Reducing NOx Emissions ... ▪ 1

Reducing Emissions in 

Connecticut on High Electric 

Demand Days (HEDD) 

A REPORT FOR THE CT Department of 

Environmental Protection and the US 

Environmental Protection Agency 

July 25, 2008 

AUTHORS 

Chris James, Jeremy Fisher, PhD 
Reducing NOx Emissions ... ▪ 2

Table of Contents 

1. EXECUTIVE SUMMARY ......................................................................................................... 4

2. INTRODUCTION AND PROBLEM DESCRIPTION ............................................................ 6

A. BACKGROUND ................................................................................................................... 7

B. DEMAND RESPONSE IN CONNECTICUT: DIESEL GENERATORS ....................................... 7

C. RELIABILITY IN CONNECTICUT: RELIABILITY-MUST-RUN (RMR) UNITS ......................... 8

D. CT EMISSIONS REDUCTIONS: 2008 STATE IMPLEMENTATION PLAN .............................. 9

E. TASKS FOR COMPLETION................................................................................................ 11

3. TASKS AND ASSUMPTIONS .............................................................................................. 12

A. GROW LOAD FROM BASELINE 2005 ............................................................................... 12

B. CONNECTICUT’S EE PROGRAMS ARE COORDINATED WITH ISO-NE ............................. 12

C. THE CONNECTICUT BOX MODEL .................................................................................... 13

D. NUCLEAR UNIT OPERATION ........................................................................................... 13

E. ENERGY EFFICIENCY LOAD SHAPE ................................................................................ 13

F. STATEWIDE ANALYSIS IS BASED STRICTLY UPON 2005 EPA REPORTED DATA ............. 14

G. ANALYSIS DOES NOT INCLUDE RGGI OR CAIR ............................................................. 14

4. METHODOLOGIES ................................................................................................................ 15

A. SCENARIOS ..................................................................................................................... 15

Scenario 1: Baseline 15

Scenario 2: Energy Efficiency (2% per year) 15

Scenario 3: RMR Units Apply Emissions Controls 15

Scenario 4: Efficiency and RMR Units Apply Controls 16

Scenario 5: Aggressive Efficiency (3%) 16

B. LOAD GROWTH AND ENERGY EFFICIENCY ..................................................................... 16

ISO-NE Load Predictions 16

Extrapolating hourly load from 2005 to 2020 17

Applying Energy Efficiency 18

C. THE LOAD-BASED PROBABILISTIC EMISSIONS MODEL (LBPEM): CORE PROGRAM ... 19

Datasets and Exclusions 20

Emissions statistics from generation (probabilistic emissions rate) 23

Monte Carlo Simulation 24
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D. LBPEM EXTENSION: LOAD GROWTH, INTERPOLATION, AND SYSTEM CHANGES ........ 25

Statistics Extrapolation: Load growth and shrinkage 26

Retiring and adding generators 27

Changing Emissions Profiles 28

Reporting Results 28

5. RESULTS ................................................................................................................................. 29

A. EGU OPERATIONS AND RMR STATISTICS ..................................................................... 29

B. OZONE SEASON LOAD AND GENERATION ....................................................................... 29

C. EMISSIONS BEHAVIOR IN SCENARIOS ............................................................................. 30

D. MEETING THE OTC MOU COMMITMENT ....................................................................... 31

Effect of Strategies on High Electricity Demand Day (HEDD) Emissions 32

Effect of Strategies on Average Ozone Season Day Emissions 34

E. POST-2009 EMISSION REDUCTION STRATEGIES .......................................................... 34

6. RECOMMENDATIONS .......................................................................................................... 36

Recommendation 1: Implement a combination of increased energy efficiency and 

reductions from units that contribute to peak day NOx emissions 37

Recommendation 2: Ensure sustained funding for EE programs 37

Recommendation 3: Increase transparency of generation data 38

B. NEXT STEPS: ENFORCEABILITY OF RECOMMENDED POLICY MEASURES ..................... 38

Bundled EE Measures 38

ECMB Measures Not Submitted to the FCM 39

Building Codes and Standards, Appliance Standards 40

Public-Private Partnerships 40

7. CONCLUSION ......................................................................................................................... 42

8. APPENDIX: FIGURES ........................................................................................................... 43

A. LOAD VS. OZONE SEASON NOX EMISSIONS .................................................................. 43
Reducing NOx Emissions ... ▪ 4

1. Executive Summary 

The Connecticut Department of Environmental Protection (DEP) asked Synapse to complete 

three tasks to analyze electricity demand during peak demand periods: 

• project Connecticut electricity demand for the period from 2005 to 2020; 

• project generation and transmission from load; and 

• project emissions and prepare a report that will be used as part of Connecticut’s SIP to 

demonstrate attainment with the Federal eight-hour ozone standard 

DEP’s commitment through a Memorandum of Understanding (MOU) to the Ozone Transport 

Commission is to reduce ozone season NOx emissions 11.7 tons per day from a 2005 baseline 

by 2009. Synapse’s analysis recommends several potential ways by which this commitment can 

be met, but not even the most aggressive one is capable of meeting the reduction goal within the 

initial agreed upon timeframe. The potential means by which DEP can reduce NOx emissions 

are: 

• continue energy efficiency measures in accordance with existing (2007 vintage) approved 

plans; 

• ramp up future energy efficiency and demand side measures as anticipated by a 2007 

Connecticut statute1

; 

• require additional NOx control measures for generators smaller than 15 MW; and 

• a combination of the above 

There are costs associated with each of these recommended measures. Achieving all costeffective energy efficiency as required by statute will mean that the state needs to approve 

additional funding for the Energy Conservation Management Board (ECMB), engage the private 

sector, or some combination. While the state has plans to implement all cost-effective energy 

efficiency, funding of these programs at a level needed to procure all such resources is beyond 

the control of the DEP. 

Installing controls on affected sources also will add costs. These costs will be passed along to 

Connecticut ratepayers through existing cost recovery mechanisms available through the CT 

Department of Public Utility Control (DPUC), and through higher hourly clearing prices in the ISONE electricity market. Energy efficiency continues to be much less expensive than the cost of new 

generation with costs of 3-3.5c/kWh, compared to 8-11 c/kWh for the cost of new generation. 

DEP’s commitment through a Memorandum of Understanding (MOU) to the Ozone Transport 

Commission is to reduce ozone season NOx emissions 11.7 tons per day from a 2005 baseline 

by 2009. Synapse’s analysis recommends several potential ways by which this commitment can 

be met, but not even the most aggressive one is capable of meeting the reduction goal within the 

initial agreed upon timeframe. The potential means by which DEP can reduce NOx emissions 

are: 

 

1

 PA07-242 requires all cost-effective energy efficiency measures to be procured 
Reducing NOx Emissions ... ▪ 5

• continue energy efficiency measures in accordance with existing (2007 vintage) approved 

plans; 

• ramp up future energy efficiency and demand side measures as anticipated by a 2007 

Connecticut statute ; 

• require additional NOx control measures for reliability must run (RMR) generating units in 

two phases, starting in 2012; 

• require additional NOx control measures for generators smaller than 15 MW; and 

• a combination of the above. 
Reducing NOx Emissions ... ▪ 6

2. Introduction and Problem Description 

In 2008, the State of Connecticut continues to experience air quality that exceeds EPA 1-hour 

and 8-hour ozone standards. The state has adopted many regulations and standards to reduce 

ozone concentrations over the past thirty years. Consequently, pollution levels and the number of 

days in which CT exceeded the EPA standard have significantly decreased from over 40-50 days 

a summer in the 1980s to less than ten days during the last few years. Connecticut participates in 

regional programs to reduce air pollution transported into the state from upwind areas along the 

Northeast Corridor (including NY, Philadelphia and Metro DC-Baltimore) and from the Ohio 

Valley. In the late 1990s, Connecticut joined New England and Mid-Atlantic states to settle 

several Clean Air Act lawsuits against power plants in the Ohio Valley. Emissions reductions 

completed by these plants as part of the consent decrees will further reduce transported air 

pollution into the Northeast2

. 

In the mid-1990s, Connecticut began to regulate air emissions for the electric utility sector. When 

these programs were adopted, the utilities were still regulated as vertically integrated monopolies 

with generation, transmission and distribution responsibilities. Initial regulatory efforts focused on 

reducing emissions from electric generating units (EGU) larger than 15 or 25 MW because of the 

significant contribution of these units to state and EPA emissions inventories. In the late 1990s, 

the passage of electric restructuring legislation coincided, but was not coordinated, with 

environmental agency efforts to control NOx emissions from EGUs. Subsequent restructuring of 

the electricity markets created opportunities for non-utility generators, as well as EGU smaller 

than 15 or 25 MW. Independent System Operator of New England (ISO-NE) demand response 

and price response programs helped provide energy, capacity, and reliability in the region, but 

have also lead to outcomes not anticipated by air regulators. State implementation plans (SIPs) 

developed in a regulated environment could not anticipate: 

• An increase in the number of generating units smaller than 15 MW, with hundreds of 

units deployed smaller than 1 MW; 

• Energy and capacity payments created significant economic incentives for new 

participants (capacity payments prior to the FCM were as high as $14/kW-month) 

• De minimis permit thresholds were high enough to enable small units to construct and 

operate without going through individual New Source Review (NSR) processes, including 

opportunities for public review and comment 

 

 

2

 Lawsuits were pursued and settled with Virginia Electric Power (Dominion); Ohio Edison (First Energy) and 

American Electric Power (AEP). The lawsuit against Duke Energy (Cinergy) has not been settled as of June 2008. 
Reducing NOx Emissions ... ▪ 7

A. Background 

As restructuring of New England’s 

electric markets created many new 

opportunities for supply side 

resources, demand increased 

through the last two decades (Figure 

1). With the recent exception of late 

2007 into 2008, the previous several 

years has been a period of significant 

economic growth, resulting in several 

factors that lead to increased peak 

electricity demand. Southwest 

Connecticut’s peak demand growth 

has been 2-3 times higher than that 

of base growth (Figure 2). Industrial 

electric demand has been flat to 

decreasing over the past decade. 

Transmission constraints and 

congestion have affected Southwest 

Connecticut (SW CT) for the past 

several years. Factors that have 

caused or contributed to this 

congestion include: 

• Differential rates of electric 

demand increases between 

SW CT and the rest of the 

state; 

• Inability to increase 

generation supplies; and 

• Interruption of energy 

efficiency programs following 

electric restructuring 

B. Demand response in Connecticut: Diesel Generators 

As a result of significant congestion in SW CT, both ISO-NE and Connecticut have focused 

attention on ensuring reliable electric service in this part of the state. Both supply and demand 

side approaches have been implemented, but, in terms of both MW and dollars, the supply side 

has received the majority of resources. The DEP has permitted or registered hundreds of diesel 

generators and several simple cycle gas turbines. The DPUC has helped to finance supply side 

resources by providing incentives of up to $500 per kW to build new smaller generation. ISO-NE 

demand and price response programs have also actively signed up resources in SW CT3

. 

 

3

 For example, see www.iso-ne,com June 18, 2008, 11th semi-annual load response program report, which is filed 

with FERC 

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

19901991199219931994199519961997199819992000200120022003200420052006

 Total Electric Industry Retail Sales (GWh)

Figure 1 Connecticut electric retail sales, 1990 to 2006. 

EIA, 2006. 

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Growth relative to 1990 level

Summer Peak (MW)

Net Energy (MWh)

Summer peak trend

Net energy trend

Figure 2 Connecticut load growth (net energy, MWh) 

relative to peak load growth (summer peak, MW), 1991 to 

2006. ISO-NE, 2008. 
Reducing NOx Emissions ... ▪ 8

Connecticut’s energy efficiency program, administered via the Energy Conservation Management 

Board (ECMB), also focused demand side resources in SW CT, directed at both peak and base 

load measures. 

The significant amount of supply side resources being constructed and operated led the DEP to 

investigate the degree to which emissions reductions achieved by regulating emissions of larger 

EGU might be overcome by emissions increases from smaller sources. DEP was unable to 

conclusively determine the number of small generators that are participating in ISO-NE programs 

or whom might be registered as an emergency engine, but is operating on non-emergency days 

to take advantage of price response programs. 

