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District energy systems: Studying building types at an urban scale to understand building energy consumption and waste energy generation
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District energy systems: Studying building types at an urban scale to understand building energy consumption and waste energy generation
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Content
District Energy Systems:
Studying building types at an urban scale to understand building energy consumption and waste energy
generation
by
Debjit Kundu
A Thesis
SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BUILDING SCIENCE
AUGUST 2018
ii
COMMITTEE
CHAIR:
Marc Schiler, FASES
Professor
USC School of Architecture
marcs@usc.edu
(213)740-4591
COMMITTEE MEMBER #2:
Karen M. Kensek, LEED AP BD+C
Associate Professor of the Practice of Architecture
USC School of Architecture
kensek@usc.edu
(213)740-2081
COMMITTEE MEMBER #3:
Kyle Konis, Assistant Professor
USC School of Architecture
kkonis@usc.edu
iii
ABSTRACT
Singular building scale energy efficiency may not be enough to combat larger issues of climate change. Instead, at a
district scale, understanding the dynamics of energy usage including heating, cooling, and electricity by different
buildings through the day and year could save overall energy. A district energy model was developed based on
collections of different building types (e.g. offices, residential, data centers, etc.) to transfer any surplus or wasted
energy among buildings and reduce the need for energy generation for building operation. The model analyzes five
districts that are composed of four specific building types in five different climate zones in the United States to
understand the dynamics of energy demand between the buildings and then suggests the possibility of having building
energy exchange and using a cogeneration plant, in those five locations. A district energy strategy could help to
minimize the use of fossil fuels to generate energy for building operation and maintenance. A simple prototype model
composed of offices, residential buildings, datacenters, and hotels was created with specifications from ASHRAE
guidelines to obtain the heating, cooling, and electricity demand for each building for every hour throughout the year.
The buildings were modelled in eQUEST to generate heating, cooling and electrical loads. The results indicated that
building energy exchange is not feasible in all the cities, but there are possible options to reuse the waste produced
from electricity generation over the course of a day. This provided a guideline for selecting building types for
achieving maximum energy efficiency in the five climate zones.
KEYWORDS
District energy systems, district heating, district cooling, energy transfer, Cogeneration
HYPOTHESIS
District energy systems could theoretically substantially reduce the need for energy generation for building operation
and maintenance in urban areas.
RESEARCH OBJECTIVE
• To determine the feasibility of district energy systems in downtown urban areas
• To understand the feasibility of cogeneration system in five different climatic locations
• To determine theoretically on a given day in a district, if energy can be exchanged.
iv
TABLE OF CONTENTS
ABSTRACT ......................................................................................................................................................... III
HYPOTHESIS ..................................................................................................................................................... III
LIST OF FIGURES ............................................................................................................................................ VII
CHAPTER 1: INTRODUCTION ........................................................................................................................ 1
1. INTRODUCTION .................................................................................................................................................. 1
1.1. COMBINED HEAT AND POWER (CHP) OR COGENERATION ............................................................................. 1
1.2. DISTRICT ENERGY SYSTEMS .......................................................................................................................... 1
1.3. BACKGROUND OF DISTRICT ENERGY SYSTEM IN USA .................................................................................. 3
1.4. DEMAND RESPONSE ...................................................................................................................................... 3
1.5. DYNAMIC PRICING ........................................................................................................................................ 3
1.6. HEATING AND COOLING LOAD ...................................................................................................................... 4
1.7. OVERVIEW OF ELECTRIC UTILITY INDUSTRY ................................................................................................ 4
1.8. SMART ELECTRICITY GRIDS........................................................................................................................... 4
1.9. PEAK ENERGY DEMAND ................................................................................................................................ 5
1.10. CENTRAL PLANT ........................................................................................................................................... 5
1.11. THERMAL STORAGE ...................................................................................................................................... 6
1.12. GLOSSARY OF TERMS .................................................................................................................................... 6
1.12.1. Thermal Zoning ................................................................................................................................... 6
1.12.2. Boilers ................................................................................................................................................. 6
1.12.3. Chillers ................................................................................................................................................ 6
1.12.4. Absorption Chillers ............................................................................................................................. 6
1.12.5. Heat pumps .......................................................................................................................................... 7
1.12.6. Vapor-compression refrigeration system ............................................................................................ 8
1.12.7. Steam system ....................................................................................................................................... 8
1.12.8. Co-efficient of performance (COP) ..................................................................................................... 9
1.12.9. Seasonal Energy Efficiency ratio (SEER)............................................................................................ 9
1.12.10. Energy Efficiency ratio (EER) ............................................................................................................. 9
1.12.11. Latent heat of fusion ............................................................................................................................ 9
1.12.12. Latent heat of vaporization .................................................................................................................. 9
1.12.13. Degree-Days ........................................................................................................................................ 9
1.13. CONCLUSION .............................................................................................................................................. 10
CHAPTER 2: BACKGROUND / LITERATURE STUDY ............................................................................... 11
2. INTRODUCTION ................................................................................................................................................ 11
2.1. DISTRICT HEATING AND COOLING ............................................................................................................... 11
2.2. FEASIBILITY OF ENERGY SYSTEMS IN DISTRICT SCALE ................................................................................ 11
2.3. THERMAL ICE STORAGE .............................................................................................................................. 12
2.3.1. USC Cromwell field ........................................................................................................................... 12
2.4. STANFORD ENERGY SYSTEM INNOVATIONS (SESI) .................................................................................... 12
v
2.5. SPATIAL DISTRIBUTION OF URBAN BUILDING ENERGY CONSUMPTION BY END USE ..................................... 12
2.6. 2030 CHALLENGE ....................................................................................................................................... 14
2.7. BACKGROUND OF DISTRICT ENERGY SYSTEM IN USA ............................................................................... 14
2.8. DISTRICT ENERGY SYSTEMS IN DIFFERENT CITIES OF NORTH AMERICA (USA AND CANADA) ................... 15
2.8.1. Cornell University (Ithaca, New York) ..................................................................................................... 15
2.8.2. St. Paul (Minnesota) ................................................................................................................................ 16
2.9. SKYWAY MINNEAPOLIS .............................................................................................................................. 17
2.10. CONCLUSION .............................................................................................................................................. 18
CHAPTER 3: METHODOLOGY ..................................................................................................................... 19
3. INTRODUCTION ................................................................................................................................................ 19
3.1. PROTOTYPICAL MODEL ............................................................................................................................... 19
3.1.1. Locations ........................................................................................................................................... 19
3.1.2. Building types .................................................................................................................................... 20
3.2. EQUEST SIMULATION ................................................................................................................................ 20
3.2.1. Building Inputs (ASHRAE 90.1) ........................................................................................................ 21
3.2.2. Building details (Area) ...................................................................................................................... 21
3.2.3. Weather file ....................................................................................................................................... 22
3.3. RESULTS- BUILDING TO CITY ..................................................................................................................... 32
3.4. CONCLUSION .............................................................................................................................................. 34
CHAPTER 4: RESULTS ................................................................................................................................... 35
4.1. INTRODUCTION ........................................................................................................................................... 35
4.2. SIMULATION USING EQUEST ..................................................................................................................... 35
4.2.1. Method 1: Energy exchange .............................................................................................................. 37
4.2.2. Method 2: Cogeneration system (CHP) ............................................................................................ 38
4.3. SIMULATION RESULTS FOR THE CITY OF LOS ANGELES: .............................................................................. 38
4.3.1. Building energy exchange for Los Angeles ....................................................................................... 40
4.3.2. Cogeneration system (CHP) for Los Angeles .................................................................................... 43
4.4. SIMULATION RESULTS FOR THE CITY OF CHICAGO ...................................................................................... 44
4.4.1. Building energy exchange for Chicago ............................................................................................. 44
4.4.2. Cogeneration system (CHP) for Chicago .......................................................................................... 46
4.5. SIMULATION RESULTS FOR THE CITY OF NEW YORK ................................................................................... 47
4.5.1. Building energy exchange for New York City .................................................................................... 47
4.5.2. Cogeneration system for New York ................................................................................................... 49
4.6. SIMULATION RESULTS FOR SEATTLE ........................................................................................................... 50
4.6.1. Building energy exchange for Seattle ................................................................................................ 50
4.6.2. Cogeneration system for Seattle ........................................................................................................ 52
4.7. SIMULATION RESULTS FOR MIAMI .............................................................................................................. 52
4.7.1. Building energy exchange for Miami ................................................................................................ 53
4.7.2. Cogeneration system for Miami ........................................................................................................ 54
4.8. CONCLUSION .............................................................................................................................................. 54
CHAPTER 5: ANALYSIS.................................................................................................................................. 56
5.1. INTRODUCTION ........................................................................................................................................... 56
5.2. SIMULATION RESULT ANALYSIS FOR LOS ANGELES .................................................................................... 56
vi
5.2.1. Building energy exchange for Los Angeles ....................................................................................... 56
5.2.2. Cogeneration system analysis for Los Angeles ................................................................................. 59
5.3. SIMULATION RESULT ANALYSIS FOR CHICAGO ........................................................................................... 60
5.3.1. Building energy exchange analysis for Chicago ............................................................................... 60
5.3.2. Cogeneration system analysis for Chicago ....................................................................................... 63
5.4. SIMULATION RESULT ANALYSIS FOR THE CITY OF NEW YORK .................................................................... 64
5.4.1. Building energy exchange for New York City .................................................................................... 64
5.4.2. Cogeneration system analysis for New York ..................................................................................... 66
5.5. SIMULATION RESULT ANALYSIS FOR SEATTLE ............................................................................................ 67
5.5.1. Building energy exchange for Seattle ................................................................................................ 67
5.5.2. Cogeneration system analysis for Seattle .......................................................................................... 69
5.6. SIMULATION RESULT ANALYSIS FOR MIAMI ............................................................................................... 70
5.6.1. Building energy exchange for Miami ................................................................................................ 70
5.6.2. Cogeneration system analysis for Miami .......................................................................................... 72
5.7. CONCLUSION .............................................................................................................................................. 73
CHAPTER 6: CONCLUSION ........................................................................................................................... 76
6.1. DISCUSSION ................................................................................................................................................ 76
6.1.1. Energy exchange ............................................................................................................................... 77
6.1.2. Cogeneration ..................................................................................................................................... 79
6.2. LIMITATIONS AND ENERGY MODELING CONSIDERATION ............................................................................. 79
6.3. FUTURE WORK ............................................................................................................................................ 79
6.3.1. Other methods ................................................................................................................................... 79
6.3.2. Improvement in Building Energy Exchange ...................................................................................... 80
6.3.3. Improvement in cogeneration system ................................................................................................ 80
6.4. SUMMARY ................................................................................................................................................... 80
BIBLIOGRAPHY .............................................................................................................................................. 81
APPENDIX A ..................................................................................................................................................... 84
APPENDIX 2 ...................................................................................................................................................... 94
vii
LIST OF FIGURES
Figure 1 - 1: Diagram showing the features of a District Energy System – how different forms of energy
sources can be integrated in a district energy model to produce the energy required by a respective
district (Boardman, 2018) ............................................................................................................................ 2
Figure 1 - 2 : Vapor compression refrigeration system (The texts in blue represents the condition of the
refrigerant) .................................................................................................................................................... 8
Figure 2 - 1 : Combustion turbine with heat recovery steam generator (Cornell University, 2018) ......... 16
Figure 2 - 2 : The Skyway system at downtown Minneapolis (NRG, 2018) ................................................ 17
Figure 3 - 1: Methodology diagram............................................................................................................ 19
Figure 3 - 2 : First part of the script, components are grouped and named for clarity ............................. 20
Figure 3 - 3 : Schedule properties .............................................................................................................. 26
Figure 3 - 4 : Space property of a particular zone ...................................................................................... 26
Figure 3 - 5: Office building (Miami) .......................................................................................................... 27
Figure 3 - 6: Data centers (Miami) ............................................................................................................. 28
Figure 3 - 7: Screenshot of the excel sheet containing energy loads for Hotel building in Los Angeles ... 33
Figure 3 - 8 : Screenshot of the tabs created in excel sheet for adding the respective energy loads ....... 33
Figure 3 - 9 : Screenshot of the excel sheet showing the electric load for all buildings of the prototype
model in Los Angeles .................................................................................................................................. 33
Figure 3 - 10 : Methodology diagram (part)............................................................................................... 34
Figure 4 - 1: Part methodology diagram showing the result analysis section ............................................ 35
Figure 4 - 2: Screen shot of the Excel file for Los Angeles (This is only a part of the spreadsheet)............ 36
Figure 4 - 3: Screenshot of the tabs created in Excel sheet for adding the respective energy loads ......... 36
Figure 4 - 4: Screenshot of the Excel sheet showing the electric load for all buildings of the prototype
model in Los Angeles. This Excel sheet represent the tab named “Electrical Load” (Figure 8) of the Excel
file named “Energy demand_Los Angeles” ................................................................................................. 37
Figure 4 - 5: Screenshot of the Excel sheet showing Calculation of the total and the peak energy loads . 37
Figure 4 - 6: BEPS report for a residential building (10 floors) in New York City ........................................ 38
Figure 4 - 7: Cooling energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy load
in Btu/hr) ..................................................................................................................................................... 39
Figure 4 - 8: Heating energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy
load in Btu/hr) ............................................................................................................................................. 39
Figure 4 - 9: Electrical energy demand (prototype model) for Los Angeles ............................................... 40
Figure 4 - 10: Total energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy load
in Btu/hr) ..................................................................................................................................................... 41
viii
Figure 4 - 11: Heating and cooling energy demand for Los Angeles (X-axis: hours, Y-axis: Energy load in
Btu/hr) ......................................................................................................................................................... 41
Figure 4 - 12: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) ... 42
Figure 4 - 13: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 43
Figure 4 - 14: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 43
Figure 4 - 15: Total energy demand (prototype model) for Chicago (X-axis: hours, Y-axis: Energy load in
Btu/hr) ......................................................................................................................................................... 44
Figure 4 - 16: Heating and cooling energy demand for Chicago (X-axis: hours, Y-axis: Energy load in
Btu/hr) ......................................................................................................................................................... 45
Figure 4 - 17: Energy demand for 91
st
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 46
Figure 4 - 18: Energy demand for 285
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 46
Figure 4 - 19: Energy demand for 216
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 46
Figure 4 - 20: Total energy demand for New York ...................................................................................... 48
Figure 4 - 21: Heating and cooling energy demand for New York (X-axis: hours, Y-axis: Energy load in
Btu/hr) ......................................................................................................................................................... 48
Figure 4 - 22: Energy demand for 275
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 49
Figure 4 - 23: Energy demand for 299
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 49
Figure 4 - 24: Energy demand for 198
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 49
Figure 4 - 25: Total energy demand for Seattle (X-axis: hours, Y-axis: Energy load in Btu/hr) ................... 50
Figure 4 - 26: Heating and cooling energy demand for Seattle (X-axis: hours, Y-axis: Energy load in
Btu/hr) ......................................................................................................................................................... 51
Figure 4 - 27: Energy demand for 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 51
Figure 4 - 28: Energy demand for 286
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 52
Figure 4 - 29: Energy demand for 3
rd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .... 52
Figure 4 - 30: Total energy demand for Miami (X-axis: hours, Y-axis: Energy load in Btu/hr).................... 53
Figure 4 - 31: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) ... 53
Figure 4 - 32: Energy demand for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) . 54
Figure 4 - 33: Energy demand for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 54
Figure 4 - 34: Energy demands comparison of all the five cities (Y-axis: Energy load in kBtu.sq.ft) .......... 55
Figure 5 - 1: Methodology diagram ............................................................................................................ 56
Figure 5 - 2: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) ..... 57
Figure 5 - 3: Cooling minus heating energy load for the 2
nd
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 57
Figure 5 - 4: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .... 58
Figure 5 - 5: Cooling minus heating energy load for the 38
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 58
Figure 5 - 6: Energy demand for 220
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 59
Figure 5 - 7: Cooling minus heating energy load for the 220
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 59
Figure 5 - 8: Analysis of recovered heat in Los Angeles .............................................................................. 60
ix
Figure 5 - 9: Analysis of recovered heat in Los Angeles .............................................................................. 60
Figure 5 - 10: Energy demand for 91
st
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 61
Figure 5 - 11: Cooling minus heating energy load for the 91
st
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 61
Figure 5 - 12: Energy demand for 285
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 62
Figure 5 - 13: Cooling minus heating energy load for the 285
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 62
Figure 5 - 14: Energy demand for 216
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 62
Figure 5 - 15: Cooling minus heating energy load for the 216
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 63
Figure 5 - 16: Analysis of remaining recovered heat for Chicago ............................................................... 63
Figure 5 - 17: Energy demand for 275
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 64
Figure 5 - 18: Cooling minus heating energy load for the 275
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 64
Figure 5 - 19: Energy demand for 299
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 65
Figure 5 - 20: Cooling minus heating energy load for the 299
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 65
Figure 5 - 21: Energy demand for 198
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 66
Figure 5 - 22: Cooling minus heating energy load for the 198
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 66
Figure 5 - 23: Analysis of remaining recovered heat for New York ............................................................ 66
Figure 5 - 24: Energy demand for 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 67
Figure 5 - 25: Cooling minus heating energy load for the 149
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 67
Figure 5 - 26: Energy demand for 286
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) 68
Figure 5 - 27: Cooling minus heating energy load for the 149
th
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 68
Figure 5 - 28: Energy demand for 3
rd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .... 69
Figure 5 - 29: Cooling minus heating energy load for the 3
rd
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 69
Figure 5 - 30: Analysis of remaining recovered heat for Seattle................................................................. 70
Figure 5 - 31: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) ... 71
Figure 5 - 32: Cooling minus heating energy load for the 2
nd
day (X-axis: hours starting 12 am, Y-axis:
Energy load in Btu/hr) ................................................................................................................................. 71
Figure 5 - 33: Energy demand for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) . 71
Figure 5 - 34: Cooling minus heating energy load for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy
load in Btu/hr) ............................................................................................................................................. 72
Figure 5 - 35: Energy demand for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr) .. 72
Figure 5 - 36: Cooling minus heating energy load for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy
load in Btu/hr) ............................................................................................................................................. 72
Figure 5 - 37: Analysis of remaining recovered heat for Miami ................................................................. 73
Figure 5 - 38: Energy exchange possibility .................................................................................................. 74
x
Figure 5 - 39: Total energy demand (kBTU/sq.ft.) per year ........................................................................ 74
Figure 5 - 40: Energy exchange possibility (Annually) ................................................................................ 75
Figure 6 - 1: Methodology ........................................................................................................................... 76
Figure 6 - 2: Energy exchange possibility .................................................................................................... 78
Figure 6 - 3: Total energy demand (kBTU/sq.ft.) per year .......................................................................... 78
1
CHAPTER 1: Introduction
1. Introduction
Built environments consume a lot of energy. This energy often comes at the cost of fossil fuels, nonrenewable energy
resources. This increases their carbon emission and hence negatively impacts the environment. Presently, many
concepts have evolved to make a building energy efficient or net zero energy buildings or even net positive energy
buildings. But with the current rapid trend of places getting urbanized it is imperative to start thinking of energy
efficiency at an urban scale. District energy systems and distributed energy systems can supply energy to the buildings
in an efficient way. But these systems should be analyzed and plan accordingly as per the requirements of a region or
neighborhood. Furthermore, smart grids also provide options to integrate intermittent renewable energy sources that
can further help to reduce the stress on electric grid, and also helps to reduce energy loss through grids.
