimated the generation-weighted 

NOx emissions rates to be 1.65 lbs/MWh in 2005, 1.24lbs/MWh in 2007, and 

0.97lbs/MWh in 2008. 

Both the RSG and DEP analyses validate the use of 1.5 lbs/MWh for 2005. The analysis 

team recommends the use of the DEP estimates beyond 2005 as they are based on more 

recent data and incorporate projected changes in control technology and the fossil fueled 

generation mix. These DEP estimates beyond 2005 have been incorporated into the 

projections for each of the four alternative scenarios in the report. 

4

 Tom McNevin, Bureau Air Quality Planning, NJ DEP, Personal Communication, September 2005. 

 

 4-3
Table 1: Fossil Fueled Generation Units Used In the Generation Weighted Analysis of 

Variably Dispatched Plants in New Jersey. 

Name Fuel

Capacity 

Factor

Capacity 

(MW)

Annual Net 

Generation 

(MWh)

Ozone Season 

Net Geneation 

(MWh)

Nox Rate 

Annual 

(lbs/MWh)

Nox Rate 

Ozone Season 

(lbs/MWh)

Chambers Cogeneration LP Coal 57% 285 1,433,629 597,345 0.8 0.8

Hudson Generating Station Coal 31% 1229 3,307,562 1,570,699 5.6 4.3

Logan Generating Plant Coal 56% 230 1,126,726 469,469 0.9 0.9

Mercer Generating Station Coal 44% 768 2,926,302 1,376,294 8.9 6.2

B L England Coal 30% 484 1,256,331 550,443 8.3 6.5

Deepwater Coal 17% 259 377,442 193,346 5.9 5.2

Howard Down Coal 10% 71 62,270 35,368 10.4 10.0

Bayonne Generating Station Distillate Oil 0% 43 596 16.8 516 8.1

Carlis Corner Distillate Oil 1% 84 9,532 5,896 7.6 5.1

Cedar Station Distillate Oil 3% 63 18,508 10,075 4.2 3.2

Middle Station Distillate Oil 1% 80 7,408 3,686 7.3 6.1

Missouri Avenue Distillate Oil 1% 56 6,503 3,686 8.0 5.9

National Park Generating Station Distillate Oil 0% 19 146 9.1 142 3.9

Werner Distillate Oil 0% 159 5,100 2,125 2.3 2.3

Forked River Natural Gas 7% 77 46,764 22,135 5.6 4.9

Anheuser Busch Inc Newark Brewery Natural Gas 66% 13 75,513 31,464 1.3 1.3

Asbury Park Press Inc Natural Gas 53% 1 6,044 2,518 1.6 1.6

Aventis Pharmaceuticals Natural Gas 81% 4 26,529 11,054 1.7 1.7

Bayonne Cogen Plant Natural Gas 84% 192 1,409,971 587,488 0.4 0.4

Bayville Central Facility Natural Gas 27% 1 3,008 1,253 1.7 1.7

Bergen Generating Station Natural Gas 22% 765 1,485,866 840,246 1.2 0.9

Bristol Myers Squibb Co Natural Gas 89% 10 74,373 30,989 1.4 1.4

Burlington Generating Station Natural Gas 3% 807 228,102 157,538 0.9 0.7

Calpine Newark Inc Natural Gas 48% 65 271,413 113,089 0.4 0.4

Calpine Parlin Inc Natural Gas 32% 141 389,001 162,084 0.2 0.2

Camden Cogen LP Natural Gas 70% 157 969,174 403,823 0.5 0.5

Cumberland Natural Gas 5% 99 40,545 19,619 1.9 1.6

Eagle Point Cogeneration Natural Gas 87% 225 1,712,749 713,645 1.0 1.0

Edison Generating Station Natural Gas 3% 510 114,502 71,238 3.8 2.5

Essex Generating Station Natural Gas 4% 596 186,746 103,326 2.9 2.2

Fiber Mark Technical Specialties Inc Natural Gas 33% 2 5,801 2,417 1.8 1.8

Gilbert Natural Gas 5% 606 243,950 101,646 1.4 1.8

Glenn Gardner Natural Gas 1% 157 16,837 7,016 2.1 2.1

Green Tree Chemical Technologies Inc Natural Gas 76% 5 30,141 12,559 2.2 2.2

Hoffmann Laroche Inc Natural Gas 61% 12 63,694 26,539 1.4 1.4

Hunterdon Cogeneration Facility Natural Gas 82% 4 29,322 12,218 1.2 1.2

Kenilworth Energy Facility Natural Gas 85% 30 224,139 93,391 1.2 1.2

Kms Crossroads Natural Gas 78% 7 47,629 19,846 1.5 1.5

Lakewood Cogeneration LP Natural Gas 26% 239 550,345 229,311 0.1 0.1

Linden Cogen Plant Natural Gas 64% 762 4,289,494 1,787,289 0.4 0.4

Linden Generating Station Natural Gas 5% 778 346,952 201,210 1.2 1.6

Lowe Paper Co Division Of Simkins Industries Natural Gas 44% 3 11,574 4,822 1.4 1.4

