# Avoided Air Emissions from Energy Efficiency and Renewable Electric Power Generation in the PJM Interconnection Power Market Area

Colin J. High and Kevin M. Hathaway
Resource Systems Group, Inc. White River Junction, VT

Supported by Funding from the U.S. Depar t ment of Energy Clean Ener gy/Air Quality Integra t ion Initia tive


# Draft Final March 15, 2007A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2



## CONTENTS

CONTENTS

............................................................................................................................... ...........................2

PREFACE

............................................................................................................................... ..............................3

LIST OF ACRONYMS

............................................................................................................................... .........4

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

2. THE PJM INTERCONNEC TION POWER MARKET AREA .................................................................6

3. ANALYTICAL METHODOLOGY ..............................................................................................................8
3.1 A NALYSIS OF 

### LED T RAFFIC 

### S IGNAL 

### R ETROFITS 

### .................................................................................10 3.2 A NALYSIS 

### OF L IGHTING 

### R ETROFITS 

### IN G OVERNMENT 

### B UILDINGS 

### .................................................11 3.3 ANALYSIS OF A IR 

### C ONDITIONING 

### R ETROFITS 

### IN G OVERNMENT 

### B UILDINGS 

### ...............................12 3.4 A NALYSIS OF 

### SOLAR P HOTOVOLTAIC 

### P OWER 

### ....................................................................................13 3.5 A NALYSIS OF 

### W IND 

### P OWER 

### ...................................................................................................................14
4. SUMMARY OF THE ANALYSIS OF AVOIDED NO X 

### EMISSION RATES ........................................15

APPENDIX A: COMPARISON OF ALTERNA TIVE METHODOLOGIES TO CALCULATE AVOIDED NOX EMISSIONS ..........................................................................................................................18

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

APPENDIX C: NREL SUPPORTING DOCUMENT- COMPOSITE HOURLY ELECTRICAL SAVINGS PROFILES FOR GOVERNMENT BUILDINGS IN THE WASHINGTON METROPOLITAN AREA

............................................................................................................................... .24A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 3



## PREFACE This report and analysis were prepared by Colin High and Kevin Hathaway of Resource Systems Group under contract to DJ Consulting LLC. The work was conducted with the financial support of the Clean Energy/Air Quality Integration Initiative of the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. Resource Systems Group wishes to thank staff from the National Renewable Energy Laboratory, who provided extensive input to the en ergy efficiency analyses included in this report, including Laura Vimmerstedt, David Kline, Gail Mosey, Sara Farrar-Nagy, and B. Griffith. In addition, we appreciate the review and comments on the entire report provided by the NREL staff, Debra Jacobson of DJ Consulting LLC, and Alden Hathaway of Environmental Resources Trust. We also would like to thank the staff of the Metropolitan Washington Counc il of Governments (MWCOG), state and local government members of MWCOG, and energy industry companies who provided data used in the analysis.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 4



## LIST OF ACRONYMS

CAA Clean Air Act
CAIR Clean Air Interstate Rule
CEM Continuous Emission Monitors
CHP Combined Heat and Power
CO 2 

## Carbon Dioxide DOE U.S. Department of Energy EERE Energy Efficiency and Renewable Energy EGU Electric Generating Units EPA U.S. Environmental Protection Agency
kWh Kilowatt-Hour ISO Independent System Operator LED Light-Emitting Diode MWCOG Metropolitan Washington Council of Governments MWh Megawatt-Hour NAAQS National Ambient Air Quality Standards
NO x 

## Nitrogen Oxides NREL National Renewable Energy Laboratory OAQPS EPA Office of Air Quality Planning and Standards OTC Ozone Transport Commission PJM PJM (Pennsylvania, New Jersey, Maryland) Interconnection
PV Photovoltaic RSG Resource Systems Group, Inc SIP State Implementation PlanA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 5



## 1. INTRODUCTION AND PURPOSE OF THE REPORT The purpose of this report is to provide an estimate of the avoided nitrogen oxide (NO x 

## ) emissions that result from electric energy efficiency (EE) and the generation of electric power from selected renewable energy (RE) sources in the PJM Intercon nection power market area (PJM). This report is designed to assist the Metropo litan Washington Council of Gove rnments (MWCOG) in efforts to support credit for avoided NO x 

## emissions resulting from electric EE and RE power generation. Specifically, the report will support the development of the State Implementation Plans for the District of Columbia, the State of Maryland, and the Commonwealth of Virginia to implement requirements under the Clean Air Act to attain the 8-hour ozone standard. The report provides a prospective evaluation of the avoided emissions from several programs undertaken by State and local governments in the DC-MD-VA non-attainment area. Theseprograms include: (1) energy efficiency programs including the retrofit of traffic signals to lightemitting diode (LED) signals; (2) wind energy purchases; and (3) the implementation of selected zero-emission renewable energy requirements of th e District of Columbia’s Renewable Portfolio Standard (RPS). All of these programs will impact electric generation within the PJM Interconnection power market area. The technical methodology developed by Resource Systems Group, Inc., (RSG) in cooperation with Environmental Resources Trust was applied in this analysis. The me thodology is based on generally accepted principles and procedures for estimating air emissions reductions from EE and RE power generation on the electric grid. The underlying assumptions and computation are consistent with the approach used by other experts in the field and similar to previous studies of avoided air emissions from EE and RE in New Jersey, Maryland and portio ns of PJM. The New Jersey report, including its methodology, was published in August 2006 and was co-authored by tec hnical experts from the National Renewable Energy Laboratory (NREL), the Nation’s premiere renewable energy laboratory, and the Global Environment and Technology Founda tion, a prominent non-profit organization in the energy and environmental field. In addition, the U.S. Environmental Protection Agency and the Department of Energy provided peer review commen ts that were incorporated in both the Maryland and New Jersey studies. 1 

## The Maryland study also was accept ed by the Environmental Protection Agency to support its first-ever approval of a renewable energy purchase for NOx emissions reduction credit in a State Implementation Plan (SIP) under the Clean Air Act. 2 

## The methodology in

1 

### The New Jersey report is titled as follows: United States Department of Energy, Final Report on the Clean Energy/ Air Quality Integration Initiative Pilot Project for the Mid-Atlantic Region, August 2006

. (See http://www.eere.energy.gov/wip/c lean_energy_initiative.html ) The Maryland study is contained in the following document: , Metropolitan Washington Council of Governments, Plan to Improve Air Quality in the Washington, DC-MD-VA Region , February 19, 2004. See http://www.mwcog.org/committee/commi ttee/archives.asp?COMMITTEE_ID=14 (Scroll down to February 19, 2004, pp. 7-77 to 7-81 and Appendix J, pp. J-71 to J-76).

2 

### 70 Fed. Reg. 24988 (May 12, 2005).A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 6



## the Maryland report also was published by NREL as a model for air emissions assessment for other wind energy projects. 3 

## Avoided emissions result from renewable electric generation and demand-side energy efficiency measures because of the way that the electric power sy stem works. Solar and wind energy generation are “must run” power sources because they have ve ry low marginal operatin g costs and zero fuel costs. When these renewable sources and energy e fficiency measures are in effect, they will displace generation at fossil-fuel units, which have much higher marginal operating costs in their power market area. As a result, the emissions from those fossil-fuel units are displaced. Renewable energy generation and energy efficiency almost never displaces generation at nuclear power plants or hydropower plants for several reasons. First, both nuclear and hydropower units have low operating costs. In addition, nuclear power is not displaced because of the high costs involved in shutting down and restarting such plants. Although the timing of the hydropower generation may shift, as a result of renewable generati on, the stored water w ill be used to provide power at a later time -- resulting in a net reduction in fossil-fueled generation. The specific fossil-fuel units displaced vary by time of day and season and with the mix of fossil-fuel units operating. This report is focused on specifi c electric energy efficiency projects and the generation of electric power from zero-emission wind and photovoltaic sources. However, much of the discussion of the analytical process and the regulatory framework for crediting avoided NOx emissions also applies to avoided emissions from other energy efficiency /conservation measures and other zero-emission renewable energy generation.