The causes of SW CT congestion have also been experienced in other parts of the Northeast, 

particularly around New York City and Boston. Through the Ozone Transport Commission 

(OTC), Connecticut participated in a regional process to assess potential stationary source 

contributors to ozone formation. One of the primary contributors were emissions units that 

operate during period of peak electricity demand, described as high electric demand days 

(HEDD). As a result of these processes, Connecticut and several other states, committed, 

through an Memorandum of Understanding (MOU)4

, to reduce emissions from sources operating 

on HEDD by 25-35%. 

C. Reliability in Connecticut: Reliability-Must-Run (RMR) 

Units 

As a general rule, Connecticut’s EGU are dispatched by ISO-NE on an economic basis, with the 

lowest cost baseload nuclear and coal plants dispatched first. The high natural gas fuel costs 

mean that these units are dispatched last, and set the hourly marginal clearing price. 

Connecticut’s electricity congestion in the southwestern part of the state (defined as 15 towns in 

the Stamford-Norwalk area or 52 towns mainly in Fairfield and New Haven Counties) causes the 

general dispatch rule to be broken frequently during the year. In order to service demand from 

this area, which accounts for over half of Connecticut’s peak load, several older oil-fired units 

must run out of economic merit order to assure continued reliability. If normal ISO-NE rules were 

followed, these units, referred to as RMR (for reliability must-run), would operate infrequently and 

displace several natural gas units to set the marginal clearing price. However, the strategic 

location of the RMR units, in relation to demand, requires them to operate even though their 

hourly costs are higher than other units. 

The combination of ISO-NE economic dispatch coupled with the out of merit operation of the 

RMR units means that Connecticut EGU emissions profiles do not align with DEP’s 

environmental preferences. Base loaded nuclear units are dispatched first, providing about 2000 

MW of generation without NOx emissions. Above nuclear though are baseloaded coal and then a 

variety of EGU including the RMR units. With the exception of the Bridgeport Energy facility, the 

natural gas combined cycle EGU installed since 1998 have limited operating hours and 

generation, mostly used as load following or peaking units. From a criteria and greenhouse gas 

pollutant perspective, increased operation of the natural gas units would reduce Connecticut’s 

emissions. 

 

4

 MOU signed March 2007. See http://www.otcair.org (click on meetings, then “materials” for 2007 meetings) 
Reducing NOx Emissions ... ▪ 9

D. CT Emissions Reductions: 2008 State Implementation 

Plan 

Connecticut’s efforts to improve air quality and to ensure reliable electricity delivery require 

cooperation and collaboration with other New England and Middle Atlantic states. Upwind air 

pollution from the I-95 corridor and the Ohio Valley contributes to elevated levels of ozone and 

fine particulates. Connecticut is part of ISO-New England, which dispatches electricity for all six 

New England states, including imports from and exports to surrounding states and provinces. 

As part of the desire to develop policies that can help to solve regional air quality concerns, 

Connecticut is an active participant in the Ozone Transport Commission (OTC). The OTC was 

formed pursuant to Section 184(a) of the Clean Air Act Amendments of 1990 to provide a 

collaborative forum to discuss and resolve interstate air pollutant transport issues, and covers a 

geographic boundary from Maine to northern Virginia. 

Air pollution emissions during high electric demand days (HEDD) were raised through the OTC in 

meetings beginning in 2006. Connecticut, New Jersey and EPA Region 1 completed initial 

evaluations that attempted to define the universe of units contributing to air pollution, and the 

degree of their contribution. Several meetings were held throughout 2006 and 20075

, including 

representatives from the three power pools with jurisdiction in OTC, distribution companies and 

operators of electric generators. These efforts led to the March 2007 OTC MOU that committed 

the signatory states to reducing NOx emissions on high electric demand days. 

One of the challenges Synapse faced in evaluating the emissions associated with high electric 

demand days was an inability to precisely determine the definition of “high electric demand day”. 

While the term is used frequently in the OTC MOU, and in presentations, it is not defined. For this 

reason, Synapse defined the high electric demand day (HEDD) period to facilitate analysis for this 

report. We chose to define the HEDD period as the 12 highest electric demand days during the 

year, following language in the stakeholder approved OTC MOU. Our reasoning is supported 

through three mechanisms: 

a) These 12 days fall during the ozone season, when air quality is unhealthy; 

b) The use of any single day to define the HEDD period could lead to erroneous results in 

compliance monitoring, where peak generation needs are unlikely to be consistent 

interannually. The use of a multi-day period ensures that average peak emissions are 

monitored consistently; 

c) Finally, in the final “whereas” paragraph of the MOU, the document makes reference to 

how, if regulatory policies were applied to HEDD in the same manner as those previously 

developed for the electric sector, that emissions on the 12 highest electric demand days 

alone would equal 74% of states’ Clean Air Interstate Rule allowances. 

At the completion of this analysis this evaluation, we learned that the OTC reduction requirement 

was based upon emissions from the single highest peak day. Comments received after the 

completion of this analysis have suggested alternative analysis periods which may have merit, 

but were outside of the agreed upon scope of this work. 

 

5 http://www.otcair.org Click on meetings and then scroll down to view HEDD presentations for 2006 and 2007 
Reducing NOx Emissions ... ▪ 10

Synapse believes it is critical to show that the results of our analysis are replicable and 

reproducible. Using the 12 highest electric demand days, as opposed to a single peak day, is 

more optimal in terms of smoothing or removing unique influences that might not be replicated 

every year. The causes of a single peak day may not be identical every year, and if our analysis 

on the benefits of policies to reduce emissions were based on a single day, their effectiveness 

could be much different from year to year. One of the primary goals of this evaluation was to 

develop policies that could confidently ensure that the anticipated emission reductions would 

occur. Using 12, as opposed to one, high electric demand days helps to increase certainty in our 

analysis, and precision in forecasting that implementation of policies to reduce NOx emissions will 

indeed lead to them being achieved. 

The March 2007 OTC MOU makes reference to earlier efforts to regulate emissions from this 

sector, and how these would be inappropriate for adoption to address emissions from high 

electric demand days. Earlier efforts applied a two-pronged strategy: 1) states required direct 

application of emissions control devices to reduce emissions at the smokestack; 2) for EGU that 

operate during peak electric demand, those sources were required to turn in allowances at a ratio 

of much greater than 1 allowance per 1 ton of NOx emissions. Connecticut’s peak shaving policy, 

developed in the mid-1990s, required certain EGU to turn in seven allowances for each one ton of 

emissions that occurred on a peak day. The policy applies to combustion turbines that have 

entered into enforceable trading agreements with the DEP ,and which are part of Connecticut’s 

ozone SIP. 

The same approaches are infeasible to apply today, for both environmental and economic 

reasons. Requiring controls, such as selective catalytic reduction (SCR), as a sole means to 

reduce emissions would mean that the costs of those controls would be passed along to 

ratepayers, in the form of higher electricity rates. EGU that have no abatement costs would enjoy 

extra profits as a result, since New England’s single price electricity market means that all EGU 

operating in a given hour are paid the marginal clearing price. 

Requiring EGU to surrender allowances at a high ratio to actual emissions would also be 

infeasible. First, to equalize the economics for units that are paid hourly electricity prices that 

approach or equal $1000/MWh, the offset ratio would need to be in the range of 30-50 or more 

allowances for each ton of emissions6

. Second, if such a program was implemented anyway, 

even a few high electric demand days would require surrender of a large portion of Connecticut’s 

emissions budget, leaving little for the remaining days. This would likely lead to temporarily 

shutting down fossil fuel generation for many days if not weeks, and electricity would have to be 

imported from elsewhere, at higher costs, into Connecticut. Existing EGU with no abatement 

requirements would also again receive extra profits during these periods. 

These two reasons lead Connecticut to investigate other possible ways to reduce NOx emissions 

during high electric demand days. The DEP was also cognizant of the potential role that demand 

side measures could play in reducing both base and peak electricity usage. The DEP is a 

member of the state’s Energy Conservation Management Board, and an active participant in the 

Regional Greenhouse Gas Initiative (RGGI), where energy efficiency is expected to be an 

important means to reduce greenhouse gas emissions. Energy efficiency is now considered to be 

a resource on par to supply side resources by ISO-NE, as part of its forward capacity market. 

 

6

 see http://www.otcair.org Meeting December 2006, Hartford, CT, EPA presentation on TRUM analysis 
Reducing NOx Emissions ... ▪ 11

DEP’s previous positive experience with requiring direct controls, and its more recent knowledge 

on the potential role that energy efficiency could play, led the agency to request that Synapse 

evaluate both approaches, and their ability to reduce NOx emissions on high electric demand 

days. 

E. Tasks for Completion 

The Clean Air Act requires that states implement plans (referred to as SIPs, for state 

implementation plans) to attain and maintain compliance with National Ambient Air Quality 

Standards (NAAQS). Over the past thirty years, Connecticut has adopted regulations to reduce 

emissions that cause or contribute to ozone formation, and these have steadily reduced both 

ozone concentrations and the frequency of exceedances of the ozone NAAQS. EPA;s recent 

promulgation of a lower ozone standard will require additional in-state emissions reductions to 

occur. 

The OTC MOU 2007 commitment was made to reduce emissions to help meet the previous 

ozone NAAQS. Since the new EPA NAAQS will require additional emissions reductions to occur, 

the DEP asked Synapse to evaluate a number of strategies that could meet both the OTC MOU 

commitment and be part of the SIP for the new EPA ozone standard. 

The DEP asked that Synapse complete three tasks to analyze electricity demand during peak 

demand periods: 

• project Connecticut electricity demand for the period from 2005 to 2020; 

• project generation and transmission from load; and 

• project emissions and prepare a report that will be used as part of Connecticut’s SIP to 

demonstrate attainment with the Federal eight-hour ozone standard 

The following sections describe our methodological approach to evaluate how various energy 

efficiency and regulatory scenarios could reduce NOx emissions in two phases through 2020. 

The first phase considered what policies could be implemented to meet Connecticut’s OTC 

commitment, which is a 11.7 tons/day reduction of NOx emissions from the peak electric demand 

day. The second phase evaluated how an extension of policy measures implemented in phase 

one could help Connecticut to satisfy its obligation to meet the newly promulgated EPA ozone 

standard. Connecticut anticipates developing regulations during 2008 to meet the OTC 

commitment. EPA will require that states submit SIPs by 2011 to demonstrate how they will meet 

the new ozone standard. Connecticut’s efforts to meet the OTC commitment will be very useful to 

plan for how the state will meet the new ozone standard. Having a longer planning horizon, out to 

2020, also helps to provide certainty to the regulated sources, and for agencies that the DEP will 

need to coordinate with to ensure that the energy efficiency programs are performing at their 

anticipated levels, and that the levels persist over time. 
Reducing NOx Emissions ... ▪ 12

3. Tasks and Assumptions 

To complete the requested tasks, Synapse needed to evaluate Connecticut’s electricity load, 

expected changes in CT load between 2005 and 2020, and determine how generation would be 

expected to change over this period, and how regulatory and incentive programs might change 

emissions from CT generators over this time period. To complete these set of tasks, we divide 

this paper into three methodological segments: 

1. Project load in CT to 2020; 

2. Create model to estimate generation required to meet load until 2020; and 

3. Modify CT load and generator emissions characteristics to simulate emissions 

reductions regulatory and incentive mechanisms. 

Below, we discuss five critical assumptions in our model. 

A. Grow Load from Baseline 2005 

For the purposes of this research, Synapse was directed to use load shape from 2005 to drive the 

analysis. Therefore, although we grow load according to predictions from ISO-NE (as detailed 

below), the fundamental load shape follows a pattern set in 2005 by that year’s demand, weather, 

fuel prices, and anomalies. 

B. Connecticut’s EE programs are coordinated with ISO-NE 

Connecticut is part of ISO-NE regional transmission organization, making up about 25% of the 

net electricity demand of ME, NH, VT, MA, RI, and CT. In the ISO-NE market structure, load is 

met by generators in economic “merit-order”; i.e. those generators which are least expensive (or 

conform to other criteria) are chosen to meet the lowest load, followed by increasingly expensive 

generators as demand increases. Except within transmission-constrained regions, generators 

responding to load are not necessarily within the proximity or even state where that load is 

required, rather the marginal generator can be anywhere on the grid. It is likely then that CT’s 

hourly load is not met exclusively by Connecticut generators, rendering the question of how CT 

electricity-sector emissions will be impacted by energy efficiency programs in CT quite difficult. 