Dominkovic and Bacekovic et al describe how smart energy systems integrate the power, heat, and gas sectors to
detect synergies between the sectors for achieving a cheaper and sustainable energy system. They also help to integrate
renewable energy systems. Using large scale systems for electricity storage in urban areas can be costly, but can be
avoided by integrating different energy sectors with district heating (DH) acting as a major link between heat and
electricity. In areas of high heating demand sharing DH systems is one of the ways to increase the efficiency in energy
systems. It facilitates to integrate the intermittent energy sources like wind and solar since it provides a better scope
of integrating heating and power sectors. Utilizing the excess heat from the industry and agriculture is an important
way for the development of future district heating systems, since it would use the heat that would otherwise have been
lost in industrial process. Moreover, it would result in an increase in the competition among the DH suppliers, and cut
down monopolism in the DH market. (Dominkovic, Bacekovic, Sveinbjornsson, Pedersen, & Krajacic, 2017).
1.1. Combined heat and power (CHP) or Cogeneration
Heat is generally lost as waste during the production of electricity. However, this waste heat can be captured and used.
The process of simultaneous production of electricity and thermal energy (waste heat) from a single source of fuel is
called co-generation. This is a good example of sustainability, contributing towards increasing the efficiency of energy
generation. It also contributes towards reducing the GHG emission. The captured waste heat can be utilized in direct
heating or indirect heating applications.
Co-generation is useful if there is a demand of heating energy for application in a building. The waste heat can also
be stored in devices like phase change materials or water tanks.
1.2. District energy systems
District energy systems are used to distribute thermal energy in a district or urban area. The emphasis is to distribute
energy to a group of buildings rather than generating energy for a single building. In this system, thermal energy is
distributed to buildings from a central plant with the help of steam or hot or chilled water pipes. The energy can be
used for various purposes like space heating and cooling, water heating.
There were previously three generations of district heating systems (1
st
generation, 2
nd
generation and 3
rd
generation):
Lund et at described that steam was used as the heat carrier in the first generation of district heating systems. This was
introduced in society primarily to replace the individual boilers in the apartment for reducing the risk of boiler
explosions and enhancing comfort. Most of the heating systems established until 1930 used this technology both in
USA and Europe. Such a system is not feasible today, since high steam pressure has caused severe explosions.
Moreover, the corroded condensate return pipe give less condensate returns causing lower system efficiency.
Pressurized hot water was used in second generation systems as the heat carrier. The supply temperatures were mostly
over 100C. This system lasted from 1920-1970. The reason behind using this system was to achieve better comfort by
utilizing combined heat and power (CHP) and reduce fuel use. In 1970, a third generation of district heating system
was introduced. It also used pressurized water as the primary heat carrier with supply temperature often below 100 C.
2
There are many societal reasons and institutional framework, behind introducing CHP in various countries. However,
the primary reason was to introduce energy savings measures, by cutting down the use of oil/gas with other sources
of energy used in a CHP plant. These sources of energy can be waste heat generated from electricity production.
(Lund, et al., 2014)
However, a framework needs to be developed in order to determine the ideal location of a district heating system and
how the use of intermittent renewable energy resources could be integrated in the system. There is a substantial
positive impact of the district heating system, but the technology needs to be further developed so as to minimize the
grid losses, use synergies with electric and gas grid. In Europe and other regions, the integration of renewable energy
resources with CHP and other energy saving measures proved to be an important factor in responding to climate
change (Lund, et al., 2014).
It is required to plan such that the heat demand is concentrated in a place, rather than spread out. This will help to
minimize the heat loss and cut down the distribution cost. Downtown location of cities can be analyzed to find out the
heat and electricity demand of the buildings.
Substantial amount of energy is needed in order to make a building habitable. This energy comes at the cost of huge
carbon emission and hence degrade our environment. It is highly required to aim for a sustainable energy system that
reduces the use of fossil fuels and maximizes the use of renewable energy sources. The 4
th
generation district heating
system combines the district heating system with the performance of the buildings (Lund, et al., 2014). This in turn
enhances the energy efficiency of the total system and includes the feature of smart thermal grids.
Smart thermal grids are a network of pipes connecting a number of buildings in a district or specific place (Borlase &
Stuart Contributer, 2012). These are served from centralized or distributed energy production units, and also from
individual contributions from the connected units. These are designed so as to reduce grid loss by improving the
components of the system and reducing the building’s heating and cooling demand (Borlase & Stuart Contributer,
2012).
Working process of district energy systems: Heating and cooling are supplied to multiple buildings from a central
energy plant. Steam, hot water and chilled water from the plant is transmitted 24x7 via underground pipes to customer
buildings. Buildings connected to district energy systems don’t need to operate their own individual boiler, chillers
and cooling towers. They can choose to connect to the district energy network that reduces upfront capital cost. It also
Figure 1 - 1: Diagram showing the features of a District Energy System – how different forms of energy sources can be
integrated in a district energy model to produce the energy required by a respective district (Boardman, 2018)
3
saves valuable space that might be dedicated to heating. Buildings connected to district energy systems are easier to
operate, have lower life cycle cost, and have reduced or no onsite emissions.
District energy systems can run on multiple fuel sources, which is more difficult for individual buildings. District
energy systems can integrate combined heat and power (CHP). There are several components involved in a district
energy system (Figure 1-1).
1.3. Background of District Energy system in USA
The district energy industry in the USA is considered to be divided into two parts. One is the pre-1960 downtown
steam systems (this is considered to be used since late 1880’s or 1890’s) and the other is the post-1960 combined
district heating and cooling systems. (International District Energy Association, 2005)
The IDEA report on District Energy Industry states that district heating systems dominated the first sector of the US
district energy industry. These were located in the downtown of many cities like New York city, Philadelphia, Boston,
San Francisco, Denver, etc. Those systems used steam to distribute energy to buildings to use for space heating and
domestic hot water. In fact, steam was used as much as 78% in US district heating system in comparison to water, and
in some cases steam was even used to operate the on-site steam driven chillers for air conditioning purpose, by
supplying steam at high pressure (125-150 psig). (International District Energy Association, 2005)
From this IDEA report, it was clear that the Consolidated Edison steam system in New York City is the largest district
steam system in the world. It was formed by merging many steam systems in the downtown, which once served
respective areas of Manhattan. It reduces the load on the electrical grid since there is no requirement of electrically
driven chillers and takes of 400 MW of peak electrical load demand during summer. In summer months, steam driven
cooling systems are used by large buildings in many cities, and in campuses they are widely used with CHP systems,
so as to use the waste heat recovered from electricity generation. Apart from that, there were rules made concerning
the restriction of emissions in downtown, and fossil fuel prices increased too. It resulted in the increment in that started
operating in the USA in 1960.the Hartford Gas Company. This set an example for gas fired district energy systems
and was developed in 10 more downtown cities in the USA, which were developed and operated by the local natural
gas distribution company. It began to grow in cities like Omaha, Minneapolis, Century City, Tulsa, Pittsburgh and
Oklahoma City as it provided better deals to the building owners, in terms of initial capital cost, no mechanical room
(more space), and maintenance costs. (International District Energy Association, 2005)
1.4. Demand response
Consumers can play an important role during peak electricity demand, by balancing their usage to reduce the strain
on the electricity demand. Like, during the peak time, the consumers can postpone some of their tasks that consume
substantial amounts of energy, to a later stage of the day. In this way it will put less strain on the electrical grid and
generating capacity. To encourage this, they are offered financial incentives by electric system operators and planners.
This helps to have a balance between supply and demand, and can bring down the cost of electricity in wholesale
market, leading to lower retail rates. It also reduces the need for new power plants, which would be used only to meet
an increased peak demand and which would remain dormant most of the time. Various time-based rates that are
offered to engage customers are-of-use pricing, critical peak pricing, variable peak pricing, real time pricing, and
critical peak rebates time (DOE, Demand Response, 2017).
The power companies also have the provision though direct load control program, to control air-conditioners and
water heaters on and off during peak periods, and offer financial incentives in exchange. (DOE, Demand Response,
2017)
Demand response plays a very important role in the electric power industry. For example, sensors can be used to detect
peak load problems / scenarios, and use automated functions to reduce or divert load, thus eliminating overload or
power failure. This type of programs helps the electricity providers to save money during peak demand and can help
in the process of decision making for constructing new power plants dedicated for peak times.
1.5. Dynamic pricing
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The cost of producing electricity is not same throughout a day. It is costly to produce electricity during the peak time,
when other expensive systems need to be added to meet the additional energy demand. Initially consumers were
charged on a flat rate, irrespective of the amount of electricity they consume at any particular point of the day. This
resulted in peak load on electricity generation based solely on need. Hence dynamic pricing was introduced. As per
dynamic pricing, a consumer has to pay penalty for consuming electricity at certain point of the day, for example
during a hot summer afternoon, a consumer will be charged more using electricity. This was done to tell the consumers
that it is not appropriate to consume any amount of electricity irrespective of time and location, and an effort to reduce
electricity demand at peak times.
1.6. Heating and cooling load
The size of a HVAC heating system (boiler) and its components (ductwork, piping) are directly related to the heating
load calculated for a building. The peak heating load calculations are derived from the building performance and heat
loss at extreme conditions (peak load). Several methods are used to calculate the heating load of a building. The
methods range from rule of thumb method to intensive computer simulation methods. One of the examples in rule of
thumb method is to consider 25000 to 75000 Btu/hr of energy required per 1000 sq.ft. of floor area, and is based on
the geographic location of that place. (Wujek & Dagostino, 2010)
To calculate the heating load precisely, the two things are required to take into consideration: the building envelope
and the properties of materials in construction assemblies, which includes various elements of a building like walls,
roofs, ceilings, doors, windows, etc.
Heating load are calculated based on zones or rooms in a building, and the size of the ducts are calculated based on
the same to deliver appropriate amount of heat to the respective zones to maintain healthy living conditions as per
building codes like ASHRAE or TITLE24.
Calculating cooling load for a building is also similar in process to heating load, and the method also range from
simple thumb rule calculation to detailed software simulation. But the thumb rule to calculate the cooling energy
required for 400-600 sq.ft. of space is one ton and is depends on the geographic location of the project. (Wujek &
Dagostino, 2010)
Thermal zones need to be accurately modeled in order to calculate the heating and cooling load, as these are the most
critical components to the energy use of a building (Sehrawat & Kensek, 2014).
1.7. Overview of Electric Utility Industry
Large power plants generate electricity and transmit it at high voltage levels for long distances. High voltage levels is
used to minimize loss during transmission. Electricity (alternating current at 110kV to 1.2 MV) is transmitted through
transmission lines to distribution substations where it is stepped down to lower voltage level by a transformer. It is
then transmitted over short distances to a network of smaller and local transformer, where electricity is further stepped
down for safe use by the end users.
Power plants use an energy source (wind steam, hot gases or water) to drive a rotating turbine which is attached to a
generator, for generating electricity. Steam is produced by the burning fossil fuel like coal, and hot gases are produced
by burning natural gas or oil. (Borlase & Stuart Contributer, 2012)
1.8. Smart electricity grids
Smart grids integrate a set of technologies, that reduces capital expenditures, operation and maintenance costs. It
integrates the electrical and communication infrastructure to provide advanced automation capabilities within the
existing electrical grid (Borlase & Stuart Contributer, 2012).
In a smart grid, power flow is monitored in real time, and voltage control is improved to optimize delivery efficiency
and eliminate waste and oversupply. This helps to reduce overall energy consumption and related emissions and
conserves nonrenewable energy resources and lowers overall electricity cost (Borlase & Stuart Contributer, 2012).
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Borlase and Stuart found that initially electricity generation and transmission was based on the idea that the load is
given, and adjustments was made on the supply side for balancing the supply and demand in real time environment
(Borlase & Stuart Contributer, 2012). The customers were also charged on flat rate, i.e. they were charged on the
amount they consume, irrespective of how much they consume. This concept of using electricity put stress on the
electric grid during the peak hours, for example during a hot summer day most buildings would require an air
conditioner (A.C.) to maintain a comfortable indoor temperature. During this time more electricity needs to be
produced, and costly equipment is required to keep up with such high infrequent demand. The cost of producing this
extra electricity is also high. It was then decided to charge more to the consumers responsible for creating the peak
demand. But there are other options too that can be integrated with the electric grid to respond during this peak demand.
This can be done with the help of smart grid, where renewable energy sources can be merged with the electric grid.
(Borlase & Stuart Contributer, 2012)
1.9. Peak energy demand
Energy is needed for building operations. There are different types of energy. One of it is electrical energy. It is needed
for a variety of purpose like running an air conditioning (A.C.) and other electrical appliances. Its need is not constant
and varies from season to season. For example, in a summer season in Los Angeles more electrical energy is needed
for the cooling purpose than in the winters and if all the buildings use an A.C. during a hot summer afternoon, it will
put more stress in the electrical grid. The electricity generating station needs to produce more electrical energy than
the normal days, at that point of time. Then it creates a peak electrical demand in the electrical grid. This requires
adding extra generating capacity, and is very most expensive to operate.
Electricity is charged based on two factors. First, it is charged based on the consumption. Second, it is charged based
on the demand of a consumer. The demand factor is based on the highest demand in a billing period, which is generally
evaluated on a 15-minute interval during the billing cycle (Woodcock, 2018).
Different types of buildings have different requirements of electricity, and this requirement is not constant. Some
buildings, especially the commercial and industrial, require huge amounts of electricity occasionally (Woodcock,
2018). Since electricity can’t be stored, and it needs to be consumed the moment it is produced, this puts substantial
pressure on the electrical grid, and high-tech expensive equipment (transformers, substations, generating stations,
wires etc.) are required to keep up with the customers’ requirements, i.e. the equipment should be able to provide
electricity during the peak times - when the demand is the highest. Hence the customers are billed based on demand
charge too, apart from consumption charge, so as to charge more to the customers creating the huge demand in
electricity (Woodcock, 2018).
1.10. Central Plant
The central plant is responsible for electricity generation along with providing energy for either cooling or hearting or
both cooling and heating together (ASHRAE, DISTRICT HEATING AND COOLING, 2016). To facilitate the
process of distribution, small satellite plants are sometimes used to distribute energy to buildings that do not fall within
the boundary of the central plant’s piping network. The available choices of the distributing medium for heating in a
district energy system is either steam or hot water. Steam is generally used for industries, hospitals or for areas using
a cogeneration system (ASHRAE, DISTRICT HEATING AND COOLING, 2016). Hot water is mostly used for large
commercial buildings. Steam depends on the latent heat capacity of water and has substantially more heat content than
hot water. Hence more mass needs to be circulated in a hot water system than steam system, both at same heat capacity,
to supply energy to buildings. But steam has lower density than water, and hence it requires a larger size of pipe for
the supply line. The size required is small for the return condensate pipe, and hence the piping cost can be comparable
for steam and condensate, and hot water supply and return (ASHRAE, DISTRICT HEATING AND COOLING,
2016).
The type of chiller plant system used in a district cooling system depends upon several factors like the availability of
water, power and steam, location of the plant, environmental rules and regulation, etc. A district cooling system has
multiple needs for air conditioning, and it economically better and also has more energy efficiency opportunities than
a conventional air conditioning system. Moreover, it is mostly not possible to use ammonia (natural refrigerant) in
single buildings, but district energy plants offer the opportunity to use ammonia. The feasibility of district energy
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plants depends on the size and range of cooling load, and its distances from the central plant. (ASHRAE, DISTRICT
HEATING AND COOLING, 2016)
1.11. Thermal Storage
A district energy system can take advantage of both hot and chilled water storage systems. But in North America
mostly chilled water storage systems are used. These systems can have a positive impact on the requirements of the
chiller system and its operating cost, based on the design of the plant and its load requirement. It can be done by
shifting a part or all of the refrigeration load at times when the energy demand is not high or during non-peak hours.
Ice energy storage can be used to do this. Generally, the utility companies have lower rates during the night time
(when the demand is low) and high rates during the peak times. So, at night water is cooled to form ice and during the
day time water is passed through it (to reduce the temperature of water) before being used as chilled water. This
process helps to meet the peak load demands while using a low size chiller plant.
1.12. Glossary of terms
1.12.1. Thermal Zoning
The air conditioning needs of different spaces or rooms in a building are different during a day. A room that is exposed
on the east side will require cooling during the early hours of the day, and a room exposed to the west will require
cooling mainly during the evening. The same applies for rooms with different orientation. The occupancy pattern and
system (HVAC) operating schedule depends upon the function of a room (Wujek & Dagostino, 2010). Hence it is
required to break the whole building space into different spaces or zones, in order to understand the ventilation needs
of different spaces in a building. It will allow the air conditioner to deliver an appropriate amount of heating or cooling
to the rooms. (Wujek & Dagostino, 2010)
To model a building precisely it is required to make thermal zones in a building, i.e. to segregate a building into
different areas having similar heating and cooling needs. Thermal zoning differs among building types as follows:
• For residential buildings, the zones are made based on the room activity. The bedrooms and the living rooms fall
in different zones.
• Commercial buildings are typically divided into two zones: perimeter zones and the core zone. The perimeter
zones consist of rooms that have external wall(s) and the core zones have rooms that do not have external walls.
