oEPA
EPA 600/R-19/124 February 2020 | www.epa.gov
United States
Environmental Protection
Agency
City-based Optimization Model for Energy Technologies:
COMET - New York City Documentation
i
ICr
Office of Research and Development
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EPA/600/R-19/124
February 2020
www.epa.gov/research
oEPA
United States
Environmental Protection
Agency
City-based Optimization Model for tnergy Technologies:
COMET-New York City
Documentation
Office of Research and Development
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EPA/600/R-19/124
January 2020
City-based Optimization Model for Energy
Technologies: COMET- New York City
Documentation
By
Ozge Kaplan, Mine Isik
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation of use.
This research was also supported in part by an appointment of Dr. Mine Isik to the Research
Participation Program for the U.S. Environmental Protection Agency, Office of Research and
Development, administered by the Oak Ridge institute for the Science and Education (ORISE)
through an interagency agreement between the U.S. Department of Energy and EPA.
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e of Contents
1 Introduction 1
2 Background on MARKAL 4
2.1 Description 4
2.2 Data Requirements 5
2.2.1 Time Horizon 6
2.2.2 System-wide Parameters 6
2.2.3 End-Use Energy Service Demands 6
2.2.4 Energy Carriers 7
2.2.5 Resource Technologies 7
2.2.6 Process, Conversion, and Demand Technologies 8
2.2.7 Emission Factors 9
2.3 MARKAL Set Definitions and Naming Conventions 9
3 COMET-NYC Structure 10
3.1 Model Workbooks 12
3.2 Units 13
3.3 Model Assumptions 13
3.4 Emission Factors 14
4 Building Sector: Base Year Calibration 15
4.1 Residential Sector 18
4.1.1 Residential Energy Demand Services 19
4.1.2 Residential Emissions Accounting 22
4.1.3 Residential Sector Constraints 23
4.2 Commercial Sector 23
4.2.1 Commercial Energy Demand Services 24
4.2.2 Commercial Technology Structure 25
4.2.3 Commercial Emissions Accounting 27
4.2.4 Commercial Sector Constraints 27
4.3 Industry Sector 27
4.3.1 Industrial Emissions Accounting 28
4.3.2 Industrial Sector Constraints 28
5 Transportation Sector: Base Year Calibration 29
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5.1 Light Duty Vehicles 29
5.1.1 Light Duty Vehicle Energy Demand Services 29
5.1.2 Technology Structure 30
5.1.3 Light Duty Vehicle Emissions Accounting 31
5.1.4 Light Duty Vehicle Constraints 31
5.2 Heavy Duty Vehicles 31
5.2.1 Energy Demand Services 32
5.2.2 Technology Structure 32
5.2.3 Heavy Duty Vehicle Constraints 33
6 Oil and gas resources 33
7 Electric Sector Representation 34
8 Reference Case 35
9 Final Remarks and Future Work 37
10 References 39
11 Appendix 41
11.1 Appendix A: Variable Types in the Model and Corresponding Data Requirements 42
11.2 Appendix B: Model Constraints and Baseline Calibration Assumptions for Buildings,
Transportation and Power Sector 43
11.3 Appendix C: NYC Borough Based Population Projection 48
11.4 Appendix D: Heating and Cooling Degree Days 49
11.5 Appendix E: Residential Sector Demand Projections 50
11.6 Appendix F: Commercial Sector Demand Projection 51
11.7 Appendix G: Industry Sector Demand Projection 52
11.8 Appendix H: Light Duty Vehicle Demand Projection 53
11.9 Appendix I: Heavy Duty Vehicle Demand Projection 54
11.10 Appendix J: Natural Gas Supply & Distribution in NYC 55
11.11 Appendix K: CHP Generation in NYC 56
11.12 Appendix L: End-use demand Shares with respect to The Building Archetypes 57
11.13 Appendix M: Building Area of Per Type of Building Per Borough (Sq.Ft) 58
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Li les
Table 1 Set Definitions
Table 2 Technology Naming Conventions
Table 3 List of Energy Carriers
Table 4 COMET-NYC Workbooks
Table 5 Time-slice fractions used to characterize load-duration curves
Table 6 Residential End-use Service Demands
Table 7 End-use Energy Service Demand Formulations for Residential Sector
Table 8 Residential Technology and Fuel Combinations
Table 9 Technology Specific Hurdle Rates for Residential Sector
Table 10 Commercial Demands
Table 11 End-use Energy Service Demand Formulations for Commercial Sector....
Table 12 Commercial Technology and Fuel Combinations
Table 13 Technology Specific Hurdle Rates for Commercial Sector
Table 14 Light Duty Vehicle Fuel and Technology Combinations
Table 15 Heavy Duty Transportation Demands
Table 16 Heavy Duty Vehicle Demand Types, Fuel, and Technology Combinations
Table 17 Technology Specific Hurdle Rates for Heavy Duty Vehicles
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List of Figures
Figure 1 Illustrative Reference Energy System
Figure 2 COMET-NYC regional coverage - New York State and Boroughs of New York City
Figure 3 COMET-NYC Model Structure
Figure 4 Data Sources for Buildings Sector
Figure 5 End-use Demand in 2010 with Respect to The Building Types in PJ
Figure 6 Share of The Building Types with Respect to Total Building Area (ft2) in Each Borough
Figure 7 Illustrative Reference Energy System for Space Heating Characterization
Figure 8 Residential Energy Demand by End-Use Type
Figure 9 Commercial Energy Demand by End-Use Type
Figure 10 Distribution of Light Duty Vehicles
Figure 11 Reference Case vs. Reported Data Fuel Consumptions (PJ)
Figure 12 Residential Sector Fuel Consumption (PJ) in the Reference Case
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List of Acronyms and Abbreviations
AEO
AHP
BAU
BBL
BC
BK
bn-lum-yr
bn-pass-miles
bn-t-miles
bn-vmt
BX
CAP
CBECS
CDD
CFL
CHP
CLT
CME
CMN
CNG
COA
COM
COMET
CSH
CWH
DOE
DSH
DSL
E15
E85
EGU
EIA
ELC
EPA
ETSAP
EV
GHG
GHGI
GHP
GJ
GPS
GSL
Annual Energy Outlook
Absorption Heat Pump
Business as usual
Borough-Block-Lot Number
Black Carbon
Brooklyn
Billion Lumens PerYear
Billion Passenger Miles
Billion Ton Miles
Billion Vehicle Miles Traveled
Bronx
Criteria Air Pollutant
Commercial Buildings Energy Consumption Survey
Cooling Degree Days
Compact Fluorescent Light
Combined Heat and Power
Commercial Lighting
Commercial Miscellaneous Electricity
Commercial Miscellaneous Natural Gas
Compressed Natural Gas
Coal
Commercial Sector
City-based Optimization Model for Energy Technologies
Commercial Space Heating
Commercial Water Heating
Department of Energy
Distillate Heating Oil
Diesel
15% Ethanol Fuel Blend
85% Ethanol Fuel Blend
Electricity Generation Unit
Energy Information Administration
Electricity
Environmental Protection Agency
Energy Technology Systems Analysis Program
Electric vehicle
Greenhouse Gas
Greenhouse Gas Inventory
Gas Heat Pump
Gigajoules
Global positioning system
Gasoline
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GW Gigawatt
H2 Hydrogen
HDD Heating Degree Days
HDV Heavy-duty vehicle
HYD Hydropower
IND Industrial Sector
KER Kerosene
LDV Light Duty Vehicles
LED Light Emitting Diode
LFG Landfill Gas
LPG Liquid Petroleum Gas
MARKAL MARket Allocation model
MN Manhattan
MOVES Motor Vehicle Emissions Simulator model
Mt Million Tons
MU Million Units
NCDC National Climatic Data Center
NEI National Emissions Inventory
NEMS National Energy Modeling System
NGA Natural Gas
NGL Natural Gas Liquids
NOAA National Oceanic and Atmospheric Administration
NOx Nitrogen Oxide
NUC Nuclear
NYC New York City
NYCDEP New York City Department of Environmental Protection
NYS New York State
NYSERDA New York State Energy Research & Development Authority
O&M Operation and Maintenance
OC Organic Carbon
ORD Office of Research and Development
PADD Petroleum for Administration Defense Districts
PJ Petajoules
PLUTO Primary Land Use Tax Lot Output
PM Particulate Matter
PTC Petroleum Coke
PV Photovoltaic
QN Queens
RA Rural Area
RECS Residential Energy Consumption Survey
RES Reference Energy System
RFH Residual Fuel Oil
ROE Residential Other Electricity
ROG Residential Other Natural Gas
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RSC
Residential Space Cooling
RSD
Residential Sector
RSH
Residential space heating
RWH
Residential Water Heating
SI
Staten Island
SOL
Solar
sox
Sulfur Oxide
SUV
Sport Utility Vehicle
TB
Transportation Bus
THS
Transportation Heavy-duty Short Haul
TL
Transportation Light-duty
TRP
Transportation Rail Passenger
VMT
Vehicle Miles Traveled
VOC
Volatile organic compounds
WND
Wind
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eduction
Meeting increased demand for energy with increased resource scarcity exacerbated by climate
change presents a significant challenge to many cities around the world. There is a strong
connection between how cities deliver and use energy either in the form of fuels or electricity
to meet demands in the buildings and transportation sectors and what air quality and GHG
outcomes these activities will yield. Beyond GHG emissions, many cities and urban areas are
faced with air quality challenges. New York City, for instance, have efforts to understand and
influence both energy sector dynamics and end-use sectors. The policy makers recognize the
importance of behavior response as a key to designing cost-effective energy evaluation and
emission mitigation goals across the city. In addition, many cities, including New York City will
require expansion and upgrades over the coming decades to serve its steadily growing
population. An integrated approach to energy planning can identify and evaluate policy
scenarios that leverage opportunities for resource efficiency, cost reductions, and long-term
sustainability. This integrated strategy could help urban and regional planners explore
possibilities to foster more resilient urban ecosystems.
There are only number of tools and frameworks that can facilitate few studies where dynamics
among the EGU sector, buildings sector and transportation sector. One must recognize the
jurisdictions of cities in how they could implement and achieve certain goals, in most instances,
the cities have no control over how the grid evolves, but there would be emission reduction
goals based on the assumptions of how the grid might evolve. The literature in city-specific
energy modeling differ in how they cover the energy sector or what type of questions they seek
for an answer. In light of these, U.S. EPA has embarked on a project to develop tools for cities
and local communities to aid them with identifying integrated strategies for energy planning.
This report describes the development of a city-scale tool designed to examine energy
consumption and management scenarios under different policy scenarios. Initial application of
the City-based Optimization Model for Energy Technologies is piloted in NYC (referred to as
COMET-NYC from now on). This report provides an overview of the COMET, data sources,
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calibration against actual energy consumption data and discusses a reference case proving
future year energy outlook.
COMET uses the MARKAL (MARKet ALIocation) energy-environmental-economic optimization
framework1 to determine the technology investment choice and related fuel consumptions for
end-use energy demand sectors such as buildings and transportation. In addition, the model
covers supply curves for primary energy carriers including fuel oil, natural gas, coal, hydrogen,
and other renewable resources. Energy technologies (e.g., power plants, refineries, combined
heat and power (CHP)) are deployed based on their initial capital cost, variable and fixed
operation and maintenance costs, and parameters such as efficiency, availability, capacity
factors. COMET determines the technology investment level and related fuel consumption that
results in least total system-wide discounted cost while keeping the constraints (sector-wide or
system-wide emission limits, renewable or electrification standards etc.) under pre-set levels
for the period between 2010-2055. The electric generating units (EGU)s dispatching electricity
through New York Independent System Operator (both base and peaking units) are included in
the model. The COMET framework includes reference building energy profiles and future
energy efficiency retrofits, distributed energy generation options (e.g. roof-top solar
photovoltaic and combined heat and power plants).
The COMET-NYC is a distinct representation of the New York City energy system (including
EGUs in New York State (NYS)) designed to be used within the MARKAL framework. The
database characterizes the flow of energy associated with the extraction or import of
resources, the conversion of these resources into useful energy, and the use of the energy in
meeting end-use demands within and between the five boroughs of New York City and New
York State. A MARKAL model run optimizes technology penetrations and fuel use within this
representation over the specified time horizon using linear programming techniques to
minimize the net present value of the energy system while satisfying the specified demands and
meeting any user-defined constraints. Outputs of the model include the technological mix at
time intervals into the future, the total system cost, criteria air pollutant (CAP), greenhouse gas
1 https://iea-etsap.org/index.php/etsap-tools/modej-generators/markal
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(GHG) emissions, and estimates of energy commodity prices. Using scenario analyses, the
model can also be used to explore how the least cost pathway changes in response to various
input changes, such as the introduction of new energy efficient technologies or a new policy to
stimulate emission reductions. The COMET will enable users to analyze portfolio of
technologies that meets building and transportation sector energy demands by facilitating case
studies in other cities.
The COMET-NYC includes data specific to NYC such as New York City Department of
Environmental Protection (NYCDEP), the New York State Energy Research & Development
Authority (NYSERDA), and a variety of other sources, where local data is unavailable, the
COMET relies on a database created for U.S. energy system. For instance, the U.S.
Environmental Protection Agency (EPA) has been maintaining a model representing the U.S.
national energy system through nine census divisions (known as the EPAUS9r model) for use
within the MARKAL energy-economy-environment modeling framework (Lenox et al., 2013).
Data for the EPAUS9r are derived primarily from the U.S. Energy Information Administration
(EIA)'s National Energy Modeling System (NEMS) model, and the results are calibrated every
two years to the corresponding Annual Energy Outlook (AEO) published by EIA. EPAUS9r
includes data specific to Northeast Census Division where the COMET-NYC draws data upon.