For the purposes of this analysis, we assume that any energy efficiency (EE) programs, or any 

shifts in Connecticut’s load, will be proportionally matched throughout ISO-NE, or at least in 

bordering states. For example, if CT implemented a rigorous EE program without the cooperation 

or engagement of the remainder of ISO-NE, we might expect that CT generators would still serve 

load outside of the state. In fact, since CT’s load is wrapped up as 25% of ISO-NE’s bulk regional 

demand, any changes in CT’s demand profile are diluted by 75% when reflected in the remainder 

of ISO-NE. For example, in an absurd case, if CT ceased demanding electricity altogether then 

the remainder of ISO-NE would see a demand reduction of 25%, and it is likely that some of CT’s 

generators would keep spinning even though the state requires no power. To adequately reflect 

the strength of a load reduction program on emissions in CT, we assume that all other states in 

ISO-NE adopt similar EE programs as CT, meaning that the EE program in CT is not diluted 

throughout the region. This assumption is supported by legislation passed in ME, RI and VT, and 

passed in July 2008 in Massachusetts that, like Connecticut, would require all cost-effective 

energy efficiency to be procured first as a resource. The neighboring state of New York has also 
Reducing NOx Emissions ... ▪ 13

directed NYSERDA to develop and implement a plan to reduce electric consumption there 15% 

by 2015. 

C. The Connecticut Box Model 

The research thrust of this paper is to explore the impact of demand reduction programs and 

emissions regulations on emissions targets, specifically in CT. As noted above, we assume that 

CT and the remainder of the region act in parallel. As a corollary to this simplification, we also 

assume that CT acts in a “box”, or as a single entity. Currently, CT can be subdivided into at least 

two regions: the constrained SW corner of the state, and the remainder of the state. During times 

of high electricity use, SW CT demands more electricity than can be carried across transmission 

lines from inexpensive generators and local, expensive generators are dispatched to meet load. 

This load constraint allows out-of-merit plants to operate, and re-arranges economic dispatch 

order. Currently, CT is building new transmission systems to alleviate this constraint. We assume 

that this new system will allow electricity to flow from plants dispatched in merit order, and 

therefore generators will be loaded in the same order as during non-constrained periods. 

D. Nuclear Unit Operation 

Connecticut and ISO-NE are served by five nuclear generators in CT, MA, NH, and VT. In CT, the 

Millstone 2 and Millstone 3 units serve over 2000 MW combined. When these units are out of 

operation, dispatch throughout the region must shift to make up the difference, thereby changing 

“typical” behavior. We assume that except for very rare events, the two nuclear units in CT will be 

in operation though every ozone season. Therefore, we only track dispatch and operations in CT 

when the Millstone units were operating at 100% in 2005. 

E. Energy Efficiency Load Shape 

Energy efficiency programs can be applied to a number of sectors, and targeted at either a broad 

array of energy reductions, or though specific technologies (such as efficient lighting or air 

conditioning). Some programs are optimized for demand reductions at peak periods (e.g. price 

response programs), while other programs implement general efficiency measures (i.e. 

weatherization, lighting technology). Different EE programs may reduce energy consumption at 

different times of the day, or under different conditions. A peak-shavings program will reduce 

peak loads only, while a lighting program may reduce use across all hours. In addition, programs 

may be targeted towards different sectors, such as industrial or residential customers. Each of 

these categories will have different load-shapes for demand reduction. Since we are not in a 

position to know which types of EE programs could be implemented in CT, we assume that load 

is reduced across all hours as a percentage of hourly demand. Therefore, a 2% EE initiative will 

reduce demand by 2% at all hours (50 MW from a 2,500 MW trough and 150 MW from a 7,500 

MW peak). 
Reducing NOx Emissions ... ▪ 14

F. Statewide analysis is based strictly upon 2005 EPA 

reported data 

This task examined statewide emissions from EGU. The performance characteristics for each 

EGU are directly based upon data reported to the EPA Clean Air Markets program7 for the year 

2005 in the state of Connecticut, as agreed upon in the stakeholder process leading up to this 

analysis. Comments received after the completion of this analysis indicated that some EGU 

anticipated emissions changes due to technological upgrades and changes in performance or 

operations. Misreported data or changes in unit operations after 2005 were not considered. It is 

feasible, and even likely, that some EGU in Connecticut will change operations during the 

analysis period (to 2020); however, for consistency with the initial dataset and the agreed upon 

analysis guidelines, we have not altered any single unit’s emissions or runtime characteristics 

except in specified sensitivity runs. 

G. Analysis does not include RGGI or CAIR 

Neither the ten-state Regional Greenhouse Gas Initiative (RGGI) governing greenhouse gas 

emissions, nor the recently overturned Clean Air Interstate Rule (CAIR) were considered in this 

analysis. RGGI, if implemented, will encourage a shift in dispatch throughout New England to 

resources with lower emissions, but is unlikely to affect the relative economic merit order of fossil 

plants operating in CT today. In addition, the trading schema is unlikely to directly effect criteria 

pollutant emissions in the relatively near-term unless generation is reduced. Similarly, CAIR was 

not implemented as an operational parameter; if a similar rule is implemented, it would likely 

change the emissions target rather than the baseline operations or dispatch. 

 

7

 http://www.epa.gov/airmarkets/ 
Reducing NOx Emissions ... ▪ 15

4. Methodologies 

A. Scenarios 

Synapse evaluated several different scenarios and their ability to reduce NOx emissions during 

the ozone season. Each scenario was based upon knowledge of existing policies or technologies. 

Scenario 1: Baseline 

In the baseline scenario, CT demand grows in pace with 2008 ISO-NE load predictions, following 

the load shape of 2005. Maximum load increases from just over 7,000 MW in 2005 to 8,000 MW 

in 2020; net energy for load also grows by about 12.4% by 2020. 

Scenario 2: Energy Efficiency (2% per year) 

Connecticut’s energy efficiency program is achieving energy savings equal to approximately 1% 

of annual electricity sales. The ECMB’s October 2007 plan anticipated increasing the annual 

savings goal. Based on conversations with ECMB consultants in May 2008, energy efficiency 

programs are expected to achieve energy savings equal to 2% of annual electricity sales during 

2008 or 2009. The 2% per year level selected for this scenario therefore is representative of how 

the current EE program is expected to perform 

Scenario 3: RMR Units Apply Emissions Controls 

The second scenario evaluated the effects of applying a two phase NOx emissions reduction 

requirement to Connecticut’s RMR units. Phase one, a 30% reduction, is approximately equal to 

the level of reduction that could be achieved if selective non-catalytic reduction (SNCR) controls 

were installed on each RMR unit. SNCR is considered to be a “reasonably available control 

technology” (RACT), which means the controls have been demonstrated to be effective during 

operation, and that they would be cost-effective to consider for application to existing emissions 

units. Phase two would apply a total of a 50% reduction to the RMR units. The 50% reduction is 

considered to be equivalent to requiring “best available control technology” (BACT) to the sum of 

all RMR units. BACT would be selective catalytic reduction (SCR). BACT is typically applied to 

new sources, and in this case, SCR would be expected to reduce NOX emissions by 90-95%. For 

existing sources, space considerations may preclude BACT level controls on each and every unit, 

so the 50% reduction level reflects that not all RMR units would individually install BACT level 

control technology. Note that the 30% and 50% reduction levels do not require emissions controls 

to be installed. RMR unit emissions reductions could be achieved by decreasing the number of 

hours they operate, ceasing operation of one or more units or through installation of controls. 

Synapse is agnostic with respect to which option may be chosen to reduce emissions from these 

units. 

For the purposes of this sensitivity scenario, Synapse manually reduced emissions from RMR 

units by 30% and 50%, in 2012 and 2015, respectively. Simulated controls were only applied to 

RMR units, which have a disproportionately high NOx emissions rate relative to other units in CT. 

Additional emissions controls were not considered for combustion turbines or other non-RMR 

units. Other units do not have the high emissions rate of the RMR units and therefore these 

reductions would have a relatively small impact if applied to non-RMR units. 
Reducing NOx Emissions ... ▪ 16

Scenario 4: Efficiency and RMR Units Apply Controls 

A third scenario evaluated the benefits of a hybrid strategy that includes energy efficiency 

program achieving energy savings equal to 2% of annual electricity sales and reducing NOx 

emissions from RMR units by 30% in phase one, and by a total of 50% in phase two. This 

scenario was chosen to determine if the OTC MOU commitment could be met within the desired 

timeframe, and whether the success of a hybrid approach could be extended to help Connecticut 

reduce emissions further to meet the new EPA ozone standard. 

Scenario 5: Aggressive Efficiency (3%) 

The Connecticut’s Public Act 07-242 (2007) requires the state to procure all cost-effective energy 

efficiency as a resource of first choice. A 2004 study of energy efficiency8

 concluded that 

significant potential exists in Connecticut to substantially increase the amount of energy savings 

achieved from demand side measures. Synapse chose a 3% savings level as an aggressive, but 

possible goal that the state could achieve. Based on work Synapse is currently performing9

, 

energy efficiency programs appear capable of achieving savings equal to as much as 5% of 

annual electricity sales. So, the 3% is an aggressive, but not extreme goal that Connecticut has 

the capacity to achieve. 

B. Load Growth and Energy Efficiency 

ISO-NE Load 

Predictions 

Load growth from a 2005 

baseline was 

extrapolated from 2007 

ISO-NE load forecasts10, 

predicting peak loads 

and net energy demand 

from 2008 to 2016. As 

part of the long-term 

forecast, ISO-NE 

provides a monthly peak 

load forecast for each 

state in the control 

region. Monthly data for 

Connecticut was extracted 

for the period of 2008 to 

 

8

 Independent Assessment of Conservation and Energy Efficiency Potential for Connecticut and the Southwest 

Connecticut Region; Final Report, GDS Associates, April 2004, prepared for the Connecticut Energy Conservation 

Management Board 

9

 Draft work to be published summer 2008 

10 ISO-NE, September 2007. CELT Forecast Data, 2007. Available online at http://www.isone.com/trans/celt/fsct_detail/2007/isone_2007_forecast_data.xls. An updated forecast (2008-2018) was made 

available since the inception of this research on April 30th, 2008. The updated report is available at http://www.isone.com/trans/celt/fsct_detail/index.html. 

Recorded peak, 

March 2005

Recorded peak, 

July 2005

JUL

MAR

4000

4500

5000

5500

6000

6500

7000

7500

8000

8500

2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

Year

Monthly Peak Load (MW)

Figure 3: Connecticut monthly peak extrapolation from ISO-NE 

monthly peak forecasts (2008-2016) for July and March (2006 & 

2007, 2017-2020).
Reducing NOx Emissions ... ▪ 17

2016. 

In each month, the annual forecast 2008 to 2016 in Connecticut conforms to a 2nd order 

polynomial of the form 

m y m y m y m L = a x + b x + c

2

, (Equation 1) 

where Lm,y is the peak load of month m in year y; and am, bm, and cm are coefficients of the 

polynomial specific to month m. In the polynomial fit, 2008 is year one (1). Values of the 

coefficients are presented below in Table 1. These best fit polynomials were used to estimate 

monthly peaks in 2017 through 2020 in a business-as-usual scenario (see Figure 3). This 

assumes that ISO-NE is using the best possible information about current demand growth rates 

and not including energy efficiency or demand reduction programs which are not currently 

deployed. 

Month 

(m) a b c r

2 

Month 

(m) a b c r

2

Jan -3.38 105.24 5807 0.9994 Jul -6.03 166.45 7006 0.9996

Feb -3.18 100.85 5615 0.9994 Aug -5.98 165.35 6945 0.9995

Mar -3.14 95.22 5188 0.9994 Sep -5.04 138.96 5836 0.9995

Apr -2.49 80.73 4546 0.9995 Oct -2.84 86.16 4711 0.9995

May -4.40 124.55 5308 0.9998 Nov -2.99 94.54 5257 0.9992

Jun -5.49 152.54 6446 0.9996 Dec -3.18 101.98 5741 0.9996

Table 1: Coefficients of monthly peak growth from 2008 to 2016 from ISO-NE forecast for equation 1. 

