Center for Energy, Economic &

Environmental Policy

Rutgers, The State University of New Jersey

33 Livingston Avenue, First Floor

New Brunswick, NJ 08901

http://ceeep.rutgers.edu/

732-789-2750

Fax: 732-932-0394

Working Paper #2

Do Combined Heat and Power Plants Perform?

Case Study of Publicly Funded Projects in New

York

Rasika Athawale, Frank A. Felder, and Leo A. Goldman

November 2015

2

Do Combined Heat and Power Plants Perform? Case Study

of Publicly Funded Projects in New York

Rasika Athawale,1 Frank A. Felder,

2 and Leo A. Goldman

3

November 30, 2015

Abstract

We investigate lower than expected capacity factors of combined heat and power plants using a

publicly available dataset of hourly performance for plants in the state of New York. Low

utilization of a CHP indicates underperformance. We examine possible causes of this

underperformance including economic arbitrage, poor maintenance and operational practices,

oversizing of plants, and reliability and resiliency needs. Based on seasonal and

weekday/weekend capacity factor averages, we find that there is not enough evidence to support

the economic arbitrage cause. Out of 99 plants in the dataset, 64 plants have average capacity

factor below 60%, indicating they are either oversized and/or poorly maintained. This suggests

that the current practice of one-time fixed incentive ($/kW) favors investment in capacity with no

incentive for utilization (unlike a production credit which incentivizes generation $/kWh). From

a policy perspective, this paper recommends better pre-engineering assessment for correct sizing,

as well as revision of incentives based on performance. Additional information should be

collected so that a more accurate ongoing analysis of the societal benefits of CHP projects can be

made. Lastly, the energy efficiency gap may be smaller than is commonly assumed and other

options should be explored to meet energy efficiency goals.

Highlights

Low utilization of CHP indicates underperformance.

Possible causes of underperformance are examined, including economic arbitrage, poor

maintenance and operational practices, oversizing of plants, and reliability and resiliency

needs.

We recommend better pre-engineering assessment for correct sizing, as well as revision of

incentives based on performance.

Keywords

Combined Heat and Power, Energy Efficiency Gap, Capacity Factor

1 Center for Energy, Economic & Environmental Policy, Edward J. Bloustein School of Planning and Public Policy,

Rutgers University, 33 Livingston Avenue, New Brunswick, NJ 08901, USA (rasika.athawale@rutgers.edu) 2 Center for Energy, Economic & Environmental Policy (ffelder@rutgers.edu)

3 Center for Energy, Economic & Environmental Policy (leogoldman1@gmail.com)

3

1. Introduction

Benefits of energy efficiency as a building block for achieving emissions reduction goals and its

viability as a ‘low hanging fruit’ are widely accepted. Combined Heat and Power (CHP or

cogeneration), which is defined as sequential or simultaneous generation of multiple forms of

useful energy through an integrated machine (EPA, 2008), is considered a proven energy

efficiency solution for industrial, commercial, and large residential customers. Overall efficiency

of CHP can range between 60% to 80%, much higher than compared to the average efficiency of

33% for fossil-fueled power plants in the U.S.4 A 2012 Executive Order by President Obama

called for setting up 40 GW of new CHP capacity by the year 2022.5 CHP can also help states

comply with their emissions reduction obligations under the EPA’s final Clean Power Plan Rule

announced on August 3, 2015.6

As of December 31, 2014 there were 4,438 CHP projects in the United States, adding up to 82.73

GW of capacity.7 Significant capacities were added during the early 1990s and afterwards during

2001 and 2002 (Figure 1).

Figure 1: U.S. Historical Trend of CHP Capacity Addition

Source: U.S. DOE Combined Heat and Power Installation Database

4 U.S. EPA Combined Heat and Power Partnership, Basic Information

http://www.epa.gov/chp/basic/efficiency.html 5 Executive Order – Accelerating Investment in Industrial Energy Efficiency https://www.whitehouse.gov/the-press-

office/2012/08/30/executive-order-accelerating-investment-industrial-energy-efficiency 6 U.S. EPA Clean Power Plan Final Rule http://www2.epa.gov/cleanpowerplan/clean-power-plan-existing-power-

plants 7 U.S. Department of Energy Combined Heat and Power Installation Database

https://doe.icfwebservices.com/chpdb/

4

Recognizing the benefits of CHP, many states have formulated financial measures (direct

incentives such as grants, rebates; and indirect incentives such as tax relief, low-interest loans,

feed-in tariffs) and policy intervention (output-based emissions regulation, including CHP in

state portfolio standards, standardizing utility interconnection agreements) to promote

investments.8 Twenty-three states recognize CHP as part of their Renewable Portfolio Standards

or Energy Efficiency Resource Standards.9 Per the ACEEE 2014 State Energy Efficiency

Scorecard (ACEEE, 2014), sixteen states rank favorably10

on the parameter of providing

financial incentives to CHP.