Since the official release of the EPAUS9r, researchers around academia, non-governmental
organizations, and federal research laboratories apply and use the model for applications from
analysis of policies to technology evaluations (Akhtar et al. (2013), Elobeid et al. (2013),
Loughlin et al. (2013), Yeh et al. (2008), Brown et al. (2013), Lenox and Kaplan (2016), Brown et
al. (2018), Kaplan and Witt (2019). The main contribution of these studies as well as this study is
the gathered insights on interactions of energy system components with economy,
technological advancements, emissions and policy. Therefore, addition of city and community
scale energy model to portfolio of publicly available tools is critical.
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kgrou " ¦ CAL
2.1 Description
MARKAL is a generic, data-driven, bottom-up energy-economy-environment optimization
framework where users can tailor it to their own data and needs. The initial version of the
model was developed in the late 1970s at Brookhaven National Laboratory. In 1978, the
International Energy Agency adopted MARKAL and created the Energy Technology and Systems
Analysis Program (ETSAP). ETSAP is a group of modelers and developers that meets every six
months to discuss model developments, extensions, and applications. MARKAL, therefore,
benefits from an active and interactive group of users and developers.2
The basis of the MARKAL model structure is a network diagram called a Reference Energy
System (RES), which depicts an energy system from resource supply to end-use demand as in
Figurel. The RES constructs an energy system up from a list of technology types, energy
carriers, and user demands. The four technology types represented are resource, process,
conversion, and demand technologies as defined in detail below:
1) Resource technologies represent the extraction cost and availability of resources such as
coal, oil, and natural gas.
2) Conversion technologies represent the conversion of fuel inputs into electricity.
3) Process technologies represent other means of converting resources into end-use fuels
including refineries and coal-to-liquid processes.
4) Demand technologies represent the technologies that meet specific user demands, such as
vehicles, air conditioners, and water heaters.
These technologies feed into a final stage consisting of end-use demands for useful energy
services. End-use demands include items such as residential lighting, commercial air
conditioning, and automobile passenger miles traveled.
2 For a detailed description of MARKAL, see the ETSAP MARKAL users-manual at
http://www.etsap.org/documentation.asp (last accessed on August 13, 2019)
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Resource
Technologies
Process
Technologies
Conversion
Technologies
Demand
Technologies
End-Use
Demand
Oil Well 1
Oil Well 2
Oil Well 3
Oil
Oil 1
Oil 2
Oil 3
Emissions
Power
Oil Collector
~
Refinery
~
Tracking
~
Plant
Gas Well 1
Gas
Gas 1
Gas 2
Gas Well 2
Gas Collector
Emissions
Power
Tracking
Plant
Electricity
Residential
Air
Conditioning
Residential
Space
Cooling
Figure 1 Illustrative Reference Energy System
The stages are connected by the various forms of energy, called energy carriers, produced and
consumed by the system. Technology specific emissions are tracked at each individual
technology, whereas the fuel related emissions are characterized under process technologies.
2.2 Data Requirements
A variety of data parameters are used to describe each element of the RES. The general
categories of data are:
Time horizon
System-wide global parameters
End-use energy service demands
Energy carriers
Resource technology profiles
Process and demand technology profiles
Environmental emission factors
User-defined constraints
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The definitions of these parameters and key inputs to the model is presented in Appendix A.
2.2.1 Time Horizon
The time horizon constitutes a user-defined number of time periods with each period having
the same number of years. The time horizon extends from 2010 to 2055 divided into 5-year
time periods.
2.2.2 System-wide Parameters
System-wide, otherwise known as global, parameters are assumptions that apply to the entire
model. Discount rate is defined to calculate annualized investment cost of technologies. All
costs must be entered in the same monetary unit and discounted to a common year; 2005 U.S.
dollars for the COMET-NYC. Also, subdivision of the year into load fractions is defined to
characterize variation in end-use demands as well as load duration curves for electricity
production. The model subdivides the year into three seasons Z (summer, winter, intermediate)
and four times of day Y (day am, day pm, night, and peak) resulting in a 12-step load duration
curve representation.
2.2.3 End-Use Energy Service Demands
End-use demands describe the specific energy services to be delivered to individuals or
commercial entities in the economy. Examples of end-use demands include residential space
cooling, personal automotive transport, and industrial process heat. The demand for an energy
service does not refer to the consumption of a particular energy commodity, but rather to the
provision of services such as manufacturing of steel, transportation, lighting of offices, and
heating of homes. These energy services are measured in units of useful energy, which may
vary within sectors. For example, the demand for the majority of transport services is specified
in miles traveled, demand for lighting is specified in billion lumens per year, and demand for
industrial process energy is specified in petajoules (PJ). Key data include per sector:
Projections for useful energy demand services by sector, and
The load shape of the demand profile by season/day-night-peak specifically defined
for end-use demands using electricity.
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2.2.4 Energy Carriers
Energy carriers are the various forms of energy produced and consumed in the RES. Energy
carriers can include fossil fuels: coal with different sulfur content, crude oil, refined oil
products, natural gas; electricity; synthetic fuels, and renewable energy: biomass, solar, wind,
geothermal, and hydro. Energy carriers provide the interconnections between the various
technologies in the reference energy system by flowing out of one or more technologies and
into others. The model requires that the total amount of each energy carrier produced in any
time period is greater than or equal to the total amount consumed. Key energy carrier related
data include:
Transmission efficiency,
Investment and operation and maintenance (O&M) cost for electricity transmission
and distribution systems, and
Reserve margin or amount of installed electricity production capacity above the
highest average annual demand.
2.2.5 Resource Technologies
Resource technologies are the entry points for raw fuels going in and out of the energy system.
These entry points include imports and exports, mining and extraction, and renewable energy.
These technologies are generally characterized using stepwise supply curves that indicate how
much of a resource can be obtained at a given price during each model period. Key resource
technology data include:
Bounds indicating the size of each step on each resource supply curve,
A corresponding resource supply cost for each supply step,
Cumulative resources limits indicating the total amount of a resource at a particular
supply step that can be delivered over the entire modeling horizon (e.g., total
proven size of a petroleum reservoir), and
Cost of transporting resources, either within a region or from region to region.
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2.2.6 Process, Conversion, and Demand Technologies
Process technologies change the form, characteristics, or location of energy carriers. Examples
of process technologies include oil refineries and hydrogen production technologies.
Conversion technologies model electricity production (e.g. conversion of one form of energy to
another, as in coal to electricity). Conversion plants are distinguished from other types of
technologies by the fact that they operate on a seasonal/day-night basis. Demand technologies
are those devices that are used to directly satisfy end-use service demands, including vehicles,
furnaces, and electrical devices. These technologies are characterized using parameters that
describe technology costs, fuel consumption and efficiency, and availability. Key data include:
Cost of investing in new capacity,
Fixed O&M costs (incurred at the level of installed capacity),
Variable O&M costs (incurred during the operation of installed capacity),
Fuel delivery costs corresponding to any sectoral difference in the price of an energy
carrier,
Technical efficiency (usually defined as the ratio between the sum of energy carrier
or useful energy service outputs to the sum of energy carrier inputs),
Model year in which the technology first becomes available for investment,
Availability factors (for process technologies) and capacity utilization factors (for
demand technologies) that describe the maximum percent annual (or season/day-
night-peak) availability for operation or a fixed percent annual (or season/day-night-
peak) capacity utilization per unit of installed capacity,
Existing installed capacity at the start of the model time horizon,
Limits on capacity in the form of incremental new investment (absolute or growth
rate) or total installed capacity, and
"Hurdle" rates, or technology specific discount rates, that can be used to represent
non-economic, behavioral aspects of investment choices (e.g., consumer
preferences, expectation of very rapid rates of return, or information gaps).
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2.2.7 Emission Factors
The COMET-NYC tracks emissions associated with energy use either through activity, installed
capacity, or new investment in capacity. Key environmental variable related data (expressed in
terms of pollutant emissions) include:
Emissions per unit of technology activity, installed capacity, or new investment, and
Emission constraints, which can take the form of a cap on total emissions in a year,
or a cumulative cap on emissions over the entire modeling horizon.
2.3 MARKAL Set Definitions and Naming Conventions
The MARKAL structure uses a pre-defined set of definitions and naming conventions to organize
the RES. Each set represents technologies, energy carriers, or constraints of a similar type.
Within any given set, MARKAL has numerous mandatory parameters that need to be specified
in the model. The main set memberships are listed in Table 1.
Tablel Set Definitions
Set Name Set Definition Set Name Set Definition
TCH
Technologies
ENT
Energy Carrier
SRCENCP
Resource Technology
ENC
Standard
SEP_EXP
Export
ECV
Conversion
SEPJMP
Import
EFS
Fossil
SEP_MIN
Extraction
ENU
Nuclear
SEP_RNW
Renewable
ERN
Renewable
SEP_STK
Stockpile
ESY
Synthetic
PRC
Process Technology
ELC
Electric
PRE
Energy
LTH
District Fleat
PRW
Material (weight)
FEQ
Fossil Equivalent
CON
Conversion Technology
DM
Demand
ELE
Electric Conversion
DM_COM
Commercial
BAS
Baseload
DMJND
Industrial
NBN
Non-baseload
DM_RES
Residential
STG
Storage
DM TRN
Transportation
DMD
Demand Technology
ADRATIO
User defined constrains
ENV
Emissions
REG_ADR
XARAT
Regional constraint
Cross-region
constraint
In addition to pre-defined sets, standard naming conventions are used to name the
technologies used in the model. For example, domestically mined fossil fuel step curves start
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with MIN (a standard convention) followed by the energy type and the supply step (i.e.
MINNGAD6 is the name for the 6th step in the supply curve for domestically mined natural gas).
The technology naming conventions are listed in Table 2.
Table 2 Technology Naming Conventions
Resource
Process
Conversion
Demand
Technologies
Technologies
Technologies
Technologies
(SRCENCP)
(PRC)
(CON)
(DMD)
MIN = Fossil Fuels
P = Process
E = Electric Conversion
COM = Commercial
RNW = Renewables
SC = Collectors
IND = Industrial
IMP = Imports
SE = Emissions Tracking
RES = Residential
EXP = Exports
X = Tracking
TRN = Transportation
STK = Stockpiles
ZZ = Dummy
The general rule for energy carrier naming conventions is to use a three- to four-character core
name for each principal energy carrier. The specific names for energy carriers would then add
on a two- or three-character descriptor to the core name. The core names are listed in Table 3.
Table 3 List of Energy Carriers
Resource Acronym Resource Acronym
Coal
COA
Landfill Gas
LFG
Compressed Natural Gas
CNG
Liquid Petroleum Gas
LPG
Conventional Gasoline
GSL
Natural Gas
NGA
Distillate Heating Oil
DSH
Natural Gas Liquids
NGL
Electricity
ELC
Nuclear
NUC
Diesel
DSL
Oil
OIL
Hydrogen
H2
Petroleum Coke
PTC
Hydropower
HYD
Residual Fuel Oil
RFH
Kerosene
KER
Solar
SOL
Wind
WND
3 IYC Struci
The COMET-NYC uses publicly available data published in New York City's annual greenhouse
gas inventory (GHGI) reports to estimate energy consumption in residential, commercial, and
industrial buildings. The modeling time period runs from 2010 until 2055 with 5-year time
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intervals for reporting. The 2010 (City of New York, 2011), 2011 (City of New York, 2012), 2014
(City of New York, 2016) and 2015 NYC GHGI reports (City of New York, 2017) are used to
calibrate the model's results in 2010 to ensure that the simulations are closely aligned with
real-world conditions.
The COMET-NYC consists of six regions including Brooklyn, Bronx, Manhattan, Staten Island,
Queens and New York State (to cover EGUs in the state). Each of the six-regions is structured in
a different RES diagram. Those diagrams are interconnected through technology links, i.e. fuel
trades. The naming conventions for each fuel type don't change from one region to another.
The naming identification of regions are presented in Figure 2. For instance, in the model, R1
represents the all the EGUs in New York State except the ones in the New York City. This region
is the source of electricity and transfers electricity to other regions via trade technologies.
In addition to the six regions, there is an outer region for model fuel supply (R0-"dummy"). This
is the supply region of the model that characterizes the fossil fuel sources located outside of the
city and the state. The fuel trade allows the commodity flows between regions. For each import
(or trade) option, a transportation cost, capacity limits, and capacity extension cost (investment
cost) are defined.
R1
New York State
R2
Brooklyn
R3
Bronx
R4
Manhattan
R5
Staten Island
R6
Ouecns
Figure 2 COMET-NYC regional coverage - New York State and Boroughs of New York City
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3.1 Model Workbooks
Figure 3 represents the simplified diagram of the reference energy system (RES) in the COMET-
NYC. There are two options to modify and edit the data in model. First, the modeler has ability
to change, create, and delete constraints, technologies, and related parameters directly from
the ANSWER interface3. Secondly, all data can be transformed from raw data in Excel
workbooks. Twelve different workbooks that provide input to the database are listed in Table 4.
Detailed requirements for inputs to the model is presented in Appendix A.
RESOURCE
RESOURCE
SUPPLY
EXTRACTION
Coal
Natural gas
Oil
IMPORT
Nuclear
Biomass/MSW
Solar
EXPORT
Wind
Energy Carrier
- Energy Supply
~ Useful Energy
Air Emissions
CONVERSION
TECHNOLOGIES
ENVIRONMENT
Industrial
Demand Devices
Transportation
Demand Devices
.
* IND
-
* TRN
Building
HH
Demand Devices
_
-J
COM
RES
Figure 3 COMET-NYC Model Structure
3 ANSWER is user friendly software with a gentle learning curve for working with energy system models. ANSWER-
MARKAL is the user interface working with inputing and analyzing results. In 2017, the support for new ANSWER-
MARKAL users is halted. International Energy Agency's Energy Technology Systems Analysis Program (ETSAP)
(https://iea-etsap.org/) strongly recommends using TIMES over MARKAL. U.S. Environmental Protection Agency
has moved its nine region U.S. database (Lenox et al., 2013) into TIMES framework. The COMET database for the
New York City will be moved to TIMES in the near future. However, the underlying input data that is critical for
case studies will not change.