Year 2008 is portrayed as x=1 in this polynomial (i.e. 2016 is year 9). Goodness-of-fit (r2) values 

exceed 0.999 in all cases. 

We also use the same polynomials to extrapolate backwards to 2006 and 2007, even though 

actual energy use and monthly peaks are known for these two years. Using actual monthly peak 

data for 2006 and 2007 would suggest that we had also used real hourly data for those two years, 

which was not requested. Therefore, we assume that these two years conform to the growth rates 

predicted by the ISO for 2008 to 2016. We use real hourly load and monthly peaks for 2005. 

Extrapolating hourly load from 2005 to 2020 

The peak extrapolation is adequate for explaining peak periods only, but not the remainder of 

hours in the hourly load profile. To extrapolate the hourly load profile of 2005 out to 2020 using 

the monthly peak data, we examine the relationship between monthly peak loads in 2005 and 

every other year, and apply the same relationship to every hour. Simply, we gather the coefficient 

for each year with a zero-intercept linear regression between monthly load in 2005 and estimated 

monthly load in every other year. The form of the equation for this regression is Ly = ayx2005, 

where L are monthly peak loads in year y, x are the monthly peak loads of 2005, and a is the 

slope between 2005 and year y. The slopes (to be used as a multiplier) are presented in Table 2. 

Year Slope Year Slope Year Slope Year Slope

2005 1.000 2009 1.058 2013 1.120 2017 1.159

2006 0.997 2010 1.075 2014 1.131 2018 1.165

2007 1.019 2011 1.094 2015 1.142 2019 1.169

2008 1.040 2012 1.108 2016 1.152 2020 1.172
Reducing NOx Emissions ... ▪ 18

Table 2: Slope relationships between monthly peaks in 2005 and every other year (including 2005). 

To obtain an estimated monthly 

peak in another year, multiply the 

peak by the slope factor. 

We assume that the same 

relationship seen between years in 

the monthly peaks can also be 

applied to all other hours. For 

example, if in 2016, all monthly 

peaks are expected to see a load 

increase of approximately 15.2% (a 

slope of 1.152) relative to 2005, 

then we assume that hours would 

expect to see a similar growth. 

More complicated factors could be 

used, including non-linear 

(polynomial) relationships, but 

these are prone to providing 

anomalous data during trough 

periods. For example, a 2nd order 

polynomial fit between monthly 

peaks in 2005 and 2016 never falls below 5,500 MW, and yet there are estimated monthly peaks 

in 2016 as low as 5075 MW (in April), indicating that we would be likely to overestimate load 

during trough periods. Therefore, we have chosen to use a simplified metric of a single slope 

factor which goes through the origin (see Figure 4). 

All 8760 points in the 2005 hourly load profile (365 days x 24 hours per day = 8760 hours) are 

multiplied by each years’ slope factor to obtain an estimate of the hourly load profile in 2006 to 

2020. These hourly load profiles become the business-as-usual (BAU) load growth scenario. 

Applying Energy Efficiency 

In three of the scenarios presented in §4.A above, we explore the impact of energy efficiency 

programs on emissions reductions in Connecticut. The application of energy efficiency in this 

analysis is applied to the load growth assumptions, after hourly loads in each year have been 

calculated. We make several simplifying assumptions about EE: 

• EE measures are comprehensive and are applied across all hours, 

• EE programs reduce loads by a constant percentage (see §3.E), 

• EE programs do not expire, and 

• EE measures are cumulative and compounded 

EE is applied in this analysis in the following way. In each hour, the BAU rate increase from the 

previous year’s same hour is calculated; the EE rate is subtracted from this growth rate to obtain 

an apparent growth rate. This new apparent growth rate is used to calculate the growth since the 

same hour in the previous year, and the result is load growth after EE. The form of the equation 

is: 

y = 1.1523x

R2

 = 0.7902

0

2000

4000

6000

8000

0 2000 4000 6000 8000

Monthly peaks in 2005

Monthly peaks in 2016

Figure 4: Obtaining the slope factor for 2016 relative to 

2005. The slope is forced through the origin (0,0) and 

represents a simplified growth rate of the monthly peak 

relative to 2005.
Reducing NOx Emissions ... ▪ 19

( ) () ( )

( ) ( , 1) ( , 1) , 1

, , 1 , ⎥ ∗ − + −

⎦

⎤ ⎢

⎣

⎡ − −

− − = EE L h y L h y L h yL h y L h y L h y EE EE

BAU

BAU BAU

EE

where LEE and LBAU are the loads (in MW) at hour h in year y after the EE measure and in the 

business-as-usual (BAU) scenarios, respectively. The term y-1 indicates the previous year, in the 

same hour h. 

C. The Load-Based Probabilistic Emissions Model (LBPEM): 

Core Program 

The Load-Based Probabilistic Emissions Model, LBPEM, is designed to estimate regional or state 

generation from hourly demand. The model uses hourly load to estimate hourly fossil generation, 

and hourly generation to estimate hourly emissions of NOx and SOx. This model is based on the 

simple precept that EGUs are dispatched to meet loads, but may not do so in a linear or easily 

interpretable reason. In addition, although EGUs have a measurable average emissions rate 

(measured in lbs or tons per MWh), it is rarely the case that the emissions rate is constant over all 

levels of generation. A warming or cooling generator will often be less efficient (per unit of 

generation) than a unit running at full rated capacity. To overcome these barriers, LBPEM is a 

statistical model using historical data to estimate potential emissions from hourly load. The 

following sections describe LBPEM and its operation, as well as some of the assumptions which 

go into the model in practice. 

To determine the relationship between load and emissions, we first determine the relationships 

between load and expected generation. EGUs are dispatched economically, or in merit order. 

Least expensive units are dispatched first to meet the lowest loads, and as more electricity is 

required, more expensive generators are dispatched in order of their increasing costs per unit. If 

all external variables remained constant, generators never required maintenance or other 

outages, and a system was fully constrained such that all load inside a region were met by 

generators in the same area, the system would be simple to simulate. In the simple case, if load 

were known, we would add the least expensive generators together until load were met. 

However, in the more common circumstance, non-fossil units such as nuclear, hydro, wind, and 

solar contribute to the grid with their own patterns of generation, electricity is imported into the 

region at rates determined by the cost of electricity in neighboring areas and the strain on the 

transmission system; forced and planned outages cause generators to cease operating for 

maintenance and inspections. Thus, building a model which relies on each EGU being 

continuously available is unlikely to reflect the range of possibilities which occur in reality. 

LBPEM gathers statistics about the operating behavior of a generator based on historical hourly 

generation and emissions information, as well as concurrent hourly regional load. The model 

structure recognizes that the electricity sector has significant underlying patterns and internal 

consistencies, and yet is filled with stochastic behaviors. We can divide dispatch models into first 

principles and statistical models. First-principles models build an electrical system from the 

‘ground up’, using information on capital costs, fuel and operating & maintenance (O&M) costs, 

economic dispatch assumptions, transmission constraints, and known outage frequencies. The 

LBPEM statistical model (with no other known analog) is backwards looking, building a statistical 

database which portrays the system behavior at a period of time. Manipulating some of the 

assumptions of this statistical model, we can predict how simple, short-term changes will impact 
Reducing NOx Emissions ... ▪ 20

dispatch operations. 

In LBPEM, increasing load must be met with new generation resources. Unlike dispatch or 

planning models, the operator of LBPEM must choose particular new resources and the year in 

which these resources become operational. Because resources all have specific probabilistic 

operational parameters in LBPEM, we simplify the addition of new resources by adding EGU 

statistically analogous to existing EGU until peak requirements are met. In this implementation of 

the model in Connecticut, acceptable new generators are analogous to the cleanest new 

combined cycle (CC) and combustion turbines (CT) units built in the state (Devon units 11-14, 

Bridgeport Energy 1&2, and Milford Power 1&2). For additional statistical breadth, we also 

included four South Meadow Stations (13A-14B) as potential new resources, although the 

emissions characteristics from these units are unfavorable and similar resources are unlikely to 

be built. 

Datasets and Exclusions 

LBPEM is built with three data sets. The fundamental dataset is from the 2005 EPA Clean Air 

Markets Dataset (CAMD) program11, designed track national NOx and SOx emissions for regional 

trading programs and acid rain controls. Every fossil power plant greater than 15 MW in 

Connecticut is required to report hourly generation (in MWh), emissions of CO2 (tons), NOx (lbs), 

and SOx (lbs). The data were extracted for the State of Connecticut for the year of 2005 and 

compiled into a large database. 

The second dataset is comprised of hourly load for the State of Connecticut for 2005, obtained 

from New England Independent System Operator (ISO-NE). This dataset records loads ending 

on the hour. The data was shifted from local daylight savings time to a continuous hourly dataset. 

Finally, the last dataset identifies the days of the year in which the nuclear units in Connecticut 

were not operating at full capacity. On these days, fossil units in the State operate at higher than 

expected generation levels to make up the difference, and may not reflect a standard merit order 

of operation. The nuclear outage data was obtained from the Nuclear Regulatory Commission12. 

Southwest Connecticut (SW CT) is currently transmission constrained, a situation in which during 

high demand periods, dispatch becomes non-meritorious as expensive EGUs within the 

constrained region come online to meet local demand. Because of this dramatic shift in dispatch 

during periods of transmission constraint, this condition provides a poor baseline for future 

conditions. Currently, new transmission capacity is being added to CT’s infrastructure intended on 

relieving this congestion and since it is unlikely that these transmission constrained periods will 

be maintained in the future, and the hours in which these conditions exist are unlikely to persist. 

We remove these hours from our analysis by targeting hours in which congestion prices exceed 

$20. 

Dates falling between January 18th and 30th of 2005 were excluded from this analysis. During a 

cold snap, natural gas shortages drove a switch from natural gas burning EGU to RMR units in 

CT. This fuel availability and price fluctuation caused units to run out of economic merit order, a 

 

11 Available at http://camddataandmaps.epa.gov/gdm/ 12 Nuclear outage data for 2005 available at http://www.nrc.gov/reading-rm/doc-collections/event-status/reactorstatus/2005/
Reducing NOx Emissions ... ▪ 21

situation which is unlikely to be repeated as dramatically in the future. Because of this switch, we 

have excluded this time period 

Generation statistics from load: Operational probability and generation probability distribution

We gather statistics from the generation dataset by examining discrete “load bins”, the hours in 

which load fell between an upper and lower bounding demand. There are 100 such load bins, 

each indexing a number of hours. In each load bin, we collect two statistics: 

• the probability that each unit is operational, and 

• if at all operational, the probability distribution function of the unit’s generation within the 

load bin 

Load Category

Probability of Units Online

2252 2781 3310 3839 4368 4897 5426 5955 6484

3: Sprague Paperboard - Sprague Mill 1

8: Waterside Pow er, LLC 4

10: Pfizer 5 11: Waterside Pow er, LLC 5

13: Waterside Pow er, LLC 6

14: Devon 7

15: Pfizer 8

16: Devon 8

19: Norw alk Harbor Station 10 23: Devon 10

41: Bridgeport Harbor Station BHB1 59: AES Thames UNITA

60: AES Thames UNITB

24: Torrington Terminal 10 21: Franklin Drive 10

31: South Meadow Station 11A

22: Middletow n 10 32: South Meadow Station 11B

17: Branford 10

44: Bridgeport Harbor Station BHB4 35: South Meadow Station 13A

36: South Meadow Station 13B

58: Norw ich TRBINE 33: South Meadow Station 12A

34: South Meadow Station 12B

26: Cos Cob 11

18: Cos Cob 10

28: Cos Cob 12

52: Capitol District Energy Center GT 37: South Meadow Station 14A

38: South Meadow Station 14B 20: Tunnel 10

27: Devon 12

29: Devon 13

30: Devon 14

25: Devon 11

42: Bridgeport Harbor Station BHB2

50: Wallingford Energy CT04

51: Wallingford Energy CT05

49: Wallingford Energy CT03

48: Wallingford Energy CT02

7: Middletow n 4

54: Lake Road Generating Company LRG1 12: Montville 6

46: Wallingford Energy CT01

9: Montville 5 1: Norw alk Harbor Station 1

57: New Haven Harbor NHB1

6: Middletow n 3 5: Norw alk Harbor Station 2

2: Pratt & Whitney, East Hartford 1 4: Middletow n 2

56: Lake Road Generating Company LRG3

45: Milford Pow er Company LLC CT01

55: Lake Road Generating Company LRG2

39: Bridgeport Energy BE1

40: Bridgeport Energy BE2

47: Milford Pow er Company LLC CT02

53: Algonquin Pow er Windsor Locks, LLC GT1

43: Bridgeport Harbor Station BHB3 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1
Reducing NOx Emissions ... ▪ 22

The probability that the unit is operational is one function of the type of generator. For example, a 

baseload unit will be operational even when very little load is demanded (off-peak hour), and thus 

generate in most of the load bins. A peaking unit, however, is unlikely to ever operate at low load 

bins, but might occasionally operate at high loads. The probability that a unit is operational at low 

loads is relatively well predicted by the unit’s capacity factor (Figure 5). In low load categories, 

only the baseload units operate. In the mid-load categories, intermediate units begin operating, 

and at peak loads, all units which run during the year are fully operational. 