M&M Mars Natural Gas 82% 9 63,277 26,365 1.7 1.7

Merck Rahway Power Plant Natural Gas 47% 11 44,164 18,402 1.0 1.0

Micketon Station Natural Gas 3% 71 21,574 14,313 4.3 2.7

Milford Power LP Natural Gas 0% 33 203 1.4 85 1.4

Montclair Cogeneration Facility Natural Gas 91% 4 32,656 13,607 0.9 0.9

Newark Bay Cogeneration Project Natural Gas 52% 135 619,417 258,090 1.1 1.1

Novartis Pharmaceuticals Natural Gas 59% 3 14,550 6,063 1.5 1.5

Pedricktown Cogeneration Plant Natural Gas 19% 135 228,139 95,058 0.4 0.4

Pharmacia Corp Natural Gas 18% 5 7,434 3,097 1.8 1.8

Prime Energy LP Natural Gas 72% 83 521,052 217,105 1.1 1.1

Roche Vitamins Inc Natural Gas 81% 45 320,477 133,532 1.2 1.2

Rowan University Natural Gas 53% 2 6,909 2,879 1.8 1.8

Sayreville Natural Gas 1% 463 21,511 8,963 1.5 2.9

Sayreville Cogeneration Facility Natural Gas 55% 430 2,063,072 859,613 1.2 1.2

Schering Corp Cogeneration Facility Natural Gas 88% 8 63,086 26,286 2.0 2.0

Schweitzer Mauduit International Inc Natural Gas 29% 4 9,056 3,773 1.6 1.6

Sherman Avenue Natural Gas 6% 113 61,976 32,070 0.9 1.2

Trigen Trenton Energy Co Natural Gas 83% 12 87,464 36,443 2.9 2.9

University Medicine Dentistry Natural Gas 89% 11 81,669 34,029 1.4 1.4

Vineland Cogeneration Plant Natural Gas 19% 53 88,095 36,706 0.4 0.4

West Station Oil 4% 27 10,169 13.5 5,721 10.0

Kearny Generating Station Residual Oil 0% 1165 39,974 29,376 4.1 3.5

Sewaren Generating Station Residual Oil 4% 576 216,431 136,897 1.7 1.3

 4-4
F1147-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

August 2006 

2. REPORT TYPE

Technical Report 

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Final Report on the Clean Energy/Air Quality Integration Initiative 

Pilot Project of the U.S. Department of Energy’s Mid-Atlantic 

Regional Office 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

D. Jacobson, George Washington University Law School 

P. O’Connor, Global Environment & Technology Foundation 

C. High, Resource Systems Group 

J. Brown, National Renewable Energy Laboratory 

5d. PROJECT NUMBER

NREL/TP-710-40477 

5e. TASK NUMBER

60014204 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

DOE/GO-102006-2354 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The MARO pilot project represents the first effort in the country to seek to obtain credit under a Clean Air Act (CAA) 

State Implementation Plan (SIP) for nitrogen oxide (NOx) emission reductions. This project came about because of 

state-funded incentive programs and projects for RE and EE. Specifically, the pilot project focuses on the New 

Jersey (NJ) SIP and efforts to facilitate attainment of the new, 8-hour ozone standard under CAA by implementing 

selected categories of RE and EE programs and projects funded by the New Jersey Clean Energy Program (NJ 

CEP) of the New Jersey Board of Public Utilities (NJ BPU). 

15. SUBJECT TERMS

maro; mid-atlantic regional office; nitrogen oxide; nox; sip; ozone standard 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18
A Strong Energy Portfolio for a Strong America 

Energy efficiency and clean, renewable energy will mean a stronger economy, a cleaner environment, and 

greater energy independence for America. Working with a wide array of state, community, industry, and 

university partners, the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy invests 

in a diverse portfolio of energy technologies. 

For more information contact: 

EERE Information Center 

1-877-EERE-INF (1-877-337-3463) 

www.eere.energy.gov