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Table 4 COMET-NYC Workbooks
Workbook Name
COMET-NYC_COAL_19_vO
CO M ET-N YC_CO M_19_v0
CO M ET-N YC_E LC_19_vO
CO M ET-N YC_I N D_19_v0
CO M ET-N YC_N G A_19_v0
CO M ET- N YC_01 L_19_v0
COMET-NYC_REF_19_vO
CO M ET-N YC_R ES_19_vO
CO M ET-N YC_TR D_E LC_19_vO
CO M ET-N YC_TR N_F U E LSV_19_vO
COMET-NYC_TRN_LDV_19_vO
COMET-NYC TRN HDV 19 vO
Workbook Description
Resource: Coal supply curves and emissions
Commercial: end-use technology and emissions
Electric Generating Units (EGU): NY State EGUs technology and
emissions
Industrial: end-use technology and emissions4
Resource: Natural gas supply curves and emissions
Resource: Oil supply curves and emissions
Refinery: technology and emissions
Residential: end-use technology and emissions
EGU: Electric trading technology and emissions
Transportation: Fuel supply chain, technology and emissions
Transportation: Light duty vehicle sector: technology and emissions
Transportation: Heavy duty vehicle sector: technology and emissions
3.2 Units
The cost data is given in 2005 million U.S. dollars. Energy carriers are given in terms of PJ. Most
end-use demands are given in terms of PJ with the following exceptions:
Commercial and Residential Lighting Demand: billion lumens per year (bn-lum-yr),
LDV Transportation: billion vehicle miles traveled (bn-vmt), and
Transportation air and passenger rail: billion passenger miles (bn-pass-miles).
3.3 Model Assumptions
There are numerous assumptions that are used to compute the annual investment cost
such as annual discount rate (DISCOUNT), also referred to as "hurdle rate." It is applied as
5% to the system-wide economy (that covers all 6 regions). This discount rate can be
adjusted for a specific technology if this technology requires a different rate (DISCRATE).
4 The facility building energy consumption is represented. In the NYC area, there are no reported heavy
manufacturing industries.
13
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The year is divided into 12 different time slices over the planning horizon. The fraction
of the year (QHR(Z)(Y)) that is specified in the database is presented in Table 5.
Table 5 Time-slice fractions used to characterize load-duration curves
Abbreviation
Description
Time Fraction
l-DAM
Intermediate day - AM
0.0822
l-DPM
Intermediate night - PM
0.0957
l-N
Intermediate night
0.1532
l-P
Summer peak
0.0032
S-DAM
Summer day - AM
0.0975
S-DPM
Summer day - PM
0.1087
S-N
Summer night
0.1253
S-P
Summer peak
0.0027
W-DAM
Winter day - AM
0.0815
W-DPM
Winter day - PM
0.1087
W-N
Winter night
0.1381
W-P
Winter peak
0.0032
The transmission efficiency (TE(ENT)) of each energy carrier is assumed to be 100%.
In the electric sector transmission losses are characterized as "transmission efficiency".
We use 93.5% (EIA data based on state profile).
The reserve capacity ((E)RESERVE) for electricity is 0.15.
Specific assumptions on end-use sectors and electric generation, including user-defined
constraints are given in Appendix B.
3.4 Emission Factors
The COMET-NYC includes emission factors for: carbon dioxide (CO2), nitrogen oxides (NOx),
particulate matter < 10 |am (PM10), particulate matter < 2.5 |am (PM2.5), sulfur dioxide (SO2),
volatile organic compounds (VOC), methane (CH4), carbon monoxide (CO), Organic Carbon (OC),
and Black Carbon (BC) for each region and sector across the whole energy system related to
fuel consumption.
14
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. Iclif tor; Be bration
The building end-use energy demands are split into residential, commercial and industrial
(facility level) buildings. The level of end-use demand in each of the three sub-sectors is
estimated using a bottom-up approach based on the U.S. ElA's AEO, Commercial Building
Energy Consumption Survey (CBECS), the NYC Primary Land Use Tax Lot Output (PLUTO) and
other related official data. Figure 4 depicts the data sources for each demand category.
The 2011 Inventory of NYC GHGI contains citywide energy consumption and emissions data that
belong to 2010 (City of New York, 2012). The citywide data is divided into several sectors
including buildings, transportation, and streetlights. Energy consumption data is reported in
liters for liquid fuels and gigajoules (GJ) for electricity and gaseous fuels. It also contains energy
content (GJ/unit) and emissions intensity (kg/unit) coefficients for each fuel type. The energy
content coefficients are used to calculate the amount of energy consumption for each fuel type
in terms of PJ/year. Total energy and emissions data for the building sector represents the sum
of "Buildings" section energy consumption provided in the emission inventory report.
In the NYC GHGI report that was published in 2016, the data is broken down into more detailed
sectors than the 2012 GHGI report. The building sector is divided into five categories: large
residential, small residential, commercial, industrial, and institutional. Since the model includes
residential, commercial, and industrial building technologies, the goal is to gather sector-level
data rather than the lump sum reported in the 2012 NYC GHGI for all buildings. To this end, we
break down city level fuel oil, natural gas, and electricity consumption into residential,
commercial, and industrial sector by applying the fuel consumption shares provided in the 2014
and 2015 GHGI reports (City of New York, 2016).
In 2014, NYC Department of Health and Mental Hygiene provided citywide site energy
consumption levels by fuel type for five different building types (1 to 4-unit family house,
multifamily, commercial, industrial, and institutional) under eight different major end-use
energy service demands, i.e., space heating, water heating, space cooling, lighting, conveyance,
process loads, and miscellaneous, to EPA.
15
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Building Sector
Residential
Commercial
industry
9 Multifamily (Pre-
war, post 1980, very
large)
£ 1 to 4 Family (Row
house, masonry, etc.)
£ Commercial (Very
large, mixed use,
financial office, hotel,
hospital, museum
etc.)
£ Institutional (K-
12 schools,
university, religious,
hospital and health
facilities etc.)
£ Warehouse &
Factory Buildings
DSL
DSL £
DSL 0
DSL £
DSL £
Space heating
NGA
NGA £
NGA £
NGA 4)
NGA £
ELC
ELC £
ELC £
ELC £
ELC
STM
STM £
STM ' £
STM 0
STM 0
DSL
DSL £
DSL Q
DSL £
DSL £
Space cooling
NGA
NGA $
NGA d
NGA £
NGA £
ELC
ELC £
ELC
ELC
STM
STM 4)
STM 4)
STM £
STM £
DSL
DSL 0
DSL 0
DSL £ £
| DSL #
Water heating
NGA
NGA £
NGA (0
NGA £
NGA £
ELC
ELC £
ELC
STM
STM £
STM £
STM £
STM #
Lighting
11
ELC £
1 ~ 1
1 "
Other
NGA
NGA £
NGA $
NGA £
NGA $
| NGA
ELC
ELC £ 0
ELC £
ELC £
r I
i ¦ ¦
City of New York, 2012. Inventory of New York City Greenhouse Gas Emissions. New York City, NY: Mayor's Office of
Long-Term Planning and Sustainability
City of New York, 2016. Inventory of New York City Greenhouse Gas Emissions in 2014, by Cventure LLC, Cathy
Pasion, Mikael Amar, and Yun Zhou, Mayor's Office of Sustainability
PLUTO: Extensive land use and geographic data at the tax lot level
LL84: Benchmarking Data 2015 Energy and Water Data Disclosure (Data for Calendar Year 2014)
NYC Department of Health and Mental Hygiene
Figure 4 Data Sources for Buildings Sector
In the COMET-NYC, we categorized conveyance, process loads, and miscellaneous under
"other" section. The aggregated fuel consumption data for each end-use energy service
demand (residential, commercial, industrial) are decomposed into end-use categories under
the assumption that end-use energy consumption shares are valid also for 2010, By processing
the data provided by the NYC Department of Health and Mental Hygiene, we have custom
energy consumption shares for different building types as depicted in Figure 5.
16
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Residential
0 50 100 150 200 250
¦ Space Heating ¦ Water Heating ¦ Space Cooling ¦ Ventilation
¦ Lighting ¦ Conveyance ¦ Process Loads ¦ Misc
Figure 5 End-use Demand in 2010 with Respect to The Building Types in PJ5
The PLUTO database gathers information on every building in NYC's five boroughs such as
address/GPS coordinates of each building, building type identifiers, total indoor floor space,
number of floors, etc. (City of New York, 2015). Each building in the city is assigned a unique
identifier number to be distinguished in various data that city collects. This number is called
Borough-Block-Lot (BBL) number. From PLUTO database, we identified the building
topographies per each borough. Figure 6 represents the PLUTO building topology classification6
per each borough.
¦ Multifamily
Institutional
Industrial
¦ Commercial
¦ lto4
Figure 6 Share of The Building Types with Respect to Total Building Area (ft2) in Each Borough7
5 Raw data for this figure is presented in Appendix L.
6 PLUTO data base divides existing building stock under 55 different property type. Each property type is allocated
under one of 3 different end-use sector presented in COMET
7 BK: Brooklyn; BX: Bronx; MN: Manhattan; QN: Queens; SI: Staten Island; Raw data for this figure is presented in
Appendix M.
17
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Furthermore, New York City enacted a Benchmarking Law requiring annual measurements of
energy and water consumption in buildings exceeding certain square footage (LL84) (NYC,
2018). The LL84 data is also organized using BBL numbers. By processing, both PLUTO and LL84
using BBL, we identified existing building stock and their associated energy use for each building
type in each borough. Building type specific end-use demand shares are then used to divide
total fuel consumption (given in GHG inventories) into borough based end-use demand specific
energy demands. Those energy consumption values are coupled with existing technology stock
(capacity and efficiency) that is provided in EPAUS9r database for Middle Atlantic Census
Division to get the existing representation of building end-use energy demands for each
borough. The parameters related to technology stock and its distribution are based on
Commercial and Residential Energy Consumption Survey (CBECS and RECS) provided by EIA.
4.1 Residential Sector
The residential sector workbook characterizes end-use energy demands for space heating,
space cooling, water heating, lighting, and other appliances to meet end-use demands. Several
technology options are defined. Figure 7 illustrates a sample RES diagram for residential space
heating (RSH) demand. Technology options that can meet RSH are furnace, heat pump, radiant,
and wood.
Grid
PV
Natural Gas
NEW YORK CITY
BUILDINGS
Figure 7 Illustrative Reference Energy System for Space Heating Characterization
18
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Total energy demand for the residential sector is classified under four main sections (space
heating, space cooling, water heating, lighting) and two aggregated fuel consumptions (other-
electricity and other-natural gas). These four main demand services represent 81.1% of the
energy use in the residential sector in 2010. The rest, 18.9% of energy demand, come from
appliances such as personal computers, TVs, clothes dryers, ovens, ventilations, conveyance,
and dishwashers, are met with "Residential Other" technologies that use electricity, or natural
gas. These "other" technologies do not have efficiency improvements over time.
4.1.1 Residential Energy Demand Services
The residential sector energy demand structure consists of six different end-use energy
demands. The nomenclature and related units are given in Table 6. The final energy
consumption for end-use services is driven by the model under set of pre-defined end-use
service demands, energy balance requirements, environmental and energy policies defined
through constraints. Residential sector end-use demands are defined in two different units. The
percent of total residential energy demand in 2010 and in 2055 is shown in Figure 8.
Table 6 Residential End-use Service Demands
Demand
Units
Description
RSC
PJ/yr
Space Cooling
RSH
PJ/yr
Space Heating
RWH
PJ/yr
Water Heating
RLT
billion lumens/yr
Lighting
ROE
PJ/yr
Other - Electricity
ROG
PJ/yr
Other - Natural Gas
For the residential sector, energy service demand for 1-4-unit family and multifamily are
merged to calculate the demand for the calibration year. Building sector end-use demand is
determined based on the change in the projected population and the change in the average
number of households. Average number of people per household is assumed to be 2.70 in 2010
and expected to decrease to 2.59 by 2035. It is assumed that until the end of the modeling
19
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horizon average number of people per household remains to be 2.59. For detailed information
on borough based projected population values, please see Appendix C.
¦ 2010
¦ 2055
50%
Figure 8 Residential Energy Demand by End-Use Type
The end-use energy service demand is calculated for square footage of heated or cooled space,
average heating degree days (HDD) and cooling degree days (CDD). The formulas used to
calculate the individual end-use energy demand services are given in Table 7. The HDD and CDD
values specific to New York City are given in Appendix D.
Table 7 End-use Energy Service Demand Formulations for Residential Sector
Demand
Formulation
RSC
Cooling coefficient * square footage of air-conditioned space * CDD
RSH
Heating coefficient * square footage of heated space * HDD
RWH
NYC total water heating demand * percent of households in a borough
RLT
NYC total lighting demand * percent of households in a borough
ROE
NYC total other electric * percent of households in a borough
ROG
NYC total other natural gas * percent of households in a borough
There is no data available for New York City on how the square footage of air-conditioned or
heated space would change, however, there is data on space heating and cooling coefficients
for New York City. We utilized AEO (2014) assumptions on the percentage of demand change
over time, % of household air conditioning use, and the average household square footage
population change. As for HDD and CDD, the data specific to New York City is used. The
20
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residential technology stock values are assumed to be in line with values for Middle Atlantic
Region as no other data is available. Similar approach is taken for building the EPAUS9r and
wherever there is no data for New York City, we utilized the data and framework adopted in
EPAUS9r (Lenox et al., 2013). The COMET-NYC calculations are based on EIA Residential Energy
Consumption Survey.
To characterize technology trends and its impacts on energy consumption, emissions and cost,
a suite of technology options with available fuel combinations and energy efficiency attributes
are included into the COMET-NYC while meeting model constraints, and energy demands. Table
8 shows main technology categories with available fuel options.