New York ranks sixth in CHP scores for the year 2014 (ACEEE, 2014). Whereas the state ranks

well on financial incentives and measures such as including CHP in the RPS, it scores low on

policy interventions such as standardized interconnection procedures and output-based emissions

regulations. New York has a total installed CHP capacity of 5,775 MW (40% installed at

commercial establishments and 60% at industrial establishments) and ranks fourth in terms of

installed capacity (after Texas 17,557 MW; California 8,797 MW; and Louisiana 6,106 MW).11

The New York State Energy Research and Development Authority (NYSERDA) has been

actively incentivizing CHP projects in the state since 2000 and expects CHP to deliver energy,

environmental, and economic benefits such as peak electric demand reduction, higher fuel-use

efficiency, emissions reduction, and lower energy costs. Over the last decade the agency has

spent around $125 million in funding CHP projects in New York.12

The CHP Demonstration

Program was run for eleven years (2000-2011), and additional incentives were provided to

projects under the ‘CHP in the Existing Facilities Program’ (during 2006-2011). New programs

launched in 2012 include both a CHP Acceleration Program,13

which provides incentives for the

installation of pre-qualified, pre-engineered packaged systems up to 1.3 MW, and a CHP

Performance Program,14

which provides incentives to systems larger than 1.3 MW and which

can provide summer on-peak demand reduction.

Investments in creating new CHP facilities (in New York State as well as throughout the U.S.),

however, have fallen short of harnessing what is believed to be CHP’s full achievable potential.

Technical potential for CHP in New York State totals up to an additional 8,500 MW at 26,000

sites (split evenly between upstate and downstate markets), with a market penetration of

8 For more information on state-specific financial and policy interventions for CHP, please see the dCHPP (CHP

Policies and Incentives Database) maintained by the CHP Partnership at EPA. http://www.epa.gov/chp/policies/database.html 9 U.S. Energy Information Administration http://www.eia.gov/todayinenergy/detail.cfm?id=8250

10 For a state to be eligible for 0.5 points, at least one available incentive must (a) apply to all CHP regardless of

fuel; (b) be a production credit, an investment credit, a credit for installed capacity, or a grant; (c) apply to both the commercial and industrial sector. 11

U.S. DOE Combined Heat and Power Installation Database https://doe.icfwebservices.com/chpdb/ 12

http://chp.nyserda.ny.gov 13

http://www.nyserda.ny.gov/PON2568 14

http://www.nyserda.ny.gov/All-Programs/Programs/Combined-Heat-and-Power-Performance-Program

5

additional 764 MW by the year 2012 for projects with a payback period of less than 8 years

(NYSERDA, 2002). In reality, the state has been able to add 580 MW between 2002 to 2014.15

A study conducted by ONSITE SYCOM Energy Corporation for the Department of Energy

(DOE) estimated a country-wide technical potential of additional 74,638 MW of CHP capacity

(potential for each type of application shown in Figure 2) over the existing 4,926 MW of

installed CHP capacity at the commercial/institutional sector (OSEC, 2000).16

This study used

the DOE Energy Information Administration 1995 Commercial Buildings Energy Consumption

Survey to estimate electric and thermal energy requirements for various building types, which

was combined with MarketPlace Database17

to identify potential CHP sites by its Standard

Industrial Classification code. A facility with moderate to high operating hours (>4000 hours per

year) was assumed to be eligible for CHP.

Figure 2: Technical Potential in Commercial/Institutional Sector (2000)

Source: (OSEC, 2000) for the U.S. DOE

A more recent study by the consulting firm ICF International for the American Gas Association

estimated 124.7 GW of additional CHP technical potential (56 GW of industrial installations and

68 GW of commercial/institutional installations) (ICF, 2013). Out of this technical potential only

15

U.S. DOE Combined Heat and Power Installation Database https://doe.icfwebservices.com/chpdb/ 16

Bruce Hedman was the Principal Investigator of this analysis. He later joined ICF and has worked on several similar studies estimating the potential of CHP in the United States. 17

The MarketPlace Database was maintained by iMarket Inc., and was based on the Dun and Bradstreet financial listings. It includes information on economic activity, location, and size of commercial and industrial facilities in the U.S.