2. THE PJM INTERCONNECTION POWER MARKET AREA The PJM Interconnection has the largest generation capacity of any power market in North America. This power market has expanded from the origin al PJM core of Pennsylvania, New Jersey and Maryland (PJM) to include all or part of 12 stat es. The analysis excludes the two non-contiguous parts of PJM in Illinois and Michigan because it is unlikely that fossil-fuel ed generation will be displaced in these areas that are so distan t from the Metropolitan Washington area. In this analysis, the import and export of power is not considered, and all generation displaced by renewable sources is assumed to occur in the PJ M area. Transmission constraints, though likely

3 

### National Renewable Energy Laboratory, Model State Implementation Plan (SIP) Document ation for Wind Energy Purchase in State with Renewable Energy Set-Aside , May 2005, Subcontract Repor t NREL/SR-500-38075. See http://www.windpoweringamerica.gov/sips.aspA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 7



## significant in PJM’s large region, are not considered . Our analysis demonstrates that none of these simplifying assumptions likely affect the result s at this level or for this report’s purposes. 4 

## The complete list of fossil- fueled plants in PJM utilized in our analysis is presented in Appendix B (Table B-1). The locations of the plants are shown in Figure B-1. We selected these plants for our analysis because they have the ability to provide vari able levels of generation into the PJM grid at short notice and are not fully committed. These plants all meet the following criteria. They all are: (1) fossil-fuel fired, (2) sell power into the contiguous portions of the PJM Grid; (3) have average capacity factors below full capacity; and (4) are large enough to have continuous emissions monitors (CEMs). Small stand-by “peak shaving” units, emergency ge nerators, and “behind the meter” generators are not included in the analysis because detaile d data for these are not readily available. The monthly average generation at fossil-fueled units in cluded in the analysis is shown by fuel type in Figure 1 below. This figure shows that generation peaks in summer and winter and that coal is the dominant fuel in all seasons. The proportion of oil increases in the May to September ozone season because electric generators need to meet the NO x 

## emissions limitations for the summer ozone

season. Figure 1 is based on plant-level fuel use reported to the Department of Energy’s Energy Information Administration and is intended for info rmational purposes only. The actual NO x 

## avoided emission analysis is based on time matching analysis at the unit level as described in section 4. The marginal fuel use may differ, especially at peak demand periods.

4 

### However, it should be noted that power imports and transmission constraints may affect the avoided emissions for specific projects. In a 2003 study of a specific wind project in western Maryland conducted by Resource Systems Group for Clipper Windpower, the average ozone season avoided NOx emission rate was 5.8 lbs/MWh, which was considerably higher than the PJM average avoided emissions at that time. 4 These results are indicative of how project-specific factors can affect avoided emission rates.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 8



### F

i g u r e 1: PJM Aver ag e Mo n t h l

y Gen e ratio n Mix (MW h

) fo r 2005

- 5, 00

0, 000 10

, 0

00

, 0

0 0 15

, 0

00

, 0

0 0 20

, 0

00

, 0

0 0 25

, 0

00

, 0

0 0 30

, 0

00

, 0

0 0 35

, 0

00

, 0

0 0 40

, 0

00

, 0

0 0 45

, 0

00

, 0

0 0

Ja n F eb M a r A p r M a y J u n Ju l A u g S e p O ct N o v D ecGeneration(MWh) Co a l Oi l Ga s



## 3. ANALYT ICAL METH OD OLOGY
Th e a n alysis obje c tive was to det e rmi n e the avera g e avoided NOx emissions th at o ccur w h e n a specifi c a m ou nt of E E sav i ng or RE ge ner atio n occu rs in PJM. The five EE a n d RE pr oje c ts discussed in this report i n cl ude the f o llowing:

• LED tr affic si gnal re tr ofits (EE )

• Lighti ng retrofits in gov e rnment building s ( EE )

• Air-co nditi oni ng ret r ofi t s in gover n me nt b u ildings ( EE )

• Solar Ph ot ovo l taic P o wer ( RE )

• Wind Pow e r ( RE )
For the thr ee EE pr oje c t c a tegories, the avoided em issions analysis assumes th at t h e proj ects wi ll be located i n the DC-MD-VA non-attain ment area and that electrici t y sa vings will displace fossil-fu eled gener ati on i n PJM. For t h e RE p r oje c ts, i t is assumed t h at gen e ra tio n could b e lo cat e d an ywh e re i n PJM and will displace fossil-fuel ed generation in PJM.
Several est a bli s hed met hods for estim ati n g avoided em issions in a n y powe rmarke t are a hav e b e e n develop e d. These al ter n a t e met hods are briefly r e vie w ed in Ap pe ndix A of t h i s repor t. Addition a l reviews of t h i s subject hav e been pre p ar e d by Syna pse Energ y

Ec on omics fo r th e Ozon e Tr ans por t Commission. 5 

## The methodology used in this report is the tim e -m at c h ed and ge ne rati on- weig hte d average of th e emissio n s o f p l an t s th at a r e va riab ly disp a t ched to m eet ch angi ng demand. Thi s met hodol og y is a refin e me nt of the gen e ra tio n -weig h t e d av erage ap pr oa c h whi c h w a s used in th e N e w Jerse y re p o rt cited in Secti on 1. The fi rst step deter m ines the E E or RE generati on profil e (secti ons 3. 1 - 3.6 provides inf o r m ati on on e a ch of t h es e p r ofiles) f o r t h e summer oz one se aso n a n d for th e full year

5 

### Synapse E n e r gy E c onomi c s, Inc

. Predicting Avoided Emissions fr om Poli cies that Encourage Ener g y

Efficiency a n d Clean Power, p r ep ared fo r th e Ozon e Tran spo r t Co mmissio n , Jun e 2002.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 9



## (by time of day, week, and month). Secondly, these hourly profiles are matched with the hourly generation of the variably dispatched fossil-fuel units at plants listed in Appendix B (Table B-1) using SQL (structured query language) software. This cust omized tool identifies the fossil-fueled units operating when renewable power is generated or energy savings are taking place, forming the list of generating units available to be di splaced in each hour of the year. Unit-level generation is estimated using the hourly carbon dioxide (CO 2 

## ) emissions from the continuous emissions monitors (CEMs) required by EPA for each fossil-fueled electric generating unit (EGU). 6 

## The relationship between CO 2 

## emissions and generation is described by different linear models for each fuel (oil, coal, and gas) and co mbustion design (boilers, turbines, and internal combustion engines). These linear models pr ovide reasonably accurate estimates of hourly generation of the EGUs in PJM. Hourly NO x 

## emissions for each fossil-fueled EGU are also derived from the CEM data. Estimated avoided emissions for each hour are based on a generation-weighted average of the units operating at that time. 7 

## These results are reported for both the ozone season (May 1 to September 30) and the full year. The current analysis is based 2005 data. The hourly, annual, and ozone season avoided emissions rates for NO x 

## (lbs/MWh) are shown in Figure 2. These avoided emission rates are almost identical for each of the EE and RE technologies. 8 

## The monthly and daily total avoided emissions (affected by the seasonal variability in energy savings and wind and PV power generation) are described in the following sections.