Table 8 Residential Technology and Fuel Combinations
End-use Demand
Technology Type
Fuel
Space Heating
Radiant
Electric
Natural Gas
Distillate
Heat Pump
Electric
Natural Gas
Geothermal
Furnace
Natural Gas
Distillate
Kerosene
Wood
Space Cooling
Room AC
Central AC
Electric
Electric
Heat Pump
Electric
Natural Gas
Geothermal
Water Heating
Electric
Natural Gas
Distillate
Solar
Lighting
Incandescent
CFL
LED
Halogen
Linear Fluorescent
Reflector
Electric
Electric
Electric
Electric
Electric
Electric
All cost and efficiency values for residential space heating, space cooling, water heating and
lighting are taken from the Annual Energy Outlook Residential Technology Equipment Type
Description File (EIA, 2014). All parameters related to residential sector technologies are
provided exogenously into the model.
21
-------
The AEO Residential Technology Equipment Type Description File provides building sector
appliances and related equipment costs and efficiency (EIA, 2011). For lighting demand, the
Lighting Market Characterization Report which shows the distribution of lamps with respect to
the technology options (e.g. fluorescent, incandescent) for each demand sector (e.g.
residential, commercial), the estimates of installed stock and lumen production provides data
for the model. For Lighting costs, the report prepared for the EIA on building sector end-use
demand technologies provides data for the COMET-NYC (EIA, 2007).
For calibration year, aggregate in-city fuel consumption value in 2010 is distributed for each
end-use demand category, in line with the shares that are provided by the NYC Department of
Health and Mental Hygiene. For the period between 2015-2055 the shares for NGA and ELC are
relaxed per time-period. For the technologies that consume NGA and ELC, if they are saturated
in the market, the hurdle rate is set as 18%. For other technology options, the hurdle rates as
provided in EPAUS9r (Lenox et al., 2013) are given in Table 9. Residential sector end use
demands are presented in Appendix E.
Table 9 Technology Specific Hurdle Rates for Residential Sector
Hurdle Rates
Technology
28%
Compact fluorescent lights
45%
LED and linear fluorescent lights
45%
Technologies that use diesel
45%
Instantaneous and solar water heaters
45%
Electric heat pumps for space heating and cooling
60%
Geothermal heat pumps for space heating and cooling
60%
Room/window air conditioners
4.1.2 Residential Emissions Accounting
The model utilizes a pass-through dummy process technology to account for the emissions
resulting from fuel activity within an end-use technology (e.g., natural gas use in furnaces to
meet space heating demand). These process technologies include an emission coefficient
parameter per PJ consumption of fuels such as NGA, DSL, etc. Depending on the level of fuel
consumed in that demand technology, the model calculates the total emissions and aggregates
it up to whole residential sector.
22
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4.1.3 Residential Sector Constraints
The COMET-NYC utilize constraints to mimic more realistic outputs in accordance with the
existing city policy implications. For instance, to model, city's plan to phase out petroleum-
based space heating options, an upper bound on diesel consumption is set for the 2015-2055
period. However, we also include a lower bound on electricity consumption on the space
heating to assure that the share of electricity-based space heating will not drop unrealistically
over the modeling period. In addition to, fuel share constraints, technology splits are also
presented to calibrate the AEO 2016 (EIA, 2016) to Residential Unit Consumption of Energy with
respect to the equipment classes. For space heating, the constraint is set for furnaces, heat
pumps, and radiant; for space cooling central heat pump, central AC, room AC; for lighting
incandescent, compact fluorescent, linear fluorescent, and reflector for the base year. These
constraints are relaxed by 27% for 2055. Please refer to Appendix B for a more detailed list of
residential sector constraints.
4.2 Commercial Sector
The commercial sector representation in the COMET-NYC database covers energy service
demands for space heating, space cooling, lighting, water heating, and other commercial uses.
Commercial sector demand makeup about 13.5% of the total energy used in the demand
sectors. The first four demands can be met in a model run by choosing from a number of
detailed end-use technologies. For example, water heating demand can be met by electric,
natural gas, or solar water heaters or an electric heat pump. These four demands represent
64% of the energy use in the commercial sector in 2010 according to AEO. The other 36% of
energy demand comes from other equipment such as office computers and printers,
automated teller machines, telecommunications equipment, medical equipment, and
emergency generators. These demands are categorized under "Commercial Miscellaneous" and
"Commercial Office Equipment" technologies that use electricity, natural gas, diesel, or fuel oil.
In the reference case, they are categorized as "other" technologies and do not have efficiency
improvements over time.
23
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4.2.1 Commercial Energy Demand Services
The commercial sector is an aggregation of "Commercial Buildings" and "Institutional
Buildings"*. The methodology and technology structure are similar to the residential sector.
Hence some sections of the commercial sector are curtailed. The commercial sector module
includes details of commercial sector energy demands and their corresponding end-used
technologies. The nomenclature and corresponding units for those end use energy demands
are listed in Table 10. Commercial sector end-use energy demands are exogenous to the model,
and baseline values are presented in Appendix F.
Table 10 Commercial Demands
Demand
Units
Description
CSH
PJ/yr
Space Heating
CSC
PJ/yr
Space Cooling
CWH
PJ/yr
Water Heating
CLT
billion lumens/yr
Lighting
CME
PJ/yr
Misc-ELC
CMN
PJ/yr
Misc-NG
The share of energy use for various end-use applications for the calibration year (2010) and end
year of the model for Reference Case is given Figure 9.
CMN
1
CME
CWH
CSH
¦ 2010
¦ 2055
CSC
CLT
0% 10% 20% 30% 40%
Figure 9 Commercial Energy Demand by End-Use Type
* For building classifications please see LL84 data
24
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Demands are calculated by determining the energy intensity (PJ per square foot) for each end-
use demand from the average stock equipment efficiency in the AEO reference case and
multiplying those intensities by the regional square footage.
End use energy service demands are calculated by determining the energy intensity per square
foot for each demand type. For calibration year, total fuel consumption values are used to
calculate base year end-use demand values. Average stock efficiency rates that are presented in
the AEO reference case are multiplied by fuel consumption values to get the aggregate end-use
demand for NYC. Building stock square footage are used to get the energy intensity value for
each end-use demand type. Space heating and space cooling demand for the rest of the
modelling period is calculated from AEO equipment stock data (including average HDD and CDD
days). Other end use energy demands are calculated similarly as in residential sector. The
equations used to calculate end-use energy demands are given in Table 11.
Table 11 End-use Energy Service Demand Formulations for Commercial Sector
End-use demand
Formulation
CSC
Cooling coefficient * square footage of airconditioned space * CDD
CSH
Heating coefficient * square footage of heated space * HDD
CWH
Water heating intensity * regional square footage
CLT
Lighting intensity * regional square footage
CME
National demand for "other" electricity uses * regional percent of households
CMN
National demand for "other" natural gas uses * regional percent of households
4.2.2 Commercial Technology Structure
31 demand technologies with numerous fuel combinations are modelled (as detailed in Table
12). Each of these technology and fuel combinations have distinct technology attributes such as
investment cost, O&M cost, starting year, and fuel efficiency. We utilized AEO's Commercial
Technology Equipment Type Description File to determine values for various end-use sectors
such as space heating, cooling etc (EIA, 2011).
Base year (2010) also known as calibration year final energy consumption is calibrated against
reported actual final energy consumption data. For the period between 2015-2055, DSL fuel
25
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share is decreased over 5% per time-period where as for ELC and NGA base year shares are
relaxed 3% per period out to the end of the modeling horizon.
The technology shares excluding incandescent lighting are relaxed 3% per time period.
Incandescent lighting shares are reduced 60% by 2020 to only 5% of the lighting technologies
used in 2055.
Table 12 Commercial Technology and Fuel Combinations
End-use demand
Technology Type
Fuel
Space Heating
Heat Pump
Air Source
Natural Gas
Ground Source
Boiler'
Electric
Natural Gas
Diesel
Furnace'
Natural Gas
Diesel
Space Cooling
Heat Pump
Air Source
Natural Gas
Ground Source
Centrifugal Chiller
Electric
Natural Gas
Reciprocating Chiller
Electric
Scroll Chiller
Electric
Screw Chiller
Electric
Rooftop A/C'
Electric
Natural Gas
Window/Wall A/C'
Electric
Central A/C'
Electric
Water Heating
Electric'
Natural Gas
Diesel
Solar'
Lighting
Incandescent
Electric
CFL
Electric
LED
Electric
Halogen
Electric
Linear fluorescent
Electric
Mercury Vapor
Electric
Metal Halide
Electric
Hurdle rates in the COMET-NYC are adopted from the values in the EPAUS9r database (Lenox et
al., 2013). For the technologies that consume NGA and ELC, if they are saturated in the market,
the hurdle rate is set as 18%. The hurdle rates for remaining technology options are given in
Table 13.
* Technology shares are defined as constraint in COMET
26
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Table 13 Technology Specific Hurdle Rates for Commercial Sector
Hurdle Rates Technology
45%
45%
24%
24%
All high efficiency technologies (except otherwise noted).
Ground source heat pumps, standard efficiency
Ground source heat pumps, high efficiency
Solar water heaters
75%
60%
125%
All high efficiency natural gas technologies
Diesel technologies
Diesel boilers
4.2.3 Commercial Emissions Accounting
The model utilizes a pass-through dummy process technology to account for the emissions
resulting from fuel activity within an end-use technology (e.g., natural gas use in furnaces to
meet space heating demand). These process technologies include an emission coefficient
parameter per PJ consumption of fuels such as NGA, DSL, etc. Depending on the level of fuel
consumed in that demand technology, the model calculates the total emissions and aggregates
it up to whole commercial sector.
4.2.4 Commercial Sector Constraints
Similar to the residential sector, an upper bound on diesel consumption is set for the 2015-
2055 period for water heating. The constraint is set in such a way that diesel consumption
shares for water heating in 2010 represents an upper bound for it. Other than fuel share
constraints, technology splits are also controlled with user defined constraints according to the
AEO 2016 Commercial Technologies Market Shares (EIA, 2016) for the base year (For space
heating the constraint is set for furnaces and boilers for space cooling rooftop, central, gas heat
pump (GHP), absorption heat pump (AHP); for lighting incandescent, compact fluorescent,
halogen, T8, T8L, T5, HID High Bay, HID Low Bay, LED). These constraints are relaxed by 27% for
2055. Please refer to Appendix B for a more detailed list of commercial sector constraints.
4.3 Industry Sector
Modeling of an end-use sector in MARKAL includes an end-use energy demand layer and
available technology options to meet those service demands. However, the industrial sector in
27
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the COMET-NYC consists of only one energy demand under the "other manufacturing" title. The
overall demand is presented in PJ/yr. The demand for the modeling period represents the
aggregate fuel consumption equivalent value under "facilities" end-use service. Industrial
sector building related end-use energy demands are exogenous to the model, and assumed
values are presented in Appendix F.
4.3.1 Industrial Emissions Accounting
Industrial emissions are accounted as the quantity of emission per PJ of each fuel consumed by
a specific technology due to fuel combustion. There exist no emissions that are related to
feedstocks.
4.3.2 Industrial Sector Constraints
Due to the lack of data, the industrial sector doesn't have facility based detailed representation
in the COMET-NYC. The fuel consumptions that belong to the industrial sector are assumed to
result from "facility building demand". The industrial sector only has constraints to shape the
fuel mix according to 2010 existing fuel consumption data and relaxed approximately 15% for
the rest of the modeling horizon.
28
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ortati :tor: Base Year Calibration
The transportation sector covers the vehicle technologies that are used to meet the
transportation demand for numerous transportation modes. Technologies are classified under
two main technology sets namely light-duty vehicles (LDV) and heavy-duty vehicles (HDV).
LDV technologies include gasoline, diesel, compressed natural gas (CNG), hydrogen (H2), and
electric powered cars including plug-in, electric vehicle (EV), and hybrid, which meet demand
measured in billion vehicle miles traveled per year (bn-vmt-yr). HDV technologies include heavy
duty short haul trucks, buses, and electric passenger rail to account for NYC's extensive public
transit system. The LDV and HDV workbooks do not currently contain biofuels or blended
products like E15 or E85.
Transportation demand for bn-vmt-yr was derived from the NYC Greenhouse Gas Inventory
(City of New York, 2012) where fuel consumption per mode is given in liters. To estimate
vehicle miles traveled based on total fuel consumption data, we need to have vehicle efficiency
ratings and car class shares. Because of lack of data for New York City, we utilized values in
EPAUS9r for vehicle efficiency ratings and car class shares from different transportation modes
given for Middle Atlantic Region to estimate vehicle miles traveled coupling with the reported
fuel consumption data.
5.1 Light Duty Vehicles
Light Duty Vehicle (LDV) demand account for personal vehicle miles travelled. Transportation
Light-duty (TL) demand is represented not only by various demand technologies (including
different fuel type and efficiency levels) but also fuel distribution networks for gasoline, diesel,
electricity, etc. Mini-compact, compact, full size, minivan, pick-up truck, small SUV, and large
SUV are the main vehicle class sizes.
5.1.1 Light Duty Vehicle Energy Demand Services
Light duty vehicle demand for base year is calculated with respect to the total fuel consumption
provided in NYC greenhouse gas emission inventory report. According to base year average
vehicle efficiency, aggregate vehicle miles travelled is calculated. Demand trajectories are taken
from the AEO forecasts (EIA, 2014) and adjusted for each borough according to population
29
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forecasts. Light duty vehicles transportation demands are exogenous to the model, and
assumed values are presented in Appendix G. 2010 LDV fleet distribution for the New York City
is set as a constraint. These constraints are in the form of engine type, maximum available
investment levels are based on the ones in EPAUS9r. For conventional vehicles, hurdle rate is
taken as 40%, and 44% is applied for the remaining technologies.