6

around 6.4 GW of capacity was estimated to have a payback period of less than 5 years. This

estimate is based on identifying applications where CHP provides a reasonable fit to the electric

and thermal needs, by quantifying the number and size distribution of target applications, and

then estimating CHP potential in MW capacity. This untapped potential analysis is based on

benefits to the private investor (potential in terms of MW was categorized into three payback

categories representing degree of economic potential), but does not consider broader social

benefits. The National Resource Defense Council reports an untapped CHP potential in the US

for a minimum 50 GW to a maximum 200 GW (NRDC, 2013), although the basis for this

estimate is not provided.

Despite numerous claims of technical and economic potential, adoption of CHP technology in

the marketplace has been rather slow. Several reasons for this so-called energy efficiency gap

(also termed energy efficiency paradox) have been studied. These include a tendency to wait for

further technological progress instead of making early decisions and risk getting locked into

irreversible investments (Soest and Bulte, 2001); preference for centralized supply over CHP

distributed generation in the residential and services sector (Gulli, 2006); lack of investment

confidence due to perceived complexity of the regulatory processes involved in CHP investments

(Mueller, 2006); poor energy management practices leading to inadequate analysis for

investment in efficiency improvement technologies (Backlund, Thollander, Palm, & Ottosson,

2012); and uncertain regulatory policies for microgrids with multiple owners (especially

residential customers) and cumbersome air permitting processes in densely populated urban city

centers (Howard, Saba, Gerrard and Modi, 2013).

Rather than attempt to explain why insufficient CHP investment occurs in the first place, this

paper tries to isolate the reasons for lower than expected capacity factors of installed CHP

systems. This paper builds on a previous study that examined the effects of understated capital

costs as well as low and volatile capacity factors on the financial viability of CHP (Athawale and

Felder, 2014). Due to data limitations, the reasons for low and volatile capacity factors were not

reported, requiring the formulation and testing of multiple hypotheses that explain the data.

Under-performance of existing CHPs (as demonstrated by low and volatile capacity factors) also

suggest that the emissions and associated environmental benefits and higher efficiencies are not

translated into reality. This paper examines whether CHP systems remain underutilized as a

result of poor economics, poor maintenance and operational practices, oversizing of plants,

seasonal factors, and/or different investment objectives such as reliability and resiliency needs.

This study makes use of the NYSERDA database of distributed generation facilities that have

been funded under the agency’s various programs.18

The CHP systems funded therein employ

various types of prime mover technologies and are installed at many different types of places

18

http://chp.nyserda.ny.gov/home/index.cfm

7

such as multi-family housing, hospitals, education centers, office buildings, supermarkets, and so

on. Capacity factors of these plants are analyzed to understand what causes poor performance of

CHP systems.

The study proceeds as follows. We review relevant literature for sizing of CHP and the expected

capacity factor. Section 3 describes the NYSERDA dataset, followed by Section 4, which

presents our hypothesis and then discusses the results and offers key insights. The final section

concludes by discussing policy implications.

2. Literature Review

A CHP can be set up to function as a standby, base load plant, or for peak shaving purposes

(EPA, 2015). Several studies discuss the minimum number of hours a CHP system should be

operational to achieve the desired level of efficiencies, and in turn financial viability. According

to the International Energy Agency, a site with annual heating and/or cooling demand for at least

5,000 hours (57% capacity factor given 8,760 hours in a year) is an ideal candidate for setting up

a CHP (IEA, 2008). The Carbon Trust (UK) Good Practice Guide recommends that only those

applications where CHP can be operated for more than 5,000 hours per year are worth detailed

investigation and applications; hotels, hospitals and similar sites with a steady base load

requirement for heat for 18 to 24 hours a day are the best candidates for CHP investment

(Carbon Trust, 2004). The Guide also warns that a “CHP does not save money if it doesn’t run!”

(p. 30).

Maidment and Tozer (2002) indicate that the main factor that determines economic viability of a

CHP is high utilization of heat and power simultaneously, and that a plant can be viable

(achieving 4 to 5 years of payback period) only when it runs for a period of 4,500 hours per

annum at the minimum (approximately 50% capacity factor). They further demonstrate that in

order to maintain a runtime of 4,500 hours and achieve payback of 4.5 years for CHP installed at

a UK supermarket, a substantial amount of heat (thermal energy output from the CHP) has to be

ejected into the atmosphere. The number of hours of operation becomes particularly important

for commercial applications (in contrast to industrial), since the overall size of CHP required for

a commercial application is smaller, thus further reducing the cost benefit due to economies of

scale. Making use of a buffer vessel, which can be utilized to store excess heat that can later be

transferred back when required, improves profitability and utilization (Kavvadias, Tosios, &

Maroulis, 2010).