1.12.2. Boilers
A boiler is a piece of equipment that generates heat to produce hot water and steam (Wujek & Dagostino, 2010). It
consists of a vessel where fuel (natural gas, fuel oil or coal) undergoes a combustion process to generate the required
heat. Boilers are used to generate steam or hot water, for the purpose of space heating and producing domestic hot
water in buildings. There are two main components in a boiler. One is the furnace and other is a closed vessel. In a
furnace, the fuel is burnt to produce heat, and the closed vessel serves the purpose of a container where the steam or
hot water is generated. The burner in the furnace is provided with fuel and air, which are converted into hot combustion
gases. The area between the furnace and the vessel provides way for the hot combustion gases to be in contact with
the water (inside the vessel). The heat from the hot combustion gases heats the water, creating steam or hot water. A
pressure/temperature safety valve is present to prevent any explosion (accident) which can be caused by enormous
high pressure, by releasing steam. (Wujek & Dagostino, 2010)
1.12.3. Chillers
A chiller is a type of equipment that produces chilled water for the purpose of removing heat from a building. It
extracts heat from water and rejects it to the surroundings by using the vapor compression cycle mechanism, often
called the refrigeration cycle (Wujek & Dagostino, 2010). One of the important type of chillers is the absorption
chiller. It produces chilled water by generating the required refrigeration effect, using a thermal or chemical process.
Pumps and cooling towers are used to pump out the waste heat from a chilled water plant (Wujek & Dagostino, 2010).
1.12.4. Absorption Chillers
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Wujek and Dagostino states that absorption chillers use a thermochemical process to produce chilled water (Wujek &
Dagostino, 2010). It differs from a vapor-compression refrigeration system where mechanical compression and
expansion of the refrigerant is used to produce the desired chilling effect. To understand the working methodology of
absorption chillers, it is required to understand the fact that water boils at a higher temperature when the ambient
pressure is high and when the ambient pressure is low water boils at a lower temperature. This forms one of the
important factors for the process used in absorption chillers. Another factor that drives the absorption chiller’s
mechanism is the affinity between the chemicals used in the process. Two types of fluids are used in an absorption
cycle. One is the refrigerant and the other is the absorbent. The refrigerant has a high affinity towards the absorbent,
i.e. it can easily dissolve into the absorbent. Two combinations of chemicals usually used in this process are water and
lithium bromide (salt), and ammonia and water. (Wujek & Dagostino, 2010)
An absorption chiller unit consists of five main parts: an evaporator, an absorber, a generator, a condenser and a heat
exchanger (Janis & Tao, 2014). The heat exchanger is used to increase the efficiency of the system. A mixture of the
refrigerant (water) and the absorbent (lithium bromide) is pumped from the absorber through the heat exchanger to
the generator (or concentrator). This mixture is dilute or weak in solution, because of the presence of water. This
mixture keeps on accumulating in the generator compartment. Then heat is added to this mixture and causes the water
to boil, and separate from the lithium bromide, increasing the saturation or saltiness. The heat used can come from
waste heat which generally comes in the form of steam or hot water, or the source of heat can be from direct firing
with oil or gas (Janis & Tao, 2014). The water then becomes vapor and rises up in the generator compartment, and
then transfers to the condenser compartment (Mindset, 2018). The lithium bromide settles at the bottom and becomes
a concentrated hot liquid. The liquid then flows down through the heat exchanger, where it transfers the heat to the
weak solution line (water and lithium bromide) and cools down. The cool lithium bromide solution then reaches the
absorber compartment to be mixed with water vapor and reused again for the process of repeating the loop. The next
step is to condense the hot water vapor in the condenser compartment. For this process, water is circulated in sealed
pipes passing through the condenser and then circulated to a cooling tower. When the hot vapor in the condenser
compartment comes in contact with the pipe, it exchanges heat with the water in the pipe and heats the water. As a
result, the vapor loses heat and condenses to form liquid water. The hot water in the sealed pipe ejects heat outside
when it is circulated to a cooling tower. The evaporator compartment is at very low pressure, almost near vacuum
condition, to allow the water (refrigerant) to boil at very low temperature. The liquid water is then transferred to the
evaporator chamber. Since the evaporator is at low pressure, when the water from the condenser unit enters the
evaporator compartment, its temperature drops to around 4 degrees Celsius. The evaporator unit is also connected to
a chilled water loop, that brings all the unwanted heat from the building, collected from air handling unit (AHU), etc.
This water is around 12 degrees Celsius when it enters the evaporator unit, and is inside a sealed pipe. The water from
the condenser unit is then sprayed over the chilled water tube where it transfers its heat to the water from the condenser
unit. This causes the water from the condenser unit to boil at this low temperature, as the evaporator unit is at low
pressure. This exchange of heat also causes the water inside the chilled water tube to cool down to circulate around
the buildings to collect all the unwanted heat again to bring back to the absorption chiller (Mindset, 2018).
An absorption chiller will be efficient when there would be a constant flow of waste heat. The waste heat can be from
industries or from electricity generation. The temperature of the source heat should be from 212 F to 390 F to produce
the desired cooling effect and be cost effective (Wujek & Dagostino, 2010).
1.12.5. Heat pumps
A heat pump is a type of system which can provide both heating and cooling (Wujek & Dagostino, 2010). It can draw
heat from a building to outside during cooling season, and can draw heat into a building from outside during heating
season. One of the types of heat pump is a factory assembled packaged unit. It consists of fan, compressor, inside coil
and outside coil. A split system is the most common type in a factory assembled packaged unit. It includes air handlers
which consists of an inside coil, fan and supplementary electric heat strip, and is present inside of a building, and a
condensing unit, consisting of an outside coil and compressor, present outside a building. Heat pumps with both
heating and cooling capabilities have a different refrigerant circuit design than that of air conditioners (Wujek &
Dagostino, 2010).
Heat pumps use outside air/water as a heat source or a heat sink, depending on whether it is heating or cooling (Wujek
& Dagostino, 2010). During heating seasons, it moves heat from outside of a building to the inside, and during the
cooling season, it removes heat from inside of a building to outside. Heat pumps make use of electricity to power
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compression refrigeration cycles for moving heat to a warm location from a cold one. Because the coefficient of
performance (COP) often exceeds 3, three times as much heat is delivered than by heating something directly with
electricity. Thus, such systems can be very efficient whenever the temperatures are within the operating range (Wujek
& Dagostino, 2010).
Geothermal heat pumps are the type of heat pumps that use a feature of renewable energy to provide heating and
cooling. It makes the use of earth’s natural heat storage ability and ground water of the earth to heat or cool a building.
This system is more efficient than air source heat pump (Wujek & Dagostino, 2010).
1.12.6. Vapor-compression refrigeration system
A vapor-compression refrigeration system consists of five components: a refrigerant, compressor, condenser,
expansion valve and an evaporator (Figure 1-2). Refrigerant in its liquid state and at a medium temperature is present
in the system (Wujek & Dagostino, 2010). It is forced to pass through the expansion valve, and then changes into a
low temperature liquid. This low temperature liquid is passed through the evaporator coil where it gets in contact with
recirculated room air or the recirculated liquid medium (can be chilled water), and extracts heat from them. It thus
reduces the temperature of the air by extracting heat from it, and hence increases its temperature by absorbing energy.
Then it is converted into gas which is at low pressure and warm temperature. In the next step, the gas is passed through
a compressor where it its pressure is increased and hence the temperature rises. Thus, it enters into the condenser
where it dissipates the heat to the outside and condenses into a liquid state by releasing its latent heat. The process is
repeated throughout a refrigeration cycle (Wujek & Dagostino, 2010).
1.12.7. Steam system
A steam system is a type of district heating system where steam produced from the steam generating stations, is used
to both heat and cool high-rise buildings. For example, in New York, the steam is carried by underground pipes in
Manhattan (Wikipedia, 2017). This type of district eating system was started by New York Steam Company on March
3, 1882. Con Edison uses the technology of cogeneration to generate 50% of the steam annually and around 30% of
its installed system’s capacity is in cogeneration system. This helps to reduce the emission of pollutants like sulfur
dioxide, carbon dioxide, and particulate matter, and reduces the city’s carbon footprint, since it increases the fuel
efficiency of the co-generated system. The company also uses the steam for cooling purpose during the summer
months by passing steam through absorption chillers. The overall system is called trigeneration, where electricity,
heating energy and cooling energy is produced by the same plant, reduces the electrical loads during the peak times
and saves the cost incurred in the construction of electrical infrastructure otherwise (Wikipedia, 2017).
Figure 1 - 2 : Vapor compression refrigeration system (The texts in blue represents the condition of the
refrigerant)
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1.12.8. Co-efficient of performance (COP)
The co-efficient of performance defines the performance of the energy systems and is different for heating and cooling
systems (Janis & Tao, 2014). For heating, it is the ratio of the rate of heat provided to the rate of energy input under
designated operating conditions. For cooling, it is the ratio of the rate of heat removed to the rate of energy input under
designated operating conditions. The unit is consistent during the process. A system is more efficient if it has higher
COP (Janis & Tao, 2014).
1.12.9. Seasonal Energy Efficiency ratio (SEER)
Seasonal energy efficiency ratio is defined as the ratio of an equipment’s (A.C. or heat pump) total cooling output
(Btu) during its normal annual usage period for cooling (A.C.) or heating (heat pump) to the total electrical energy
input (W-hr) during the same period. Higher SEER denotes higher efficiency of an equipment. It is applicable to A.C.
which has a cooling capacity of less than 65000 Btu/hr. or 5400 tons (Janis & Tao, 2014).
1.12.10. Energy Efficiency ratio (EER)
An energy efficiency ratio is defined as the ratio of an equipment’s (A.C. or heat pump) cooling capacity (Btu/hr.) to
the total electrical (Watts) input, performed under industry designated operating conditions (Janis & Tao, 2014). It is
used to measure the instantaneous energy efficiency of a cooling equipment. Higher EER suggests higher efficiency
of an equipment. It is applicable to A.C.’s which have a cooling capacity similar to or greater than 65000Btu/hr. or
5.4 tons (Janis & Tao, 2014).
1.12.11. Latent heat of fusion
The latent heat of fusion is the heat required to change the phase of a substance from solid state to a liquid state. The
latent heat of fusion of water is 144 Btu/lb., i.e. 144 Btu of heat is absorbed when 1 pound of ice is converted to water
or 144 Btu of heat is released when 1 pound of water is converted to ice (Janis & Tao, 2014).
1.12.12. Latent heat of vaporization
The latent heat of vaporization is the heat required to change the phase of a substance from liquid state to gaseous
state. The latent heat of vaporization of water is 970 Btu/lb., i.e. 970 Btu of heat is required when 1 pound of water is
converted to gas or 970 Btu of heat is released when 1 pound of gas is converted to liquid (Janis & Tao, 2014).
1.12.13. Degree-Days
Degree-days are used to measure the amount of energy required during the cooling and heating seasons to maintain
comfortable indoor temperature (Wujek & Dagostino, 2010). The concept was conceived by the utility companies
several years ago to understand the relation between energy demand and weather conditions. The calculation of degree
days is based upon two factors – dry bulb temperature recorded daily and an indoor base temperature. The base
temperature is 65 F, and was developed on the fact that on an average indoor temperature is 75 F, and combining the
internal heat gains with the balance point temperature (65 F), the indoor temperature of 75 F can be achieved. There
are two types of degree-days. One is heating degree days and the other is cooling degree days (Wujek & Dagostino,
2010).
Heating degree-days (HDD) are used to calculate the amount of heating energy required during the cool seasons
(Wujek & Dagostino, 2010). It is the difference between the base temperature and the average daily temperature. The
average daily temperature is measured by taking the average of the highest and the lowest temperature of a day. When
the average temperature for a day is more than the 65 F, then the heating degree-day amounts to zero, however, if the
daily average temperature is less than 65F then the heating degree-day is the difference between the base temperature
(65 F) and the average temperature. Similarly, the cooling degree-days are calculated (CDD), but in this case if the
daily average temperature is higher 65 F, then the CDD is calculated as the difference between the average temperature
and the base temperature, or else if the average temperature is lower than 65 F then the CDD for that day is zero. The
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total number of degree days are added to calculate the severity of an entire season – heating and cooling (Wujek &
Dagostino, 2010).
1.13. Conclusion
Dealing with building energy systems can be challenging if the concepts pertaining to it are not understood properly.
Combined heat and power is an important system to reuse the rejected heat from electricity generation. It requires the
understating of different types of refrigeration cycles (vapor compression refrigeration system and absorption
refrigeration system) to analyze the various energy systems that produce, reuse, or store energy. It is basically a process
to improve the efficiency of energy use for building operation. Hence, the efficiency of the systems is also very
important and needs to be analyzed. Initiative to save energy also stems up from the fact of dynamic pricing, where
the consumers has the option to save electricity bill by using energy wisely. But energy is very precious and needs to
be saved as much as possible. Hence implementing various strategies and at an urban scale will help to understand the
dynamics of energy use and also help to identify synergies in energy demand for various building types, to use energy
efficiently. Overall, the feasibility of the district energy systems needs to be analyzed before implementing it any
specific location.
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CHAPTER 2: Background / Literature Study
2. Introduction
District energy systems are used to distribute energy to a group of buildings. This is achieved with the help of a central
plant, where the energy is generated and distributed using steam or hot or chilled water pipes. There are several types
of techniques that can be integrated in district energy systems, like a cogeneration plant, where rejected heat can be
used further to meet the energy demands for building operation. This increases the overall efficiency in terms of energy
usage for building operation. All of these concepts have been described in chapter 1, but applying these concepts
practically will help to determine the actual performance of these energy systems. Hence to understand these concepts
further, actual buildings were studied (online resources) where district energy systems have been used, to get an
overview of the performance of these systems in the real world. These are discussed in this chapter. In addition, the
history of district energy systems is summarized.
2.1. District heating and cooling
Interconnecting different district heating grids sometimes results in increasing the overall energy efficiency of an
energy system. In a major study done by Dmonkovic, et al, five disconnected DH grids distributed geographically in
Sonderborg municipality were assessed along with the impact of Industrial waste heat on district heating grids
(Dominkovic, Bacekovic, Sveinbjornsson, Pedersen, & Krajacic, 2017). It was observed that out of four
interconnections, two were economically viable. The benefits of the interconnections were as follows:
• Reduces energy usage (9.5% reduction in primary energy supply)
• Reduces CO2 emissions (11.1% reduction in CO2 emissions)
• Reduces total system cost by 6.3%
Using the industrial waste heat further reduces the primary energy supply by 3%, CO2 emissions by 2.2% and total
system cost by 1.3% (Dominkovic, Bacekovic, Sveinbjornsson, Pedersen, & Krajacic, 2017). Excess heat from
agriculture and industry can be used in district heating systems. It will result in increasing the efficiency of the systems
and cut down the monopolism in the heat suppliers market. But, from a regional case study, in Sweden, it shows that
the system is beneficial for the long term, and at a loss in case of short run. (Dominkovic, Bacekovic, Sveinbjornsson,
Pedersen, & Krajacic, 2017)
2.2. Feasibility of Energy systems in district scale
A substantial amount of energy is involved in the operations of buildings. Energy is required to make a building
habitable. It can be used for space heating, space cooling, domestic hot water or to operate electrical equipment within
a building. Fuel is used to produce the required energy. The type of fuel used can be obtained from both renewable
and non-renewable sources of energy. Renewable sources of energy (like Sun, wind, etc.) are intermittent and
appropriate technology must be installed to store the energy obtained from renewable sources or augment it when it
is insufficient. However, the present infrastructure uses fuels mostly obtained from non-renewable resources to
generate the amount of energy required for the operation of buildings, and the overall system is not fully efficient.
Energy is wasted during the process. For example, heat energy is wasted when electricity is generated in a plant. This
waste heat can be utilized for the purpose of space heating or domestic hot water in buildings. Utilizing waste heat
will make the energy system more efficient, prevent additional cost incurred in the construction of extra utility
infrastructure, and reduce the carbon footprint.
Understanding the organization of building types in a particular area is very important for the purpose of installing
district energy systems in that location. It gives the idea of the type of energy required by a particular type of buildings
in that location. For example, office buildings in New York use substantial amounts of electricity, and the residential
buildings need lot of energy for space heating and domestic hot water. Waste heat produced from the electricity
generation could potentially be used in residential buildings. So, if the office buildings are located in close proximity
12
to that of the residential buildings, then it might be feasible to install a CHP in that location. So, the spatial proximity
of the energy loads is a very important factor in determining the location of a CHP plant in this case. The ratio of
water heating to base electric ratio of each block in New York is fundamental in determining the various type of energy
generating system that could be installed in each block, since the ratio varies with block and have impact on the type
energy system chosen (Howard, et al., 2011).
Wujek & Dagostino describes when waste steam or hot water is produced as a result from an industrial process, the
absorption cooling in that case is able to provide a cost-effective solution for cooling. The same is the case for waste
heat that is generated in an electricity plant. It can be used for space cooling in summer and absorption cooling in
summer in a cost-effective way. The absorption chillers use this waste heat as a source for its mechanism and generates
cooling. (Wujek & Dagostino, 2010)
2.3. Thermal Ice storage
2.3.1. USC Cromwell field
The University of Southern California uses an ice energy storage method to efficiently use energy for building
operations (FMS, 2018). Underground of Cromwell field is used for the operation. It is 125’ in diameter and 40’ deep
and connected to a water chiller system. The chillers run at night to cool the water when electricity is cheapest and
power plants are underutilized. Chilled water from the tank is used during daytime hours, when electricity is most
expensive, for the use of producing heating or cooling as required by the buildings inside the university campus. (FMS,
2018)
2.4. Stanford Energy System Innovations (SESI)
Stanford University Energy and Climate Plan describes that the natural gas -powered cogeneration system in Stanford
was responsible for producing 90% of Stanford’s GHG emission (Office of Sustainability, 2015). In this system, the
unwanted heat from the buildings was collected by the chiller system to transport it to a central energy facility to
dispose to the atmosphere via evaporative cooling towers. It was decided to capture the waste heat from the chilling
system and use it to produce hot water for the heating system. Substantial heating is done in the winter and cooling in
summer, and with the help of a study it was inferred that 70% of the waste heat from the chilled water system could
be reused to meet 90% of the heating loads in the campus. But to achieve that the hot water should be used in the heat
distribution system instead of steam. Stanford came up with the SESI when the cogeneration plant approached the
end of its life. It was to achieve a trigeneration system which includes heating, cooling, and power. (Office of
Sustainability, 2015)
Several options were considered for the new energy system at Stanford, and the option “electrically-powered heating
and cooling plant with heat recovery and water distribution” was chosen. It has the lowest life cycle cost and is also
among the options of lowest up-front capital cost.