6 

### U.S. Environmental Protection Agency, Office of Ai r and Radiation, Continuous Emission Monitoring Database, , Acid Rain Hourly Emissions Data 2005 , CD 2 disks. Distributed by National Technical Information Service, Springfield, VA.

7 

### The analysis includes only the variably dispatched fossil fueled units at plants which are included in the analysis area of PJ M and listed in Table 1 of Appendix B.

8 

### The similarity in annual and ozone season avoided NOx emission rates (lbs/MWh) extends to five or more decimal places. The reason for this similarity is because of PMJ’s large generation ba se and the small increment of EE/RE that is being added to th e system in this analysis. A more refined analysis of specific projects for smaller areas and larger increments of EE/RE may show some differences between technologies. The seasonal differences in generation patterns between EE/RE technologies are, however, significant and result in marked differences on a tons per day basis for specific increments of generation or electric ity savings.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 0



### Figure 2 : Ann u a l a nd Ozon e Se a s on Hourly Av oide d Emis s i on Pote ntia l i n

the PJ M Are a

- 1

2 3 4 5 6



### Ja n F eb M a r A pr M ay J un J u l A ug S e p O ct N o v D ecAvoided Emissionslbs/MWh 

## NO

x O Z O N E SEA SO N : 1

. 7 lb s / M

W

h

A NNU AL : 3

. 2 l b s / M

W

h



### 3

. 1 A N A L YSIS OF LED TR A FFIC SI GN A L RETROFITS


## The e n ergy s a vings from re trofi t s of L E D traffic sig n als has be en c o mpil ed from the repor t s of the state and municipal age n ci es in the MW C O G that ha ve conducted su ch r e tr ofits. The e n erg y

savi ngs have bee n c a l c ulated b y using th e pr ot oc ol est a blish e d by t h e EP A E NER GY STA R Progr a m by th e use of data d e rived from t h e EPA E N ERG Y STA R Calcula t or W o rkshe e t. 9 

## The results of these calcula t io ns a r e re por ted in Sectio n 6.5 of the SI P docu ment. The el ectri city savings from the L E D traffi c sig n al r e tro f its oc cur at a ne arly co nsta ntlevel o v er a 24-h o ur peri od a n d t h rough o ut th e year

. Theref ore, the anal ysis does not co nt ain a n y daily or se a s onal v a ria t io n. The ele c tri c ity savings p r ofile is const a n t a t all hours as s h own bel o w in Figure 3.

9 

### U.S. En viron m en tal Pro t ectio n Agen cy, EN ERG Y STA R LED Traffic Sign als Calc ul ato r

. See http://www.energystar.gov/index.c fm?c=traffic.pr_ traffic_signalsA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 1



### Figure 3 : Mo n t hly Pe rc e n tage of Ann u a l NOx Emi s s i ons Av oide d & Annua l E l e c t ricity Sa v i ngs from LED Tra ffic Si gna ls Re trofits

12 % 12 % 12 % 10% 12% 11 % 11 %

4% 4% 4% 4% 4%



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### N O xE m issi o n s A v o i d e d

8

. 33 % 8 .33 % 8. 33% 8. 33% 8.3 3 % 8 .3 3% 8

. 33 % 8

. 33% 8. 33% 8.3 3 % 8.3 3 % 8.3 3 %



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### E l e c t r ic it y S a v i n g s fro m LE DTra f f i c S i g n a l Re t r o f it s



### 3.2 A N A L YSIS OF LIGHT I NG RETR OFI T S IN GOVERNM ENT BUILDINGS


## Th e e n ergy s a vings fr om l i ghti ng re trofi t s in g o vern men t building s has b e e n c o mpil ed fro m t h e reports of the state and mu nicipal agenci es in th e MW COG th at ha ve co nducted such re trofi t s. Th e met hodol ogy is described in a me mor a n d um included in App e ndix C 10 

## . The summary results of the savings c a lcul atio n s are r e p o rted i n Se cti on 6.5 of the SIP docume nt. The hourly energy savi ng s profil e h a s be en simula ted by N R EL using th e pe rfor man ce o f a sa mple of 20 go vernm e n t buil dings in the Metropolitan Wash ington area and on meteorological data for 2003. The monthly electricity savings pr ofil e is show n in Figure 4.

10 

### B. Grif f i th, Co mpo s ite Hourly E l ectrical Savings Pro f iles from Govern ment Buildings in t h e Washingt on DC. Area, Buildin gs an d Th ermal Systems Cen t er, Nation al Ren e wa b l e En ergy Lab o r ato r y, Decemb e r 5, 2006.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 2



### Figure 4 : Mo n t hly Pe rc e n tage of Ann u a l NOx Em is s i ons Av oide d & Annua l E n e r gy Sa v i ngs from Ligh tin g Re trofits of Gov e r

nme n t Build in gs

10 % 9% 12 % 10% 10% 11 % 11 %

4% 5% 5% 6% 5%



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### N O xE m issi o n s A v o i d e d

7% 6% 8% 8% 9% 10% 9% 8% 8% 7% 10

% 10 %



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### E l ec t r i c i t y Sa v i n g s f r o m L i g h t i n g Ef f i c i en c y I m p r o v e m en t s



### 3.3 A N A L YSIS OF A I R CONDITIONING R E TROFITS IN GOVERNMENT BUILDINGS


## Th e en ergy s a vings fr om r e tro f its o f air conditi o ning in gover n m e nt buildings has b e e n co mpile d from t h e r e p o rts o f th e st ate and muni cipal age n cies in th e MWC O G th at hav e co nducted such
retr ofits. The met hodol ogy is describ e d in a mem o r a nd um included i n A p p e ndix C

. 11 

## Th e summ a r y results of savings calculations are reported in Section 6.5 of the MWCOG SIP document. The hourly e n er gy savings pro f il e has b een si mulated b y N R EL using t h e perf orm a n c e of a sam p le of 20 gover n ment buildings in the Metr opoli t an Washi n gton area and on m e teor ol ogical data for 2003. The monthl y el ec tricity savings profil e is s h own i n Figure 5. The energ y

savings show a v e ry mar k ed summer peak.

11 

### B. G riffith , Com p osite Hourly Elec tr

ical Savings Profiles from Gov e rnment Buildings in t h e Washingt on DC. Area . Buildin gs an d T h ermal Systems Cen t er, Natio n a l Ren e wab l e En ergy Lab o r ato r y, Decemb e r 5, 2006.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 3



### Figure 5 : Mo n t hly Pe rc e n tage of Ann u a l NOx Emi s s i ons Av oide d & Annua l E n e r gy Sa v i ngs from Air Condit i on ing Effic i e n c y

Re trofits in Gov e r

nme n t Buildi ngs

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

5% 8% 10 % 11 %

7%



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### N O xE m issi o n s A v o i d e d

4% 5% 6% 8% 12% 11 %

6% 6% 4% 4% 17 % 15 %



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### E l e c t r ic it y S a v i n g s f r o m A i r Co n d i t io n i n g E f f i c i e n c y I m p r o v e m e n t s



### 3.4 A N A L YSIS OF S O L A R PHOTOVOL T A I C POWER


## Th e an alysis f o r solar p h o t o volt aic p o w e r is based on gener ati on da t a derived fro m the p e rf or man ce of existi ng facilities. The photov oltai c data are based on the perfor mance of a standard silicon PV system using Typical Met e orol ogic al Y e ars ( T MY2 ) s o lar radia t io n data co mpil ed by N R EL fo r t h e Metro poli t a n Washi n gt on a r ea. 12 