Kaikko and Backman (2007), while trying to determine the effect of recuperation and other

control methods on the overall economic performance of a 100 kW CHP, assume a high value of

operating hours (between 7364 to 7899 per year). This translates into an average capacity factor

of around 87%. In the EPA Catalog of CHP Technologies, it is implicitly assumed that CHP is

set up for a baseload function and is operated for 8,000 annual operating hours (translating into

8

around 91% capacity factor) for calculation of costs and fuel input (EPA, 2015), which is similar

to the assumption used by EPA in the past (EPA, 2008).

According to the Massachusetts guide to submitting CHP application for incentives, economic

performance of a CHP plant is improved in the case of “high annual hours of operation and

continuous thermal load” (Massachusetts, 2014). The guide advocates the use of thermal storage,

which can lead to a more even spread of thermal load over a greater number of hours (at least

5,000 hours per year); the result is in an increase in benefit-cost ratio for a potential CHP

incentive applicant.

However, Athawale and Felder (2014) found a capacity factor range between the higher 40s

(47%) to lower 60s (62%) for 1250 CHPs in the United States with an installed capacity of more

than 1 MW.19

Given these low capacity factors the authors found, that there is a 48% chance that

a CHP project is economically unviable, with an internal rate of return less than its weighted

average cost of capital. That may be one of the reasons contributing to the estimated gap between

potential and actual CHP capacity. Similar observations regarding capacity factors were made in

a study for California which found that actual CHP performance20

falls short of readily available

best-case scenarios, and that customers should apply rigorous project management tools to

mitigate operational risks (ACEEE, 2009). KEMA found the average daily capacity factor of

CHPs in Massachusetts to range between 40% to 60% in summer months and between 60% to

80% in winter months (KEMA, 2012).

Assuming that lower utilization of a CHP indicates economic underperformance, accurate plant

sizing and routine maintenance appear to be the prerequisites for achieving project profitability.

In cogeneration (and trigeneration systems where cooling is the third functionality), heat and

electricity cannot be produced independently of each other. A boiler is commonly employed,

which can meet additional thermal energy needs over and above the output from a CHP.

Several studies have been conducted in the past that discuss approaches for sizing of CHP and

the factors that affect sizing. Blakemore, Davies and Jones (1995) present an option appraisal

approach whereby scores are appointed to various decision parameters such as minimization of

energy costs, operational management, minimization of capital and maintenance costs. Ren, Gao

and Ruan (2008) use a sensitivity analysis to understand the influence of key parameters such as

natural gas and electric prices, carbon tax rates, electricity buy-back prices, and optimization of

storage tank etc. on the sizing of residential CHP systems. Utility demand charges and

transmission and distribution costs have also been found to guide choice of CHP size (Maribu

and Fleten, 2008).

19

Data was collected from three national sources: eGRID database, EIA Form-923, and ICF. Plants larger than 1 MW installed capacity are reported in these databases. 20

A sample of 53 industrial CHP projects were studied for their performance from 2001 to 2008.

9

Oversizing of CHP has been anecdotally referred to as one of the primary reasons for

underperformance. A consulting firm arguing for the benefits and risks of CHP investments

notes: “The sizing of CHP plant is critical to its efficient operation. Many installations are

oversized and this results in excessive thermal output being wasted”.21

Potential investors have

been warned of oversizing (invested with a rationale of 10% for luck, that is, sizing more than

what is required just in case more capacity is required) and that a bigger plant does not

necessarily mean better performance.22

In fact, oversizing (as well as underutilization later on) is

believed to cause physical damages due to excessive shutdowns and failures.23

Other possible explanations for low CHP capacity factors, such as poor maintenance and

reliability/resiliency based investments, have not been discussed in the literature but are

examined below.

3. Data

NYSERDA has developed a web-based DG integrated data system portal,24

which provides

system-level performance data for 312 facilities aggregating to a capacity of 283 MW. These

facilities have received funding from NYSERDA under one of its various incentives programs

for development of distributed generation. Facilities include Combined Heat and Power systems

(141 nos., 192 MW), Anaerobic Digester Gas (ADG) systems (27 nos., 11 MW), Fuel Cell

systems (7 nos., 3 MW), Photovoltaic Solar systems (133 nos., 72 MW) and Main-Tier RPS25

sites (4 nos., 4.5 MW).

Plant-level operating information is available for various time periods for various plants; the

reasons for when the operational information starts flowing and when it stops are not

documented in the database. Out of the 141 CHP systems, detailed hourly data is available for 99

facilities. Of these, the plant-level M&V monitoring report is available for only two facilities that

have capacity greater than 1.3 MW and were installed after 2012. The prime mover technology

used by these projects is summarized in Table 1 below.