5.1.2 Technology Structure
The light duty demand (TL) is met by twelve different engine types for seven car classes.
Available fuel-technology pairs for seven car classes are presented in Table 14.
Table 14 Light Duty Vehicle Fuel and Technology Combinations
Mini-
Compact
Compact
Full-
size
Minivan
Pickup
Small
SUV
Large
SUV
GSL
Conventional
~
~
~
~
~
~
~
Advanced
~
~
~
~
~
~
~
Hybrid
~
~
~
~
~
~
Plug-in Hybrid (20
miles per charge)
~
~
~
~
~
~
Plug-in Hybrid (40
miles per charge)
~
~
~
~
~
~
DSL
Conventional
~
~
~
~
~
~
Hybrid
~
~
~
~
~
CNG
Conventional
~
~
~
~
Flex fuel
~
~
~
~
h2
Fuel Cell
~
~
~
~
~
~
ELC
100-mile range
~
~
~
~
~
~
~
200-mile range
~
~
~
~
~
~
~
From 2010 to 2035, the share of LDV car classes are adopted from the EPAUS9r Middle Atlantic
region, with a greater percentage of light-duty transportation demand being met by smaller,
more efficient vehicles (Lenox et al., 2013). From 2035 on, the splits remain the same. Figure
10 shows the distribution of car classes in 2010 and 2035.
30
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Figure 10 Distribution of Light Duty Vehicles
5.1.3 Light Duty Vehicle Emissions Accounting
Two distinct emission tracking structures are included in the COMET-NYC for the transportation
sector. The first one is a pass-through process technology where CO2 emissions are tracked.
Similar to other end-use demand sectors, CO2 emissions are calculated by means of the total
flow of the fuel entering into the dummy fuel CO2 related technologies. The emission factors
are based on carbon content of the fuel. For criteria air pollutants, emission factors are defined
on the technology itself to represent transportation related air regulations. Those coefficients
are gathered from the Motor Vehicle Emission Simulator (MOVES) (EPA, 2010) model. Emission
factors for the period of 2010-2055 are taken from EPAUS9r.
5.1.4 Light Duty Vehicle Constraints
The LDV sector includes seven car classes including Mini-compact, Compact, Full-size, Minivan,
Pick-up, Small and Large SUV. Car shares are based on Regional car and truck sales (sales data
by class) for the Middle Atlantic region presented in AEO (EIA, 2014).
5.2 Heavy Duty Vehicles
Table 15 presents demand naming conventions for the heavy-duty vehicles (HDV) sector. The
HDV sector includes buses (TB), short haul heavy duty trucks (THS), medium size (TMS),
passenger rail transport (TRP) and subway in the COMET-NYC database. The aggregate end-use
demand for heavy-duty vehicles are characterized under TH.
31
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5.2.1 Energy Demand Services
Input data that are concerning heavy duty technologies are collected from NYC 2010 fuel
consumption data (City of New York, 2012), AEO 2014 demand projections (EIA, 2014) and
EPAUS9r fleet constraints.
TH end use energy demands are calculated with the assumption that calibration year existing
technology combinations in EPAUS9r are also valid for NYC. NYC energy consumption value for
transportation sector is combined with the average efficiency of existing fleet to calculate the
TH demand, then the demand is extended according to the AEO demand projections.
All heavy-duty vehicles transportation demands are exogenous to the model, and assumed
values are presented in Appendix H.
Table 15 Heavy Duty Transportation Demands
Name
Description
Units
Unit Description
TB
Bus
bn-vmt
billion vehicle mi
es traveled
TMS
Medium Duty Trucks
bn-vmt
billion vehicle mi
es traveled
THS
Short Haul Heavy Duty Trucks
bn-vmt
billion vehicle mi
es traveled
TRP
Passenger Rail (includes Subway)
bn-pass-miles
billion passenger
miles
5.2.2 Technology Structure
Table 16 represents the available engine and fuel type pairs in the COMET-NYC and
distinguishes them with respect to the efficiency improvements and different vintage years
with available fuel options. User defined constraints are set for the calibration year to mimic
the real fuel investment data. The technology share constraints are relaxed 1% to 3% per
period. Those shares are taken from EPAUS9r. For CNG consumption maximum fuel
consumption share is bounded by user defined constraint to meet AEO projections in 2035. The
average hurdle rate is set to 18% for base efficiency technologies. Table 17 presents the hurdle
rates for other technologies. The rates applied for heavy duty vehicles are taken from EPAUS9r
(Lenox et al., 2013).
32
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Table 16 Heavy Duty Vehicle Demand Types, Fuel, and Technology Combinations
End-use demand
Fuel
Efficiency improvements
Transportation Bus Demand
Diesel
Improved Eff
Adv. Tech
Adv. Hybrid
(TB)
ELC
Improved Eff
Adv. Tech
CNG
Improved Eff
Adv. Tech
Adv. Hybrid
Hydrogen Fuel
Hybrid
Cell
Transportation Medium and
Diesel
Improved Eff
Adv. Tech
Adv. Hybrid
Heavy-Duty Vehicles - Short
CNG
Improved Eff
Adv. Tech
Adv. Hybrid
Haul Demand (THS)
Hydrogen Fuel
Cell
Hybrid
Transportation Rail Passenger
Diesel
Demand (TRP) - Commuter
Electricity
Transportation Rail Passenger
Electricity
Demand (TRP) - Passenger Rail
Subways & Streetcars
Table 17 Technology Specific Hurdle Rates for Heavy Duty Vehicles
Hurdle Rates Technology
20%, 24%, 26% Improved efficiency (modelers choice between the three)
24% Hydrogen fuel cell technologies
28% Advanced technology improvements
5.2.3 Heavy Duty Vehicle Constraints
In HDV sector, the model only has two constraints. CNG powered buses are given a fixed
amount of investment for 2010 to represent existing stock of CNG bus fleet. Compared to
EPAUS9r, the COMET-NYC includes "subway". The model has both commuter rail and subway
to meet TRP demand. To keep the balance in order to mimic the actual sector conditions the
percent of total demand that can be met by commuter rail is protected by lower bounds that
belong to the actual NYC transportation data for 2010 (City of New York, 2012).
and gas resources
The COMET-NYC_NGA_19_vO workbook provides natural gas to the commercial, residential,
industrial, electricity, refinery, and transportation sectors. The NYS supply region (Rl) and the
five boroughs (R2-R6) all receive domestic natural gas using the price and supply curves from
33
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the EPAUS9r model. Commodities NGAD1-NGAD6 are supplied by MINNGAD1-6 resource
technologies, which are then collected into NGAR1-6 and delivered to end use customers
through pipeline technologies and trade linkages. Additional information about NYC's natural
gas supply and infrastructure are included in Appendix J.
The COMET-NYC_OIL_19_vO workbook provides domestic crude oil from PADD I, II and III to the
NYC supply region (Rl), and refined products imported from PADD I, II and III to the five
boroughs (R2-R6). Crude oil commodities are produced by resource technologies MINDOILD1-5,
collected into OILD1, and sent to refineries in the supply region (Rl) which produce distillate
heating oil (DSL) and low sulfur fuel oil (RFL) in the COMET-NYC database. In the database, the
refineries also produce low sulfur (500ppm) and ultra-low sulfur (15ppm) highway diesel for use
in the transportation sector (TRNFUEL/LDV/HDV workbooks). The five boroughs (R2-R6) can
import refined products from the refineries in Rl using trade links and pipelines or import
refined products directly from PADD l-lll. For other fuel resource information please see
EPAUS9r Database Documentation (Lenox et al., 2013).
7 Electric Sector Representation
The ELC workbook contains technology characterization for all EGU technologies located in New
York State. The EGUs in New York City are dual fuel generators using natural gas or oil. Some
coal fired generation capacity still exists in the New York State. The EGU sector also includes
CHP capacity, based on the U.S. Department of Energy (DOE) CHP database. The transmissions
and distribution technologies for electric trade linkages are presented in the COMET-
NYC_TRD_ELC_19_vO workbook, represents electric trade linkages from Rl to each of the five
boroughs (R2-R6) and between neighboring boroughs.
This sector consists of 115 different conversion technologies plus in state combined heat and
power plants that take fuel resources in and generate electricity for the use of city. 16 Solar
photovoltaic energy conversion technologies (for 4 different generation class under 5 cost
categories from A to E), 77 wind conversion technologies including onshore wind and offshore
shallow wind (for 4-6 generation class of wind resource under 5 cost categories), three different
nuclear power plants (LWRs with recirculating cooling and open loop cooling), 9 coal conversion
34
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technologies (coal steam with bituminous and subbituminous with recirculating cooling and
open loop cooling), 10 natural gas conversion technologies (including steam with recirculating
cooling and open loop cooling, combined cycle with recirculating cooling, open loop cooling, dry
cooling), 3 hydroelectric power plants (conventional and reversible). All the above-mentioned
conversion technologies have capacity in gigawatts. To mimic the real electricity generation in
2010, we add generation capacity that based on "Annual Electric Generator Report" values
which represents the data collected by EIA-860 forms (this form collects data on the status of
the existing electricity generation facilities with respect to the equipment specifications (i.e.
boilers, cooling system, generator type). The electricity is produced in PJ (the conversion factor
is taken as 31.536 PJ/GW). As any other technology they are characterized in the system with
residual capacity (if they are existing technologies), life-time, variable O&M cost, availability
factor and efficiency parameters. NEMS and AEO 2016 data are used to set constraints on
electricity generation levels. In the COMET-NYC model, NYS electricity generation sector is
included as the main electricity provider.
nice Case
The methodology and data sources described in the previous sections are utilized to generate a
baseline reference case for New York City such that the energy consumption values reported in
the NYC GHG Inventory is matching sector-by-sector energy consumption for 2010 and 2015.
Figure 11 compares the fuel consumption results of the reference scenario model run for the
base year 2010 with 2010 output data from the NYC GHGI. The 2010 data was broken down
into residential, commercial, industrial and transportation sectors. The estimated energy
consumption in 2010 and 2015 (calibration years) closely tracks with the reported data across
the three major building sectors and transportation energy use. The data combines energy used
to meet end-use demands (i.e. space heating/cooling) with energy used by the building
archetypes in lieu of end-use demand technologies. Figure 12 compares electricity generation
estimates with actual generation in New York State. The CHP capacities in the Reference Case is
presented in Appendix K.
35
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155.6
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Figure 11 Reference Case vs. Reported Data Fuel Consumptions (PJ)
36
-------
50 49
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96
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Figure 12 Electricity Generation Results from COMET-NYC vs. Reported Data (GWh) in 2010 and
2015
9 Final Remarks and Future Work
The COMET is developed to perform scenario analysis at the city and regional level. The
COMET-NYC is calibrated to current technology stock and fuel consumption values for New York
City. The model can be used to make long-term projections for energy consumption. The
COMET-NYC uses several official data sources to provide useful model outcomes. The data is
planned to be updated on a regular basis in accordance with the release of the document
updates including borough-based population forecasts, AEO forecasts, local GHG emission
reports, EIA state level electricity generation reports, etc.
Local and regional authorities are facing challenges caused by climate change, urbanization,
limited natural resources, environmental goals that conflict with economic development, and
aging infrastructure that will require significant upgrades or replacement in the coming
decades. The COMET-NYC model serves as an example for other cities and communities who
are interested in leveraging the benefits of performing energy planning as population growth
and climate change place increasing pressure on aging infrastructure. The COMET framework
37
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can be adapted for use in other cities or communities where underlying necessary data is
available.
This type of modeling framework could aid policy making process through generating
technically robust and high-fidelity technology evaluations. Therefore, leading to more efficient
policy design to influence change and result in mitigation of emissions while properly
identifying costs (the current database available upon request).
In light of the needs, the next steps in this research is to build generic platform for cities to
populate their own energy data and conduct evaluations for their energy policies. Applications
of this type of model can aid local, state, and regional decision makers to understand the
environmental (climate and air quality) and health implications of energy supply and use in
their regions, as well as the extent to which energy resources and technologies may contribute
to achieving current and future environmental goals. Future specific applications could include:
evaluation of building energy efficiency benchmarking programs, city and regional emissions
reduction strategies (how much would it cost to reduce a ton of pollutant from a sector?),
effectiveness of city and regional level renewable energy standards, forecasting energy
consumption, and forecasting growth of emissions in light of changes in how energy is utilized
by the consumers.
38
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nces
Akhtar, F.H., R.W. Pinder, D.H. Loughlin, and D.K. Henze, GLIMPSE: A Rapid Decision Framework
for Energy and Environmental Policy. Environmental Science & Technology, 2013. 47(21): p.
12011-12019.
Bhatt, V., Crosson, K., Horak, W., Beisman, A., (2008). New York City Energy-Water Integrated
Planning: A Pilot Study. Upton, NY: Brookhaven National Laboratory. Report # BNL-81906-2008.
https://www.bnl.gov/isd/documents/43878.pdf (Last accessed on September 16th, 2019)
Brown, K.E., D.K. Henze, and J.B. Milford, Accounting for Climate and Air Quality Damages in
Future U.S. Electricity Generation Scenarios. Environmental Science & Technology, 2013. 47(7):
p. 3065-3072.
Brown, K.E., T.A. Hottle, R. Bandyopadhyay, S. Babaee, R.S. Dodder, P.O. Kaplan, C.S. Lenox, and
D.H. Loughlin, Evolution of the United States Energy System and Related Emissions under
Varying Social and Technological Development Paradigms: Plausible Scenarios for Use in Robust
Decision Making. Environmental Science & Technology, 2018. 52(14): p. 8027-8038.