## O t h e r l o cati ons in PJ M would h a v e simila r se as on al pat t er ns, an d ther ef ore, th e y would have similar avoid e d emissio n r ates (lbs/MW h ) even if th e ac tual g e ne ra tio n rate in w a tts per m e ter diff ered. The su mmar y resu lts of the avoide d NOx cal c ulations are r e porte d

12 

### Nati o n al Re ne w a bl e E n e r gy Laboratory , TMY2 U sers Manual

. See http://rredc.nrel.gov/solar/old_d ata/nsrd b/tmy2/A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 4



## in Sec t io n 6.5 of t h e MW C O G SIP docu men t

. Th e m o n t hly pe rce n tage of to tal a nnual g e n e ra ti on and th e h o url y NO x 

## avoide d emission ra t e for p h o t ov o l taic pow er is show n in Figu re 6.
Figure 6 : Mo n t hly Pe rc e n tage of Ann u a l NOx Emis s i ons Av oide d & Annua l P hot ov olta ic Ene r gy Produc e d

7% 13 % 15%

5% 9% 7% 10 %

6% 8% 8% 7% 5%



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### N O xE m issi o n s A v o i d e d

5% 8% 10% 12 % 13%

7%

3% 4% 9%

4% 13 % 11 %



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### P h o t ov ol t a i c E n e r gy P r o d u c e d



### 3.5 A N A L YSIS OF WIND POWER


## Th e an alysis f o r wind p o w e r is based o n wind gen e r atio n dat a der ived from exi s ting wind e n ergy faciliti e s. 13 

## The data ar e fo r h o urly ele c tri c gener ati on for a on e-y e ar pe riod. The wi n d data are bas e d

on th e p e rf o r ma nce s h o w n in t h e c o mple te an nua l records of several wind turbines, i n the

moun tai nous i n teri or are a s o f the Ap pal a c h ia n Moun tai ns in PJM. Th is loca tio n is t y pic a l of th e wind pow er purc ha ses plan ned b y munici pali ti es in th e DC

- M D-VA n on- att ain m e n t ar e a
.

13 

### The l o c a ti on and owne rs hi p of t h e wi nd tu rbi n e s a r e c onf i d e n ti al to pro t e c t the c o m m e r c i al i n te re sts of the owne rs wh o prov i d e d pe rf ormanc e data.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 5



## Th e m o n t hl y perc en tag e o f to tal an nual gener ati on a n d the h o urly NO x 

## avoided emissio n r ate for wind p o wer i s show n in Fi gure 7 wit h a marked wint er p e ak. The summary resu lts of th e av o i ded NOx emissions calcul ati ons are re por ted in Secti on 6.5 of the SIP document.

Figure 7

: Mo n t hly Pe rc e n tage of Ann u a l NOx Em is s i ons Av oide d & Annua l Win d E

n e r gy Produc e d

14 % 13 % 14 %

10 % 15 %

2% 1% 2% 2% 3% 8% 16 %



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### N O xE m issi o n s A v o i d e d

12 % 11 % 12%
9% 8%

5% 4% 7% 13% 13 %

3% 3%



### Jan F e b M ar A p r M ay Ju n J u l A u g S ep O c

t N o

v D e c%ofannualtotal 

### Wi n d E n er g y P r o d u c ed



## 4. SUMMA R Y OF TH E ANALY S IS OF AV OID E D N O x 

## EMI SSION RAT ES
Th e m o n t hl y and se aso n al an alysis of t h e avoided e m issions f o r eac h o f t h e f ive NO x 

## reduct ion

me a s ure s de sc rib e d in Sect ion 3 we re co mp ut e d by a n Micro s fo ft ® 

## Excel TM 

## wo rkbo ok c a lcul ato r pre p ar ed by Resour ce Systems Grou p, Inc. 14 

## The cal c ulator will be posted on the website of the

14 

### Reso urce Systems Gro u p In c. MWAQC EERE Avo i de d NOx Emissio n s Calculato r2007. availab l e at http://www.mw cog.org/environment /airA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 6



## Metropolitan Washington Air Quality Committee of the Metropolitan Washington Council of Governments at http://sharepoint.mwcog.org/airquality/default.aspx

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

• The first sheet (named “Instructions and Docu mentation”) provides instructions & general methodology while graphically displaying the se asonal variation in hourly and daily avoided NO x 

## emissions rates. Seasonal patterns in energy efficiency savings and power generation for each of the five EE/RE measures are also provided.

• The second worksheet (named “Emissions Standard”) calculates the monthly, annual and ozone season avoided NO x 

## emissions (in tons per day) based on the seasonal profiles of the five EE/RE measures described in Section 3. There are separate sections for input and results for Maryland, Virginia and the District of Columbia.

• The third worksheet (named “Emissions Season Specific”) provides the same calculations as the ”Emissions Standard”, except that the user can specify the energy generated or saved in the ozone and non-ozone seasons. This worksheet can be used when the energy amounts are known or specified, such as in a power purchase agreement or a contract to purchase Renewable Energy Certificates (RECs). This worksheet provides a more accurate result than the “Emissions Standard” worksheet where th e specified ozone season and non-ozone season MWh differ from the regional generation or savings profile. This worksheet would be most applicable where a contract for RECs or energy efficiency savings binds the provider to supply a specified number of RECs during the summer ozone season. The average avoided NO x 

## emission rates for the five NO x 

## reduction measures are 1.7 lb/MWh for the ozone season and 3.2 lb/MWh annually. The average avoided ozone season and annual NO x 

## emissions (in tons per day) for each of the five measures and each of the three jurisdictions are provided in Section 6.5 of the SIP document. The average avoided emissions that may be applied in the SIP depend on the absolute amount of generation or savings and the monthly profile of these measures. Avoided emissions apply to PJM descri bed in Section 2 for 2005 and are based on the methodology described in this report and the set of fossil-fueled generating units at power plants listed in Appendix B. They are based on electricity savings from energy efficiency and renewable generation located in PJM without regard to location. Renewable energy project location will influence the avoided emission rate, especially in areas with significant transmission constraints. For projects located in PJM, but not in the immediate MWCOG area, the upwind or downwind location may also affect the ozone impact of th e avoided emissions. Avoided emissions from wind plants that are located in the Ap palachian Mountains to the west w ill provide upwind reductions for the MWCOG area. The average avoided NO x 

## emission rates of western PJ M, where more generation is fueled by coal, are typically higher than in east ern PJM (i.e. more natural gas). If fossil-fueled units in eastern PJM’s were excluded from the analysis (i.e. they are unlikely to be displaced by wind power), the avoided NO x 

## emission rates would be higher. In this instance, the analysisunderestimates the avoided emission from wind plants upwind of the Washington DC-MD-VA nonattainment area.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 7



## These avoided emission rates are likely to have been similar in 2006. In future years, the avoided emission rates will be affected by changes in po wer generation, emissions control technology, and emissions control regulations.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 8



## APPENDIX A: COMPARISON OF ALTERNATIVE METHODOLOGIES TO CALCULATE AVOIDED NOX EMISSIONS 15 

## To model the avoided emissions or marginal emission reductions, several methods may be employed. These methods include the following:

1) A system mix analysis of allelectric generating units; 2) A surrogate plant analysis; 3) A generation-weighted average of variably di spatched fossil-fueled generating units; 4) A time-matched and generation-weighted average of variably dispatched fossil-fueled generating units; and 5) A complete grid-system dispatch analysis of fossil-fueled generating units. The analysis in this report relies on methodology (4) for the reasons set forth below. 1) The system mix analysis takes the generation-weighted average of all the fossil-fueled units in the electric generating system. This is a simple me thod. However, this method includes nuclear and hydropower plants that are almost never displaced by EE/RE measures. As a result, this approach significantly underestimates the em issions displacement, which occurs almost entirely at fossil-fueled plants. The EPA eGrid datarelies on this approach. 16 