21

http://www.greenconsultancy.com/intelligent-energy-insights/article-25-2013-09/18-combined-heat-and-power-opportunity-or-liability 22

http://www.modbs.co.uk/news/fullstory.php/aid/13695/Ensuring_a_successful_outcome_for_a_CHP_project_.html 23

http://www.shentongroup.co.uk/blog/challenges-chp-sizing/ 24

http://chp.nyserda.ny.gov/home/index.cfm 25

New York’s RPS Program (established in 2004) consists of two tiers – Main Tier and Customer-Sited Tier – which are administered by NYSERDA. The objective of the Main Tier, which accounts for 98% of the RPS Program target, is development of large-scale renewable generation facilities that can sell their output into NYISO’s wholesale power market.

10

Table 1: Summary of Prime Movers funded by NYSERDA (from 2001 to 2015) for which

information is captured in the DG integrated data system portal

Prime Mover

(Technology Type)

No. of Projects

(n)

Installed

Capacity (kW)

Combined Cycle Gas Turbine 2 37,500

Combined Cycle Gas/ Steam Turbine 1 40,000

Fuel Cell 9 2,855

Microturbine 17 6,530

Microturbine/Wind Turbine 1 1,000

Reciprocating Engine 64 55,230

Simple-Cycle Gas Turbine 2 9,000

Steam Turbine 3 3,200

TOTAL 99 155,315

Projects comprise various sizes, with the smallest one being 5 kW and the largest being 40,000

kW. About 50% of the systems have only one unit, while the rest have multiple units (mostly 2

or 3, though one of the projects has 26 units with a cumulative capacity of 1,000 kW). Unit level

data for operations are not reported. The database does not mention if a particular unit is set up

for resiliency purposes, and if so whether it is blackstart capable26

or not.

These CHPs are installed at various locations across the state of New York (Figure 2). They are

designed for various applications, primarily multi-family residences, educational facilities, and

hospitals (Figure 3).

26

Blackstart capability is the ability of a CHP system to independently start on its own without receiving any power from the grid. This can be achieved by way of a battery powered starting device or a backup generator.

11

Figure 2: Location Map of CHPs

Figure 3: NYSERDA Funded CHP Facilities by Application

The average capacity factor of all plants in the dataset is 49%, which is right around the

breakeven point reported in the literature discussed above. The standard deviation is 23%, and

the average capacity factor per plant varies widely from 2% to 91%. About 68,684 kW

12

cumulative capacity equivalents demonstrates an average capacity factor of more than 60%

(Figure 4). These represent about 1/3 of the total plants.

Peak capacity factor of a plant is the maximum value of hourly capacity factor for that plant; 58

plants have a peak capacity factor exceeding 90%. These plants make up 118,909 kW of

cumulative capacity (Figure 5).

Figure 4: Avg.CF versus cumulative capacity

Figure 5: Peak CF versus cumulative capacity

13

Projects listed in the NYSERDA database are funded from one of the following three programs.

Prior to 2012, CHPs were funded under the NYSERDA’s CHP Demonstration Program, for

which the first solicitation was issued in 2000. This program was operational between 2000 and

2011, and a total of 123 demonstration projects aggregating to 127 MW were funded. Post 2012,

two new programs – CHP Performance Program, and CHP Acceleration Program – were

introduced, and the previously available CHP Demonstration Program was discontinued.

Incentives for projects eligible under the CHP Performance Program (systems larger than 1.3

MW) are based on summer peak demand reduction (600 $/kW upstate and 750 $/kW downstate),

electricity generation (10 cents/kWh for both upstate and downstate), and performance achieved

by the project on an annual basis over a two year measurement and verification (M&V) period.27

M&V performance payments are determined by NYSERDA, based on metered data collected

from each facility. Incentives under this program are disbursed in tranches as below:

(a) 15% of the base incentive (sum of kW incentive and kWh incentive) issued based upon

submission of proof of purchase and delivery of generation equipment and heat

exchanger;

(b) next 15% of base incentive issued upon submission of receipt of interconnection letter

from the applicable utility;

(c) an additional 10% of Base Incentive at the start of the M&V period;

(d) M&V year 1 payment equivalent to 30% of base incentive after the successful

completion of the first year M&V period;

(e) M&V year 2 payment equivalent to 30% of the base incentive after the successful

completion of the second year M&V period.

Projects are also eligible for up to 30% bonus incentive above the base incentive, which includes

(a) 10% additional incentive for those projects serving critical infrastructure; (b) 10% additional

incentive for those projects that are within a targeted zone as established by a utility; and (c) 10%

additional incentive to those projects that demonstrate superior performance as determined by the

measured fuel conversion efficiency (FCE).