2.5. Spatial distribution of urban building energy consumption by end use
Howard et al analyzed the character of local energy use for the building sector (Howard, et al., 2011). A prototype
model was built, for the city of New York to study the energy end use intensity (measured in kwh/m^2 of floor area)
for buildings in the following areas: space heating, electricity for space cooling and non-space cooling applications
and domestic hot water. It was assumed that a building’s function (commercial or residential) was primarily
responsible determining the energy end use intensity, and the result was calculated on the basis of New York city’s
tax lot, i.e. based on the Zip code. (Howard, et al., 2011)
The study focused on the scope of distributed generation by referring to the model developed, which is developed
without detailed building characteristics (Howard, et al., 2011). The model developed is based on a bottom up
approach, and is different from other energy modelling approaches performed by other people to analyze the energy
13
requirements of buildings at a city scale. The difference is the energy analysis done at zip code level. (Howard, et al.,
2011)
Buildings with 8 different type of functions were modelled as per their total fuel intensities and electricity, and then a
multiple linear regression process was applied to obtain the annual end use consumption intensities (Howard, et al.,
2011). Next, using the ratio from the CBECS (Commercial Building Energy Consumption Survey) and RECS
(Renewable Energy Consumption Survey), the fuel intensities and electricity were divided among space heating and
cooling, water heating and base electric. The distribution of energy consumption across New York in the four areas
(base electric, space heating and cooling, and water heating) was determined by applying the results of annual end use
intensities to the building floor area in the City of New York. High values of energy consumption resulted for densely
packed areas as the consumption was calculated per the zip code (block area). Blocks located in the mid-town of
Manhattan reported for high energy end use intensity. Substantial amount of such blocks is primarily located in the
financial and central business districts and consists of Tall buildings. The results also analyzed the area of Manhattan
only on the basis of the four energy consumption categories – space heating and cooling, base electric and water
heating. This analysis showed different parts of Manhattan are responsible for higher amounts in each of the energy
consumption categories, and that consumption depends on the building’s function. For example, space heating was
found to be in higher demand in the central business district and at the upper east and west side of Manhattan, but base
electric and space cooling was in high demand only at the central business district. This was mainly because of the
fact that residential buildings and commercial stores consume more energy for space heating than office buildings.
(Howard, et al., 2011)
Energy consumption by building types was illustrated by the study, which will help to determine the location of
appropriate energy systems (like CHP) (Howard, et al., 2011). For example, the study showed that a particular block
in Manhattan, which is mixed use (residential, office and store space), requires 1.2 MW of base electric power and 0.5
MW of domestic hot water. These two energy demands are almost static throughout a year, and since this block does
not have access to the Con Edison’s district steam system, it could be a potential area for combined heat and power
(CHP) system as the substantial waste heat produced from the process of electric power generation would easily meet
the energy demand for domestic hot water in that area. (Howard, et al., 2011)
This type of analysis is required to understand the feasibility of CHP system as it depends on the proximity of the
respective buildings and moreover, with such district level building energy model, it becomes easier to implement
different types of energy system (CHP in this case) (Howard, et al., 2011). The study also calculated the water heating
to base electric ratio for all the blocks in the city of New York, and the ratio varies from blocks to blocks. It shows the
feasibility of different energy systems across the Manhattan area. In terms of planning decision, it can be useful to
decide on the combination of different building types required for the implementation of different energy systems
under consideration. The ratio between the thermal and electric can also help to determine the feasibility between solar
energy systems, like solar PV systems and solar thermal systems. Both of the aforementioned solar energy systems
depend on the roof area or façade area, and by having an idea of the thermal to electric ratio, best possible combination
between these techniques can be made to utilize an available space to be used for solar energy systems. But the result
of the energy analysis was determined annually. The solar technologies (solar PV and solar thermal) is dependent on
the energy resource which is intermittent, and so an hourly profile of the energy demand of each building would prove
to be more useful to decide on the implementation of the energy systems more accurately. The paper suggests
calculating the energy consumption of the buildings per hour and also by end use and regards those for future study
(Howard, et al., 2011).
It was suggested that cogeneration systems are most effective when they run at a constant load and all the waste energy
is reused (Howard, et al., 2011). Office buildings consume substantial amount of electricity for applications under
base electric usage, and that is constant throughout a year. So, placing it near residential buildings, that require high
amounts of heat energy for hot water, could reduce the wasted heat loss, that is produced during electricity generation.
This would make the electricity a less carbon intensive process, as well as would reduce the stress on fossil fuels to
make hot water for domestic applications. This type of information would help the policy makers and urban planners
14
to plan framework to reduce energy consumption or enhance energy efficiency at an urban scale. (Howard, et al.,
2011)
2.6. 2030 Challenge
To deal with the problems of climate change, a non-profit organization named Architecture 2030 was established by
architect Edward Mazria. Its mission is to change the impact of the global built environment as a main contributor of
GHG (greenhouse gas) emission. It has two primary objectives:
• “To achieve the dramatic reduction in global fossil fuel consumption and GHG emissions of the built
environment by changing the way cities, communities, infrastructure, and buildings, are operated, planned,
designed, and constructed (Architecture 2030, 2018).”
• “To advance the regional development of just and sustainable, resilient, carbon-neutral built environments
that can manage the impacts of climate change, protect and enhance natural resources and wildlife habitats,
provide clean air and water, generate local low-cost renewable energy, and advance more livable buildings
and communities. (Architecture 2030, 2018).”
One of the key strategies of Architecture 2030 is 2030 Districts. It is an initiative by private and public organizations,
involving various stakeholders (like property owners, planners, architects, etc.) of the built environment to reduce the
amount of energy consumption and water use at a district level, as well as achieve reduction in transportation
emissions. The challenge lays out the plan to achieve carbon neutral results by 2030, i.e. not to use fossil fuel to
generate energy for building operation. (Architecture 2030, 2018)
2.7. Background of District Energy System in USA
The district energy industry in the USA is considered to be divided into two parts. One is the pre-1960 downtown
steam systems (this is considered to be used since late 1880’s or 1890’s), and the other is the post-1960 combined
district heating and cooling systems (International District Energy Association, 2005).
International district energy association states that district heating systems dominated the first sector of the US district
energy industry (International District Energy Association, 2005). These were located in the downtown of many cities
like New York City, Philadelphia, Boston, San Francisco, Denver, etc. Those systems used steam to distribute energy
to buildings to use for space heating and domestic hot water. In fact, steam was used as much as 78% in US district
heating system in comparison to water, and in some cases steam was even used to operate the on-site steam driven
chillers for air conditioning purpose, by supplying steam at high pressure (125-150 psig). (International District
Energy Association, 2005).
The Consolidated Edison steam system in New York City is the largest district steam system in the world (International
District Energy Association, 2005). It was formed by merging many steam systems in the downtown, which once use
to serve respective areas of Manhattan. It serves approximately 350 buildings where 650,000 tons of cooling energy
is produced by steam driven chillers. The chillers produce chilled water for the purpose of air conditioning, using
steam. It reduces load on the electrical grid since there is no requirement of electrically driven chillers and takes off
400 MW of peak electrical load demand during summer. In summer months, steam driven cooling system is used by
large buildings in many cities, and in campuses it is widely used with CHP system, so as to use the waste heat recovered
from electricity generation. The beautiful skyline of Manhattan is somewhat attributed to the district steam system, as
it eliminates the need for huge chimneys and boilers on the top of the high-rise buildings. (International District Energy
Association, 2005)
The steam used in these district steam systems was a by-product of electricity generated at the downtown. A CHP
system was used to recover the steam (International District Energy Association, 2005). The local investor-owned
utility or municipal utility originally owned the steam systems. During the time of 1960’s and 70’s, huge power
generating stations, consisting of coal and nuclear stations, were constructed, and these in turn negatively affected the
15
power plants in the downtown which were small in scale and less efficient. Those huge power plants were constructed
in remote locations and were funded by several utility companies. Apart from it, there were rules made concerning to
the restrictions of emissions in downtown, and fossil fuel prices increased too. This led a number of investor-owned
electric utilities to move out from the steam system. It resulted in the increment of the price of steam as it was produced
by running boilers and also without any electricity generation. (International District Energy Association, 2005)
The steam system needed upgrades and the distribution system also needed to be maintained and repaired at the
beginning of the 1980’s (International District Energy Association, 2005). The commercial offices began to be
designed without manually operating windows, since the price of energy went up and the offices needed to cut down
the intake of outside air. Moreover, due to the introduction of personal computers and more employee density, the
internal heat gain began to increase, which resulted in the reduction of space heating in the conditioned spaces. This
also negatively impacted the steam revenues. But, during the 1990’s new district energy industry associations invested
to use steam for cooling purposes. This supplemented the district steam business (International District Energy
Association, 2005).
Apart from the steam system, there was another category of district energy system that started operating in the USA
in 1960 (International District Energy Association, 2005). It was combined heating and cooling system at a district
scale. Hartford, CT has the world’s first district energy (both heating and cooling) system. Steam and chilled water
were used for the purpose of heating and cooling respectively. The system began operating in 1962 and was
constructed by Hartford Gas Company. This set an example for gas fired district energy systems and was developed
in 10 more downtown cities in the USA, which were developed and operated by the local natural gas distribution
company. It began to grow in cities like Omaha, Minneapolis, Century City, Tulsa, Pittsburgh and Oklahoma City as
it provided better deal to the building owners, in terms of initial capital cost, no mechanical room (more space),
maintenance costs (International District Energy Association, 2005).
In the early 1990’s, district cooling systems were developed using chilled water systems as an addition to the existing
district heating system (steam system) in some cities (International District Energy Association, 2005). It was done to
leverage the facilities to the existing customers under district heating system and it also added a second option of
revenue from cooling services. Joint ventures were formed for the district cooling services, and huge electric driven
chiller plants were constructed in dense downtown locations. A strategy using ice thermal storage was employed to
drive the chiller plants. This helped in cutting down the cost as electricity was used to cool down the ice at night when
the unit price of electricity is less (there is hardly any peak demand during night time). Energy is stored in the ice and
is used to cool water to be supplied to the chiller plant. District cooling also became popular during the early 1990’s
because of two reasons. First, there was a sharp increase in the peak electric rates and there was an impending phaseout
plan of the CFC (chlorofluorocarbons), which is the main chemical used in chillers (International District Energy
Association, 2005).
2.8. District Energy Systems in different cities of North America (USA and Canada)
2.8.1. Cornell University (Ithaca, New York)
Cornell University has a Central Energy Plant (CEP) that supplies energy to around 150 campus buildings covering
almost 1.2 million sq. meters (Figure 2-1). The system consists of various energy systems like steam heating
components, Lake Source Cooling (LSC), cogeneration facility and a thermal storage system, and produces different
forms of energy (electricity, steam and chilled water) required for building operation and maintenance (Cornell
University, 2018). During the time period from 1990 to 2010, the system has reduced GHG (Greenhouse gas)
emissions by 30% while its serviceable area was increased by 15%. Cornell University further incorporated Lake
source cooling (LSC) by using cold water from Cayuga Lake, and also added a CHP system (Cornell Combined Heat
and Power Project) to their existing CEP system. The LSC system was used in cooling the campus building, and has
replaced electrical chillers, thus reducing the electrical energy use by 86% and cutting out the release of CFCs
(chlorofluorocarbons) (Cooper & Rajkovich, 2012). The CHP system is composed of combustion turbines with
integrated heat recovery steam generators. The turbines use natural gas to generate the power for running the electric
16
generator, and the excess heat (waste heat) from the gas turbines is recovered using the heat recovery steam generators
for steam production to meet the campus building energy needs. This CHP system substantially reduces the GHG
emission and has a positive environmental impact on the Cornell University’s energy needs. The CEP system did not
use any coal to generate energy for building operation because of using natural gas, and has achieved a 20% reduction
in CO2 emission which is equivalent to 50,000 tons/ year. (Cornell University, 2018)
2.8.2. St. Paul (Minnesota)
St. Paul has a district heating and cooling system and also uses a CHP system to supply energy to buildings (District
energy, 2018). It was started in 1983, and it serves around 80% of St. Paul’s downtown area. It uses hot water to
transfer energy for its district heating system process, and serves the purpose for domestic hot water (laundry, hotels,
restaurants), space heating, and also in the industry (Cooper & Rajkovich, 2012). A central plant along with satellite
facilities generates hot water, and then is transported to buildings using a hot water loop (underground pipes). The
temperature of the supply water during winter is around 250F and in the remaining months is around 190F (District
energy, 2018).
The district cooling system is operated by District Energy, St. Paul Minnesota (District energy, 2018). It provides
service throughout the year to more than 100 buildings in St. Paul, covering an area of over 19 million sq.ft. Absorption
chillers and electric centrifugal chillers are used in the district cooling system, and this system substantially reduces
the use of refrigerants (ozone depleting). Like district heating, underground pipes are used to transfer energy in the
form of chilled water (38F to 42F). The underground pipes are connected to a central chiller plant (with thermal
storage facilities) that produces the chilled water and serves a great deal for managing high energy loads. Both the
district cooling and heating system have more than 99.99% reliable service. (District Energy S. P., 2018)
The CHP system at St. Paul was developed in 1990 (District energy, 2018). It improved the efficiency of energy
production, by producing electrical and heat energy from the same fuel source. The system uses biofuel, natural gas
and boilers to generate energy, simultaneously producing around 33 MW of electricity and 65 MW of heat. The waste
thermal energy is reused to meet the demand of 65% of the district’s heating load. (District Energy S. P., 2018)
Figure 2 - 1 : Combustion turbine with heat recovery steam generator (Cornell University, 2018)
17
2.9. Skyway Minneapolis
Minneapolis has a district energy system in the downtown area, that began operation in 1972, and grew throughout
the 1980’s and 90’s to serve more buildings (over 120 buildings under district heating and around 40 buildings under
district cooling) (NRG, 2018). A unique and interesting feature about downtown Minneapolis energy system is its
skyway system, in which more than 40 million sq.ft. of area at second floor level is interconnected and primarily
heated by the NRG Energy Center Minneapolis (International District Energy Association, 2005). This energy center
has grown along with the downtown Minneapolis and provides reliable energy (track record of 99.9999%) that is
environmentally sound as well (NRG, 2018). The skyway system allows people to travel around the city (the
conditioned space) comfortably, even when the outside space is extremely cold (Figure 2-2). Minneapolis has extreme
weather conditions, with the summer time temperature exceeding 100 F and winter temperature reaching minus 10 F,
and these conditions call for peak demands for heating and cooling, which require high amounts of steam and chilled
water to meet the building energy needs (NRG, 2018).
Figure 2 - 2 : The Skyway system at downtown Minneapolis (NRG, 2018)
18
2.10. Conclusion
Chapter 2 provided a brief overview of the practical applications of the various types of energy systems at an urban
scale. It is required to understand the scope as well as the practical limitation of the concepts discussed in chapter 1.
District energy systems exists for a long period of time, and in US district heating systems dominated the first half of
the district energy industry. New York sets a good example for the use of seam to produce cooling. Skyway
Minneapolis is an exceptional project describing the application of heating and cooling at an exceptionally large scale,
since the conditioned spaces are under one roof. The energy systems used at Cornell University describes the
possibility of using different techniques (energy systems and storage) to achieve efficiency from the perspective of
building energy usage. The ice cooled storage facility at the University of Southern California gives an example of
storing energy. All of these techniques show the possibility of the theoretical concepts described in chapter 1 and has
helped in generating ideas to analyze the results described in chapter 4.
19
CHAPTER 3: Methodology
3. Introduction
Chapter 3 discusses the methodology of the process of generating heating and cooling loads for the districts and
combining them. It concentrates on the process of developing a prototype model and then obtain a baseline energy to
analyze the building operation energy demand in an urban scale (Figure 3-1). eQUEST is used for the purpose to run
simulation on the prototype model to generate the baseline energy demand. The results obtained from simulation are
described and analyzed in chapters 4 and 5.
3.1. Prototypical model
Five cities were chosen for the prototype districts. These areas were considered by providing the right input (climatic
condition at the downtown area) for the climate file in the energy simulation software (eQUEST). Then, four different
building types were created for each of the location to fulfill the requirements of the prototypical site model. The
prototypical model is considered to be an ideal model, where the energy demand of the building types is required to
understand the dynamics of energy usage at an urban scale. Here, various factors like shading (due to buildings, trees),
wind speed or other site-specific design constraints is not taken into consideration.
3.1.1. Locations
Five different types of areas were selected to cover a wide range of climate types. These districts include the downtown
areas of five different cities – Los Angeles, Chicago, New York, Seattle and Miami.
Figure 3 - 1: Methodology diagram
20
3.1.2. Building types
Four different types of buildings were selected to understand the possibility of energy exchange between different
buildings in various climate zones. The building types are residential, office, hotel, and data center. Depending on the
weather conditions, a building may require heating or cooling energy to create a habitable atmosphere for the residents.
Moreover, different buildings have different demands to use the same type of energy for different purpose. For
example, an office building generally has high demand for space heating and a residential building has high demand
for domestic hot water (DHW). In this case, the waste heat of one building can be used to meet the heating demand of
this two building types. Hence a data center was selected to be simulated in the prototype model. Data Centers require
cooling energy all the time and can possibly provide heating energy or using cooling energy from the other buildings
(that needs heating energy and rejects cooling energy) to create an energy exchange between the buildings. A hotel
building was an add on to include more building types and see how it affects process of energy building energy
exchange in different climates.
3.2. eQUEST Simulation
eQUEST was used to perform the energy simulation of the buildings in the prototype model for five different cities.
It is used to evaluate the performance of the whole building, considering the various features in a building like the
building envelope, façade, door, window, lighting requirement, energy demand (heating, cooling and electricity). The
simulation results can be used by various stakeholders like architects, owners, utility company, to determine the hourly
energy usage of the buildings. eQUEST generates a building model based on the various inputs provided by the user.
These inputs appear on wizard screens requiring the features of the building (Figure 3-2). This wizard screen comes
under the schematic design wizard (explained later), and it requires information of the building footprint. Appendix 1
describes the screens in more detail.
Four different types of buildings were selected for the analysis. It includes three office buildings, one hotel and data
center, and two residential buildings. The buildings were simulated in eQUEST to generate the hourly energy demand
for each hour of the year. The energy profile includes heating, cooling and electrical energy demand for the each of
the respective buildings. The heating energy of all the buildings were calculated and added to get the net heating
Figure 3 - 2 : First part of the script, components are grouped and named for clarity
21
energy demand of every hour for the hypothetical district. The same process was repeated for the cooling and electrical
energy demand.
3.2.1. Building Inputs (ASHRAE 90.1)
Guidelines from ASHRAE 90.1 has been used (Table3.1 & Appendix 2) to provide the details for building inputs like
properties of the wall, glass material, ceiling properties, etc.