## 2) The surrogate plant analysis calculates the emissions of the next new plant or unit that is likely to be added to the electric grid as a basis for determining what emissions would be avoided if the demand were reduced by energy efficiency measures or displaced by renewable energy generation. In the Mid-Atlantic, the most likely new plant in recent years would be combined-cycle natural gas with best available NO x 

## control technology. The re sult is a very low NO x 

## avoided emission rate. This approach is unrealistic in the short-term because actual generation and energy efficiency displacement are spread across a wide range of fossi l-fueled generation units, some of which have relatively high NO x 

## emission rates. This approach may provide a reasonable estimate of the long-term avoided emissions if current trends continue. However, the actual mix of plants may be very different in the future depending on fuel prices and public policy. 3) The generation-weighted average of the emissions of the fossil-fueled electric generating units that are var ably dispatched to meet changing demand. This approach provides a reasonable approximation of the marginal emissions rate with out the time and cost of a complete grid-system dispatch analysis, as described below. This method was used in the New Jersey Report. i

17

15 

### This description is based , in large part , on Appendix 4 of the Final Report on the Clean Energy/ Air Quality Integration Initiative Pilot Project for the Mid-Atlantic Region, August 2006

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

16 

### U.S. Environmental Protection Agency Emissions & Ge neration Resource Integrated Database (eGRID) 2002 http://www.epa.gov/cleanenergy/egrid/index.htmA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 1 9



## 4) The time-matched and generation-weighted averag e of the emissions of the fossil-fueled generating units that are variably dispatched to meet changing demand. This methodology has been developed by Resource Systems Group in cooperation with Environmental Resources Trust. It is a refinement of the generation-weighted average approach. It matches the hour-by-hour output of the renewable energy source or energy saving units with the generation from fossil-fuel units in each power market area that can be displaced by “must run” re newable energy or energy efficiency savings. Under the RSG methodology, the profiles of the EE/RE for the summer ozone season (by time of day, week, and month) and for the full year are determined. Th e next step matches the EE/RE hourly profile data for each source type against the hourly generation of the variably dispatched fossil-fueled units at plants in the power market analysis area. This comparison forms the basis for matching and identifying the set of generation units which can be displaced in each hour. The method requires intensive computational power and use of the CEM’s CO 2 

## data to estimate hourly generation for individual units (because exact data is not publicly available). Th is method should providea better approximation of the true marginal emission rate without the time and cost of a complete gridsystem dispatch analysis. 5) A complete grid-system dispatch analysis of fossil-fuel units consid ers the dispatch order and scheduling of specific combustion units at each facility in the power market area in detail, providing the most comprehensive estimate of the avoided emissions. An analysis of this type may be based on historical data on dispatch schedules or on a proprietary unit dispatch model. This approach allows for matching the EE/RE measures at specific hours with the actual generation of variably dispatched fossilfuel fired units. In principle, this method takes into consideration the actual dispatch order at every hour, including operational cost, system reliability, transmission constraints and other operational factors. This approach was utiliz ed by the State of Ma ryland to support NO x 

## emissions reduction credit for a regional wind purchase led by Montgomery County, Maryland in the SIP to meet the 1-hour ozone standard. 18 

## In 2005, this wind purchase received the first-ever approval by the U.S. EPA for NO x 

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

## This grid-system dispatch analysis approach is workable for the analysis of a renewable energy purchase from a single renewable energy plant (as in the case of the Montgomery County wind purchase). However, this approach is extremely difficult and ve ry expensive to apply when multiple EE/RE projects or an entire EE/RE program is the subject of the analysis . This expense is hard to justify, particularly if it is intended for the sole purpose of validating an avoided emissions rate stipulated in a State NO x

17 

### U.S. Department of Energy, Final Report on the Clean Energy/ Air Quality Integration Initiative Pilot Project for the Mid-Atlantic Region, August 2006

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

18 

### See http://www.mwcog.org/envir onment/committee/committee /archives.asp?COMMITTEE_ID=14 Scroll down to February 19, 2004 and Click SIP Chapter 7 (pages 7-77 to 7-80) and Appendix J, pp. 7-71 to 7-76.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 0



## emissions trading regulation, such as the Virginia Cl ean Air Interstate Rule, which stipulates an avoided emissions rate of 1.5 pounds per MWh for 2009 to 2014. However, this detailed dispatch analysis approach can be justified to provide accurate estimates of displaced NO x 

## emissions resulting from a large renewable energy project, such as a single wind farm. 20 

## The use of a proprietary economic unit dispatch models also makes this approach non-transparent, which may create problems for public agencies in reviewing the results. The system si mulation dispatch models were primarily designed for optimizing new construction and operating decisions of relatively large plants in competitive markets. In many power markets, ther e is insufficient experience with the dispatch of small intermittent renewable power, such as wind and PV, to establish clear precedents for how the system will respond at the level of detail implied by the dispatch simulation models.

19 

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

20 

### See National Renewable Energy Laboratory, Model State Implementation Plan (SIP) Documentation for Wind Energy Purchase in State with Renewable Energy Set-Aside, May 2005, Subcontract Report NREL/SR-500-38075 See, http://www.eere.energy.gov/windandhy dro/windpoweringamerica/sips.aspA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 1



## APPENDIX B: ELECTRIC GENERATING UNITS INCLUDED IN THE ANALYSIS

The list of plants with fossil-fueled units included in the analysis of the PJM Interconnection in this report is shown in Table B-1. In a few cases, re newable-fueled units, such as wood-fired units, are included because they also burn fossil fuels. We c onducted the analysis at the level of the individual electric generation unit.
The NO x 

## and CO 2 

## emissions data was obtained from the Continuous Emission Monitor database of the U.S. EPA for calendar year 2005. 21 

## This information is supplemented by data from the U.S. Energy Information Administration (EIA ), the Federal Energy Regulato ry Commission, and from utility company websites.
RSG used data from the EPA’s Emissions & Generati on Resource Integrated Database (eGRID) 2002 to check and supplement the list of electric generating units. 22 

## The emissions data in eGRID 2002 are based on data collected in 2000. Additiona l information has been obtained from technical staff at the Virginia Department of Environmental Quality and the New Jersey Department of Environmental Protection.

21 

### U.S. Environmental Protection Agency, Continuous Emission Monitoring Database, Office of Air and Radiation, “Acid Rain Hourly Emissions Data 2005,” CD 2 disks. Distributed by National Technical Information Service, Springfield VA.