There is a provision for incentive reduction in case of non-performance. When the project is able

to achieve FCE (a) between 55% and 60% there is no penalty; (b) for FCE between 50% and

55%, the M&V payment would be reduced by 50% for that year of M&V; and (c) for FCE less

than 50%, the applicant would not be eligible for receive an M&V payment for that year of

M&V.

FCE only measures actual output derived for a given input and not necessarily total potential

output (against the assumed operation of the plant). This parameter therefore fails to capture the

effect of lower capacity factor because it does not measure how much the plant could have

achieved, but simply how well the plant performed (for whatever hours it did perform).

27

http://www.nyserda.ny.gov/All-Programs/Programs/Combined-Heat-and-Power-Performance-Program

14

For the CHP Acceleration Program (eligible systems smaller than 1.3 MW), NYSERDA has

developed a detailed CHP system catalog that lists multiple technology and vendor choices for

various project size and usage requirements, and provides applicable incentives (location specific

for upstate and downstate) for each combination.28

4. Results

Using CHP facilities data from NYSERDA, we investigate possible reasons for lower than

expected capacity factors of plants. Table 2 below lists our hypothesis for low capacity factor,

along with indicators and possible evidence to support a hypothesis.

Table 2: Hypothesis, Indicators, and Evidence for Underperformance

Hypothesis Indicators Evidence

A. Economic

Arbitrage

Identifiable pattern in the way a plant is

operated either (i) over a year, and/or (ii)

over a week, and/or (iii) over a day –

indicating that it is economical to run the

plant during some time(s) and not during

other time(s)

Overall low CF; though

higher CF during a

particular season

(summer/winter) or days

(weekdays/weekends) or

hours (morning/evening) is

observed

B. Poor

maintenance

/ operations

A plant either (i) consistently runs for

lower number of hours (fuel

unavailability, managerial issues), and/or

(ii) runs intermittently

(technical/operational glitch), indicating

there are frequent breakdowns

Overall low CF; erratic

distribution over hours,

days and inter-seasons

C. Oversizing A plant may be running consistently but

never reaches full 100% potential

Peak CF is always far

below 100%

D. Reliability/

Resiliency

as primary

reason for

investment

Very low average utilization because plant

has been set up for redundancy and

resiliency, with 100% utilization achieved

once in a while

No direct evidence to

support this finding based

on data availability

E. Some

combination

of above

factors

Usage pattern does not fit the above

categorization

No direct evidence to

support this finding based

on data availability

28

NYSERDA Combined Heat and Power System Catalog, CHP Acceleration Program (PON 2568 Attachment C) version 3.0 September 2014

15

Hypothesis A: Economic Arbitrage

CHPs installed at multi-family residences and educational facilities were selected to investigate

the effect of economic arbitrage on capacity factor. This is because multi-family residence CHP

and education facility CHP represent the first and second largest application categories in the

dataset (25 projects and 11 projects, respectively, out of 99). These may be the ones that have a

higher possibility of variation in capacity factor between seasons and days (for example,

weekdays vs. weekends). Tables 3 and 4 below list the calculated average seasonal and daily

capacity factors for multi-family and educational facilities. Not much evidence can be gathered

for economic arbitrage as a reason for overall underperformance of CHP. Except for few plants,

the capacity factor variation between seasons and days is minimal. Data suggests that for most of

the plants, the capacity factor is quite consistent across different seasons and days.