Table 3.1: Building inputs as per ASHRAE guidelines
3.2.2. Building details (Area)
The buildings modelled were designed to have a floor area of 150’ by 150’ for each floor (Table 3.2). A square building
was taken for the analysis in all the cities. Only the building energy loads is considered for comparing across all the
cities. Building size vary as per project requirement and site constraint. Hence, the building size was not the primary
consideration behind the energy use calculation, rather it was the building type and the locations, which was considered
primarily to understand each building’s energy requirement.
Table 3.2: General building details
Building description Floor dimension
(ft.)
Area per floor
(sq.ft.)
No of floors
Total building
area (sq.ft.)
Office 12 floors 150' * 150' 22500 12 270000
Office 20 floors 150' * 150' 22500
20
450000
Office 60 floors 150' * 150' 22500
60
1350000
Residential 10 floors 150' * 150' 22500
10
225000
Residential 20 floors 150' * 150' 22500
20
450000
Data Centers 150' * 150' 22500
8
180000
Hotel 20 150' * 150' 22500
20
450000
The areas of the building are also critical to evaluate the heating, cooling and electrical energy demand of the interior
spaces of the buildings. The total area of all the buildings that is being considered for each location is 3375000 sq.ft.
Residential Hotel Office Data Center
Construction Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c.
Ext. Finish / Color Roof, built-up Roof, built-up Roof, built-up Roof, built-up
Ext Insulation 2 in. polyurethane (R-12) 2 in. polyurethane (R-12) 2 in. polyurethane (R-12) 2 in. polyurethane (R-12)
Add Insulation R-19 batt, no rad barrier R-30 batt, no rad barrier R-19 batt, no rad barrier R-19 batt, no rad barrier
Construction Metal Frame, 2x6, 16 in. o.c. Metal Frame, 2x6, 24 in. o.c. Metal Frame, 2x6, 24 in. o.c. Metal Frame, 2x6, 24 in. o.c.
Ext. Finish / Color Aluminium Aluminium Aluminium Aluminium
Add Insulation 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5)
Exposure Earth Contact Earth Contact Earth Contact Earth Contact
Construction 6 in. Concrete 6 in. Concrete 6 in. Concrete 6 in. Concrete
Ext Insulation no perimeter insulation no perimeter insulation no perimeter insulation no perimeter insulation
Interior Finish no surface finish Carpet with rubber pad no surface finish no surface finish
Int. Finish Lay-In Acoustic Tile Lay-In Acoustic Tile Lay-In Acoustic Tile Lay-In Acoustic Tile
Batt Insulation no ceiling insulation no ceiling insulation no ceiling insulation no ceiling insulation
Int. Finish Carpet with fiber pad Carpet with rubber pad Carpet with rubber pad Carpet with rubber pad
Construction 4 in. Concrete 4 in. Concrete 4 in. Concrete 4 in. Concrete
Concrete Cap 1.25 in. LW Concrete 1.25 in. LW Concrete 1.25 in. LW Concrete 1.25 in. LW Concrete
Rigid Insulation 1 in. polystyrene (R-4) 1 in. polystyrene (R-4) 1 in. polystyrene (R-4) 1 in. polystyrene (R-4)
Door type 1 Construction Steel Hollow core w/o Brk Steel Hollow core w/o Brk Steel Hollow core w/o Brk Steel Hollow core w/o Brk
Door type 2 Construction Double Viracon Double Viracon Double Viracon NA
Glass Category Single PPG Single PPG Single PPG Single PPG
Glass type Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9)
Frame type Aluminium, Fixed Aluminium, Fixed Aluminium, Fixed Aluminium, Fixed
Window
Roof Surfaces
Above Grade
Walls
Ground Floor
Ceilings
Floors
22
3.2.3. Weather file
Four different building types were modelled in 5 different climate zones to understand the heating and cooling
requirements of the different building types. Each of the buildings were modeled in Schematic Design wizard of
eQUEST. It contains multiple wizard screens starting with the “General Information” tab. The process of modelling
each of the building type differs only in the step of specifying the location. This step provides the file with the specific
climate file for each location. This step is accomplished by the following process:
1. Visit the site - http://www.doe2.com/
2. A file name “eQ_WthProc” was downloaded from the site. This is used to convert epw (EnergyPlus) files
into bin files. Only bin files are used in eQUEST to specify the climate type of a particular location. The
file was downloaded as a zip file and was extracted. The required file “eQ_WthProc.exe” was present in the
folder name “Processor” inside the zipped file.
3. EnergyPlus weather files were downloaded for the respective locations
4. The epw file was converted into bin file and was saved into the folder containing the weather files for
eQUEST.
5. The “Mode” tab in the eQUEST file was changed to “detailed data edit”, and the “project properties” tab was
opened from the “component tree” dialogue box.
6. Under the “project properties” the “project data” tab was selected, and the name of the weather file was
changed. The name was changed by writing the name of the converted bin file, preceding with the word
“TMY2”. For example, “TMY2\USA_IL_Chicago-OHare.725300_TMY2.bin”.
3.2.4. Procedure
The working procedure (Figure 3-1) in eQUEST is described in detail for a residential building and is compared in a
few instances with other building type as required. It is done as the procedure is generally the same for all the
building types.
Working in eQUEST is different from other energy modelling software programs like IES VE or Honeybee in terms
of creating a model. No 3d model that looks like a building is initially created by creating walls, floors, and roofs and
then assigning properties (materials) to them. Instead a model was created by inputting the values in wizards, and then
eQUEST generates a model based on those values. A very simple model (square shaped plan) was developed for the
purpose of building energy simulation. A complete series of wizards is required to generate models and run simulation
(see Appendix1).
There are generally two types of modes in eQUEST to model a building. One is Schematic Design Wizard, and the
other one is Design Development Wizard. The Schematic Design Wizard is used to create simple structures and
functions; for example, it can be used to create only a single building shell and two HVAC system type templates. But
Design Development Wizard can be used to create complex shapes and to create multiple building shells and many
HVAC system type templates. Once a project is in Design Development wizard, it cannot be edited in the Schematic
Design Wizard.
Other basic building related data like project name, building type and number of floors above and below grade were
also provided in the “General information” tab. Basic building structure information were provided in the third wizard
screen – “Building footprint” tab. It includes building footprint shape, orientation, dimension, and floor and ceiling
heights. The type of roof, whether pitched roof or flat, whether there is space for attic or not is also to be specified in
the third wizard screen. Apart from it, this screen consists of the zoning pattern. “One per floor” zoning pattern is used
in all the eQUEST models.
23
Subsequently, a few wizard screens provide the option of inputting building materials information. There are default
values already present in these tabs, but information from ASHRAE 90.1 energy standard has been used. The first
wizard screens contain information on “Building Envelope Constructions”, which has three sub sections – “Roof
surfaces”, “Above grade walls”, and “Ground exposure”. The sub-sections require further detailed information about
the materials used in the building. Default values have been mostly used, apart from few values that were required to
be changed to follow the ASHRAE code. For the “roof surfaces” tab, the material for exterior insulation have been
changed to “2-inch polyurethane (R-12). In “Above grade walls” category, exterior finish, exterior insulation, and
added insulation have been changed to Aluminum, 1 & ½ inch polyisocyanurate (R-10.05) and no batt respectively.
For the ground floor option, only the “construction” tab have been changed to 6-inch concrete.
The next wizard screen consists information of the building’s interior construction. It has two sections- Ceilings and
floors, and same procedure as the last one has been followed to input the data. In the “ceiling” section, “interior finish”
have been changed to the option of “Lay-In Acoustic Tile”, and ceiling insulation option have been opted out. For the
“Floors” section, only the “construction” and “rigid insulation” sub section has been changed to “4-inch concrete” and
“1-inch polystyrene (R-4)” respectively. The option of “1-inch polystyrene has been selected on the basis of R-value
of the material polystyrene, and the same thing has been followed throughout the process in selecting materials for
other wizard screens.
The next wizard consists the details of exterior doors: number of doors in each side (as per the orientation), the size
of the doors and the construction materials used for the door(s). The number of doors being used for this project was
selected by the user and was not based on any guidelines. Opaque door has been selected for south, east and west side,
since no external shading has been used for this project, and glass has been used as a door type in north side. Other
factors like construction and glass type has been kept at the default value, except for the height and glass type for the
glass option in door type.
The next wizard consists the details of the exterior windows. This wizard screen has three main sections. First, is the
window area specification method, in which the default method has been used. Second section consists of the glass to
be used in the windows. Here the glass category and frame work has been modified as per the ASHRAE energy
standard. Third section consists of the window dimension and positions and was left unchanged to the default values.
Next two screens consist of the generic information of the buildings like exterior window shades and blinds, and
skylights, both of which are not used in the project. In eQUEST few wizard screens are dependent on other wizard
screens and appear only on by inputting specific values in the precedent wizard screens. Information for “Roof
Skylights” was wizard no. 9 and then the next wizard screen that appears is no. 13.
Occupancy schedule
Wizard 13 provides options for the time frame for which a building will be occupied in a year. It is named as “Season
Definitions”, and has the option of editing the observed holidays schedule. It has been kept at default.
The next wizard screen contains information about “Building Operation Schedule”, and the following values have
been used for the occupancy schedule in the respective buildings, but in “detailed data edit” mode. The default option
of building occupancy was selected which is “Daytime unoccupied, Typical use.” The time period of building
occupancy for each day has default values related to the option of the type of use – “Daytime unoccupied, Typical
use.” It has three different types of building occupancy – one for the weekdays (Monday to Friday), one for weekends
(Saturday and Sunday), and one for holidays and this schedule changes with the building types. But, the occupancy
schedule has been modified in the “Detailed Data Edit” mode (Table 3.3, 3.4, 3.5, 3.6 & 3.7), to approach for a better
and accurate energy simulation results. In the “Detailed Data Edit” mode, under the “Component Tree”, all the
24
schedule options are there, including occupancy schedule, lighting schedule, equipment schedule, etc. Default values
of all these schedules are loaded into the eQUEST file initially, and these values have to be modified as necessary to
get close to an accurate result. To edit or make a schedule from scratch, any of the existing schedule options from the
component tree needs to be selected. This will open a window named as “Schedule Properties” (Figure 3-3), and it
has three sub categories in it, which are “Day Schedules”, “Week Schedules” and “Annual Schedules”.
Table 3.3: Occupancy schedule for office buildings
Table 3.4: Occupancy schedule for residential buildings
Table 3.5: Occupancy schedule for hotels (Saturday)
25
Day Schedules provides the option of entering the hourly values for each of the schedule options. So, to modify the
occupancy schedule, the option “create” was selected from the “currently Active Day Schedule” tab, and then from
new window named “Create Day Schedule”, the option of “Load Component from Library” was selected. It provides
the option of “category” and “Entry”. From Category option, the type of schedule can be selected. Occupancy
Schedule was selected in this case, and from the “Entry” category the “APT OCC WD” option was selected. This
loads the default value of occupancy schedule for residential buildings for weekdays in the eQUEST file. Then this
schedule was renamed and the hourly values were modified as per the above table (Table 3.4) for residential buildings.
Other buildings have different occupancy profile for weekdays and weekends and in similar way the occupancy
schedule for weekends is also created. Once the daily schedules are created, the schedule for a week is formed by
specifying the weekdays, weekends and holiday values for a week in the “Week Schedules” tab. This schedule for a
week is given a new name, and then is specified to an annual schedule, which is created from scratch. The overall
process constitutes only the creation of occupancy schedule for the building for every hour throughout the year. The
next step is to assign this schedule to the building. This is done by using the “Space properties” option of each floor
from the “component tree” tab. This opens a new window, where the newly created occupancy schedule can be applied
to the building (Figure 3-4).
The schedule (occupancy schedule in this case) needs to be applied to each space separately. Figure 3-3 shows that
the currently active space is “Spc (T.3)” which represent one of the many spaces (or zones) in the building. The
simulation process will take into consideration only those schedules which have been assigned to any of the spaces.
Table 3.6: Occupancy schedule for hotels
Table 3.7: Occupancy schedule for hotels (Sunday)
26
The next wizard screen consists of the details of the room specific occupancy details, which includes maximum
amount of sq. Foot a person covers, design ventilation and percentage of the room area with respect to the total area.
Default values have been used in this case, and this wizard screen is specific for the respective building types. For
example, the type of room areas it shows for a residential building are “Residential (Multifamily dwelling unit),
corridor, storage”, etc. But for an office building it shows totally different area like “office (open plan), office
(Executive / private), Lobby (office reception / waiting), conference room”, etc. Few items can be similar like
“corridor”, which is in case of residential and office buildings. In the subsequent wizard screen, information related
to non-HVAC end uses is provided. eQUEST has separate profiles (data contents in the wizard) for different types of
Figure 3 - 4 : Space property of a particular zone
Figure 3 - 3 : Schedule properties
27
buildings, and to get a basic energy simulation (heating, cooling and electricity) result, sections like these were kept
at default values, unless it is necessary to modify a certain value for meeting the requirement of a particular building
type. For data centers, eQUEST does not have any specific profile. So, to set up an eQUEST file for data centers, the
building was modelled as an office building and the internal plug loads were set high, at 200 W/sq.m (18.58 W/sq.ft).
This was done in the wizard screen name “Occupied loads by activity area”, and there is a difference between the load
input profile for an office building and a data center (Figures 3-5 & 3-6). This wizard screen does not appear for
residential buildings. Instead different screens appear for residential buildings to perform similar task. This is because
both the building types (office and residential) has different load requirements. These loads depend on the different
areas (zones) of a building and how those areas are used in a day. For example, a residential building will have more
demand for domestic hot water, where as an office building will have more demand for electrical loads, and to calculate
these different loads for different building types separate wizards are available in eQUEST program.
For residential building types, the wizard screen used to calculate some part of energy loads are “Interior Lighting
Loads and Profiles, Cooking Loads and Profiles, Self-Contained Refrig Loads and Profiles, Miscellaneous Loads and
Profiles, Exterior Lighting Loads and Profiles and Domestic Water Heating Hourly Profiles”. These wizard screens
are exclusively designed for residential building types and have different input requirements for other type of
buildings. Default inputs have been used in these wizards too to get a general overview of energy loads. The default
inputs are based on the respective profiles of each wizard. The default profile is used if not specified to use a particular
schedule for lighting. Like Occupancy schedule, the lighting schedule are also created from scratch for each of the
separate buildings.
The lighting schedule for residential building is same as the occupancy schedule, and the following tables describe
the lighting schedule for all the types of building.
Figure 3 - 5: Office building (Miami)
28
The lighting schedule for office buildings is kept as 100% for the occupied time during the weekdays, i.e. from 7 am
to 7 pm, and is kept at 0% for the unoccupied time. This is done to take into account the maximum lighting load that
will be encountered during the building operation, and even if the occupancy load is low during a particular time (like
50% during 12-1 pm on weekdays, Table 3.8), the lighting load may be 100% because of the presence of the remaining
50% of the occupants. Lighting schedules vary between the building types and also on the type of days it is used for
(Tables 3.9, 3.10, 3.11 & 3.12).
Figure 3 - 6: Data centers (Miami)
Table 3.8: Lighting schedule for office building
29
Table 3.9: Lighting schedule for residential building
Table 3.10: Lighting schedule for Hotels (Weekdays)
Table 3.11: Lighting schedule for Hotels (Sunday)
30
This is used to change or modify the default inputs that is used to model occupancy and non-HVAC end uses specified
by the user. The Wizard screen for DHW (Domestic Hot Water) was next for the residential building types. Default
profile was used for this category.
The next wizard screen used for calculating the energy loads and is similar for all the building types is “HVAC system
Definitions”, but the wizard screen number is different for different buildings. The type of HVAC system used is VAV
(Variable air volume). It is not the default option in an eQUEST programme, instead it has to be set up in the
programme. The schematic design wizard screen in eQUEST runs under the mode “Wizard Data Edit”, and the mode
has to be in “Detailed Data Edit” to set up the requirements for VAV system. The next step is to select the “component
tree” tab, located on the lower left-hand corner of the screen. Any of the energy systems (Water-side HVAC or Air-
Side HVAC) has to be selected to make the required changes. Air-Side HVAC system was selected in this process,
and then on the main screen (right hand side of component tree) the spreadsheet tab was selected. Then the display
mode has to be selected as “Basic Specifications”, and under the HVAC System Type drop down menu the option of
“Variable Air Volume” has to be selected which is displayed in red color. Two HVAC system types can be selected
in an eQUEST programme under “Schematic Design Wizard”, but only the VAV system “Standard VAV with HW
reheat” was selected for this project, and the type of return Air Path for this project was kept default as “Ducted”. In
this system, the cooling source was changed to “Chilled water coils” and the heating source was kept to default input
as “Hot water coils”. Like occupancy schedule and lighting schedule, system schedule
Table 3.12: Lighting schedule for Hotels (Saturday)
Table 3.13: Equipment schedule for office buildings (Weekdays)
31
Table 3.15: Equipment schedule for Hotels (Weekday)
Table 3.14: Equipment schedule for residential buildings
Table 3.16: Equipment schedule for Hotels (Saturday)
32
Next few wizard screens ask for inputs regarding the HVAC system, like thermostat temperature for occupied and
unoccupied space and its location, fan schedules, etc. Defaults input has been used in these wizard screens and has not
been described further in detail.
3.3. Results- Building to City
Simulation results (energy loads) for each building was obtained from eQUEST, and then the energy loads were added
to determine the total energy demand of the prototype model. eQUEST simulations provide the result of energy loads
for each building (Figure 3-7). This consists of an Excel spreadsheet containing the cooling, heating and electric loads
of the building. After obtaining the results of all the building a separate excel file is created. Three tabs are created
within this Excel file – one for cooling loads, one for heating loads and one for electric loads (Figure 3-8). These
loads account for the total energy loads of the respective category (cooling, heating or electric) of all the buildings in
the prototype model. For example, the electric loads of all the buildings are copied into the tab of electrical loads, and
then the total electric load for the prototype model is calculated by adding the electric load of each of the individual
building (Figure 3-9). The same process is repeated for cooling and heating loads.
The next step was to analyze these energy loads. For this purpose, two methods were considered.
First, is the energy exchange, where the cooling and the heating energy were compared to find the possibility of
instantaneous energy exchange between the buildings within the prototype model. For this purpose, a separate column
was formed where the heating values was subtracted from the cooling values, the result will show the values which
will indicate that either there is a demand of excess heating or excess cooling. If the values are zero, then it will
indicate that there is similar demand for both heating and cooling, and it will be a good sign for the feasibility of
building energy exchange.