22 

### U.S. Environmental Protection Agency Emissions & Ge neration Resource Integrated Database (eGRID) 2002 http://www.epa.gov/cleanenergy/egrid/index.htmA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 2



### T

a b l e B-1: Plan ts In clud ed

in PJM In tercon n ectio n An alysis

N a m e S t at

e N am e S t a t

e N a m e S t at

e N am e S t a t

e B E NNI N G DC NA T I O N A L P

A R K S T A

. NJ CRO Y D O N S T A T I O N PA B I RCH W O O D P O W

E R V A C E N T R A L H E AT I N G D C N EW A R K B AY C O G E N PR T N J EBE N SBU R G P O W

E R C O M P PA B R EM O V A CH RI S T I A NA S U B S T A T I O N DE NO RT H J E RS E Y E N E R G Y NJ E D D Y S T O N E PA B UCHA NA N G E N E RA T I NG V A C O N E C T I V
, E D G E M O OR D E O C EA N PE AKI N G PO W

E R N J E D G AR T H O M SO N P L AN T PA C C C P O W

E R ST A T I O N V A D E L

A W

A R E C I T Y D E PED R I C K T O W

N C O G E N N J E D I S O N S T AT IO N PA C EL AN ESE AC ET AT E V A H A Y R O AD D E PEL P N J E L R AM A PA C H ESA PEAK E V A I N D I AN R I VE R G E N E R A T I O N D E SAL E M ST AT I O N N J E N T R I KE N PA C H EST ER F I EL D V A M A D I SON ST R EET D E SAYR EVI L L E N J F AI R L ESS EN E R G Y , L L C PA C L I N C H R I V E R V A M

C KEE R U N D E SH ER M A N AVEN U E N J F A I R L ESS H I L L S SGS PA C L O VE R V A NR G E N E R G Y DO V E R D E V A L E R O R E F

. CO

. - N. J

. NJ F A Y E T T E E N E R G Y F A CI L I T Y PA C O G E N T RI X HO P E W E L L V A PR EM C O R R E F I N I N G GR P D E W ER N E R N J F PL EN ER G Y M H 5

0 L

. P. PA C O G E N T R I X PO R T SM OU T H VA V A NS A N T D E W E S T S T A T I O N N J F P L E M A RCUS HO O K PA C O G E N T RI X RI CH M O N D V A W

A R R E N F

. ( SAM ) BEAS D E F U L T O N C O G E N E R A T I O N N Y G A N S PA D A N R I VER VA W

E S T S U B S T A T I O N D E H I L L B URN G T NY G I L B E R T O N P O W

E R CO MP PA D A RB Y T O W

N CT V A M

O N T P E L I ER EL E C T R I C I N N I SS EQ U O G U E C O G E N N Y G L A T F E L T ER , P. H

. C O

. PA D O S W E L L

L P VA B I G S A N DY K Y W A T E RS I D E G E NE RA T I NG NY G R E Y S F E RRY CO G E N PA E L I Z A B E T H RI V E R V A C A T L ET T SBU R G R F N G KY AK ST EEL O H H A N D SOM E L

A KE E N ER GY PA GL EN LY N V A RI V E R S I DE G E NE RA T I O N K Y CA RD I N A L O H HA T F I E L D ' S F E RRY P S PA G O RD O N S V I L L E V A A E S W A RRI O R RU N M D C A R G I L L I N C O H H A Z E L T O N G E NE RA T I O N PA G R AV E L NE CK C T V A BR AN D O N SH O R E S M D C H I L L I C O T H E PAPER I N C O H H O M ER C I T Y PA H O PEW EL L V A C H AL K PO I N T M D C O N E SVI L L E PL AN T O H H U N L O C K PO

W

E R PA H O PEW EL L V A C H AR L ES P. C R A N E M D D A R B Y EL EC T R I C G E N E R A T I O N O H H U N T E R S T O W

N PA H O PEW EL L C O G E N V A D I C KER SO N M D D AYT O N P O W

E R AN D L I G H T O H KEY ST O N E PA I N T ER N A T I ON AL PAPE R V A H

. A. W A G N ER M D D I C K ' S C R EEK S T AT IO N O H K I M BER L Y

- C L A R K PA L A D Y S M I T H V A M

E AD W EST VA C O M D
, IN C M D G AVIN O H L I B E R T Y EL EC T R I C L L C PA L O U I S A G E N E R A T I O N F A VA M

O RG A N T O W

N M D G RNV I L L E E L E C T R I C O H L O W

E R MO UN T B E T HE L PA M A R S H RUN G E N E RA T I O N V A PA N D A

- B R A N D YW I N E M D H AM IL T O N G E N E R A T I N G O H M A R T I N S C R E EK PA M EAD W EST VAC O O F VA VA PE R R Y M A N M D H AM IL T O N M U N I C I PA L O H M ER C K &

C O M PAN Y I N C PA M EC KL EN BU R G P

. S V A R P SM I T H P S M D H AN GI N G R O C K EN E R G Y O H M I T C H E L L P S PA P O SSU M POI N T V A R I VER S I D E M D KYG E R C R EEK O H M O N T O U R PA P O T O M AC R I VER VA RO CK S P RI N G S M D M A D RI V E R O H M O U N T A I N PA R E M I N G T O N V A V I E NNA G E N E RA T I NG S T M D M A DI S O N S T A T I O N O H M T

. CA RME L C O G E N PA S O U T H A M P T O N V A W

E ST P O R T M D M U SK I N G U M R I VER O H N O R T H E AST C O G E N PA T ASL E Y PEA KI N G ST A T I O N V A I N T E R N AT I O N A L PAPER N C O

. H

. H U T C H I N G S O H N O R T H A M PT ON G E N C O PA T E N A S KA V I R G I N I A G E N V A W

E YER H AEU SER C O

. N C O SU

- M C C R AC K E N O H N O R T H E AST E R N P OW ER C O PA W O L F H I L L S EN E R G Y VA G R AN I T E R I D G E E N ER GY N H PI C W AY O H O N T E L A U N EE EN ER G Y C E PA Y O R K T O W

N VA A E S RE D O A K

F A C I L I T Y NJ RE B URG E R O H P A N T H E R CRE E K PA A L B R I G HT P S W V B.L

. EN G L AN D N J R I C H A R D G O R S U C H G E N E O H PI N E Y C R EEK L

. P. PA B I G SA N D Y PEAK ER PL W V BE L V I D ER E P L AN T N J R O B E R T P

. M O N E O H PO R T L A N D PA C E R E D O W V BE R G E N G E N

. ST A T I O N N J R O L L I N G H I L L S G E N E R

. O H PR OC T E R & G A M B L E PAP ER PA D U PO N T BE L L E P L AN T W V BU R L I N G T O N ST A T I O N N J S M A R T PA PER S L L C O H R I C H M O N D ST AT I O N PA E L KEM
, ST E A M P L

AN T W V CA L P I N E NE W A RK
, I N C N J S UNO CO
, I NC. (R& M ) O H S CH UY L K I L L PA F O R T M A RT I N P S W V CA L P I N E P A R L I N, I NC. NJ T A I T E L E C T R I C G E NE R O H S CH UY L K I L L S T A T I O N PA G RA NT T O W

N P O W

E R P L T W V CA RL L S CO R N E R NJ W

. H. S A M MI S O H S CR UB G R A S S G E N P L A N T PA H A RRI S O N P S W V C A R N E Y PO IN T GEN E R A T I ON N J W ASH I N G T O N E N ER GY O H SEW AR D PA I N S T I T U T E PL AN T W V C E D A R ST AT I O N N J W AT E R F O R D O H SH A W VI L L E PA J O H N E. AM O S W V C O

AST AL EA G L E P O I N T N J W O O D S D A L E ST AT I O N O H S H E N A N G O I N C PA K AM M

E R W V CO G E N T E CH
, L I N D E N NJ A E 3
, 4

& 5 P A S HE RM A N S DA L E PA K A NA W

H A

R I V E R W V C U M B E R L A N D N J AES B EAVE R VA L L E Y P A S T

. N I C H O L AS C O GEN

. PA M I T C H E L L W V D E EPW AT ER ST AT I O N N J AES IR O N W O O D
, I N C P A S U N C O M PAN Y PH IL A PA M O R GAN T OW N E N ER GY W V EA G L E PO I N T C O GEN N J A L L E G H EN Y EN ER G Y L L C PA SU N B U R Y PA M O U N T AI N E ER W V ED I S O N ST A T I O N N J A R C H BAL D PO W

E R P A S U N O C O I N C

. ( R &M ) PA M T

. ST O R M W V EF KEN I L W O R T H N J A R M A G H P A T I T U S PA N A T R IU M P L

AN T W V ES SEX G E N

. ST AT I O N N J A R M S T R O N G E N R G Y
, L L P PA T O L N A PA N O R T H BR A N C H W V F O RK E D RI V E R N J A RM S T RO NG P S P A U