Table 3: CF for multi-family residence CHPs

Facility Name Average

Capacity

Factor

Average

Winter

CF

Average

Spring

CF

Average

Summer

CF

Average

Fall CF

Average

Weekday

CF

Average

Weekend

CF

10 West 66th

Street Corp 47% 52% 54% 27% 52% 48% 44%

Archstone Chelsea 65% 62% 66% 75% 61% 65% 65%

Archstone Midtown 57% 67% 66% 33% 40% 57% 58%

Bay Club Tower 1 16% na 15% 18% na 19% 9%

Bay Club Tower 2 16% na 17% 14% na 15% 18%

Birchwood Towers – The

Kyoto

21% na 17% 36% na 22% 18%

Birchwood Towers – The

Toledo

55% 75% 46% 9% 54% 55% 55%

Brevoort East 34% na 35% 29% na 35% 30%

Cabrini Terrace 50% 52% 52% 47% 50% 50% 50%

Concord Court

Apartments

46% 47% 43% 47% 49% 47% 45%

Grenadier Realty – AAF

Sea Rise 1

34% 42% 38% 25% 25% 36% 27%

Grenadier Realty – AAF

Sea Rise 2

48% 48% 44% 50% 51% 49% 46%

Hazel Towers 52% 55% 56% 43% 52% 52% 50%

Octagon 88% 85% 84% 94% 89% 88% 89%

River Point Towers 41% 60% 34% 27% 49% 42% 40%

Schwab House 42% 60% 40% 28% 39% 42% 41%

Seapark East – Brooklyn 70% 63% 73% 73% 66% 70% 69%

16

Seapark West - Brooklyn 80% 83% 78% 78% 80% 80% 79%

Seaside Apartments 43% 37% 45% 48% 42% 43% 44%

Silver Towers 33% 24% 31% 43% 37% 34% 30%

Terrace Gardens #1 33% 25% 34% 42% 39% 33% 31%

Terrace Gardens #2 22% 26% 17% 33% 20% 22% 23%

Toren Condominiums 53% 50% 49% 60% 54% 53% 52%

Trump Tower 75% 79% 74% 70% 76% 75% 75%

Tudor Gardens 83% 85% 83% 81% 83% 82% 85%

Table 4: CF for education facilities CHPs

Facility Name Average

Capacity

Factor

Average

Winter

CF

Average

Spring

CF

Average

Summer

CF

Average

Fall CF

Average

Weekday

CF

Average

Weekend

CF

Burrstone Energy Center

St. Luke’s Utica College

57% 65% 50% 57% 57% 65% 36%

Clarkson University 80% 93% 78% 68% 83% 80% 81%

Cooper Union 66% 76% 54% 69% 75% 67% 62%

Cornell University 72% 93% 67% 57% 75% 72% 73%

East Irondequoit Central

School District

44% 44% 41% 47% 44% 47% 35%

East Rochester

Elementary School

87% 79% 91% 83% 66% 88% 82%

Fonda-Fultonville CSD 25% 27% 23% 25% 28% 27% 19%

Hudson Valley

Community College

29% 30% 27% 29% 29% 31% 23%

SUNY Buffalo 79% 73% 91% 73% 41% 79% 79%

Syracuse Green Data

Center

2% Approx. 70% of the CF values are negative

Vassar College 3% 4% 4% 0% 4% 3% 3%

Hypotheses B and C: Poor Maintenance and Oversizing

To investigate the other two hypotheses (poor maintenance and oversizing), we segregate the

plants into four groups using a 2x2 matrix of average capacity factor and peak capacity factor, as

follows:

1. Plants with average CF >= 60% and peak CF >= 90%: high peak CF indicates that the

plant is rightly sized, and high average CF indicates that it operates well most of the time

(Code A)

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2. Plants with average CF >= 60% and peak CF <90%: lower peak CF indicates that the

plant is probably oversized, while a high average CF indicates that it operates well most

of the time (Code B)

3. Plants with average CF <60% and peak CF >= 90%: high peak CF indicates the plant is

rightly sized, but a lower average CF indicates that the plant is underutilized for a

significant time (Code C)

4. Plants with average CF <60% and peak CF <90%: lower peak CF indicates that the plant

is probably oversized, and a lower average CF indicates that the plant is underutilized for

a significant time (Code D)

Of the 99 facilities, 39% of the CHP plants fall under code D, 33% under code A, 25% under

code C, and 2% under code B. Figure 6 below represents the number of projects and total

capacity (kW) of all projects that fall under an assigned code.

Figure 6: Segregating plants based on performance

Table 5 summarizes how the number of plants falling under a particular code can change if the

criteria for minimum average capacity factor and peak capacity factor are altered. As is evident,

when the criteria for average and peak capacity factors are relaxed, more projects become

eligible for code A and code C, while the number of projects under code B and D are reduced. In

general, it is rare to find plants that are well utilized, that is, with high average capacity factor but

a rather low peak capacity factor.

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Table 5: Classification based on Capacity Factor

Classification Criteria No. of

Projects (n)

under Code A

No. of

Projects (n)

under Code B

No. of

Projects (n)

under Code C

No. of

Projects (n)

under Code D

Average CF>65% (5,694

hours) and Peak CF>95%

25 2 26 46

Average CF>60% (5,256

hours) and Peak CF>90%

33 2 25 39

Average CF>55% (4,818

hours) and Peak CF>80%

39 0 36 24

Average CF>50% (4,380

hours) and Peak CF>70%

46 0 39 14

Plants that appear to be rightly sized but underutilized

Of the 25 plants that fall under code C (average CF below 60% and peak capacity factor above

90%), around 19 use reciprocating engines as the prime mover, 5 use a microturbine as the prime

mover, while only 1 plant uses a combined cycle gas turbine. Three of the plants are CHPs

installed at office buildings and report high peak CF (96% - 98%) but very low average CFs

(22%, 23% and 28%). However, this does not lead to a conclusion that CHPs installed at office

complexes are rarely used (though rightly sized) as there are two other CHPs in the complete

dataset that have demonstrated good utilization and fall under code A. As stated by the OSEC

(2000): “A low capacity factor is indicative of peaking applications that derive economic

benefits generally through the avoidance of high demand charges.”