When units do run, their generation is often also a function of the total demand. We collect 

statistics for each generator on how much energy the unit is producing in each load category. The 

information is translated into a discrete probability distribution function with 20 different generation 

options. In essence, this process creates a histogram of potential power outputs for a generator 

when a particular load is demanded. We illustrate an example in Figure 6. Montville 5, an oilburning unit from 1954, runs as an intermediate generator, turning on when loads exceed 2400 

MW. This unit maintains a minimum generation of about 45 MW as spinning reserve. As loads 

climb above 4500 MW, the generator is dispatched more often towards its capacity (~86 MW). At 

peak loads (>6500 MW), the unit runs almost exclusively at capacity. These behaviors are 

aggregated in the load probability distribution function. We see that at the lowest load categories 

(first black line), the unit does not operate at all, and therefore this load bin is empty. At a load bin 

2500

3000

3500

4000

4500

5000

5500

6000

6500

10

20

30

40

50

60

70

80

0

0.2

0.4

0.6

0.8

1

Load Category (MW)

Montville 5

Generation Category (MW)

Probability

Figure 5: Color histogram indicating the probability of operation for 60 EGUs in CT in 2005. In each 

of 60 load categories across the X-axis, each power plant (in the Y-axis) displays a color indicating 

the probability that the unit was in operation when a particular load was demanded. For example, 

at a load of ~5000 MW, Bridgeport Energy BE1 runs about 70% of the time (orange), but the Pratt 

and Whitney station only runs about 30% of the time (light blue).

Figure 6: Montville 5 generation behavior. When in operation, this unit (and all others) is dispatched 

to meet load according to a variety of factors. We can see in this graph that Montville 5 generates 

approximately 42 MW during most hours of operation when load is below 5000 MW. Above 5000 MW, 

the unit is increasingly likely to generate 82 MW.
Reducing NOx Emissions ... ▪ 23

from 3785 to 3866 MW (the second black line), the unit runs, generating mostly at spinning 

reserve, indicated by the steep peak at about 45 MW. At a load bin closer to 5500 MW (the third 

black line), the unit runs about 20% of the time at spinning reserve and 70% of the time at 

capacity13. As peak loads are demanded (the last black line), the unit is dispatched at full capacity 

(indicated by steep peak at 86 MW). 

The operational probability and generation probability distribution functions are recorded for each 

generating unit and each load category. These data will be used later to re-construct the 

probabilistic generation of LBPEM. 

Emissions statistics from generation (probabilistic emissions rate) 

Unit emissions statistics relative to unit generation are gathered from the database similarly to the 

way in which generation statistics were gathered relative to load. For many types of units, 

emissions are a reasonably straightforward function of generation (higher emissions when more 

power is generated). In other datasets14, emissions are calculated as a rate relative to generation 

(Lbs NOx / MWh, or T CO2 / MWh), assuming a linear increase with generation. However, 

emissions (particularly NOx and SOx) are not always tightly correlated with generation (see Figure 

7), and can vary depending on running conditions, operating temperatures, and whether 

emissions controls are in operation. 

We use 20 generation bins, or categories, for each unit, bounded by zero and the highest 

possible generation capacity for that unit. We find all hours in which the unit generated the 

amount in each bin, and record the emissions during these running hours for the ozone and nonozone season separately. Within each generation bin, we create a histogram, or PDF, of likely 

emissions. For some units, this is a very tightly bounded constraint, while for other units, this 

 

13 About 10% of the time, this unit is transitioning between 45 MW and 86 MW when the load is near 5500 MW. 14 For example, see the US Environmental Protection Agency’s eGRID dataset 

(http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html), one of the most comprehensive resources 

for plant-level, state and regional emissions reporting data, based on the CAMD dataset. 

Figure 7: NOx emissions versus generation for two CT EGUs. (A) Montville 5 (RMR) 

unit emissions rise at approximately 2.02 lbs per MWh. (B) Bridgeport Energy BE1 

has a non-linear emissions path, which is simulated in the Monte Carlo model. The 

average emissions rate for this unit is 0.19 lbs per MWh.
Reducing NOx Emissions ... ▪ 24

distribution can be quite wide. For example, Bridgeport Energy BE1 in Connecticut is a new, low 

emissions natural gas combined cycle generator with best available emissions control 

technology. The unit tends to run as a baseload unit in continuous operation (Figure 8). However, 

during the times that the unit is ramping up, the emissions controls are not as effective, and the 

unit’s operation during these periods has high emissions. However, as the unit is ramping down, 

the emissions controls are very effective, and emissions are very low. We capture this behavior in 

the very wide PDF at low generation categories, even thought the unit is very predictable and well 

constrained at high generation categories. 

Monte Carlo Simulation 

To estimate generation and emissions when a particular load is demanded, we run a Monte Carlo 

simulation. A Monte Carlo simulation is a method of obtaining both likely average system 

behavior and error bounds when there are a large number of uncertain variables. It creates 

hundreds or thousands of manifestations of a model run by drawing randomly from within 

probability distribution functions, rather than just using the expected value (the mean). The 

average behavior of the system is the average of all of these manifestations, and the standard 

deviation of the behavior can also be obtained from these multiple runs. In our system, there is 

uncertainty on: 

1. the number of units operating when a particular demand is required, 

2. the generation level of those units which are operating when a particular demand is 

required, and 

3. the emissions level of those units at a particular generation. 

We solve for expected generation and load by running 100 manifestations of the model in the 

Monte Carlo simulation. We can divide this process into three distinct steps: 

1. choosing which units operate, 

2. choosing the generation of each of these units, and 

Figure 8: Generation of two CT EGUs. Generation in MW is portrayed as colors ranging from 

0 (blue) to 175 MW (red). The y-axis are hours of the day and the x-axis are days of the year. 

Every vertical line is a daily cycle. (A) Montville 5 operates about 25% of the time, and cycles 

from zero to 80 MW, holding at 40 MW during evening hours. (B) Bridgeport Energy BE1 

operates 69% of the time and also cycles, but generally provides more of a baseload 

operation between 140 and 175 MW.
Reducing NOx Emissions ... ▪ 25

3. choosing the emissions level of each of these units. 

Each manifestation of the Monte Carlo approach runs as follows. First, given a particular load, we 

determine which load bin it falls into. Within this load bin, each plant has a certain probability of 

operating. For the 60 units in Connecticut, we roll a random die (actually choosing a randomly 

generated number) and determine if it is higher or lower than the probability that the unit is in 

operation. If it is higher or equal to the unit probability, than the unit is operational, if it is lower 

than the unit is offline. This gives us a list of all of the units running in this particular manifestation. 

Second, we determine how much each unit is generating. For each unit, we choose a random 

number (0-1) and determine where within the unit’s generation PDF this random roll falls15. The 

nearest generation value (in MW) below the random roll is taken as the unit generation. Finally, to 

determine the unit’s emissions, we reference the unit’s chosen generation bin and pull a random 

number (0-1), which chooses an emissions level given the amount of energy the particular unit is 

generating. We sum all of these emissions together to obtain net system emissions in this 

particular manifestation and record the value. 

The Monte Carlo simulation runs 100 times for each load category. At the end of these 100 runs, 

the average and standard deviation of the generation and emissions are recorded and reported. 

D. LBPEM Extension: Load Growth, Interpolation, and 

System Changes 

The core of LBPEM, described above, is able to generate an assessment of emissions in 

Connecticut in the base year, simply by recalculating average behavior from statistics gathered 

from the same base year. While this system provides a useful check on the reference case, it 

does not help to identify how changes in the system would result in changes in emissions or 

system generation. However, because of the nature of this system, it is able to dynamically adapt 

to changes in the base case. For example, simply shifting around load within the dynamic range 

of the reference year (2005) is simple: since the emissions in each hour are calculated 

independently, the order in which loads occur through the year is relatively insignificant. 

Therefore, from just the LBPEM core program alone, we can determine how different loads will 

result in emissions differences. How can we determine emissions in the case that load grows or 

shrinks outside of the dynamic range seen in the reference year, or a new plant is added or old 

plants retired? The extension of LBPEM allows this functionality. 

The basic concept in the following sections are that (a) the amount of generation expected at any 

given load category remains constant in all circumstances16, and (b) units respond to load at a 

given level of generation (or are dispatched to respond to load) based on a “perceived load” 

requirement. Using the first precept, if we interpolate up the system load vs. generation curve, we 

can determine how much generation would be expected at any given level of load (even if the 

load wasn’t demanded in the reference year). Using the second assumption, we can add and 

 

15 This is accomplished by transforming the PDF into a cumulative distribution function (CDF), with values from 

zero to one. When the random variable is drawn, it is compared to the CDF and chooses the generation with a 

cumulative probability less than or equal to the random variable. If we repeat this operation multiple times, the 

histogram of all chosen generation values converges on the shape of the PDF. 16 The constancy of the generation vs. load relationship is critical to this statistical approach. We assume that the 

amount of energy generated by fossil units is a relatively constant ratio, and other types of generators and 

transmission remain relatively constant as well. 
Reducing NOx Emissions ... ▪ 26

subtract generators by shifting the perceived load of all other units as generators are built or 

taken offline. 

Statistics Extrapolation: Load growth and shrinkage 

The statistics which are gathered for the core version of LBPEM have a critical shortfall, in that 

they are only able to portray a world in which the load falls in the dynamic range of the base year 

(in this case, 2005). If projected loads extend above or below the base year dynamic range (in the 

case of Connecticut, approximately 2,200 to 7,000 MW), then the non-extended version of 

LBPEM is unable to identify a load category and is unable to use the available statistics. The first 

expansion module extrapolates available statistics out to load categories that did not exist in the 

base year to estimate how existing generators would operate in these unknown conditions. The 

following four steps are taken: 

4. Create new load categories above and below the reference dynamic range, 

5. Extrapolate unit probabilities of operation into new load categories, and 

6. Extrapolate generation level 

PDFs into new load categories. 

New load categories are defined for 

loads up to 50% above and 30% 

below the existing dynamic range. For 

each generator, we extrapolate (both 

up and down) the probability that the 

unit is in operation using the first third 

and last third of the probability at a 

load category as a basis for the 

extrapolation. If a unit always operates 

at the historical peak load, then it will 

also always operate at any higher 

loads (see Figure 9). If a unit never 

operates at minimum loads, then at 

any loads below it will also never 

operate. If an extrapolated line goes 

above a probability of 1, the line stays level at 1; the same rule applies if an extrapolated line 

goes below a probability of zero. 

0 2000 4000 6000 8000 10000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Load Category (MWh)

Probability of operating

Middletown

Figure 9: Extrapolating run-time probability for a Middletown 

(RMR) unit. The EGU always runs at the highest loads (>7000 

MW), and therefore maintains this behavior if even higher 

loads are demanded.
Reducing NOx Emissions ... ▪ 27

When a unit is actually in operation, 

it may follow a probability of 

generating some amount of 

electricity with a pattern similar to 

that seen in Figure 10. But what 

happens to its expected generation 

as loads increase beyond those 

seen in the base year? Do the 

generators all operate all the time at 

peak capacity? In some cases, it is 

likely that this will be the case, but in 

others, even at peak loads, the plant 

may have sometimes operated in 

spinning reserve. When this is the 

case, we must extrapolate out the 

PDF functions of generation in 

each load category. We do so by 

comparing the probability that a 

unit will generate at each 

probability and extrapolating this 

pattern up and down into new 

load categories (see Figure 11). In only a few cases does the plant have a probability of operating 

in multiple modes at higher load categories, and most of the plants cease operation at load 

categories below the base. Once 

these statistics are gathered, the 

Monte Carlo approach can be run at 

higher and lower loads than are 

otherwise available in the reference 

year. 