The second method is the combined heat and power, where the rejected heat generated due to electric loads was
analyzed to see if it can meet the heating and the cooling energy loads of the prototype model. In this method, the
peak electrical demand was calculated of the prototype model (collection of buildings) at a particular location. Using
the peak electrical demand all the estimations were made for the cogeneration system. A gas fired co-generation
system was considered for the process. So, a gas turbine needs to be included in the system, to produce electricity as
well as reuse the waste heat. The efficiency of the gas motor was considered to be at 33%. This means 1 unit of fuel
Table 3.17: Equipment schedule for Hotels (Sunday)
33
is required to produce 0.33 units of electricity, and the remaining 0.67 units is lost as waste heat, exhausted from the
combustion process (more precisely, if the electrical demand is 1kW, then the required generator size should be 3kW
of which 2kW would be wasted as heat, exhausted from the combustion process, and is calculated at 3412.142
Btu/kWh). Apart from that, 15% of losses are considered that are unaccounted for. So, the remaining waste heat can
be used for heating purpose and also can be used in absorption chillers to produce chilled water. So, evaluating the
electrical demand for each location was an important factor in determining the amount of load (heating and cooling)
that could be met by utilizing the waste heat. And if that waste heat is not enough, then supplementary energy would
be required to meet the heating and cooling demands. This supplementary energy can come from any source-
renewable energy or non-renewable energy.
The two methods are independent of each other.
Figure 3 - 9 : Screenshot of the excel sheet showing the electric load for all buildings of the prototype model in Los
Angeles
Figure 3 - 8 : Screenshot of the tabs created in excel sheet for adding the respective energy loads
Figure 3 - 7: Screenshot of the excel sheet containing energy loads for Hotel building in Los Angeles
34
3.4. Conclusion
Developing a prototype model from scratch requires information for every part of a building, like building height,
amount of glazing and its properties, HVAC system types, occupancy schedules, etc. However, all these data can be
obtained with the help of a code (building or energy). ASHRAE standards have been used to get those data and has
been put into the eQUEST software to get a baseline of building energy usage. The way eQUEST works is different
from other energy simulation software programs, the details that has already been described. However, the results
obtained from the energy simulation has been analyzed using two methods (Figure 3.10) to determine the feasibility
of a district energy systems and is elaborated in chapter 4 and 5.
Figure 3 - 10 : Methodology diagram (part)
35
CHAPTER 4: Results
4.1. Introduction
Chapter 4 describes the results of the simulations that were carried out in Chapter 3 (Figure 4-1). eQUEST was used
since only the basic energy loads were required. There are two separate items that are being calculated: energy
exchange using cooling and heating loads only and combined heat and power (cogeneration) using cooling, heating,
and rejected heat from electricity. Section 4.2 discusses values from the calculation in eQUEST for all the values
needed for both district analyses.
4.2. Simulation using eQUEST
Four building types were selected and simulated in eQUEST for understanding the basic energy demands of the
buildings. The buildings types are office buildings (3 total), data center (1), hotel (1) and residential buildings (2).
Each of the buildings in the respective climate zones (Los Angeles, Chicago, Miami, New York and Seattle) were
simulated to get the results of the energy loads.
The results are calculated for each hour of the year for each of the energy loads. The results are exported into a “csv”
format which can be opened in Excel (Figure 4-2). The Excel file contains the list of the calculated data (heating,
cooling and electricity load) for each hour of a year.
There is a single Excel file for the results for each of the buildings and location, exported directly from eQUEST.
The spreadsheet has 8 columns. The first column represents all the hours of a year. The next 3 are month, day and
hour (this hour column represents the 24 hours of a day). The loads are displayed for the respective months, days and
hours of the year, starting from the 1
st
hour in the month of January and to the last hour of the month of December.
The remaining four columns are the building energy and electricity loads. The results are obtained in Btu/hr for
building heating and cooling load and in kW for the electrical load. The electric loads are converted into Btu/hr for
comparing against the building energy loads (cooling and heating).
Figure 4 - 1: Part methodology diagram showing the result analysis section
36
There are 35 Excel sheets created initially from the eQUEST program, 7 buildings simulated at 5 different locations.
These files directly get saved at a default location that is created after the eQUEST program has been installed. Then
5 different Excel sheets (named “Energy demand_City name”, ex. “Energy demand_Miami”) were created, each for
a different location, and with 4 tabs in each of the Excel sheet (Figure 4-3).
The four tabs are used for calculating the heating, cooling, electrical and total energy loads of all the buildings at a
particular location (the five cities), and the values used in those tabs are taken from the original Excel files generated
by the eQUEST program (Figure 4-2). For example, for the city of Los Angeles, an Excel sheet was created (Energy
demand_Los Angeles), and for each energy load category seven different columns were made. These columns refer
to all the buildings that have been selected for the purpose of simulation (Figure 4-4). The three tabs – “Heat load,
cool load and Electric loads” are then filled in with the data from the initially generated Excel file and is explained as
follows. For example, a hotel building in Los Angeles is taken for analysis to calculate the total electricity load. In
this case the entire column (“Building elec load” from figure 8) is selected, copied, and then pasted in the “Electrical
Load” tab of the Excel file (Figure 4-4) named “Energy demand_Los Angeles”. The same process was performed for
the other buildings and this was how the electric load of all the buildings was compiled into a single Excel file (Figure
4-4). Then all the columns (each column containing the electric load for each building type are added to get the total
electric load of all the buildings in the prototype model. This total electric load is obtained on the basis of each hour
throughout the year (figure 4-4). The same process was repeated for the other energy loads – cooling and heating. The
last tab “Loads” refers to the total energy load of all the buildings for each of the category - heating, cooling and
electricity. The electricity load was also converted from kW to Btu/hr using the formula 1 kW = 3412.14 BTU/hr.
Figure 4 - 2: Screen shot of the Excel file for Los Angeles (This is only a part of the spreadsheet)
Figure 4 - 3: Screenshot of the tabs created in Excel sheet for adding the respective energy loads
37
The energy loads obtained were then analyzed using two different methods.
4.2.1. Method 1: Energy exchange
The next step was to generate a graph that contains all the three energy loads – cooling, heating and electrical, and
analyze the feasibility of energy exchange between the different building types (Figure 4-10). The total cooling,
heating and electrical consumption was derived and also the peak loads (because it is required for later use to
understand the size of the generator) from the Excel sheet (Figure 4-5). This gave a rough idea of energy demand of a
prototype model in a particular city (all the selected cities have different climatic properties) and the possibility to
reuse waste energy. A similar method has been used to calculate energy loads for all other cities and generate graphs.
This process is explained further in detail for each of the cities.
To get an overall idea of the energy use of the prototype model, a graph (Figure 4-6) was generated to compare the
energy demand among the various cities selected. This consists of total energy use in kBtu per sq.ft. throughout the
year. This is a very rough analysis to consider the energy exchange in various cities.
Figure 4 - 4: Screenshot of the Excel sheet showing the electric load for all buildings of the prototype model in Los Angeles.
This Excel sheet represent the tab named “Electrical Load” (Figure 8) of the Excel file named “Energy demand_Los Angeles”
Figure 4 - 5: Screenshot of the Excel sheet showing Calculation of the total and the peak energy loads
38
4.2.2. Method 2: Cogeneration system (CHP)
As mentioned in previous chapters, a substantial amount of heat is wasted as a result of producing electricity, and this
heat can be reused to meet the heating demand or to use in absorption chillers for the purpose of cooling. So, the
feasibility of a cogeneration system was then analyzed, based on the following procedure.
First of all, the peak electrical demand was calculated of the prototype model (collection of buildings) at a particular
location. Using the peak electrical demand all the estimations were made for the cogeneration system. Gas fired co-
generation system was considered for the process. So, a gas turbine needs to be included in the system, to produce
electricity as well as provide reusable waste heat. The efficiency of the gas motor was considered to be at 33%. This
means 1 unit of fuel is required to produce 0.33 units of electricity, and the remaining 0.67 units is lost as waste heat,
exhausted from the combustion process. More precisely, if the electrical demand is 1kW, then the required generator
consumption would be 3kW of which 2kW would be wasted as heat, exhausted from the combustion process, and is
calculated at 3412.142 Btu/kWh. Apart from that, 15% of losses are considered that are unaccounted for (Email
exchange with Peter Simmonds, 2017). So, the remaining waste heat can be used for heating purposes and also can be
used in absorption chillers to produce chilled water. So, evaluating the electrical demand for each location was an
important factor in determining the amount of load (heating and cooling) that could be met by utilizing the waste heat.
And if that waste heat is not enough, then supplementary energy would be required to meet the heating and cooling
demands. This supplementary energy can come from any source- renewable energy or non-renewable energy.
Detailed results of the eQUEST simulation are described for each of the five locations. The feasibility process of
Cogeneration system is described in detail only for Los Angeles, since the process is same for other cities as well.
Only the numbers used in the process are different, which are the energy loads.
4.3. Simulation results for the city of Los Angeles:
It is important to provide correct inputs for respective energy models and at last check the accuracy of the results.
Initially, the simulation results were not accurate. This was determined by the “BEPS report” generated by the
eQUEST program (Figure 4-7). For the city of Los Angeles, it was ensured that the number of unmet hours (an hour
in which the interior temperature is outside the throttling range) was not more than 300 (ASHRAE Standard 90.1-
2013, 2013) , for each of the buildings in the prototype model.
Figure 4 - 6: BEPS report for a residential building (10 floors) in New York City
39
The simulation results were generated for each of the buildings, and the results of all the three energy loads (heating,
cooling and electricity) were added individually for all the buildings in an Excel sheet. The total energy loads from all
the buildings was used to determine the peak and the total energy loads, for each of the three energy categories.
The cooling, heating and electrical demand are represented in separate graphs (Figure 4-7, 4-8 & 4-9). The units for
measuring the energy loads are in Btu/hr and are represented in the vertical axis. The horizontal axis represents the
number of hours in a year.
Figure 4 - 7: Cooling energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy load in Btu/hr)
Figure 4 - 8: Heating energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy load in Btu/hr)
40
For the graphs, the following results were observed (Figure 4-7, 4-8 & 4-9)
• There is a cooling load which requires 43586226 KWh per year with a load peak of 21078 kW
• There is a heating load which requires -8884243 kWh per year with a peak load of -8275 kW
• The total annual electrical consumption is 42,583,213 kWh and the peak demand is 8374 kW
All the three energy loads from the graphs were combined into a single graph (Figure 4-10) to compare the loads
against each other and find possible synergies among them. The energy demand consists of heating, cooling and
electrical load. The horizontal axis of the graph in Figure 4-10 represents all the hours of a year, and the vertical axis
represents the value of the energy demand in Btu/hr. The cooling load is plotted in the positive direction (in blue color)
and heating load in the negative direction (in orange color) of the vertical axis. It is represented in such way (vertical
consideration) because heating load means heat is being supplied to the buildings (heat is given off by the HVAC
system) and hence shown in the negative direction. For cooling load heat is taken away by the HVAC system from a
particular place to maintain temperature within comfort range, and hence shown in the positive direction of the vertical
axis. The electrical load (in gray color and is transparent) is plotted in the positive direction of the vertical axis. It is
simply the result of the electrical load demand of the prototype model, as per the analysis of the eQUEST simulation.
For the simulation process no particular year is chosen and the simulation result depends upon the property of the
climate file (weather file) chosen. The process of choosing the right climate file for a particular location is described
in Chapter 3 – Methodology.
4.3.1. Building energy exchange for Los Angeles
From the simulation results (Figure 4-10), it can be seen that Los Angeles has substantially high demand for cooling
loads and low demand for heating loads, but there is a consistent demand of both the loads throughout the year. This
suggests the possibilities of energy exchange between the buildings of the prototype model. For further analysis, the
heating load column was modified in the Excel file so that its value could be represented in the positive direction of
the vertical axis (Figure 4-11). This allowed to visually analyze any overlap between cooling and heating energy
demand.
Figure 4 - 9: Electrical energy demand (prototype model) for Los Angeles
41
The next step was to get an idea about the possibility of energy exchange in a day. This step consists of two parts –
Part 1 and part 2. It was taken into consideration that any energy that is recovered in a day would be used in that day
itself.
Figure 4 - 10: Total energy demand (prototype model) for Los Angeles (X-axis: hours, Y-axis: Energy load in
Btu/hr)
Figure 4 - 11: Heating and cooling energy demand for Los Angeles (X-axis: hours, Y-axis: Energy load in Btu/hr)
42
Part 1
To analyze the energy exchange in a day, a method was formed that consists of 5 steps:
1. The cooling and the heating energy were obtained for each day of the year. This was obtained using an Excel
formula that sums up the hourly demand of the energy loads into a single day.
2. The values of the heating energy demand were converted to positive values, keeping the magnitude the same.
3. A separate column in was created in Excel, in which heating demand is subtracted from the cooling demand.
Any value close to zero in this column will indicate that there is difference between cooling and heating
energy demand. So, the lesser the value, the more chances there are of having similar energy demand for
cooling and heating, which will be a better option for building energy exchange.
4. These values were separated into two columns. One column (column A) was for values above zero (cooling
energy is more in this case) and the other column (column B) was for values below zero (heating energy was
more in this case).
5. Three potential days were selected (Two best days for energy exchange, one worst day for energy exchange,
more days can be selected in future work) to analyze the cooling and the heating energy demand in detail for
a period of one day. From the three days selected, the first two days were the ones having less value
magnitude. The less value will indicate the days with less difference in cooling and heating energy, hence
better option of energy exchange. So, the lowest value from column A was selected and the highest value
from column B (since values in column B are negative) was selected. The third day was the one with highest
value, which means there is a huge difference in the cooling and the heating energy, and energy exchange is
not a good option in this case. This day was selected to have an idea on the worst possible case for feasibility
on energy exchange in period of one day.
The same process has been used for the other cities, but will not be shown in detail.
Part 2
The three days selected for Los Angeles are 2
nd
day (less difference in heating and cooling energy demand), 38
th
day
(less difference in heating and cooling energy demand) and 220
th
day (highest difference in heating and cooling energy
demand) of a year. The result of these days was put in a graph (Figure 4-12, 4-13 and 4-14) to understand the thermal
overlap (overlap between cooling and heating energy) throughout the day. These results were further analyzed in
chapter 5.
Figure 4 - 12: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
43
4.3.2. Cogeneration system (CHP) for Los Angeles
There is a constant and substantial demand for electricity (Figure 11). As discussed previously, three units of fuel is
required to generate one unit of electricity and out of which two-thirds of the fuel used is exhausted as waste heat. So,
the next step was to determine the alternatives to use this waste heat to meet a building’s energy need. This waste heat
can be used for the purpose of space heating in winter or can be used to produce chilled water using absorption chillers
to be used for cooling purpose in summer seasons. This simultaneous production of electricity and use of waste heat
to meet the building’s thermal energy needs is possible by the use of a co-generation system. Therefor the next step
was to determine the feasibility of a co-generation system, and analyze whether the system can meet the heating and
cooling energy demands by utilizing the waste heat from electricity generation.
So, the first step was to determine the size of the gas motor generator to be used in the co-generation system. Its size
is determined by its ability to meet the electrical demand during peak conditions. From the simulation results, the peak
electrical demand as calculated for the prototype model is 8374.38 kW in the case for Los Angeles. So, the size of the
gas motor would be 25376.9 kW, considering the efficiency of the generator to be 33% (33% of 25376.91 kW is
8374.38 kW). The maximum amount of heat exhausted by this gas motor on the day of its peak electrical demand
would be 17002.53 kW (2/3rd of 25376.9 kW is 17002.53). So, for days with different electrical demand, varying
amounts of waste heat would be generated. About 15% of this waste heat would be lost, and the remaining 85% can
be recovered. The maximum recovered heat on the day of peak electrical demand is 11391.69 kW. The recovered heat
Figure 4 - 14: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 13: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
44
for each day can be used to meet the energy demand (heating and cooling) of the buildings in the prototype model.
When sizing equipment, these numbers would typically be sized upward to the nearest round number.
4.4. Simulation results for the city of Chicago
Like Los Angeles, the accuracy of the simulation results for the buildings in Chicago was also verified by the BEPS
report. It was made sure that the numbers of hours outside the throttling range was not more than 300. Energy
simulation was run for each of the buildings, and the results were later added in an Excel sheet to get the total loads
for each of the energy category – heating, cooling and electricity, for every hour of the year.
The energy demand of all the buildings in the prototype model for the city of Chicago is represented visually in a
single graph (Figure 4-15). The graph contains information on all the three types of energy loads – Heating (blue
color), cooling (orange color) and electricity (grey color)
The following results were observed from the graph (Figure 4-15)
• There is a demand of cooling load, mainly in the middle of the year and requires 28542668 kWh of energy
with a peak demand of 26477 kW
• There is a constant demand of heating load, mainly during the beginning and end of the year and requires
17473645 kWh of energy with a peak demand of 19468 kW
• The total annual electrical consumption is 42574256 kWh with a peak demand of 8491kW.
4.4.1. Building energy exchange for Chicago
Unlike Los Angeles, the energy demand for Chicago shows that there is no substantial demand of simultaneous heating
and cooling energy, for most of the times in a year. But Chicago has more heating demand in a year as compared to
Los Angeles. Now, the same process, as used for Los Angeles, was repeated for Chicago to get a detailed analysis of
Figure 4 - 15: Total energy demand (prototype model) for Chicago (X-axis: hours, Y-axis: Energy load in Btu/hr)
45
the graph to determine simultaneous demand for heating and cooling load profile. The values of heating energy loads
were converted to positive values only, while keeping the magnitude same. When these values of heating loads were
compared against the cooling load, a graph was obtained where the simultaneous heating and cooling energy demands
would be overlapped (Figure 4-16).
The overlapped area as seen from the graph showed that there is a slight possibility of simultaneous heating and
cooling energy demand. So, a detailed analysis was carried out to understand the energy exchange in a day, and like
other cities, three days have also been selected for Chicago as well, using the method described for Los Angeles.
Part 1
The first step of the method was to select the three days, similar to the method used for Los Angeles.
Part 2
The three days selected for Chicago are the 91
st
day (less difference in heating and cooling energy demand), 285
th
day
(less difference in heating and cooling energy demand) and 216
th
day (highest difference in heating and cooling energy
demand) of a year. The result of these days was put in a graph (Figure 4-17, 4.4-18 and 4-19) to understand the thermal
overlap (overlap between cooling and heating energy) throughout the day. These results were further analyzed in
chapter 5.