Plants that appear to be oversized and underutilized

Of the 39 plants that fall under code D (average CF below 60% and peak capacity factor below

90%) there are 4 CHPs which operate at an average CF of less than 10%, which means a runtime

of less than 876 hours per annum. These include one wastewater treatment plant (average CF

9%), one food processing facility (average CF 9%) and two educational facilities (with average

CF of 2% and 3%, respectively). However, the other wastewater treatment plant, food processing

facility and education facility based CHPs in the database have better average capacity factors,

ranging from 20% to a high utilization of 87%. Thus, a correlation between application and

average capacity factor can not be established.

Additionally, 12 of these 39 plants (almost one-third) are installed at multi-family residences, all

with reciprocating engines and with average CF ranging from a minimum of 16% to a maximum

of 48%. There are 13 more multi-family CHP installations in the database, out of which 7 fall

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under code C and 6 under code A. There does not seem to be a correlation between usage (such

as multi-family residence) and CHP utilization.

Six of the 39 plants under code D use a microturbine as prime mover technology. However, there

are 5 more facilities that use microturbines and are classified as code C, in addition to 6 facilities

with microturbines and classification code A. Again, there seems to be no correlation between

type of prime mover and utilization.

While there is enough evidence for oversizing and poor maintenance, a common minimum

theme – specifically, a correlation between these factors and size/technology/application of a

CHP – is hard to establish. The sizing and performance of these 64 plants suggests motivation

for incurring a fixed one-time incentive (which is calculated per kW) but not on output (kWh of

electricity generated and MMBtu heat recovered).

Hypothesis D: Reliability/Resiliency as primary reason for investment

The NYSERDA database does not capture whether or not a particular CHP project (or one of the

units in case of multiple unit project) is invested in to provide standby services. Logically, a

standby CHP would have lower capacity utilization because it is not run to meet the regular

demand for electricity and heating (and/or cooling). Athawale and Felder (2014) suggest that the

benefit of reliability depends both on the Value of Lost Load (VOLL29

) and on the probability

and duration of outage.

Estimates for VOLL and outages can be back-calculated for lower utilization plants using the

CBA model (Athawale and Felder, 2014). For example, for a 1MW reciprocating engine CHP,

with the model assumptions at capacity factor of 65%, and an outage of 24 hours in a year with

VOLL of 2,500 $/MWh, the benefit to cost ratio (B/C) comes to 2.96. This takes into account the

project investor’s energy savings benefit, reliability benefits, and related costs. If the capacity

factor is changed to 30%, which is the average capacity factor of all projects falling under code

D, then the corresponding B/C ratio falls to 0.96. In order to bring this ratio back to its original

value, either the VOLL has to be increased from 2,500 $/MWh to 17,300 $/MWh; or the

duration of outage has to be increased from 1 day per year to approximately 7 days per year.

5. Public Policy Implications

From a policy perspective, this paper recommends better pre-engineering assessment for correct

sizing as well as revision of incentives based on performance. Some initiatives have been already

underway for production-based incentives. For example, the New Jersey Energy Resilience Bank

(NJ ERB) provides 60% of the project cost as a loan and 40% of the project cost as incentives to

potential CHP applicants. Half of the 40% incentive is a grant paid at the start of the project. The

29

VOLL is the value that represents a customer’s willingness to pay for reliable electricity services.

20

balance half is in the form of loan forgiveness over a period of five years, subject to measured

performance. If the CHP does not meet its required performance level in a particular year then

the forgivable portion of that year’s loan will not be forgiven. Performance is measured to assess

actual versus claimed minimum run hours and production capacity (ERB, 2015).

Additional information should be collected so that a more accurate ongoing analysis of the

societal benefits of CHP projects can be made. Unit-wise reporting of operation data and more

clarity about usage (such as in the case of multiple units whether one of the units is earmarked

for standby purposes only, capacity oversize due to unavailability of smaller size options, etc.)

should be documented.

It may be worthwhile to study the effect of volatility of wholesale energy prices (electricity and

natural gas) on the runtime of CHP. Also, utility demand standby charges (and in some cases

demand charges) levied to CHP consumers and their effect on operation of a CHP can be studied

to understand if these charges have an effect on investment decisions. Last, the energy efficiency

gap may be smaller than is commonly assumed, so other options should be explored to meet

energy efficiency goals.

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