Retiring and adding generators 

The process of retiring a generator is 

relatively simple. When a generator 

is taken offline, the units remaining 

must make up the difference that this 

unit otherwise would have generated. 

The demanded load plus the 

generation which is no longer offered 

by the retired unit becomes the 

perceived load. The perceived load is 

the load which all operating units 

“see”. When a 150 MW unit is retired, 

all other units must make up this 

difference. An example serves for illustrative purposes: in year one at a given hour, the systemwide demand is 5500 MW, which will be served by many of the generators, including unit R, 

which will be retired in the next year. Forty-five out of 60 generators respond to the load and 

0 2000 4000 6000 8000 10000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Load Category (MWh)

Probability of operating

Cos Cob

Figure 11: Extrapolation of generation behavior in Bridgeport 

Harbor Station for higher and lower load categories. 

Probabilities are extrapolated out by load category.

Figure 10: Extrapolating run-time probability for a Cos Cob 

(peaker) station. At low loads, the unit never runs (zero 

probability of operation); at high loads, the unit becomes 

increasingly likely to run. This high probability is extrapolated 

up to loads not seen in the 2005 dataset.
Reducing NOx Emissions ... ▪ 28

together generate about 2200 MW. The following year, unit R is retired, and when 5500 MW are 

demanded, 45 of 60 generators respond. However, together these units still need to make up for 

the generation which has been lost with unit R. Together, they perceive a higher load than is 

actually demanded and thus are more likely to be dispatched at a higher generation level to 

answer that load. In fact, the load that they perceive is the total system demand plus whatever 

unit R would have otherwise generated. In this extension of the Monte Carlo simulation, first we 

run unit R to determine how much it could have generated when demand was 5500 MW. We find 

that it would could generated 100 MW (based on a random draw), and add this to the 5500 MW 

system-wide load. The remaining 45 active generators (not including R) now perceive a load of 

5600 MW and respond accordingly, generating 2200 MW. 

To add a generator, we copy the statistics from an existing generator, and add it into the cohort of 

plants. Similarly to the process of removing a generator, by adding a generator we reduce the 

perceived load of all other plants. Again, the new generator is run independently to determine 

how much electricity it could have generated, and then this generation is subtracted from the 

system-wide load to yield the perceived load. In this way, a new power plant does not change the 

relationship between system-wide load and expected system-wide generation; it only changes the 

dispatch order of the plants responding to this load.

Changing Emissions Profiles 

To simulate the effect of RMR units adopting increasingly rigorous air quality (NOx) controls, we 

manually set reduction by 30% in 2012 and 50% (total) in 2015 for the RMR units. The Monte 

Carlo analysis is still run in completion, but the RMR unit emissions are simply multiplied by 70% 

or 50% to simulate the effect of sprayed water controls and later SCM-type controls in 2012 and 

2015, respectively. The reductions are applied to: 

• Norwalk Harbor Station 1, 2 

• Middletown 2, 3, & 4 

• Montville 5 and 6 

• New Haven Harbor NHB1 

• Bridgeport Harbor 2 

Simulated controls were only applied to RMR units, which have a disproportionately high NOx

emissions rate relative to other units in CT. Additional emissions controls were not considered for 

combustion turbines. 

Reporting Results 

Results are reported as statistics on unit operations, as well as in tons of emissions (NOX and 

SOX) during the HEDD period, and pounds of emissions per MWh for every load category. 
Reducing NOx Emissions ... ▪ 29

5. Results 

A. EGU operations and RMR statistics 

In 2005, there were a wide variety of plant operational types in CT, including peakers, 

intermediate units, baseload units, and EGUs which did not operate at all, but were reporting to 

the EPA Clean Air Markets Database (CAMD). The operational characteristics are well matched 

by capacity factor, as seen in Figure 5. As explained in the methods section, this figure shows the 

probability that any given EGU was dispatched when a particular load was called. One of the 

interesting features of this figure is that the RMR units appear to have been dispatched in 2005 

as intermediate units. With the exception of one Norwalk Harbor EGU which did not operate in 

2005, the remainder of the RMR units (Montville, Middletown, New Haven Harbor, and Norwalk 

Harbor) all ran at even very low loads. Most of these units had a 20-30% probability of running at 

loads under 5000 MW, well under peaking conditions (>6500 to 7000 MW). While this statistic 

yields no information on the congestion, reliability, or pricing conditions under which these units 

operated, it is interesting to note that these units ran frequently under non-peaking conditions and 

aside from two Montville and one Middletown unit, ran almost without exception when loads 

exceeded 5000 MW. 

B. Ozone season load and generation 

Forecasted loads rose according to the extrapolation of the ISO-NE forecast. In the baseline load 

growth scenario, ozone season load rose from just over 15 TWh in 2005 to 17 TWh in 2020, 

rising at over 1.3% in 2010, but 

slowing to just over 0.5% in 2020 

(see Figure 12). In the 2% EE 

scenario, load is reduced by 1% 

in 2008, and 2% (compounded) 

starting in 2009. The departure 

from the baseline scenario can 

be seen immediately in 2008, as 

growth slows to zero and then 

begins dropping to 13.5 TWh in 

2020. The more ambitious 3% 

scenario drops to 12.3 TWh in 

2020. 

Fossil generation on high 

electricity demand days (HEDD) 

follows a similar pattern, rising in 

step with demand. Figure 13 

shows expected average 

generation on the twelve HEDD 

days (cumulative over a 24 period). The error bars indicate the potential spread (1st standard 

deviation) of generation which could be called to answer the load. The standard deviations are 

derived from the Monte Carlo model, in which there a large number of probable ways for each 

EGU to respond to load. Averaging all of the possibilities yields the mean probable behavior, but 

CT Ozone Season Demand

0

2

4

6

8

10

12

14

16

18

20

2005 2010 2015 2020

Year

Demand (TWh)

Baseline

2% EE in 2009

3% EE in 2012

Figure 12: Ozone season generation in CT in terawatt 

hours. 
Reducing NOx Emissions ... ▪ 30

the spread of options is captured in the standard deviation. 

C. Emissions behavior in scenarios 

Core results are available in the 

comparison between load and 

generation, and load and 

expected emissions (see Figure 

19 through Figure 22 in the 

Appendix). In the baseline 

scenario (Scenario 1, Figure 16) 

as load demand increases, fossil 

generation rises; the slope of this 

line becomes steeper towards 

higher loads as CT has fewer 

options for importing electricity 

and generates more in-state. In 

the baseline scenario, higher 

loads require more generation 

along a similar slope. This model 

inserts new, natural gas power 

plants to make up the generation 

deficit. In the right-hand graph, 

as load increases, the slope of 

the emissions line becomes 

increasingly steep after 5000 MW as RMR units are dispatched. Over the years until 2020, clean 

new generators are built and dispatched as intermediate units. Because of the collectively lower 

emissions, the system can handle higher loads with lower emissions. Therefore, we would expect 

that although loads continue to increase, emissions do not increase markedly. 

In Appendix Figure 19 (Scenario 2), energy efficiency reduces the number of hours in which peak 

loads (and thus peak emissions) are experienced and thus overall emissions drop. Fewer hours 

are spent above 5000 MW in the steep part of the emissions curve implying, in this case, that the 

RMR units operate less frequently and thus overall emissions are reduced. 

In Appendix Figure 21 (Scenario 3), RMR units adopt cleaner emissions technologies in 2012 

(reducing emissions by 30%) and 2015 (reducing emissions by 50% from 2005 levels). Therefore, 

even though load rises towards 2020 (as in the baseline scenario), the emissions slope past 5000 

MW becomes significantly less steep, and emissions during peak periods are lower than in 2005. 

While in 2005, peak emissions could exceed 8000 lbs NOx (4 tons) per hour, emissions controls 

in the RMR units (as well as dilution with new clean plants) reduce peak emissions to about 5000 

lbs NOx (2.5 tons) per hour. 

Appendix Figure 22 (Scenario 4) indicates what occurs if both energy efficiency and RMR 

reductions occur simultaneously. No new generators come online in this scenario because load 

falls, and thus all reductions are due to efficiency and RMR reductions alone. In this case, the 

steep part of the emissions curve falls due to implementation of RMR control technologies, and 

CT HEDD Average Generation

0

20,000

40,000

60,000

80,000

100,000

2005 2010 2015 2020

Year

Generation (MWh)

Baseline

2% EE

3% EE

Figure 13: CT HEDD average daily generation in megawatt 

hours. Average expected generation in solid lines; error bars 

indicate 1st standard deviation of generation according to 

Monte Carlo model.
Reducing NOx Emissions ... ▪ 31

less time is spent in the steep portion of the curve because of energy efficiency. 

D. Meeting the OTC MOU Commitment 

Figure 14 below illustrates the reference case which assumes that as Connecticut’s electricity 

demand grows, new generation is installed with emissions characteristics equal to those 

associated with natural gas fired turbines (i.e. like the Milford power station). The reference case 

shows that NOx emissions on the peak and average days are essentially flat after 2011, showing 

neither significant increases or decreases. Peak day emissions remain well above that 

anticipated to be met via the OTC MOU. 

Figure 15 below shows the effects of a 2% energy efficiency program in Connecticut’s on HEDD 

and average ozone season NOx emissions. The results in the figure assume that Connecticut’s 

current energy efficiency program, which is achieving energy savings levels equivalent to about 

1% of total annual electrical sales will ramp up to achieve savings levels equivalent to 2% of 

annual electrical sales starting from 2009. This level of savings is consistent with the October 

2007 plan submitted by the Connecticut Energy Conservation Management Board to the DPUC. 

Baseline (Reference Case)

0

5

10

15

20

25

30

35

40

45

50

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

Tons of Nox per day

Average Tons of NOX per day, HEDD

Average Tons of NOX per day, Ozone Season

Target

Figure 14: Scenario 1: Baseline reference case average NOx emissions on HEDD (orange) and 

during the ozone season (green). Target line represents OTC MOU for HEDD days.
Reducing NOx Emissions ... ▪ 32

Increasing the level of savings from energy efficiency reduces both peak and average ozone 

season NOx emissions such that the level of the OTC reduction commitment is reached in 2017. 

Effect of Strategies on High Electricity Demand Day (HEDD) Emissions 

Results from scenarios 1 and 2 indicate that the DEP’s goal to achieve the OTC MOU reduction 

commitment by 2009 cannot be met through a business as usual approach (even when all new 

generation is as clean as possible) or through a program that relies solely on existing levels of 

energy efficiency. Figure 16 shows results from several different scenarios that were evaluated 

to determine their ability to reduce NOx emissions during peak electric demand periods. 

Five scenarios are included in Figure 16: 

1. the reference case, reflecting business as usual with growth in electric demand met 

through the installation of new natural gas fired turbines; 

2. an assumption that the RMR units reduce NOx emissions by 30% starting in 2012 

and by a total of 50% by 2015; 

3. increasing the trajectory of savings achieved from Connecticut’s energy efficiency 

program from 1% to 2%, as contained in the ECMB 2007 filing to the DPUC 

4. a combination of RMR units reducing their NOx emissions 25% and Connecticut’s EE 

Scenario 2: 2% EE in 2009

0

5

10

15

20

25

30

35

40

45

50

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

Tons of Nox per day

Average Tons of NOX per day, HEDD

Average Tons of NOX per day, Ozone Season

Target

Figure 15: Scenario 2: Energy efficiency case average NOx emissions on HEDD (orange) and during 

the ozone season (green). Target line represents OTC MOU for HEDD days.
Reducing NOx Emissions ... ▪ 33

program achieving 2% savings as a percent of sales; and 

5. Connecticut’s EE program achieving 3% savings as a percent of sales 

Scenario 1 was discussed earlier. The reference case meets Connecticut’s electricity demand, 

but does not result in any NOx emissions decrease. 

Reducing emissions from RMR units by 30% in 2012, as shown in Scenario 2, decreases peak 

day NOx emissions by 2012, but not enough to meet the commitment of the OTC MOU, and after 

2012, statewide emissions decrease no further. 