City of New York (2011) Inventory of New York City Greenhouse Gas Emissions. Mayor's Office
of Long-Term Planning and Sustainability, New York, 2011.
http://www.nvc.gov/html/om/pdf/2011/pr331-ll report.pdf (Last accessed on September
16th, 2019)
City of New York (2012) Inventory of New York City Greenhouse Gas Emissions. Mayor's Office
of Long-Term Planning and Sustainability, New York, 2012.
http://www.nyc.gov/html/dem/downloads/pdf/greenhousegas 2012.pdf (Last accessed on
September 16th, 2019)
City of New York (2015) "PLUTO and MapPLUTO." http://wwwl.nyc.gov/site/planning/data-
maps/open-data/dwn-pluto-mappluto.page (Last accessed on September 16th, 2019)
City of New York (2016) Inventory of New York City Greenhouse Gas Emissions in 2014.
https://wwwl.nyc.gov/assets/sustainability/downloads/pdf/publications/NYC GHG Inventory
2014.pdf (Last accessed on September 16th, 2019)
City of New York (2017). Inventory of New York City's Greenhouse Gas Emissions in 2015.
Mayor's Office of Sustainability, New York.
https://www.dec.rT ministration pdf/nvcghg.pdf (Last accessed on September
16th, 2019)
EIA (United States Energy Information Administration) (2007) Technology Forecast Updates
Residential and Commercial Building Technologies - Reference Case Second Edition (Revised).
https://www.eia.gov/analysis/studies/buildings/eauipcosts/ (Last accessed on September 16th,
2019)
EIA (United States Energy Information Administration) (2011) Annual Energy Outlook 2011,
Residential Technology Equipment Description File.
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https://www.eia.eov/outlooks/aeo/pdf/0383(2011).pdf (Last accessed on September 16th,
2019)
EIA (United States Energy Information Administration) (2014) Annual Energy Outlook 2014,
https://www.eia.eov/outlooks/archive/aeol4/ (Last accessed on September 16th, 2019)
EIA (United States Energy Information Administration) (2016) Annual Energy Outlook 2016,
https://www.eia.eov/outlooks/aeo/pdf/0383(2016).pdf Last accessed on September 16th, 2019)
Elobeid, A., S. Tokgoz, R. Dodder, T. Johnson, 0. Kaplan, L. Kurkalova, and S. Secchi, Integration
of agricultural and energy system models for biofuel assessment. Environmental Modelling &
Software, 2013. 48(0): p. 1-16.
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Simulator). http://www.epa.gOv/otaq/models/moves/index.htm#ghg (Last accessed on
September 16th, 2019)
Kaplan, P.O. and J.W. Witt, What is the role of distributed energy resources under scenarios of
greenhouse gas reductions? A specific focus on combined heat and power systems in the
industrial and commercial sectors. Applied Energy, 2019. 235: 83-94.
Lenox, C. and P.O. Kaplan, Role of natural gas in meeting an electric sector emissions reduction
strategy and effects on greenhouse gas emissions. Energy Economics, 2016. 60: p. 460-468.
Lenox, C., Dodder, R. S., Gage, C., Kaplan, P. 0., Loughlin, D. H., & Yelverton, W. H. (2013). EPA
U.S. Nine-region MARKAL Database Documentation. (EPA/600/B-13/2013). Cincinnati, OH
45268: U.S. Environmental Protection Agency.
Loughlin, D.H., W.H. Yelverton, R. Dodder, and C.A. Miller, Methodology for examining potential
technology breakthroughs for mitigating C02 and application to centralized solar photovoltaics.
Clean Technologies and Environmental Policy, 2013. 15(1): p. 9-20.
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Yeh, S., A. Farrell, R. Plevin, A. Sanstad, and J. Weyant, Optimizing U.S. Mitigation Strategies for
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Science & Technology, 2008. 42(22): p. 8202-8210.
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11 Appendix
41
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11.1 Appendix A: Variable Types in the Model and Corresponding Data Requirements
Variable Type
Input Requirements
End-Use Energy Service Demands
Projections for energy service demands for;
o Light duty vehicle demand (bn-vmt-yr), bus transportation demand
(bn-vmt-yr), heavy duty short haul truck transportation demand (bn-
vmt-yr), passenger rail transportation demand (pn-passs-miles),
medium duty truck transportation demand (bn-vmt-yr),
o Residential space cooling (PJ/yr), residential space heating (PJ/yr),
residential water heating (PJ/yr), residential lighting (billion
lumens/yr), residential other electricity demand (PJ/yr), residential
other natural gas demand (PJ/yr),
o Commercial space cooling (PJ/yr), commercial space heating (PJ/yr),
commercial water heating (PJ/yr), commercial lighting (billion
lumens/yr), commercial other electricity demand (PJ/yr),
commercial other natural gas demand (PJ/yr),
o Industrial sector facilities (PJ/yr).
o The load shape for electricity demand profile
Energy Carriers
any kind of entity which is a form of
energy that is produced or consumed in
the energy system (e.g., coal, refined oil,
natural gas, gasoline, electricity, etc.)
Transmission efficiency
Transmission capacity
Investment cost
Operation and maintenance cost
Electricity transmission and distribution cost
Reserve margin for electricity
Resource Technologies
technologies that characterize raw fuels
exported or imported into the energy
system
Resource supply cost for each supply step
Cumulative resource limits for an energy carrier for each period
Cumulative resource limits for an energy carrier over the entire
modeling horizon (e.g., an aggregate proven capacity for a coal reserve)
Cost and capacity limits of resource transportation
Cost of extraction and production of resources
Process, Conversion, and Demand
Technologies
any kind of technology that can change
the location, form, and/or structure of
the energy carriers
New capacity investment cost
Fixed operation and maintenance cost
Variable operation and maintenance cost as a function of activity
Fuel delivery charges
Technical efficiency as a ratio between input and output
Technology investment availability year
Availability factor
Capacity utilization factors
Base year installed capacity
Upper bound on new capacity investment (if exists)
Upper bound on incremental new investment (growth rate)
Upper bound on total capacity installed over the modeling horizon
"Hurdle" rate for a technology
Emissions
Emission factor per unit of fuel consumed
Emission factor for per unit of activity
Emission factor for per unit of installed capacity
Upper bound for emission for each period
Emission constraints over the entire modeling horizon
Emission constraints for any given sector
42
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11.2 Appendix B: Model Constraints and Baseline Calibration Assumptions for Buildings, Transportation and Power Sector
Sector
End Use Demand
Type of Constraint
Residential
Buildings
Space Heating
Base year diesel share of total energy use for residential space heating is set as an upper bound for the period 2015-
2055.
Lower bound on the electricity's share of total energy consumption for residential space heating is set as the actual
consumption share reported for 2014. This share is relaxed by AEO forecast for 2055.
Base year natural gas share of total energy use for residential space heating is set as a lower bound for 2015 and is
relaxed 3% per time period.
Space heating technology shares (e.g., percentage of furnaces and radiant heat) for 2010 and 2055 are pulled from ElA's
Residential Energy Consumption Survey space heating characteristics. Specifically, Northeast Census Division's Mid-
Atlantic region technology residual values are taken.
Space Cooling
Space cooling technology shares (Central Heat Pump, Central AC, Room AC) for 2010 and 2055 are pulled from ElA's
Residential Energy Consumption Survey space cooling characteristics. Specifically, Northeast Census Division's Mid-
Atlantic region technology residual values are taken.
Lighting
Lighting technology shares (incandescent, CFL, LFL, reflector, exterior) for 2010 and 2055 are pulled from ElA's
Residential Energy Consumption Survey lighting characteristics. Specifically, Northeast Census Division's Mid-Atlantic
region technology residual values are taken.
It is assumed that incandescent lighting wattage (i.e., utilization) is reduced by 65% according to phase-out calculations in
the Energy Independence and Security Act8, and no new incandescent light investment is allowed due to unavailability in
the market after 2030.
Commercial
Buildings
Space Heating
Lower bound on the share of electricity consumption with respect to total consumption for commercial space heating is
set as the actual consumption share reported for 2014. This share is relaxed by AEO forecast for 2055 for Northeast
Census Division's Mid-Atlantic region. Between 2015 and 2055, there is no upper limit constraint for space heating
electricity consumption.
Base year natural gas share of total energy use for commercial space heating is set as a lower bound for 2015 is relaxed
by AEO forecast for 2055. Between 2015 and 2055, there is no constraint for space heating natural gas consumption.
Technology shares for boilers and furnace is set as a lower bound for 2015 is relaxed by AEO forecast for 2055. Between
2015 and 2055 there is no constraint for space heating natural gas consumption.
Market share of technologies are taken from AEO 2016 Commercial Demand Module results and adjusted according to
the actual fuel consumption values for space heating for 2014.
8 https://www.energystar.gov/ia/products/lighting/cfls/downloads/EISA_Backgrounder_FINAL_4-ll_EPA.pdf
43
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Sector
End Use Demand
Type of Constraint
Commercial
Buildings
Space Cooling
A lower bound is set on the electricity and natural gas share of total energy consumption for commercial space cooling
for base year. It is derived from the actual consumption share reported for 2014. This share is relaxed by AEO forecast for
2055. Between 2015 and 2055, there is no upper limit constraint for space cooling electricity consumption.
Steam from CHP systems is constrained for 2015. The commercial space cooling met by CHP steam is calculated with
respect to the "steam" consumption reported in 2015 NYC GHG report and set as an upper bound.
AEO's Commercial technology report provides market share for Rooftop, Central, Wall/Window room AC, GHP and AHP
technologies. We used these values as a technology investment lower bound for 2015. The value for 2055 is relaxed by
AEO reference forecast for Northeast Census Division's Mid-Atlantic Region. Between 2015 and 2055, there is no
constraint for space cooling technology choice.
Water Heating
Lower bound on fuel shares (electricity, natural gas, diesel and steam) for commercial water heating is set as the actual
consumption share reported for 2014. The shares for electricity, natural gas and steam are relaxed in accordance with
AEO forecast for 2055.
Lighting
EIA Residential Energy Consumption Surveys provides technology shares for lighting technologies (incandescent,
compact fluorescent, halogen, T8, T8L, T5, HID high bay, HID low bay, LED) in Northeast Census Division's Mid Atlantic
region. These values are applied to commercial sector for 2010, and the share is relaxed by AEO forecast for 2055.
It is assumed that incandescent lighting wattage (i.e., utilization) is reduced by 65% according to phase-out calculations in
the Energy Independence and Security Act9, and no new incandescent light investment is allowed due to unavailability in
the market after 2030.
Transportation
Light Duty
Vehicles
New vehicle sales shares for mini-compact, compact, full size, minivan, pick-up truck and small utility vehicles provided
by AEO are set as lower bound constraint.
The maximum penetration values are limited for the following vehicles;
100-mile and 200-mile electric vehicles
Fuel cell vehicles
Hybrid
Hybrid electric
Advanced gasoline fueled vehicles
Light duty vehicle demand for base year is calculated with respect to the total fuel consumption provided in NYC
greenhouse gas emission inventory report and projected in line with national miles travelled reported in AEO National
transportation demands and regional breakdowns report for Middle Atlantic Region. It is assumed in this model that
transport demand for light duty vehicles will have the same pattern that belong to Middle Atlantic Region from 2015 to
2055.
9 https://www.energystar.gov/ia/products/lighting/cfls/downloads/EISA_Backgrounder_FINAL_4-ll_EPA.pdf
44
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Sector
End Use Demand
Type of Constraint
Transportation
Heavy Duty,
Medium Duty,
Bus,
Rail
Heavy duty vehicle demand for base year is calculated with respect to the total fuel consumption provided in NYC
greenhouse gas emission report and projected in line with national miles travelled reported in AEO National
transportation demands and regional breakdowns report for Region 2. It is assumed in this model that transport demand
for heavy duty vehicles will have the same pattern that belong to Middle Atlantic Region from 2015 to 2055. Subway and
commuter rail technological efficiency rates are updated according to APTA10 values.
Power
Coal
U.S. EPA's National Electric Energy Data System (NEEDS)11 v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
Natural gas
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
Nuclear
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
Residual Oil
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
10 Source: https://www.apta.com/wp-content/uploads/Resources/resources/statistics/Documents/FactBook/2018-APTA-Fact-Book.pdf
11 https://www.epa.eov/airmarkets/national-electric:energy-data-systeni-needs-v6
45
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Sector
End Use Demand
Type of Constraint
Power
Wind
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
New York State Energy Research and Development Authority (NYSERDA) provides technical potential for onshore wind
and offshore wind. We utilized their projections as an upper bound constraint.
Power Sector
Solar
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
NYSERDA provides technical potential for utility scale solar. We utilized their projections as an upper bound constraint.
Power Sector
Hydro
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. The plants with operating (OP) and Stand by (SB) status are included into
the residual capacity whereas the plants with Out of service (OA/OS) status is not included in the COMET-NYC.
NYSERDA provides technical potential for conventional and kinetic hydro power plants. We utilized their projections as
an upper bound constraint.
Utility scale
combined heat
and power plants
U.S. EPA's National Electric Energy Data System (NEEDS) v6 provides detailed information on prime mover technology,
initial year of operation, cooling system type, name plate capacity with respect to the generator type and fuel type for
existing generating units. By coupling "Plant name" in Annual Electric Generator Report (EIA-860) and NEEDS v6 data, the
name plate capacity values in 2010 and 2015 for each fuel type and prime mover pair are calculated for each borough of
New York City and the rest of New York State. Commercial and institutional CHP DOE CHP installation database are
cleaned from the utility scale CHP capacity. The data for utility scale solar generation capacity is taken from EIA-860. The
plants with OP (operating) and SB (Stand by) status are included into the residual capacity whereas the plants with
OA/OS (Out of service) status has been removed.
CHP capacities that are located in Bronx, Kings, New York, Queens and Richmond counties are added to NYC residual
capacity. The rest is added as NYS residual utility scale CHP capacity.