. S

. S T E E L CL A I RT O N PA P H I L I P S P O R N W V G I L BER T N J B M

A N S F I E L D P A W AR R E N PA P L EAS AN T S EN E R G Y
, L L C W V G L EN

_ G AR D N ER N J BER N V I L L E PA W E S T W O O D OPER C O

. PA P L EAS AN T S P S W V H O

W

A R D M D O

W

N N J BET H L EH E M POW E R PL AN T P A W H E EL AB R A T O R PA R I VES VI L L E P S W V HU DS O N S T A T I O N N J B RUN NE R I S L A ND P A W I L L A M E T T E I ND. I N C. PA U N I O N CA R B I D E CO RP

. W V L I N D E N S T A T ION N J C A M B R IA C OGE N P A Z IN C C O R P OF A M E R IC A PA W IL L O W

IS P S W V L O G A N G E NE RA T I NG P L A N T N J CHE S W I C K P A E A S T M A N CHE M I CA L CO M T N M

E R C E R G E N

. ST AT I O N N J C O L V E R P O W

E R PR OJ EC T P A K I N GSPOR T PAPER M I L L T N M

I C K L E T O N N J C O N E M AU G H PA AL T AVI ST A VA M

I DDL E S T R E E T NJ CO NO CO P H I L L I P S CO M P

. P A B E L L E M E A D E VA M

I SSO U R I AV EN U E N J C R O M B Y P A B I G I S L A N D M I L L VAA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 3

Figure B-1 : L o c a tion s o f Plan ts Used i n

An alys isA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 4



## APPENDIX C: NREL SUPPORTING DOCUMENT- COMPOSITE HOURLY ELECTRICAL SAVINGS PROFILES FOR GOVERNMENT BUILDINGS IN THE WASHINGTON METROPOLITAN AREA

Composite Hourly Electrical Savings Profiles for Government Buildings in the Washington Metropolitan Area
B. Griffith Buildings and Thermal Systems Cent er, Commercial Buildings Group National Renewable Energy Laboratory January 16, 2005

INTRODUCTION 

## Building energy efficiency programs can reduce electricity use and dema nd. Because of the variability in the mix of power plants and their operating conditions over time, emission reductions are actually a function of the time of day and time of year. The Metropolitan Washington Air Quality Committee requested NREL’s Commercial Buildings Group to prov ide a composite profile for the hourly electricity savings during the summer ozone season in the Metro politan Washington Area for use in estimating the associated emissions reductions. This report prov ides a brief summary of the methodology used to develop the hourly profiles.

METHODOLOGY 

## The NREL analysis relies on a database compiled by the U.S. Energy Information Administration (EIA) entitled the “Commercial Buildings Energy Consum ption Survey (CBECS).” NREL utilized a computer simulation program called EnergyPlus to generate composite profiles for electricity savings using a set of twenty building models derived from the EIA survey. The set of buildings was selected to represent government buildings in the Metro politan Washington area. The larger, national survey included EnergyPlus models for all 4,820 buildings in the 2003 CBECS public use data. NREL developed the subset for the current study by selecting every gove rnment-owned building that had been assigned to either of two airport weather locations in the Metropolitan Washington area. Although the 2003 CBECS survey data actually masks the locations of the survey buildings, NREL was able to determine the modeled locations by fitting degree day information in the su rvey data to the 2003 historical weather data for major airports used for weather data in buildin g simulation. The DC area airports included Washington Dulles (IAD, WMO#724030) and Baltimore (BWI, WMO#724060). Of the 4,820 buildings in the national survey, a total of 69 were assigned to IAD or BWI. Of these sixty-nine building, twenty were government-owned. These twenty building models were used in the current study to generate the composite electrical savings profiles summarized in the following table.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 5

F l oor a

r e a Fl oor a

r e a

m

2 ft 2

1 2 2 3 1 1 3
, 35

0 R e p ai r

sh

o p 25 46

. 8

3 2 1: F o r c ed

ai r

c

o o l i n

g (

n o he

at

)

2 4 7 2 1 9
, 0

45 20 5, 00 0 G o v e

r nm

en

t o

f f i c

e 4. 2 8 43 : F a n c

o i l w i t h d

i s

t r i c

t c

h i l l e

d an

d h

o t w

a t e r
3 6 2 9 2
, 9

7 3 3 2
, 0

0 0 O t h e r c

l a

s sr

o o m

e

d u c a t i o

n 3 6 4

. 5 5 : PVAV w / r

e h e a t

4 7 2 3 2 6
, 0

12 28 0, 00 0 G o v e

r nm

en

t o

f f i c

e 6. 0 4 43 : F a n c

o i l w i t h d

i s

t r i c

t c

h i l l e

d an

d h

o t w

a t e r
5 7 55 71 5 7
, 7

0 0 P o s t of

f i c

e 25 46

. 8

3 2 9: ho t w

a t e r b

a s e b o a r ds

(

n o c

o o l )