Scenario 3 was also discussed earlier. Connecticut’s existing energy efficiency program does 

reduce peak ozone emissions such that the OTC commitment can be met, but in the year 2017, 

rather than 2009. Peak NOx emissions continue to decline at a relatively shallow rate after this 

year. 

Scenario 4, the combination of reducing RMR unit NOx emissions by 30% in 2012 as well as a 

2% savings from EE has the effect of meeting the OTC commitment in 2012. NOx emissions 

continue to decline in subsequent years. 

Scenario 5, a scenario where Connecticut’s energy efficiency program achieves savings equal to 

3% of annual electricity sales produces about the same effect as Case 4, with the OTC 

commitment met in 2012, and a continued decline in peak NOx emissions in subsequent years. 

Figure 16: NOx emissions during HEDD in each of the five scenarios. The target line represents OTC 

MOU for HEDD days. The combination of RMR unit reductions and efficiency, as well as aggressive 

efficiency, are the most effective scenarios for meeting future targets by 2020.

HEDD Average NOx emissions

in Connecticut 

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

2005 2007 2009 2011 2013 2015 2017 2019

Year

NOx emissions (tons per day)

Baseline NOx emissions

RMRs reduce emissions

2% EE in 2009

RMRs reduce emissions & 2% EE

3% EE in 2009

Target
Reducing NOx Emissions ... ▪ 34

Effect of Strategies on Average Ozone Season Day Emissions 

The previous section discussed the effects of various strategies that could reduce NOx 

emissions, and their benefits on peak ozone season days. Figure 17 below shows the results of 

the same strategies when evaluated for their benefits across the entire ozone season. 

Applying the same five cases as were evaluated for their benefits on the peak ozone season day, 

the same approximate benefits also occur across the entire ozone season, with one difference. 

Observing only HEDD days, emissions from Case 2 (reducing RMR emissions 30% to 50%) 

mostly remain above the levels of Case 3 (2% energy efficiency), however, in the average ozone 

season day, Case 2 emissions decrease initially to lower than the levels from Case 3 in 2012 and 

remain essentially flat through 2020. Case 3 NOx emissions continue to decline, reflecting the 

cumulative benefits of energy efficiency, and drop to below the levels of Case 2 after 2018. 

E. Post-2009 Emission Reduction Strategies 

The US EPA recently promulgated an eight-hour ozone standard at 75 parts per billion, an 

effective reduction of 9 ppb from the previous 0.08 parts per million level17. Connecticut’s 

commitment through the March 2007 OTC MOU addresses emissions reductions that are needed 

to comply with the previous 0.08 ppm ozone standard. Additional emissions reductions will be 

 

17 March 27, 2008, Federal Register, vol 73, no 60, pp 16436-16513. Previous ozone standard of 0.08 ppm 

effectively permitted ozone concentrations of up to 84 ppb due to rounding and number of significant figures. 

Figure 17: Average daily NOx emissions during the ozone season in each of the five scenarios.

Ozone Season Average NOx emissions

in Connecticut 

0.00

5.00

10.00

15.00

20.00

25.00

2005 2007 2009 2011 2013 2015 2017 2019

Year

NOx emissions (tons per day)

Baseline NOx emissions

RMRs reduce emissions

2% EE in 2009

RMRs reduce emissions & 2% EE

3% EE in 2009
Reducing NOx Emissions ... ▪ 35

needed to comply with the new 75 ppb ozone standard. Per Section 110(a)(1) of the Clean Air 

Act, states are required to submit plans within three years of the date that new air quality 

standards are promulgated. The plans are due to EPA by March 12, 2011, and must describe 

measures that the state will implement to ensure compliance with the air quality standard. 

The same five cases analyzed for their ability to meet the OTC commitment were also evaluated 

to assess the degree to which these same policies could also be effective in helping to meet the 

new ozone standard. Figure 14 through Figure 16 show a dotted line going from left to right, with 

a downward step in the year 2012. The timing of this decrease and the level approximate what 

the DEP believes will be necessary to achieve from this sector in order to demonstrate progress 

to meet the 75 ppb ozone standard. This second step is equivalent to applying BACT level 

emissions controls to the RMR units, effective in 2012. 

Using the policies evaluated as shown in Figure 16, only two of the scenarios reach the 2012 to 

2020 target and none reach the more immediate 2009-2012 target. Scenario 1, business as 

usual, achieves neither of the two emissions reductions goals. NOx emissions continue at the 

same level for the entire period evaluated. Reducing emissions from RMR units in a two-step 

process, as shown in Scenario 2, by 30% in 2012 and by a total of 50% by 2015, also does not 

meet the post-2009 emission reduction requirement. Existing levels of energy efficiency (Scenario 

3) reflect a trend of decreasing NOx emissions over time, but the needed reductions do not occur 

by 2020 using this policy measure by itself. Scenario 4, the combination of reducing emissions 

from RMR in a two-step process plus continued energy efficiency savings equal to 2% of annual 

electricity sales, reduces NOx emissions sufficiently to nearly achieve the lower emissions goal 

by 2020. Scenario 5, energy efficiency program savings equal to 3% of annual electricity sales, 

has the same approximate trajectory as emissions from Case 4, and also nearly achieves the 

lower NOx emissions goal by 2020. 
Reducing NOx Emissions ... ▪ 36

6. Recommendations 

Policies to reduce emissions from the electric sector should consider the benefits and impacts 

from environmental, energy and economic perspectives. Evaluating these effects is especially 

important in a restructured electric market, where any costs incurred to meet standards will be 

built into the hourly market clearing price and passed along to all ratepayers. Additional costs can 

also have the effect of increasing the profits of generators whom are unaffected by the 

environmental decisions made. Connecticut’s base loaded nuclear and coal plants will see 

additional revenue from any policies that result in increasing the marginal price of electricity. Any 

differential increases in hourly electricity prices also increases the likelihood for electricity to be 

imported into Connecticut from other New England states, from NY, and wheeled from PJM 

through NY into Connecticut. 

These additional potential economic impacts can be balanced with the economic costs to 

Connecticut ratepayers from the continued operation of the RMR units. The recent Integrated 

Resource Plan for Connecticut, completed pursuant to Section 51 of Public Act 07-242, reported 

that the annual fixed operating and maintenance costs for Connecticut’s RMR units is greater 

than $140 million per year18. 

The recommendations in this section reflect upon the complex intersection of environmental, 

energy and economic elements, each of which is important alone, but which also has direct and 

indirect effects to the other elements: 

• Connecticut’s ozone attainment status precludes construction of new large central 

electricity generating plants unless older plants shut down or otherwise enforceably limit 

their operation; 

• Connecticut’s energy efficiency program is ranked in the top of all such programs 

nationally. Implementation of section 51 of Public Act 07-242 would substantially increase 

the amount of EE savings, potentially to levels even higher than those evaluated as part 

of this report; 

• Funding of energy efficiency programs needs to be sustained over the long-term (i.e. 

decades) to ensure that program savings accumulate and that all cost-effective measures 

are implemented. While the state has plans to implement all cost-effective energy 

efficiency, funding of these programs at a level needed to procure all such resources is 

beyond the control of the DEP; 

• Past EE funding swings create uncertainty as to the ability of Connecticut to achieve its 

full potential; and 

• Requiring emissions controls to be installed on EGUs will enable NOx reductions to be 

achieved and directly measured, but the costs of these controls will likely be passed 

along to ratepayers, who already are paying some of the highest electricity rates in the 

United States 

 

18 Integrated Resource Plan for Connecticut; January 1, 2008; The Brattle Group (at appendix A, page A-6) 
Reducing NOx Emissions ... ▪ 37

Recommendation 1: Implement a combination of increased energy efficiency and 

reductions from units that contribute to peak day NOx emissions 

Case 4 evaluated a scenario where energy efficiency programs achieved a level of 2% savings 

as a percent of annual electricity sales, and where emissions from RMR units were reduced in 

two phases: by 30% in 2012, and by a total of 50% by 2015. Energy efficiency programs are 

currently avoiding about 50 MW of demand each year, and this amount accumulates. This 

amount of EE alone is insufficient to achieve the level of savings needed to offset all demand 

growth. Additional EE savings will likely be identified by October 2008, per the required updated 

maximum achievable potential study that is to be completed by Public Act 07-242. Funding for 

such increased level of savings is uncertain. This is discussed further in a later section related to 

Recommendation 2 

Reducing NOx emissions from RMR units can be achieved via: 

• Installing controls to directly reduce emissions; 

• Reducing the number of hours that the units are needed; and/or 

• Reducing the number of RMR units that are running 

For the RMR reductions, installing emissions controls or reducing the number of RMR units that 

are operating will ensure that the anticipated level of NOx reductions from this sector is achieved. 

Reducing the number of operating hours could also be made to be enforceable through permit 

restrictions and other administrative means, but this might also result in increased regulatory 

burdens, along with additional permit fees to process the revisions, for affected sources, and 

additional permitting resources for the DEP. 

Recommendation 2: Ensure sustained funding for EE programs 

Connecticut’s energy efficiency program is nationally recognized19 and has the potential to 

achieve substantially greater savings. Energy efficiency programs are funded via a 3 mil 

surcharge on Connecticut ratepayers. Funds collected are adminstered by the Energy 

Conservation Management Board (ECMB), which reviews and comments upon plans submitted 

by the two main distribution companies, CL&P and UI and those who service a much smaller 

municipal service territory. The level of funding needed to achieve the 3% level suggested to 

reduce NOx emissions will likely exceed the amount administered by the ECMB , even with the 

additional RGGI revenue. While the state has plans to implement all cost-effective energy 

efficiency, funding of these programs at a level needed to procure all such resources is beyond 

the control of the DEP. Higher savings levels would require the following elements to be met: 

• Revenue from the auction of RGGI allowances directed to the ECMB and re-invested in 

additional EE measures; 

• Private sector interest in the business case for EE to direct investments that could 

achieve additional savings without requiring public funding; 

• Innovative financing schemes that would use a mix of public and private funding. Public 

funding could buy down interest rates, serve as first loss to permit a focus on sectors that 

 

19 ACEEE Scorecard of State Energy Efficiency Programs, 2007 (for the year 2006). CT tied for first with Vermont 

and California. 
Reducing NOx Emissions ... ▪ 38

have higher risks, such as low-income and small businesses, and help to guarantee or 

insure that the anticipated savings persists over time. Private capital would be directed 

towards the measures themselves. 

• Continued updating of building codes and appliance standards, and enforcement of them, 

to maintain and increase the baseline level of savings that can occur without requiring 

incentives 

Recommendation 3: Increase transparency of generation data 

Since ISO-NE began demand response programs in 2000, the RTO has maintained that 

operation of inefficient generating units has environmental as well as energy benefits. 

Conclusively determining the degree of benefit, or not, has been hampered by an inability to 

obtain data from ISO-NE related to generating units that are called upon to operate during peak 

electric demand periods. The RTO has claimed confidentiality concerns, but the same type of 

information is publicly available from the NY ISO. Increasing data transparency would improve 

trust between the environmental agencies and ISO-NE, and would help to ensure that decisions 

considered by these respective parties took into account potential impacts and avoided 

unintended consequences. The following information would be beneficial to environmental 

agencies: 

• Event type, date and hours (already available through ISO’s semi-annual filing to FERC 

on demand response programs) 

• Generator name, location and amount of load provided or load shed (not available and 

claimed confidential) 

The lack of transparency means that air regulatory agencies may again be designing control 

programs that do not include all affected sources. 

B. Next Steps: Enforceability of Recommended Policy 

Measures 

Connecticut’s state implementation plan submittal to EPA will require a demonstration that any of 

the policies adopted are enforceable, and that the anticipated emissions reductions are 

permanent. Traditional regulatory programs have relatively straight forward enforceability, since 

the emissions controls and their effectiveness, can be directly and precisely measured at the 

stack. Demand side programs have equally precise methods to account for their benefits. 

Determining their effectiveness however requires attention and coordination with the DPUC and 

ECMB, rather than directly at the smokestack. 