Figure 4 - 16: Heating and cooling energy demand for Chicago (X-axis: hours, Y-axis: Energy load in Btu/hr)
0
10000000
20000000
30000000
40000000
50000000
60000000
70000000
80000000
90000000
100000000
1
252
503
754
1005
1256
1507
1758
2009
2260
2511
2762
3013
3264
3515
3766
4017
4268
4519
4770
5021
5272
5523
5774
6025
6276
6527
6778
7029
7280
7531
7782
8033
8284
8535
Heating and cooling
Total cooling load Total heating load (+)
46
4.4.2. Cogeneration system (CHP) for Chicago
For the city of Chicago, the peak electrical demand of the prototype model is 8491.15kW, and hence the size of the
motor gas generator is 25730.76 kW. As per the process described before, the recovered heat for each hour was
Figure 4 - 17: Energy demand for 91
st
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 18: Energy demand for 285
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 19: Energy demand for 216
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
47
compared against the cooling load and the heating load. Then a graph is produced, showing the recovered heat, that is
left after meeting the heating and the cooling energy demands, and is analyzed in chapter 5.
4.5. Simulation results for the city of New York
The energy simulation results of the buildings in the prototype model for New York city was also verified by the
BEPS report and was checked that the number of hours outside the throttling range was not more than 300. The energy
simulations were run for each building, and the results were added in an Excel sheet to get the total cooling, heating
and electrical loads individually. A graph (figure 4-20) was generated to get visual representation of the results.
The graph (figure 4-20) represents the energy demand for every hour of the year, and has the same properties as
were the graph for Los Angeles (Figure 4-10) and Chicago (Figure 4-15).
The following are the observations from the graph:
• There is a demand for cooling load, mainly in the middle of the year and requires 29881470 kWh of energy
with a peak demand of 24053 kW
• There is a constant demand of heating load, mainly during the beginning and end of the year and requires
14989140.81 kWh of energy with a peak demand of 14945 kW
The total annual electrical consumption is 42535151 kWh with a peak demand of 8379 kW
4.5.1. Building energy exchange for New York City
The electrical load profile has a constant demand throughout the year. The heating load profile is placed at the negative
region of the graph, but to get an idea of any overlap between the heating and the cooling energy, the values of the
heating load were converted to positive values, keeping the magnitude same. Then a graph (Figure 4-21) was generated
that consists only of the heating and cooling load on the same side of the graph, to visually analyze any overlap
between the heating and the cooling energy demand. It gave a rough idea of the overlap region, and it can be clearly
seen that the overlap period is dominated mostly by cooling energy demand (Figure 4-21). So even if there is a
possibility of energy exchange, supplemental energy would be required to meet the cooling energy needs. The next
step was to get an idea about the possibility of energy exchange in a day.
Part 1
The first step of the method was to select the three days, similar to the method used for Los Angeles.
Part 2
The three days selected for New York are 275
th
day (less difference in heating and cooling energy demand), 299
th
day
(less difference in heating and cooling energy demand) and 198
th
day (highest difference in heating and cooling energy
demand) of a year. The result of these days was put in a graph (Figure 4-22, 4-23 and 4-24) to understand the thermal
overlap (overlap between cooling and heating energy) throughout the day. These results were further analyzed in
chapter 5
48
Figure 4 - 21: Heating and cooling energy demand for New York (X-axis: hours, Y-axis: Energy load in Btu/hr)
Figure 4 - 20: Total energy demand for New York
49
4.5.2. Cogeneration system for New York
For New York city, the peak electrical demand is 8379.69kW and the size of the motor gas generator is 25393 kW.
Based on the waste heat recovered, and the energy demand, a graph was obtained, that visually represents the
recovered heat, left after meeting both the heating and the cooling energy load, and is analyzed in detail in Chapter 5.
Figure 4 - 22: Energy demand for 275
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 23: Energy demand for 299
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 24: Energy demand for 198
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
50
4.6. Simulation results for Seattle
The process of energy simulation for the buildings (prototype model) in Seattle and the data compilation of the results
has been done in the same way as for other buildings. The results have been verified through the BEPS report and it
has been ensured that the hours outside the throttling range is not more than 300. The heating, cooling and the electrical
demand of the prototype model in the city of Seattle is represented in a graph (Figure 4-25)
The following are the observations from the graph:
• There is a demand of cooling load, and it requires 15924404 kWh of energy with a peak demand of 18276
kW
• There is a constant demand of heating load, and it requires 15401252 kWh of energy with a peak demand of
15157 kW
• The total annual electrical consumption is 42459140 kWh with a peak demand of 8359 kW
4.6.1. Building energy exchange for Seattle
There is a demand of heating energy throughout the year (Figure 4-25), and the demand of cooling energy is mostly
in the middle of the year. The electrical load has a profile that is constant throughout the year.
To understand the possibilities of energy exchange between the buildings, a new graph was created in which the
heating and the cooling energy loads are on the same side (Figure 4-26). It can be observed from the graph that the
cooling and the heating energy loads can overlap only during some time of the year, and still at that time the cooling
load is way higher to be met by building energy exchange. So, a detailed analysis was carried out to understand the
energy exchange in a day, and like other cities, three days has also been selected for Seattle as well.
Part 1
The first step of the method was to select the three days, similar to the method used for Los Angeles.
Part 2
The three days selected for Seattle are 149
th
day (less difference in heating and cooling energy demand), 286
th
day
(less difference in heating and cooling energy demand) and 3
rd
day (highest difference in heating and cooling energy
Figure 4 - 25: Total energy demand for Seattle (X-axis: hours, Y-axis: Energy load in Btu/hr)
51
demand) of a year. The result of these days was put in a graph (Figure 4-27, 4-28 and 4-29) to understand the thermal
overlap (overlap between cooling and heating energy) throughout the day. These results were further analyzed in
chapter 5.
Figure 4 - 26: Heating and cooling energy demand for Seattle (X-axis: hours, Y-axis: Energy load in Btu/hr)
Figure 4 - 27: Energy demand for 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
52
4.6.2. Cogeneration system for Seattle
The peak electrical demand generated by the buildings in the prototype model in Seattle is 8360kW, and hence the
size of the gas motor is 25333 kW. Like other cities, a graph was generated for the recovered heat that is left after
meeting the heating and the cooling energy needs of all the buildings in the prototype model for Seattle and is analyzed
in detail in Chapter 5.
4.7. Simulation results for Miami
The energy simulation for the buildings in Miami indicates that it is a cooling dominated area. Cooling is required
throughout the year, as well as heating but in a substantially low quantity (Figure 4-30)
The following results were observed from the graph (Figure 4-30)
• There is a demand of cooling load, mainly in the middle of the year and requires 84399615 kWh of energy
with a peak demand of 28166 kW
• There is a constant demand of heating load, mainly during the beginning and end of the year and requires
4433390 kWh of energy with a peak demand of -6996 kW
• The total annual electrical consumption is 42853471 kWh with a peak demand of 8383 kW.
Figure 4 - 28: Energy demand for 286
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 29: Energy demand for 3
rd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
53
4.7.1. Building energy exchange for Miami
The heating demand of the buildings is very low, and has a possibility of getting it from the waste heat that is being
rejected from cooling energy generation (Figure 4-30). So, like other cities, a detailed analysis has also been performed
for the city of Miami.
Part 1
The first step of the method was to select the three days, similar to the method used for Los Angeles.
Part 2
The three days selected for Miami are 2
nd
day (less difference in heating and cooling energy demand), 42
nd
day (less
difference in heating and cooling energy demand) and 12
th
day (highest difference in heating and cooling energy
demand) of a year. The result of these days was put in a graph (Figure 4-31, 4-32 and 4-33) to understand the thermal
overlap (overlap between cooling and heating energy) throughout the day. These results were further analyzed in
chapter 5
Figure 4 - 30: Total energy demand for Miami (X-axis: hours, Y-axis: Energy load in Btu/hr)
Figure 4 - 31: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
54
4.7.2. Cogeneration system for Miami
The peak electrical demand generated by the buildings in the prototype model in Seattle is 8382.9 kW, and hence the
size of the gas motor is 25333 kW. Like other cities, a graph was generated for the recovered heat that is left after
meeting the heating and the cooling energy needs of all the buildings in the prototype model for Seattle and has been
analyzed in detail in Chapter 5 (section 5.6.2).
4.8. Conclusion
The overall energy demand for a year of all the cities (Figure 4.8) suggest that Miami has the highest demand for
cooling energy load and Chicago has the highest demand for heating energy load, whereas the electrical energy load
is almost the same for all the cities. This gives a very, very rough idea on the possibility of energy exchange, and
hence a more detailed analysis was performed by selecting three potential days for each of the cities to identify the
cities having good potential for energy exchange. The complete analysis of the three days for each cities is discussed
in chapter 5.
There is a difference in the heating and the cooling energy demand of the prototype model for all the cities as shown
in the more detailed analysis. In Los Angeles, the heating and cooling energy gets overlapped for substantial portion
of the year. The other three cities (New York, Chicago and Seattle), have a similar type of heating and cooling energy
load profile. In this case there is a huge demand of cooling load, mainly in the middle of the year and hardly any
demand in the beginning and end of the year. So, building energy exchange is not a viable option to meet the heating
Figure 4 - 32: Energy demand for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 4 - 33: Energy demand for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
55
and the cooling energy needs of the buildings throughout the year. Miami has a high demand for cooling energy, and
hence has a high chance that it may be able to supply for the heating energy needs for the buildings (by using the
rejected heat from the chillers), as both have a constant demand throughout the year, and heating demand is relatively
very low. There are two other methods in the category of building energy exchange, which could be used for future
analysis. One of them is based on the possibility of energy exchange per minute (which may not be practical) and the
other one is on determining the duration of time for energy storage for future use. See chapter 6 for further discussion.
Electrical energy demand profile is almost the same for all the cities. This plays an important role in determining the
feasibility cogeneration system in the different cities. The cogeneration system is one of the two main methods for
reusing waste energy. Further analysis will be done in Chapter 5.
Figure 4 - 34: Energy demands comparison of all the five cities (Y-axis: Energy load in kBtu.sq.ft)
56
CHAPTER 5: Analysis
5.1. Introduction
Chapter 4 presented the results of the simulation performed on all the buildings of the prototype model, for five
different locations (New York, Los Angeles, Chicago, Seattle, and Miami). In each city, there were four types of
buildings (office, residential, data center and hotel), with seven buildings in total. A total of 35 building energy
simulation results were obtained and were analyzed for the instantaneous exchange of energy among the building, and
for the feasibility of a cogeneration system in the five different locations. The analyses were done independently and
had no effect on each other (Figure 5-1).
5.2. Simulation result analysis for Los Angeles
Two separate items were calculated for analyzing the results for each city. These are building energy exchange and
cogeneration or CHP. To understand the results in detail a method was developed that was discussed in chapter 4.
Those results need to be analyzed to understand the feasibility of both building energy exchange and cogeneration.
There were few assumptions taken into consideration. One of them is that storage of energy is needed and utilized but
not described in detail on how it works. This will be discussed in Chapter 6, future work. The second consideration is
that all the recovered waste heat is stored for a period of one day, and no loss of heat is taken onto account during
process of storing and reusing it.
5.2.1. Building energy exchange for Los Angeles
Three days were selected to analyze the heating and cooling energy demand. These are 2
nd
, 38
th
, and 220
th
days of the
year. The first two days are selected on the basis of having high possibility of similar heating and cooling energy
demand, whereas the third day was selected to understand the energy load profile on the day of having less similarity
in demand for cooling and heating. Future work would include running simulations on all of the days of the year.
Figure 5 - 1: Methodology diagram
57
A method was developed to analyze each of the days to understand the feasibility of energy exchange. For each of the
days cooling and heating energy loads were obtained, and this were put in a graph to understand the energy demand
throughout the day (Figure 5-2). From the graph the cooling and the heating energy load profile were observed, as
well as the period of thermal overlap between them. This period of thermal overlap is the time when theoretical
instantaneous energy exchange is possible, i.e. the waste heat from the chillers (generating cooling) could be used to
meet the energy demand of heating energy. Again, an assumption was considered in this case that there is no loss of
energy is taken into account during the transfer of energy for the process of energy exchange. Another graph was
generated by subtracting the heating load from the cooling load (Figure 5-3). This graph would show the amount of
energy that could not be met by energy exchange and is further explained below for each of the days.
For the 2
nd
day (Figure 5-2 and 5-3), there is thermal overlap starting 6 am in the morning till midnight. During these
periods, there is possibility of theoretical instantaneous energy exchange. During this day (Figure 5-2), the total
cooling energy demand (area under the blue curve) is 50.7 MMBtu, total heating energy demand (area under the
orange curve) is 51 MMBtu and energy involved during the thermal overlap (the area shaded by green color) is 23.4
MMBtu. So, around 46% ((thermal overlap/cooling energy) *100) of the cooling energy demand, 45.7% of the heating
energy demand and 22.9% of the overall energy demand can be met by the process of energy exchange. The graph
(Figure 5-3) shows the amount of energy that cannot be met by energy exchange process and supplemental energy
need to be provided to meet this energy. The lines (Figure 5-3) in the positive direction represent the amount of
supplemental energy required for cooling load, and the lines in the negative direction represent the amount of
supplemental energy required for heating load.
For the 38
th
day (Figure 5-4 & 5-5), there is a substantial thermal overlap starting at 9 am in the morning till 6 pm in
the evening. Apart from it, there is some overlap during the remaining hours of the day (midnight to 3am and 6pm to
midnight). So, there is also a possibility of energy exchange during this day. The total cooling energy demand (area
Figure 5 - 2: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 3: Cooling minus heating energy load for the 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in
Btu/hr)
Supplemental cooling
Supplemental heating
58
under the blue curve) is 161 MMBtu, total heating energy demand (area under the orange curve) is 157.6 MMBtu and
energy involved during the thermal overlap (the area shaded by green color) is 100.3 MMBtu. So, around 62% of the
cooling energy demand, 63.6% of the heating energy demand and 31.5% of overall energy demand can be met by the
process of energy exchange. The graph (Figure 5-5) shows the amount of energy that cannot be met by energy
exchange process and supplemental energy need to be provided to meet this energy.
For the 220
th
day (Figure 5-6 & 5-7), there is hardly any thermal overlap. This day is dominated by cooling energy
load and there is very less load for heating energy. The total cooling energy demand (area under the blue curve) is
941.8 MMBtu, total heating energy demand (area under the orange curve) is 64 MMBtu and energy involved during
the thermal overlap (the area shaded by green color) is 64 MMBtu. Only 6.8% of the cooling energy demand and 6.4%
of the overall energy demand can be met by the process of energy exchange, but for heating energy load it is 100%
(since the heating energy load is lower than the cooling energy load for all the hours of the year). The graph (Figure
5-7) represent the amount of energy that cannot be met by energy exchange process and it is almost for cooling energy.
Figure 5 - 4: Energy demand for 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 5: Cooling minus heating energy load for the 38
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in
Btu/hr)
Supplemental cooling
Supplemental heating
59
5.2.2. Cogeneration system analysis for Los Angeles
A method was developed to analyze the process of reusing this waste heat. It is a two-step method. First, the recovered
waste heat was compared against the cooling load for every hour of the year. It was performed in an Excel sheet, by
comparing the values of the recovered heat against the cooling load for every hour of the year. A graph (Figure 5-8)
was generated out of the comparison. It shows the value of the remaining recovered heat, i.e. the amount of recovered
heat left after meeting the cooling energy demand.
The graph (Figure 5-8) is similar to the energy demand graphs shown earlier. The horizontal portion of the graph
indicates the hour of a year and the vertical portion indicates the amount of energy. The blue lines indicate the amount
of recovered heat left for every hour of the year, after meeting the cooling energy demand. The lines in the positive
direction indicates that recovered heat is still left and can be used to meet the heating energy demands of the buildings
in the prototype model. But the lines in the negative direction indicate that the recovered heat is not sufficient to meet
the cooling energy demands, and supplemental energy is required for the purpose.
The second step of reusing the waste heat was to compare the remaining recovered with the heating energy demand
of the prototype model, and a similar graph (Figure 5-9) like Figure 5-8 was created to evaluate the comparison.
From both the graphs (Figure 5-8 & 5-9), it can be observed that the heat recovered from the electricity generation is
not sufficient to meet the heating and the cooling energy needs for all the hours of the year. However, the analysis was
done in a macro scale and for every hour of the year. Though recovered heat is not sufficient to meet the energy
demands (heating and cooling), for all the hours of the year, yet there are sufficient lines in the positive direction of
both the graphs (Figure 5-8 & 5-9) indicating that there is still recovered heat left after meeting the energy demands.
This remaining recovered heat is also obtained on the basis of every hour of the year, and can be stored to meet the
heating and the cooling energy needs of the buildings, when required.
Figure 5 - 6: Energy demand for 220
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 7: Cooling minus heating energy load for the 220
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in
Btu/hr)
Supplemental cooling
60
5.3. Simulation result analysis for Chicago
Like Los Angeles, a similar process was performed to evaluate the heating and the cooling energy demands of the
prototype model in Chicago, and a separate method was developed to analyze the feasibility of cogeneration plant.
Both the methods are independent of each other.
5.3.1. Building energy exchange analysis for Chicago
Figure 5 - 8: Analysis of recovered heat in Los Angeles
Figure 5 - 9: Analysis of recovered heat in Los Angeles
61
The three days selected to analyze the energy demand of the prototype model in Chicago are the 91
st
day, 285
th
day
and 216
th
day of a year (Chapter 4, section 4.4.1). Among those three days, the first two days have high similarity in
cooling and heating energy demand, whereas the third one has low similarity in cooling and heating energy demand.
These three days were selected to have an idea of the best and worst possible energy load profile in a calendar year.
For the 91
st
day (Figure 5-10 and 5-11), there is a substantial thermal overlap starting midnight till 6am in the morning
and some overlap from 8 am to 5pm. During these periods, there is possibility of instantaneous energy exchange.
During this day (Figure 5-10), the total cooling energy demand (area under the blue curve) is 55 MMBtu, total heating
energy demand (area under the orange curve) is 61.5 MMBtu and energy involved during the thermal overlap (the
area shaded by green color) is 20.8 MMBtu. So, around 38% ((thermal overlap/cooling energy) *100) of the cooling
energy demand, 34% of the heating energy demand and 17.8% of the overall energy demand can be met by the process
of energy exchange. The graph (Figure 5-11) shows the amount of energy that cannot be met by energy exchange
process and supplemental energy need to be provided to meet this energy. The lines (Figure 5-11) in the positive
direction represent the amount of supplemental energy required for cooling load and the lines in the negative direction
represent the amount of supplemental energy required for heating load.