46
-------
Sector
End Use Demand
Type of Constraint
Distributed
Energy
Resources
Solar PV
Borough based installed solar PV generation capacity values provided by NYSERDA Solar Electric Programs are added to
the model for residential and commercial sectors. The installations completed before 2010 are added to residual
capacity. The installations completed between 2010 to 2015 are added to residual capacity for 2015, for the installations
completed between 2015 to 2019 are set as a lower bound constraint for 2020 solar PV technology investments for
residential and commercial sector separately.
Combined heat
and power
U.S. DOE maintains a database that provides information on utility, commercial and institutional scale CHP plants in the
U.S. The database includes types of CHP units such as microturbine, combustion turbine, reciprocating engine, fuel cell
and boiler/steam turbine. We processed this data to come up 2010 and 2015 stock values of CHP capacities in the New
York City's boroughs. Capacity installed before 2010 are aggregated into the 2010 capacity. Capacities installed between
2010 and 2015 are assumed to be installed and available for 2015. The values reported between 2015- 2019 is set as a
lower bound for 2015 technology investments. Commercial sector CHP growth upper bound is set as 2.6% according to
AEO CHP growth projections. CHP capacities that are located in Bronx, Kings, New York, Queens and Richmond counties
are added to NYC residual capacity. The rest is added as NYS residual utility scale CHP capacity.
47
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11.3 Appendix C: NYC Borough Based Population Projection
The following table includes the population projections that are used to calculate the end use energy service demands. Projections
from 2010 through 2040 is provided for each of the city's five boroughs by the City of New York Department of City Planning12. For
the period between 2040 and 2055 basic regression model is used to extend the projections for each borough.
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
NYC
8,242,624
8,550,405
8,550,971
8,685,999
8,821,027
8,923,086
9,025,145
9,292,840
9,446,272
9,599,703
Bronx
1,385,108
1,455,444
1,446,788
1,482,893
1,518,998
1,549,122
1,579,245
1,625,955
1,660,588
1,695,220
Brooklyn
2,552,911
2,636,735
2,648,452
2,701,231
2,754,009
2,797,267
2,840,525
2,908,245
2,959,464
3,010,683
Manhattan
1,585,873
1,644,518
1,638,281
1,657,501
1,676,720
1,684,169
1,691,617
1,733,166
1,753,312
1,773,457
Queens
2,250,002
2,339,150
2,330,295
2,351,923
2,373,551
2,393,100
2,412,649
2,496,539
2,533,666
2,570,792
Staten Island
468,730
474,558
487,155
492,452
497,749
499,429
501,109
528,936
539,243
549,551
12 Source: httpsi//wwwl,nvc,gov/assets/planning/download/pdf/data-maps/nvc-population/proiections report 2010 2040.pdf
48
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11.4 Appendix D: Heating and Cooling Degree Days
Heating Degree Days
Cooling Degree Days
2010
4,447
1,549
2011
4,335
1,331
2012
3,978
1,277
2013
4,670
1,272
2014
4,875
1,128
2015
4,460
1,581
2016
4,252
1,489
Ref:13
13 https://wwwl.nvc.Kov/assets/systainabilitv/downloads/pdf/pyblications/GHG%20lnventorv%20Report%20Emission%20Year%202016.pdf
49
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pencil 1 ;siden1 ¦¦ ' 1 r Demand Projections
Regional Cooling Demand PJ/a
= cooling coefficient
* sq ft of AC housing space *
CDD
Region
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
Brooklyn
19.01
19.86
19.75
20.77
22.35
23.99
26.12
28.44
30.67
33.30
36.15
Bronx
10.32
10.77
10.90
11.34
12.27
13.23
14.47
15.81
17.15
18.68
20.35
Manhattan
12.11
12.34
12.34
13.32
14.71
16.16
17.40
18.74
20.22
21.82
23.55
Staten Island
3.54
3.65
3.56
3.94
4.20
4.47
4.81
5.17
5.75
6.25
6.80
Queens
17.03
17.50
17.55
18.94
20.87
22.87
24.72
26.73
29.12
31.53
34.14
Total NYC
62.02
64.12
64.10
68.31
74.40
80.72
87.52
94.90
102.91
111.59
121.00
Regional Heating Demand PJ/a
= heating coefficient
* sq ft of
heated housing space * HDD
Brooklyn
49.12
46.88
46.67
42.15
41.04
39.86
38.64
37.42
35.89
34.66
33.47
Bronx
26.66
27.32
27.68
24.74
24.20
23.62
22.99
22.35
21.56
20.89
20.24
Manhattan
31.22
45.25
45.23
40.52
39.13
37.71
36.15
34.62
33.23
31.90
30.63
Staten Island
9.14
8.75
8.54
7.88
7.60
7.32
7.01
6.71
6.63
6.42
6.21
Queens
44.00
23.54
23.60
21.14
20.36
19.58
18.84
18.11
17.56
16.91
16.28
Total NYC
160.13
151.74
151.71
136.42
132.35
128.08
123.62
119.21
114.87
110.78
106.83
Regional Lighting Demand billion lumens/yr
= NYC lighting demand * Regional % of
total households
Brooklyn
7.04
7.04
7.21
7.46
7.79
8.11
8.38
8.66
9.01
9.33
9.65
Bronx
4.06
4.06
4.23
4.33
4.54
4.75
4.93
5.11
5.35
5.56
5.77
Manhattan
6.80
6.80
6.83
7.01
7.26
7.51
7.67
7.83
8.16
8.40
8.64
Staten Island
1.37
1.37
1.36
1.43
1.49
1.53
1.57
1.60
1.71
1.78
1.84
Queens
3.60
3.60
3.65
3.74
3.87
3.99
4.09
4.20
4.42
4.56
4.70
Total NYC
22.86
22.86
23.27
23.98
24.95
25.89
26.64
27.40
28.66
29.62
30.60
Regional Miscellaneous
Electric
Demand
PJ/a
= NYC misc electric demand *
Regional
% of total
households
Brooklyn
20.49
24.19
24.59
24.02
23.64
22.80
22.48
22.15
21.62
21.25
20.88
Bronx
11.12
12.18
12.59
12.17
12.04
11.67
11.55
11.42
11.21
11.06
10.91
Manhattan
13.02
16.91
17.26
16.72
16.33
15.62
15.23
14.85
14.50
14.17
13.84
Staten Island
3.81
6.18
6.15
6.14
5.99
5.73
5.58
5.43
5.47
5.38
5.30
Queens
18.35
12.38
12.66
12.27
11.95
11.41
11.16
10.92
10.78
10.56
10.35
Total NYC
66.79
71.83
73.25
71.31
69.95
67.23
65.99
64.78
63.58
62.43
61.29
Regional Miscellaneous
Gas Demand PJ/a
= NYC misc NG demand * Regional % of total households
Brooklyn
0.76
0.65
0.92
0.91
0.90
0.89
0.87
0.85
0.85
0.85
0.85
Bronx
0.41
0.49
0.70
0.69
0.69
0.68
0.67
0.66
0.66
0.66
0.66
Manhattan
0.48
1.00
1.40
1.38
1.36
1.33
1.29
1.25
1.24
1.24
1.23
Staten Island
0.14
0.07
0.09
0.09
0.09
0.09
0.09
0.08
0.08
0.08
0.09
Queens
0.68
0.13
0.19
0.18
0.18
0.18
0.17
0.17
0.17
0.17
0.17
Total NYC
2.49
2.34
3.29
3.26
3.22
3.17
3.09
3.01
3.00
3.00
3.00
50
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11,6 Appendix F: Commercial Sector Demand Projection
Space Heating Demand
PJ/a
Region
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
Brooklyn
16.48
16.54
16.72
16.32
16.16
16.08
15.99
15.78
15.65
15.52
Bronx
8.70
8.89
8.89
8.72
8.67
8.67
8.65
8.59
8.55
8.51
Manhattan
52.76
53.17
53.30
51.61
50.70
49.90
49.07
48.47
47.79
47.13
Staten Island
2.76
2.71
2.81
2.71
2.66
2.62
2.57
2.51
2.47
2.44
Queens
7.67
7.75
7.77
7.51
7.36
7.27
7.17
7.16
7.08
7.00
Total NYC
88.38
89.06
89.48
86.87
85.56
84.53
83.46
82.50
81.54
80.60
Space Cooling
Demand
PJ/a
Brooklyn
26.98
28.42
28.74
30.26
31.76
33.61
35.63
37.49
39.65
41.92
Bronx
14.25
15.27
15.28
16.17
17.05
18.11
19.28
20.40
21.65
22.97
Manhattan
86.38
91.35
91.64
95.70
99.65
104.30
109.36
115.17
121.07
127.28
Staten Island
4.52
4.65
4.82
5.03
5.24
5.47
5.73
5.96
6.26
6.58
Queens
12.56
13.32
13.36
13.92
14.46
15.19
15.99
17.01
17.94
18.92
Total NYC
144.69
153.00
153.86
161.08
168.15
176.69
185.99
196.03
206.56
217.67
Water Heating
Demand
PJ/a
Brooklyn
3.29
3.56
3.87
4.11
4.29
4.45
4.62
4.75
4.91
5.08
Bronx
1.74
1.91
2.06
2.19
2.30
2.40
2.50
2.59
2.68
2.78
Manhattan
10.54
11.44
12.33
12.99
13.46
13.82
14.17
14.60
15.01
15.43
Staten Island
0.55
0.58
0.65
0.68
0.71
0.73
0.74
0.76
0.78
0.80
Queens
1.53
1.67
1.80
1.89
1.95
2.01
2.07
2.16
2.22
2.29
Total NYC
17.65
19.17
20.71
21.87
22.72
23.41
24.10
24.84
25.60
26.39
Lighting Demand Billion
lumen-years/a
Brooklyn
19.61
20.70
22.14
23.79
24.94
26.25
27.55
29.00
30.69
32.47
Bronx
10.36
11.12
11.77
12.71
13.39
14.15
14.90
15.78
16.76
17.79
Manhattan
62.80
66.53
70.58
75.24
78.25
81.47
84.55
89.09
93.71
98.57
Staten Island
3.29
3.39
3.71
3.96
4.11
4.28
4.43
4.61
4.85
5.10
Queens
9.13
9.70
10.29
10.94
11.36
11.87
12.36
13.16
13.88
14.65
Total NYC
105.20
111.44
118.50
126.64
132.04
138.02
143.80
151.64
159.88
168.57
Miscellaneous
Natural Gas Demand PJ/a
Brooklyn
0.86
0.85
0.91
0.98
1.10
1.30
1.56
1.56
1.56
1.57
Bronx
0.45
0.46
0.48
0.53
0.59
0.70
0.84
0.85
0.85
0.86
Manhattan
2.74
2.74
2.89
3.11
3.46
4.04
4.79
4.78
4.77
4.76
Staten Island
0.14
0.14
0.15
0.16
0.18
0.21
0.25
0.25
0.25
0.25
Queens
0.40
0.40
0.42
0.45
0.50
0.59
0.70
0.71
0.71
0.71
Total NYC
4.60
4.60
4.85
5.24
5.84
6.84
8.14
8.14
8.14
8.14
Miscellaneous
Electricity
Demand
PJ/a
Brooklyn
22.49
25.00
28.03
31.32
34.55
38.07
41.96
45.85
50.35
55.29
Bronx
11.88
13.43
14.90
16.73
18.54
20.52
22.70
24.95
27.50
30.30
Manhattan
72.01
80.38
89.38
99.06
108.41
118.16
128.80
140.84
153.76
167.88
Staten Island
3.77
4.09
4.70
5.21
5.70
6.20
6.75
7.29
7.95
8.68
Queens
10.47
11.72
13.03
14.41
15.73
17.21
18.83
20.80
22.78
24.95
Total NYC
120.62
134.62
150.05
166.74
182.93
200.17
219.06
239.73
262.35
287.11
51
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11.7 Appendix G: Industry Sector Demand Projection
(PJ)
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
Brooklyn
47.62
47.83
52.26
54.71
57.27
59.36
62.14
61.53
63.06
64.59
Bronx
16.68
16.79
19.21
20.57
22.00
23.18
24.75
24.46
25.34
26.23
Manhattan
14.93
15.06
17.80
19.28
20.80
22.00
23.57
23.22
24.07
24.92
Staten Island
29.29
29.48
33.37
35.47
37.61
39.33
41.62
41.23
42.51
43.78
Queens
3.99
4.03
4.85
5.28
5.74
6.09
6.55
6.52
6.80
7.09
Total NYC
112.50
113.19
127.50
135.31
143.42
149.96
158.64
156.96
161.78
166.60
52
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11.8 Appendix H: Light Duty Vehicle Demand Projection
(Billion vmt)
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
Brooklyn
7.23
7.40
7.52
7.70
7.96
8.21
8.33
8.51
8.69
8.87
Bronx
4.00
4.06
4.11
4.23
4.39
4.54
4.63
4.73
4.84
4.95
Manhattan
4.58
4.47
4.65
4.67
4.79
4.92
4.94
4.96
5.02
5.07
Staten Island
1.35
1.35
1.37
1.41
1.42
1.44
1.44
1.45
1.46
1.48
Queens
6.44
6.29
6.59
6.64
6.80
6.96
7.02
7.08
7.17
7.26
Total NYC
23.59
23.57
24.24
24.64
25.37
26.07
26.37
26.73
27.18
27.63
53
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11.9 Appendix I: Heavy Duty Vehicle Demand Projection
Mode
Region
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
Brooklyn
0.20
0.20
0.21
0.22
0.23
0.25
0.26
0.28
0.29
0.31
Bronx
0.11
0.11
0.12
0.12
0.13
0.14
0.15
0.16
0.16
0.17
Heavy Duty
Short Haul
(Billion vmt)
Manhattan
Staten Island
0.12
0.04
0.13
0.04
0.13
0.04
0.14
0.04
0.14
0.04
0.15
0.04
0.16
0.05
0.17
0.05
0.17
0.05
0.18
0.06
Queens
0.18
0.18
0.19
0.20
0.20
0.21
0.22
0.24
0.25
0.26
Total NYC
0.64
0.66
0.68
0.72
0.75
0.79
0.83
0.89
0.93
0.98
Brooklyn
9.08
9.48
10.00
10.64
11.22
11.77
12.54
13.18
13.96
14.78
Bronx
4.92
5.23
5.46
5.84
6.19
6.52
6.97
7.37
7.83
8.32
Passenger Rail
Manhattan
5.64
5.91
6.19
6.53
6.83
7.09
7.47
7.86
8.27
8.71
(Billion pass-
mile)
Staten Island
1.67
1.68
1.84
1.94
2.03
2.10
2.21
2.40
2.54
2.70
Queens
8.00
8.41
8.80
9.26
9.67
10.07
10.65
11.32
11.95
12.62
Total NYC
29.3
0
30.71
32.29
34.21
35.94
37.55
39.83
42.13
44.56
47.13
Brooklyn
0.09
0.09
0.10
0.10
0.10
0.11
0.11
0.12
0.12
0.13
Bronx
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.07
0.07
0.07
Buses
Manhattan
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.07
0.08
(Billion vmt)
Staten Island
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Queens
0.08
0.08
0.08
0.09
0.09
0.09
0.10
0.10
0.11
0.11
Total NYC
0.31
0.29
0.31
0.32
0.33
0.35
0.36
0.37
0.39
0.41
Brooklyn
0.11
0.12
0.12
0.12
0.12
0.12
0.12
0.13
0.13
0.13
Bronx
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.07
0.07
0.07
Medium Duty
Trucks
(Billion vmt)
Manhattan
Staten Island
0.07
0.02
0.07
0.02
0.07
0.02
0.07
0.02
0.07
0.02
0.07
0.02
0.07
0.02
0.08
0.02
0.08
0.02
0.08
0.02
Queens
0.10
0.10
0.10
0.10
0.10
0.10
0.11
0.11
0.11
0.11
Total NYC
0.37
0.37
0.37
0.38
0.39
0.39
0.39
0.41
0.41
0.42
54
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11.10 Appendix J: Natural Gas Supply & Distribution in NYC
The COMET-NYC model uses the Staten Island (Transco/Tetco), Manhattan (Transco), and Bronx
(Iroquis) pipelines as the sole supply of natural gas to NYC from exterior markets. Queens and
Brooklyn, which are not directly connected to interstate pipelines, are allowed to import
natural gas from neighboring boroughs with pipeline capacity that is 20% above the borough's
estimated annual demand.