Bundled EE Measures 

For energy efficiency, the establishment of the ISO-NE forward capacity market creates a vehicle 

by which the benefits of demand side measures can be reported, measured and verified using 

internationally accepted protocols. The bundle of existing EE measures achieved to date has 

already been submitted and qualified by ISO-NE as a resource in their capacity market. To 

assure continued performance, and that the benefits of EE persist, Connecticut should continue 

to participate in the FCM, and also enroll all new qualified resources to ensure continued 

monitoring and verification of the program’s benefits. 
Reducing NOx Emissions ... ▪ 39

The emissions avoided from energy efficiency measures can be calculated from the ISO-NE 

Marginal Emissions Analysis report (published annually). Avoided NOx emissions would be the 

product of the number of MW of qualified energy efficiency measures and the Connecticut 

specific marginal emissions rate. Measure life, or persistence, also needs to be factored into the 

calculation. The benefits of energy efficiency assume a certain measure life, and that these 

benefits persist at or close to the same level of performance for their entire life. The DEP, through 

its statutory role on the ECMB, continue to coordinate closely in the planning and implementation 

of the state’s EE programs to ascertain and confirm assumptions about the types of EE measures 

that are included in the bundle of resources that qualify for the ISO FCM. For example, a series of 

lighting measures may have an expected performance of 7-8 years, while those for motors, 

chillers and other equipment may have an expected life of 15 years or more. Alternatively, all 

measures may be combined into one bundle that uses an expected life of 10 years. 

The second important factor to accurately account for energy efficiency measures is to recognize 

their cumulative nature. Consider the following example to illustrate this characteristic. In year 

one, a bundle of 50 MW qualifies for a resource in the FCM. In year two, another 50 MW bundle 

of EE qualifies. Year two’s measures are in addition to those from year one, so the total benefits 

at the end of year two are 100 MW, and the avoided emissions are calculated on this basis. In 

year three, another 50 MW qualifies, making the avoided emissions benefit equal to 150 MW and 

so forth, until the end of the expected measure life. 

ECMB Measures Not Submitted to the FCM 

Implementation of the “all cost-effective energy efficiency procurement” requirement of PA 07-242 

could result in a doubling or more of energy efficiency savings as a percent of electricity sales. 

Not all of these measures may be submitted for qualification in the ISO FCM. The ECMB program 

also includes a programs that are directed at reducing natural gas consumption. Quantifying the 

electric benefits of these additional programs can be calculated using the same methodology as 

is used for those which qualify for the FCM. Natural gas efficiency program benefits can also be 

determined, but methodology is less consistent that that now used for the FCM, as highlighted in 

the January 2006 NEEP report on common protocols to measure the benefits of energy efficiency 

programs20. Connecticut has recently begun a program to improve end-use efficiency for oil 

consumption (also required by PA 07-242). The oil program and the ECMB are supposed to 

collaborate on policy measures that could complement both programs, and work towards 

achieving an “all fuels” or “fuel blind” approach to energy efficiency. Since the oil program has just 

 

20 International Performance and Measurement Verification Protocols. See also 

http://www.neep.org/files/Protocols_report.pdf The Need for and Approaches to Developing Common 

Protocols to Measure, Verify and Report 

Energy Efficiency Savings in the Northeast Final Report ,January 2006 , Northeast Energy Efficiency 

Partnerships, Inc. 

 
Reducing NOx Emissions ... ▪ 40

begun, its programs and potential benefits are not yet known, but this could also be an important 

component to reducing air emissions in future years. 

Building Codes and Standards, Appliance Standards 

All energy efficiency programs will not be operated or administered through the ECMB, or 

overseen by the DPUC. Providing incentives to either reduce differential costs, or to help pay for 

the installation of more efficient appliances and measures is only part of a comprehensive energy 

efficiency program. Adopting and revising building codes and standards, and updating appliance 

standards substantially improve energy performance while requiring minimal levels of incentives. 

Enforcing these codes and standards is equally important, as is training for building operators and 

staff to ensure that the efficient level of performance is sustained. Building life is also decades 

longer than that of appliances or other EE measures, so having the most efficient buildings 

possible helps to assure that substantial energy performance occurs and is maintained. 

Calculating energy use from buildings is facilitated by use of the EPA Energy Star tool called 

Portfolio Manager21. This tool is used to benchmark building performance, based on existing or 

designed electricity demand. Performance after commissioning or on a year to year basis is 

calculated using the same tool, and results can be compared to determine how the building’s 

energy use has changed. 

Public-Private Partnerships 

An emerging area that appears to offer promise to substantially increase the level of energy 

efficiency savings is one involving private capital and/or public-private partnerships. Private 

capital has been invested in energy efficiency measures for several years in Central and Eastern 

Europe, where it has been used to improve industrial and process efficiency. The decrease in 

energy demand is calculated and those benefits have been quantified, and then monetized, after 

qualifying under the Clean Development Mechanism (CDM), implemented as part of the Kyoto 

Protocol22. While the US is not yet a signatory to the Kyoto Protocol, the idea of private sector, or 

public-private sector, capital investing in American energy efficiency projects has gained a 

foothold. Pennsylvania Governor Rendell launched an Energy Independence Fund initiative in 

March 2007. This is a combined public benefits fund, like that of the CT ECMB, that would also 

provide access to venture capital and for clean energy economic development23. Implementing 

legislation has not yet been passed. Connecticut could take advantage of venture capital offered 

through the Connecticut Development Authority, whose mission focuses on local economic 

development and growth. Since this is a still emerging area, enforcing and committing to a plan 

that would include private sector resources is premature. But, Connecticut’s electricity costs, at 

20c/kWh retail, coupled with underlying market dynamics that favor continued high fuel prices, 

should encourage efforts to implement substantial energy efficiency programs. As shown in 

Figure 19, energy efficiency programs are achieving savings levels at costs of 2 - 5 c/kWh 24, with 

 

21 See https://www.energystar.gov/istar/pmpam/ 22 The FE Clean Energy Group, based in Darien, CT, was formed in 2000 to provide capital for energy efficiency 

projects in former Soviet bloc countries in Eastern Europe. It has since branched out to Mexico and Asia. 

http://www.fecleanenergy.com 23 http://www.depweb.state.pa.us/energindependent/site/default.asp 24 Synapse Energy Economics. CERES Conference April 2008. “Prudent Planning and New Coal Fired Power 

Generation”. Bruce Biewald. 
Reducing NOx Emissions ... ▪ 41

an average of approximately 3 c/kWh. The costs of energy efficiency savings compare favorably 

to the cost of new generation, which has been at 8 - 11c/kWh 25. 

The table below describes what steps DEP will need to make in order to ensure that the energy 

efficiency measures perform and persist as anticipated. 

Figure 18: Utility costs of saved energy (over expected efficiency measure lifetime) versus 

incremental savings (MWh) as a percentage of annual sales (MWh). Data as publicly available in 

2007. 

 

25 The Keystone Center, Nuclear Power Joint Fact Finding, June 2007 (for nuclear) and “Paying to Warm the 

Globe: Climate Crises and Opportunities”, Presentation to Chicago Area Mensa, January 19, 2008, Sean Casten 

(for coal and natural gas) 
Reducing NOx Emissions ... ▪ 42

 

Description 

Responsible 

Agency(ies) 

How to 

Calculate EE 

Benefits 

Measurement 

Protocols Comment 

EE bundled into FCM 

ECMB- program 

administration; ISO-NEqualifying resources; CT 

DPUC- program review 

and approval 

ISO Marginal 

Emissions Analysis 

(published annually); 

Avoided emissions 

data based on EPA 

ETS 

ISO requires 

protocols consistent 

with those from 

IPMVP26

Coordinate with 

ECMB to 

determine 

measure life of 

qualified bundle 

of measures 

EE not part of FCM 

ECMB-program 

adminstration, CT 

DPUC-program review 

and approval 

ISO Marginal 

Emissions Analysis; 

avoided emissions 

data IPMVP 

Coordinate with 

ECMB on 

measure life 

Natural Gas efficiency 

programs 

ECMB-program 

administration; DPUCprogram review and 

approval 

EPA avoided 

emissions data 

Requires 

collaboration with 

ECMB and NEEP 

Coordinate with 

ECMB on 

measure life 

Building Codes and 

Standards, Appliance 

Standards 

Office of Policy and 

Managementregulations; ECMBtraining programs for 

building operators 

EnergyStar Portfolio 

Manager (for retrocommissioning and 

modifications to 

existing buildings); 

ISO marginal 

emissions and EPA 

avoided emissions 

data 

Building inspectors 

and code enforcers; 

manufacturer’s 

certification 

Requires 

coordination 

with Federal 

standards, to 

ensure 

reductions are 

not already 

included in EPA 

baseline. May 

only get to 

account for 

codes and 

standards that 

are more 

stringent than 

their Federal 

counterparts 

Public-Private 

Partnerships 

Emerging area, 

potentially: Connecticut 

Development Authority, 

Treasurer and other 

state agencies working 

with banks, insurers, 

venture capital firms 

For electric savings, 

same as above 

Use IPMPV 

protocols, including 

those used to qualify 

CDM projects 

Requires 

coordination 

between state 

agencies, may 

require 

regulations or 

changes in 

policies 

7. Conclusion 

Connecticut DEP can meet the OTC MOU commitment to reduce NOx emissions through a 

combination of reducing emissions from the RMR units and continuing to have sustained 

performance from the state’s energy efficiency programs. Achieving the second phase, with NOx 

emissions decreasing a total of 50% from 2005 levels, will require additional reductions from the 

RMR units and ramping up energy efficiency programs to levels higher than 2008 in order to 

achieve these levels by 2020. 

 

26 International Performance and Measurement Verification Protocols. See also NEEP January 

2006 report accessed via http://www.neep.org/files/Protocols_report.pdf
▪ 43

8. Appendix: Figures A. Load vs. Ozone Season NOx Emissions Figure 19: Scenario 1: Baseline. Load versus average fossil generation (A, left) and ozone season NOx emissions (B, right) for 

(red line). (A) As load demand increases, fossil generation rises; the slope of this line becomes steeper towards higher loads afor importing electricity and generates more in-state. In the baseline scenario, higher loads require more generation along a sim

inserts new, natural gas power plants to make up the generation deficit. (B) As load increases over time, new (clean) generatoranswer the load, and because of the collectively lower emissions, the system can handle higher loads with lower emissions. 

2000 3000 4000 5000 6000 7000 8000

500

100015002000250030003500400045005000

Load (MWh)

Fossil generation (MWh)

2000 3000 4000 5000 

0

10002000300040005000600070008000

Load (MWh)

Ozone NOX Emissions (lbs)
▪ 44

Figure 20: Scenario 2: Energy efficiency at 2% after 2009. (A) Load vs. fossil generation. Generation falls with load as less ener

shape of the load vs. generation curve remains the same. (B) Load vs. ozone season NOx emissions. The shape of the curve re(fundamental dynamics of the system remain intact), but less time is spent at higher loads and thus higher emissions. 

2000 3000 4000 5000 6000 7000 8000

500

10001500200025003000350040004500

Load (MWh)

Fossil generation (MWh)

2000 3000 4000 5000 6

0

10002000300040005000600070008000

Load (MWh)

Ozone NOX Emissions (lbs)
▪ 45

Figure 21: Scenario 3: RMR units reduce emissions. (A) Load vs. generation increases in step with load requirements, similarly 

scenario. (B) Load vs. ozone season emissions. As load increases, the RMR units apply control technologies to reduce source 

clean natural gas plants are brought online to answer load. New plants are dispatched simultaneously with pre-existing generadisplace RMR units. The emissions curve is displaced down and to the right. 

2000 3000 4000 5000 6000 7000 8000

500

100015002000250030003500400045005000

Load (MWh)

Fossil generation (MWh)

2000 3000 4000 5000 

0

10002000300040005000600070008000

Load (MWh)

Ozone NOX Emissions (lbs)
▪ 46

Figure 22: Scenario 4: Energy efficiency (2%) and RMR reductions. (A) Load reductions are identical to Scenario 2 (2% EE). (B) 

technologies in 2012 and 2015, reducing source emissions. In addition, energy efficiency reduces peak demand and peak emisof a lower slope and fewer hours at high loads results in a lower emissions profile. 

2000 3000 4000 5000 6000 7000 8000

500

10001500200025003000350040004500

Load (MWh)

Fossil generation (MWh)

2000 3000 4000 500

0

10002000300040005000600070008000

Load (M

Ozone NOX Emissions (lbs)