6 2 1 5

3 5 5
, 74

0 6 00

, 0

00 V a c a nt 6

. 04 4 4 : F a n c

o i l w i t h c

h i l l e

r an

d d

i s

t r i c

t ho t w

a t e r
7 2 19

3 3 2, 5

1 5 3 5 0
, 0

0 0 H i gh s

c ho

ol 13 3. 8

7 3: P S Z

- A C

8 2 20

0 3 99 4, 3 0 0 V eh i c

l e

S t

or

ag

e/

m

ai n

t e n a n c e 2 5 4 6

. 8

3 1 9 : f

o r c ed

ai r

f

u r n a c e w

/ O A s

y s

t em
9 2 3 8 0 1 39
, 3

5 0 1, 5 0 0
, 0

0 0 h ospi t al 4

. 5

9 4 4: F a n c

o i l w i t h c

h i l l e

r an

d d

i s

t r i c

t ho t w

a t e r
10 2 5 1 2 13 9, 3

5 0 1
, 5 0 0
, 0

0 0 ho sp

i t al 3. 8 8 44 : F a n c

o i l w i t h c

h i l l e

r a

n d di s t r i c

t h

o t w

a t e r
1 1 27 26 1 3 9
, 35

0 1
, 5 00

, 0

00 G o v e

r n m

e nt

of

f i c

e 46

. 6

2 4 4: F a n c

o i l w i t h c

h i l l e

r an

d d

i s

t r i c

t ho t w

a t e r
1 2 27 53 1 3 9
, 35

0 1
, 5 00

, 0

00 G o v e

r n m

e nt

of

f i c

e 3

. 88 4 3 : F a n c

o i l w i t h di s t r i c

t c

h i l l e

d a

n d ho

t w

a t e r
1 3 2 8 0 5 1
, 2 3 1 1 3
, 2

5 0 L i b

r a r y 4 1 9

. 2

8 7 : VAV w / r e h e a t

14 3 2 5 8 5 5
, 7

40 60 0, 00 0 G o v e

r nm

en

t o

f f i c

e 6. 0 4 44 : F a n c

o i l w i t h c

h i l l e

r a

n d di s t r i c

t h

o t w

a t e r
1 5 32 96 4
, 50

6 4 8
, 5

0 0 C ol l e ge

/ u n i v e

r s i t

y 1 33

. 8

7 4 3: F a n c

o i l w i t h di s t r i c

t c

h i l l e

d a

n d ho

t w

a t e r
1 6 42 99 1 3 9
, 35

0 1
, 5 00

, 0

00 G o v e

r n m

e nt

of

f i c

e 6

. 47 4 3 : F a n c

o i l w i t h di s t r i c

t c

h i l l e

d a

n d ho

t w

a t e r
17 5 7 2 2 6, 5 0 3 7 0, 00 0 E l e m

e nt

ar

y

s

c ho

ol 13 6. 8

8 40 : F a n c

o i l w / d

i s

t r i c

t c

h i l l e

d w

a t e r an

d b

o i l e

r
1 8 5 8 2 4 1 3
, 7

4 9 1 4 8
, 0

0 0 H i g h s

c h o o l 1 4 2

. 3

9 7 : VAV w / r e h e a t

1 9 59 70 5
, 11 0 5 5
, 0

0 0 E l e m

e n t a r y

s

c h o o l 36 4. 5 2 7: V A V w

i t h el e c t r i c

ba

s e bo

ar

d

2 0 6 1 5

1 1 3 9
, 3

5 0 1
, 5 0 0
, 0

0 0 G o v e

r n m

e n t o

f f i c

e 3

. 8

8 8 : VAV w / PF P b o x e

s HV AC s y s t e m

t

y pe No

. CBE CS No

. T y pe

o

f Bu i l d i n g CBE CS

we

i g h t



## For ea ch savi ngs scen ari o described b e l o w, N R EL ge ner ated a c o mposi t e savi n g s profile . The pr ofiles are hourly and annual and include values for each of the 8760 hours in the year. Each of the twenty buildings used to model DC govern me nt buildings pr o d uces its own energ y

savin g s profile . Be c a use th e buildings hav e different sizes, the po wer use by e a c h is n o rm alized b y t h e flo o r ar e a t o o b tai n v a lues th at

are pow er in t e nsit y pe r uni t are a . W e de velop e d a sin g le co mp osite hourly en erg y

savings pro f ile b y
com p uti n g a weighted aver age fr om all twenty buildings for each hour of the ye ar. The e n erg y

ef ficiency sc enario is al w a ys c o mpared to a refer e nc e sce n ario, and the difference i n e l ectri city use is “savings” attri b uted to t h e ef ficie n cy m e asure. Note th at t h e use of hig h ly inte gra t ed anal yse s provided by Energ y Plus al lows cal c ulating the i n te grated eff ects of savings m e as ures. For ex ampl e, aA v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 6



## lighti n g meas ure will decrease the energ y

use for lighti n g, increas e the energ y

use for heating and decrease the ene r gy use d for cooling.
Th e me th odol ogy is summa rized in t h e f o llowing equ ati ons:



# ∑ ∑

=

= 

## = 20

1 20

1

j j j j ij j j

i 

## A W PSI A W PSI (1)

wher e, i is the index repres ent the hour of the year (on [1..8760] ) j is the index repres enting the building in the anal ysis ( on [1..20])

i 

## PSI is the resulting power savings intensity (W/m 2 

## or W/ft 2 

## ) at hour i is the weig ht fact or f o r buil ding j from 20 03 CBECS j 

## W

is the ar ea ( m

j 

## A 2 

## or ft 2 

## ) of building j from 2 003 CBECS is the p o wer i n te nsit y (W/ m ij 

## PSI 2 

## or W/ft 2 

## ) for building j at hour i

Equation 1 is repeated for each of 8760 hours in the year using a s m all comp uter program to proces s simulation res u lts.
Th e resulti n g com posi t e pr ofile is p o w e r savings in te n s ity in u n its of pow er per u n it area. When applied for subs eque nt emissio n s resear ch, t h e profil e’s valu e s should be multiplied by the t o t a l buil ding flo o r area af fec t ed by the e n erg y

effi ci ency pr ogram(s). F o r exampl e, on ce weig hted, the tw enty building sampl e used in t h e m o deli ng re prese n t a p opula tio n of gov e rnment buildings i n the Was h ing t on, D.C. region with a maximum of 25,469,458 m 2 

## (274,159,938 ft 2 

## ) of fl oor ar ea.

SCEN A R IOS


## NREL ev aluated two s e par ate e n er gy effi ciency sce n ari o s: one for lig hti n g and one for air c onditi oni ng. Lighti ng impr ovem en ts wer e modeled b y assuming a 2 5 % improve m en t in t h e li ghti ng equip m en t. The lighti n g pow er densities in each reference building we re reduced by 25% and the models rerun with the change and c o mpared to the refere nc e cas e
. Air co nditio ni ng imp r ove m ents w e re mo deled by assu ming a 25% i m pr ovem en t in co olin g eq uipme n t efficiency and fans. The Coefficient of Performanc e was increased by 25% on all chillers and direct expa nsio n pri m ary c ooli n g equipme n t. F a n to tal effi ci enci es wer e al so in cre a sed by 25%. N o t e t h a t six of the buildings happen to use district chilled wate r and without pri m ary cooling equipment on-site, the only chang e w a s in fan effi ci enc y
.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 7



### RESULTS 

## The results were obtained using historical weather data for the year 2003. These historical weather data files in EnergyPlus format were developed by Drury Crawley of the US. Department of Energy using data from the National Solar Resource Database (NSRDB) from NREL. The composite Power Savings Intensity profile for 2003 for a 25% lighting improvement is shown in the figure below. The figure represents all hours of the year, organized in two dimensions. Hours of the day proceed from left to right, starting at midnight. Be cause lighting is used during the day time, the highest power savings intensities appear in the middle as hours proceed left to right. Days of the year proceed from top to bottom, starting with January 1. Thus, the upper left corner represents midnight on January 1, the upper right corner represents 11:59 on January 1, the lower left corner represents midnight on December 31, and the lower right corner represents 11:59 on December 31.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 8



## The composite Power Savings Inte nsity profile for 2003 for a 25% ai r conditioning improvement is show n in t h e figure bel o w.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 2 9



## Separate computer fi les accompany this report wi th the raw data for each hour of the year, for 2003. Not e th at th e tim e seri es d ata ar e i n S t a n dard Time f o r the e n tir e year, bu t occ u pan c y a n d o p er atio n a l patter ns included the eff e c t s of Daylig ht Savings Ti me.A v o i d e d Em issi ons A n al y s is PJ M Interc on necti o n Resour ce S y ste m s Gr ou p, Inc.

Febru a r y2007 Draft page 3 0



### DISCUSSION 

## CBECS is a national survey and was not (necessarily) designed to represent a relatively small geographic area such as the Metropolitan Washington area. NR EL selected the twenty go vernment buildings in the Metropolitan Washington area to model the problem at hand as well as possible. For purposes of the SIP analysis, these estimates provide important information to help assess the hourly impact of energy savings. However, the set of twen ty buildings should not be viewed as a comprehensive nor robust representation of government buildings in the Metropolitan Washington area. The underlying simulations are actually run with 15-minute time steps and the hourly results represent an average. It would be possible to provide 15-minute power savings profiles in addition to hourly profiles. Because DC offices appear to make considerable use of district chilled water, a separate study of the central plants that provide district chilled water may be needed to pr operly understand the implications of cooling equipment upgrades. This is probably beyond the scope of modeling that is appropriate for EnergyPlus.

REFERENCES 

## Commercial Buildings Energy Consumption Survey (CBECS) , Energy Information Administration, U.S. Department of Energy. Public Use Microdata 2003. http://www.eia.doe. gov/emeu/cbecs/ EnergyPlus Energy Simulation Software , Building Technologies Program, Office of Energy and Renewable Energy, U.S. Department of Energy. http://www.eere.energy.gov/buildings/energyplus/ National Solar Resource Database (NSRDB) , National Renewable En ergy Laboratory. 2003 http://rredc.nrel.gov/solar/old_data/nsrdb/