A December 2014 natural gas forecast by Morningstar states that maximum capacity at the
Tetco-M3 citygate in Manhattan is 734 mmcf/day, maximum capacity at the Transco Zone 6
citygate in Manhattan is 1,773 mmcf/day, and maximum capacity at the Transco-Rockaway
citygate in Staten Island is 547 mmcf/day. In total, Morningstar's peak-day scenario assumes
that NYC has access to 4,364 mmcf/day from interstate natural gas pipelines and 1,091
mmcf/day of capacity from 'peak shaving" LNG facilities. ConEdison and National Grid
customers consume 3,793 mmcf/day leaving 1,662 mmcf/day for electricity generation from
power plants located within the city. The 3.9 GW that primarily burn natural gas can consume
up to 1,091 mmcf/day at an average heat rate of 11 mmBtu/MWh leaving a surplus of 559
mmcf/day. These maximum capacity values are used to determine natural gas imports in the
COMET-NYC model.14
14 http://www.morningstarcommodity.com/Research/WinterGasOutlook_NY_Basistm%20FINALpdf
55
-------
11.11 Appendix K: CHP Generation in NYC
The COMET-NYC model uses data from ElA's Form 860 database as the basis for residual
(existing capacity) in New York's five boroughs, as well as DOE's combined heat and power
(CHP) database to identify existing CHP capacity in the five boroughs. The following table
summarizes the existing electric generating capacity in NYC's five boroughs. This data is
contained on the 'CHP Capacity' tab in the ELC workbook. Electricity generation (PJ) outlook for
future years representing the results from the reference scenario includes CHP technology
investments as reported below.
Prime-mover type (PJ)
2015
2020
2025
2030
2035
2040
2045
2050
2055
Commercial DST RICE
0.11
0.34
0.34
0.34
0.05
0
0
0
0
Commercial DST Turbine
0.04
0.04
0.04
0.04
0
0
0
0
0
Commercial DSTSTM Turbine
0
0
0
0
0
0
0
0
0
Commercial NGA RICE
0.56
0
2.03
0.37
0
1.32
0
0
0.65
Commercial NGA Turbine
0
0.92
4.67
5.33
3.99
2.42
2.42
2.42
0.74
Commercial NGA STM
1.84
0
0
0
0
0
0
0
0
Commercial NGA Microturbine
0.1
0.01
0.81
0.81
0.8
0.8
0.8
0.8
0.26
Commercial NGA Fuel Cell
0.01
0
0
0.39
0
0.09
0
0.09
0.01
Commercial New DST RICE
0.39
1.1
2.55
5.77
11.8
22.77
34.53
34.53
0
Commercial New DST Turbine
0
0
0
0
0
0
0
0
0
Commercial New NGA RICE
0
0
0
0
0
0
0
0
0
Commercial New NGA Turbine
1.46
3.73
7.15
7.15
7.15
6.85
2.66
4.07
8.53
Commercial New NGA Microturbine
0
0
0
0
0
0
0
0
0
Commercial New NGA Fuel Cell
0
0
0
0
0
0
0
0
0
Commercial Advanced NGA RICE
0
0
0
0
0
0
0
0
0.02
Commercial Advanced NGA Turbine
0
0
0
0
0
0
0
0
0
Commercial Advanced NGA Microturbine
0
0
0
0
5.49
4.93
4.93
9.15
27.09
Commercial Advanced NGA Fuel Cell
0
0
0
0
0
0
0
0
0
56
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11.12 Appendix L: End-use demand Shares with respect to The Building Archetypes
Building Archetype
Ito 4 Family, Freestanding, Wood Frame
Space
Heating
55.42%
Domestic
Hot
Water
20.78%
Space
Cooling
4.26%
Ventilation
0.00%
Lighting
4.76%
Conveyance
0.00%
Process
Loads
0.00%
Plug
Loads/
Misc
14.78%
lto4 Family, Row House, Masonry
55.23%
20.97%
4.26%
0.00%
4.76%
0.00%
0.00%
14.78%
Multifamily, Post-1980 >7 stories
56.91%
24.65%
4.28%
1.43%
3.65%
1.43%
0.91%
6.73%
Multifamily, Post-1980 up to 7 stories
57.32%
24.52%
1.94%
1.15%
5.01%
0.71%
0.46%
8.90%
Multifamily, Post-war >7 stories
54.45%
26.18%
6.04%
1.17%
3.54%
1.39%
0.84%
6.38%
Multifamily, Post-war up to 7stories
58.86%
24.55%
2.50%
0.80%
5.41%
1.49%
0.68%
5.70%
Multifamily, Pre-war >7stories
56.16%
25.51%
2.25%
0.47%
4.80%
1.90%
1.05%
7.86%
Multifamily, Pre-war up to 7 stories
59.34%
24.78%
2.24%
0.26%
5.58%
1.40%
0.45%
5.95%
Multifamily, Very Large
50.11%
32.08%
6.04%
0.67%
3.72%
1.02%
0.53%
5.83%
Commercial, Post-1980 >7stories
34.01%
26.45%
8.41%
8.10%
9.05%
1.59%
3.43%
8.97%
Commercial, Post-1980 up to 7 stories
51.93%
7.59%
7.52%
5.66%
10.20%
1.87%
2.09%
13.15%
Commercial, Post-war >7stories
34.97%
11.95%
18.18%
5.39%
9.89%
2.43%
1.28%
15.91%
Commercial, Post-war up to 7 stories
43.45%
7.20%
10.83%
2.38%
9.28%
0.59%
2.21%
24.05%
Commercial, Pre-war >7 stories
40.10%
10.93%
11.50%
4.14%
10.43%
3.50%
3.97%
15.43%
Commercial, Pre-war up to 7stories
45.00%
10.37%
9.96%
4.34%
10.47%
1.04%
2.47%
16.34%
Commercial, VeryLarge
33.18%
8.62%
21.08%
7.62%
10.20%
1.41%
3.09%
14.79%
Commercial, Mixed use
40.43%
17.73%
7.64%
5.29%
9.93%
1.78%
2.04%
15.16%
Industrial, 3 stories of more
warehouse/factory building
Industrial, 3 stories or less
warehouse/factory building
Industrial, Transportation, Garages, and
Utilities
Institutional, Hospital and Health Facilities
50.18%
48.83%
50.40%
54.98%
3.85%
4.53%
3.08%
12.65%
6.38%
7.51%
4.61%
7.04%
5.67%
2.21%
6.27%
6.14%
23.35%
20.68%
16.72%
9.24%
4.05%
1.92%
3.96%
0.85%
0.93%
7.95%
8.46%
1.46%
5.59%
6.38%
6.50%
7.66%
Institutional, Institutional General
49.72%
8.86%
14.07%
9.87%
6.04%
0.77%
2.01%
8.66%
Institutional, K-12 Schools
52.57%
6.51%
13.50%
5.54%
12.65%
0.54%
2.41%
6.27%
Institutional, Religious
61.72%
9.03%
6.21%
0.16%
11.13%
1.48%
1.78%
8.49%
Institutional, University
43.46%
21.37%
5.48%
4.45%
4.59%
0.59%
0.11%
19.94%
57
-------
11.13 Appendix M: Bui
Iding Area of Per Type of!
Building Per!
Borough (Sq.Ft)
Building Area (SqFt)15
BK
ex
MN
QN
SI
TOTAL
1 to 4 Family, Freestanding, Wood Frame
100,238,397
61,656,101
605,237
92,312,897
124,702,895
379,515,527
1 to 4 Family, Row House, Masonry
350,813,707
80,143,652
29,579,569
148,501,259
64,134,866
673,173,053
Commercial, Post-1980 >7 stories
11,175,112
4,687,612
100,174,639
8,460,567
227,761
124,725,691
Commercial, Post-1980 up to 7 stories
24,928,580
9,631,034
8,644,389
16,393,935
10,040,532
69,638,470
Commercial, Post-war >7 stories
1,841,492
-
55,266,499
2,416,419
503,024
60,027,434
Commercial, Post-war up to 7 stories
12,579,348
8,116,321
8,294,436
16,562,110
5,806,579
51,358,794
Commercial, Pre-war >7 stories
8,862,811
1,414,088
188,053,925
2,157,023
166,056
200,653,903
Commercial, Pre-war up to 7 stories
45,165,708
17,994,206
68,498,322
21,127,957
3,828,790
156,614,983
Commercial, Very Large
15,983,878
11,001,670
309,058,726
14,499,093
683,200
351,226,567
Industrial, 3 stories of more
warehouse/factory building
19,359,520
5,161,979
10,231,449
8,209,800
1,569,692
44,532,440
Industrial, 3 stories or less warehouse/factory
building
49,343,718
20,164,414
2,134,926
44,986,188
3,961,842
120,591,088
Industrial, Transportation, Garages, and
Utilities
56,575,732
18,548,958
26,905,448
23,858,469
4,968,196
130,856,803
Institutional, Hospital and Health Facilities
20,102,803
20,259,704
43,465,088
8,212,732
6,020,716
98,061,043
Institutional, Institutional General
45,702,338
20,574,306
45,403,463
6,569,454
4,522,730
122,772,291
Institutional, K-12 Schools
59,939,504
35,313,570
36,690,485
22,921,793
11,155,804
166,021,156
Institutional, Religious
21,861,810
7,916,013
13,939,383
7,726,422
2,421,406
53,865,034
Institutional, University
6,873,370
8,353,434
23,642,097
3,123,968
1,344,822
43,337,691
Multifamily, NYCHA
55,763,715
44,540,254
47,863,138
9,399,763
3,981,592
161,548,462
Multifamily, Post-1980 >7 stories
11,732,766
12,414,969
59,308,583
9,902,591
409,499
93,768,408
Multifamily, Post-1980 up to 7 stories
35,677,148
14,016,762
11,316,483
11,812,709
3,094,076
75,917,178
Multifamily, Post-war >7 stories
23,050,098
29,482,617
112,087,072
21,421,455
644,740
186,685,982
Multifamily, Post-war up to 7 stories
57,199,987
29,161,513
13,169,973
75,619,951
8,499,339
183,650,763
Multifamily, Pre-war >7 stories
5,882,842
8,589,220
139,624,673
454,302
-
154,551,037
Multifamily, Pre-war up to 7 stories
271,498,137
197,523,809
260,397,109
91,787,001
2,954,823
824,160,879
Multifamily, Very Large
27,268,944
30,201,907
99,906,525
15,120,511
1,051,067
173,548,954
No Data
86,118,897
20,923,590
41,718,143
84,241,760
13,784,064
246,786,454
#N/A
9,183,619
3,393,581
25,754,210
2,610,683
1,136,460
42,078,553
No Data + N/A percentage
7.12%
3.49%
3.94%
12.71%
5.59%
6.15%
TOTAL Building Area
1,339,421
696,868
1,714,262
683,558
266,694
4,700,804
(Thousand SqFt)16
Share of building types (%)
BK
BX
MN
QN
SI
NYC
1 to 4
33.68%
20.35%
1.76%
35.23%
70.81%
22.39%
Commercial
9.00%
7.58%
43.05%
11.94%
7.97%
21.58%
Industrial
9.35%
6.30%
2.29%
11.27%
3.94%
6.30%
Institutional
11.53%
13.26%
9.52%
7.10%
9.55%
10.30%
Multifamily
36.44%
52.51%
43.38%
34.45%
7.74%
39.44%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
15 https://wwwl.nvc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page
16 "Nodata" and "N/A" are excluded from calculations.
58
-------
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