For the 285
th
day (Figure 5-12 & 5-13), there is some thermal overlap starting midnight till 4 pm in the evening. The
total cooling energy demand (area under the blue curve) is 161.7 MMBtu, total heating energy demand (area under
the orange curve) is 163.6 MMBtu and energy involved during the thermal overlap (the area shaded by green color)
is 60.7 MMBtu. So, around 37.5% of the cooling energy demand, 37% of the heating energy demand and 18.7% of
the overall energy demand can be met by the process of energy exchange. The graph (Figure 5-13) shows the amount
of energy that cannot be met by energy exchange process and supplemental energy need to be provided to meet this
energy.
Figure 5 - 10: Energy demand for 91
st
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Supplemental heating
Figure 5 - 11: Cooling minus heating energy load for the 91
st
day (X-axis: hours starting 12 am, Y-axis: Energy load in
Btu/hr)
Supplemental cooling
62
For the 216
th
day (Figure 5-14 & 5-15), there is hardly any thermal overlap. This day is dominated by cooling energy
load and there is very less load for heating energy. The total cooling energy demand (area under the blue curve) is
1187.5 MMBtu, total heating energy demand (area under the orange curve) is 42 MMBtu and energy involved during
the thermal overlap (the area shaded by green color) is 42 MMBtu. Only 3.5% of the cooling energy demand and 3.4%
of the overall energy demand can be met by the process of energy exchange, but for heating energy load it is 100%
(since the heating energy load is lower than cooling energy for every hour of the year for this day). The graph (Figure
5-15) represent the amount of energy that cannot be met by energy exchange process and it is almost for cooling
energy.
Figure 5 - 12: Energy demand for 285
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 14: Energy demand for 216
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 13: Cooling minus heating energy load for the 285
th
day (X-axis: hours starting 12 am, Y-axis: Energy load
in Btu/hr)
Supplemental cooling
Supplemental heating
63
5.3.2. Cogeneration system analysis for Chicago
As discussed in chapter 4, to analyze the feasibility of cogeneration systems in Chicago, a graph is produced, showing
the recovered heat, that is left after meeting the heating and the cooling energy demands (Figure 5-16).
From the graph (Figure 5-16), it is clear that the recovered heat is unable to meet the energy demands (heating and
cooling), in the middle of the year. However, in the beginning and end of the year, the recovered heat is sufficient to
provide energy for both the heating and the cooling needs. There are substantial lines both in the positive and negative
direction of the graph, in the middle of the year. The lines in the positive direction indicates that the recovered heat is
left even after meeting the energy demands. These are calculated on an hourly basis, and this remaining recovered
heat can be stored and reused at a later time, when the recovered heat is not sufficient to meet the heating and the
cooling energy loads. Hence, a CHP system would be feasible to use for the prototype model in Chicago.
Figure 5 - 15: Cooling minus heating energy load for the 216
th
day (X-axis: hours starting 12 am, Y-axis: Energy load
in Btu/hr)
Supplemental cooling
Figure 5 - 16: Analysis of remaining recovered heat for Chicago
64
5.4. Simulation result analysis for the city of New York
Like Los Angeles and Chicago, two methods were developed to analyze the energy demand of the prototype model
in New York. One is the building energy exchange (involving only cooling and heating energy) and the other one is
cogeneration system (involving cooling, heating and electrical energy). The two methods are independent of each
other.
5.4.1. Building energy exchange for New York City
Three days were selected to analyze the heating and cooling energy demand. These are 275
th
day, 299
th
day and 198
th
day of a year. The first two days are selected on the basis of having high possibility of similar heating and cooling
energy demand, whereas the third day was selected to understand the energy load profile on the day of having less
similarity in demand for cooling and heating.
For the 275
th
day (Figure 5-17 and 5-18), there is a substantial thermal overlap starting 8 am in the morning till 5pm
in the evening and also during the end of the day from around 7pm. During these periods, there is possibility of
instantaneous energy exchange. During this day (Figure 5-17), the total cooling energy demand (area under the blue
curve) is 148 MMBtu, total heating energy demand (area under the orange curve) is 158 MMBtu and energy involved
during the thermal overlap (the area shaded by green color) is 82 MMBtu. So, around 55.4% ((thermal overlap/cooling
energy) *100) of the cooling energy demand, 52% of the heating energy demand and 26.8% of the overall energy
demand c can be met by the process of energy exchange. The graph (Figure 5-18) shows the amount of energy that
cannot be met by energy exchange process and supplemental energy need to be provided to meet this energy. The
lines (Figure 5-18) in the positive direction represent the amount of supplemental energy required for cooling load
and the lines in the negative direction represent the amount of supplemental energy required for heating load.
Figure 5 - 17: Energy demand for 275
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 18: Cooling minus heating energy load for the 275
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Supplemental heating
Supplemental cooling
65
For the 299
th
day (Figure 5-19 & 5-20), there is a substantial thermal overlap starting 7 am in the morning till 5 pm in
the evening. Apart from it, there is some overlap during the remaining hours of the day. So, there is also a possibility
of energy exchange during this day. The total cooling energy demand (area under the blue curve) is 149 MMBtu, total
heating energy demand (area under the orange curve) is 149 MMBtu and energy involved during the thermal overlap
(the area shaded by green color) is 92 MMBtu. So, around 61.7% of the cooling energy demand, 61.9% of the heating
energy demand and 30.9% of the overall energy demand can be met by the process of energy exchange. The graph
(Figure 5.5.1-d) shows the amount of energy that cannot be met by energy exchange process and supplemental energy
need to be provided to meet this energy.
For the 198
th
day (Figure 5-21 & 5-22), there is hardly any thermal overlap. This day is dominated by cooling energy
load and there is hardly any load for heating energy. The total cooling energy demand (area under the blue curve) is
1,136 MMBtu, total heating energy demand (area under the orange curve) is 20 MMBtu and energy involved during
the thermal overlap (the area shaded by green color) is 19 MMBtu. Only 1.7% of the cooling energy demand and 1.7%
of the overall energy demand can be met by the process of energy exchange, but for heating energy load it is around
94% (since the heating energy load is very low). The graph (Figure 5-22) represents the amount of energy that cannot
be met by energy exchange process and it is almost for cooling energy.
Figure 5 - 19: Energy demand for 299
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 20: Cooling minus heating energy load for the 299
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Supplemental cooling
Supplemental heating
66
5.4.2. Cogeneration system analysis for New York
As discussed in Chapter 4 (section 4.5.2), a graph was obtained that visually represents the recovered heat, left after
meeting both the heating and the cooling energy load (Figure 5-23). The profile of the recovered heat left in the city
of New York is very similar to the one for Chicago. A CHP system would be useful, especially in the beginning and
the end of the year
Figure 5 - 21: Energy demand for 198
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 22: Cooling minus heating energy load for the 198
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Supplemental cooling
Figure 5 - 23: Analysis of remaining recovered heat for New York
67
5.5. Simulation result analysis for Seattle
A similar process used for the other cities was performed to evaluate the heating and the cooling energy demands of
the prototype model in Seattle, and a separate method was developed to analyze the feasibility of cogeneration plant.
Both the methods are independent of each other.
5.5.1. Building energy exchange for Seattle
The three days selected to analyze the energy demand of the prototype model in Seattle are the 149
th
day, 286
th
day
and 3
rd
day of a year (Chapter 4, section 4.6.1). Among those three days, the first two days have high similarity in
cooling and heating energy demand, whereas the third one has low similarity in cooling and heating energy demand.
These three days were selected to have an idea of the best and worst possible energy load profile in a calendar year.
For the 149
th
day (Figure 5-24 and 5-25), there is a thermal overlap starting 8 am in the morning till midnight. During
these periods, there is possibility of instantaneous energy exchange. During this day (Figure 5-24), the total cooling
energy demand (area under the blue curve) is 44 MMBtu, total heating energy demand (area under the orange curve)
is 48 MMBtu and energy involved during the thermal overlap (the area shaded by green color) is 12 MMBtu. So,
around 28% ((thermal overlap/cooling energy) *100) of the cooling energy demand, 26% of the heating energy
demand and 13.4% of the overall energy demand can be met by the process of energy exchange. The graph (Figure 5-
25) shows the amount of energy that cannot be met by energy exchange process and supplemental energy need to be
provided to meet this energy. The lines (Figure 5-25) in the positive direction represent the amount of supplemental
energy required for cooling load and the lines in the negative direction represent the amount of supplemental energy
required for heating load.
Figure 5 - 24: Energy demand for 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 25: Cooling minus heating energy load for the 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load
in Btu/hr)
Supplemental heating
Supplemental cooling
68
For the 286
th
day (Figure 5-26 & 5-27), there is a substantial thermal overlap starting 5 am in the morning till 4 pm in
the evening. Apart from it, there is some overlap during the early hours of the day and also from 4-7pm. So, there is
also a possibility of energy exchange during this day. The total cooling energy demand (area under the blue curve) is
161.5 MMBtu total heating energy demand (area under the orange curve) is 163.6 MBtu and energy involved during
the thermal overlap (the area shaded by green color) is 111.1 MBtu. So, around 69% of the cooling energy demand,
68% of the heating energy demand and 34.2% of the overall energy demand can be met by the process of energy
exchange. The graph (Figure 5-27) shows the amount of energy that cannot be met by energy exchange process and
supplemental energy needed to be provided to meet this energy.
For the 3
rd
day (Figure 5-28 & 5-29), there is hardly any thermal overlap. This day is dominated by heating energy
load and there is hardly any load for cooling energy. The total cooling energy demand (area under the blue curve) is
1314 Btu, total heating energy demand (area under the orange curve) is 421 MMBtu and energy involved during the
thermal overlap (the area shaded by green color) is 1314 Btu, although cooling energy demand and the thermal overlap
cannot be visualized from the graph. Only 0.000312218% of the heating energy demand and 0.000312217% of the
overall energy demand can be met by the process of energy exchange, but for cooling energy load it is 100% (since
the cooling energy load is the lowest for every hour of the year for this day). The graph (Figure 5-29) represent the
amount of energy that cannot be met by energy exchange process and it is almost for heating energy.
Figure 5 - 26: Energy demand for 286
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 27: Cooling minus heating energy load for the 149
th
day (X-axis: hours starting 12 am, Y-axis: Energy load
in Btu/hr)
Supplemental heating
Supplemental cooling
69
5.5.2. Cogeneration system analysis for Seattle
As discussed in Chapter 4 (section 4.6.2), a graph (Figure 5-30) generated for the recovered heat that is left after
meeting the heating and the cooling energy needs of all the buildings in the prototype model for Seattle. For most of
the time the values of the graph lie in the positive direction, which indicates that using CHP would be a good choice
in Seattle.
Figure 5 - 28: Energy demand for 3
rd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 29: Cooling minus heating energy load for the 3
rd
day (X-axis: hours starting 12 am, Y-axis: Energy load in
Btu/hr)
Supplemental heating
70
5.6. Simulation result analysis for Miami
As observed in chapter 4, Miami is a cooling dominant area. The annual demand for heating energy is also very low,
compared to the annual demand for cooling energy. However, the option of building energy exchange seems to be
only feasible for meeting the heating energy demand, and supplemental energy is required for cooling demand. Still,
like other cities, a similar process was performed to evaluate the heating and the cooling energy demands of the
prototype model in Miami, and a separate method was developed to analyze the feasibility of cogeneration plant. Both
the methods are independent of each other.
5.6.1. Building energy exchange for Miami
The three days selected to analyze the energy demand of the prototype model in Miami are the 2
nd
day, 42
nd
day and
12
th
day of a year (Chapter 4, section 4.7.1). Among those three days, the first two days have high similarity in cooling
and heating energy demand, whereas the third one has low similarity in cooling and heating energy demand. These
three days were selected to have an idea of the best and worst possible energy load profile in a calendar year.
For the 2
nd
day (Figure 5-31 and 5-32), there is a substantial thermal overlap starting 8 am in the morning till 8pm in
the night and also during midnight to 2am. During these periods, there is possibility of instantaneous energy exchange.
During this day (Figure 5-31), the total cooling energy demand (area under the blue curve) is 44 MMBtu, total heating
energy demand (area under the orange curve) is 45 MMBtu and energy involved during the thermal overlap (the area
shaded by green color) is 14 MMBtu. So, around 32% ((thermal overlap/cooling energy) *100) of the cooling energy
demand, 32% of the heating energy demand and 16% of the overall energy demand can be met by the process of
energy exchange. The graph (Figure 5-32) shows the amount of energy that cannot be met by energy exchange process
and supplemental energy need to be provided to meet this energy. The lines (Figure 5-32) in the positive direction
represent the amount of supplemental energy required for cooling load and the lines in the negative direction represent
the amount of supplemental energy required for heating load.
Figure 5 - 30: Analysis of remaining recovered heat for Seattle
71
For the 42
nd
day (Figure 5-33 & 5-34), there is a thermal overlap throughout the day. So, there is a good possibility of
energy exchange throughout this day. The total cooling energy demand (area under the blue curve) is 78 MMBtu, total
heating energy demand (area under the orange curve) is 32 MMBtu and energy involved during the thermal overlap
(the area shaded by green color) is 29 MMBtu. So, around 37% of the cooling energy demand, 87% of the heating
energy demand and 26% of the overall energy demand can be met by the process of energy exchange. The graph
(Figure 5-34) shows the amount of energy that cannot be met by energy exchange process and supplemental energy
need to be provided to meet this energy.
Figure 5 - 31: Energy demand for 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 32: Cooling minus heating energy load for the 2
nd
day (X-axis: hours starting 12 am, Y-axis: Energy
load in Btu/hr)
Supplemental heating
Supplemental cooling
Figure 5 - 33: Energy demand for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
72
For the 12
th
day (Figure 5-35 & 5-36), there is some thermal overlap. The total cooling energy demand (area under
the blue curve) is 114 MMBtu, total heating energy demand (area under the orange curve) is 142 MMBtu and energy
involved during the thermal overlap (the area shaded by green color) is 34 MMBtu. Around 30% of the cooling energy,
around 24% of the heating energy demand and 13.2% of the overall energy demand can be met by building energy
exchange. The graph (Figure 5-36) represent the amount of energy that cannot be met by energy exchange process.
5.6.2. Cogeneration system analysis for Miami
As discussed in Chapter 4 (section 4.7.2), the peak electrical demand generated by the buildings in Miami is 8382.9
kW. So, the size of the gas motor generator should be 25402.8kW. The buildings in Miami has a very high cooling
Figure 5 - 34: Cooling minus heating energy load for 42
nd
day (X-axis: hours starting 12 am, Y-axis: Energy
load in Btu/hr)
Supplemental heating
Supplemental cooling
Figure 5 - 35: Energy demand for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy load in Btu/hr)
Figure 5 - 36: Cooling minus heating energy load for 12
th
day (X-axis: hours starting 12 am, Y-axis: Energy
load in Btu/hr)
Supplemental heating
Supplemental cooling
73
load. A graph (Figure 5-37) was generated for the recovered heat, that is left after meeting the demands for the heating
and cooling energy loads. The graph has most of the values in the negative direction, which means of supplemental
energy is required during those times to meet the energy demands of the area. It is concluded from the result that using
a CHP system for the combination of this type of buildings in Miami is not at all a viable option.
5.7. Conclusion
Building energy exchange and a CHP system can both play a big role in using the waste energy to meet the energy
demands of the buildings. The theoretical possibilities of those systems in a prototype model for five different locations
was analyzed. This can serve as a starting point to frame building planning rules for different climatic locations and
can help in reducing the carbon footprint by reusing waste energy in building operation. The feasibility of building
energy exchange needs to be analyzed along with a CHP system for a particular location, for each day of the year. It
will help to determine the amount of actual supplemental energy, that would be needed for building operation.
With respective to the two potential good days selected to analyze the feasibility of energy exchange (Figure 5-38),
Los Angeles and New York showed a good consistency for energy exchange in both the days. Seattle and Miami
performed better in only one day, and for Chicago the values were low in both the days. But, the difference between
the total cooling and heating energy load for a year is highest for Miami and then Los Angeles, where as it is lowest
for Seattle and then Chicago (Figure 5-39 and 5-40). This shows that a more detailed analysis is required for each day
of the year to get a precise idea of the building energy exchange, as these three days are not enough to predict the
energy exchange feasibility when compared to the total energy demand in a year. Only the cooling energy exchange
possibility is taken into consideration as the annual heating load is lower than the annual cooling load for all the cities
(Figure 5-39).
It is observed that the city of Miami is not at all a good option for having a CHP plant, where as for Seattle, a CHP
plant can be very good option to meet the building energy demands. This is a very valuable point for analyzing the
option of having CHP plant based on the climatic property of a location.
Figure 5 - 37: Analysis of remaining recovered heat for Miami
74
Figure 5 - 38: Energy exchange possibility
Figure 5 - 39: Total energy demand (kBTU/sq.ft.) per year
75
Figure 5 - 40: Energy exchange possibility (Annually)
76
CHAPTER 6: Conclusion
6.1. Discussion
Climate change has become a huge concern in the present-day world, and thinking from the perspective of an architect,
few issues are addressed from the standpoint of the built environment. Buildings need energy for their operation and
maintenance. For a specific type of building, the amount of energy required, can be controlled or reduced, by
integrating the energy efficiency features in it. This is one way to reduce the carbon footprint of individual buildings.
Another method is to consider multiple buildings’ energy consumption at an urban scale. Buildings need cooling
energy, heating energy and electrical energy for their operation, and generating these energy types also causes energy
waste in some other forms. So, apart from making buildings energy efficient (which still uses energy), it is also
required to address this waste energy.
Two types of methods are discussed to reuse the waste energy (Figure 6-1). These are building energy exchange
(Figure 6-2) and cogeneration systems (CHP – combined heat and power). Both the methods are independent of each
other. Energy simulations were run for four building types and the total energy loads were analyzed in five different
locations for both methods. All the buildings are modelled by following the ASHRAE standard and have the same
floor dimension of 150’*150’ but differ in the number of floors. The process of running the energy simulation is same
in all the locations except for the fact that the weather files were different. The weather file was the main factor for
which the simulation result varied between different location. The energy simulation results are obtained in a csv file
format, which was opened in Excel for analysis. The result contained three types of energy loads for each of the
buildings. These are cooling, heating and electrical energy load. The simulation result of all the buildings are
consolidated into a single Excel file to get the total cooling, heating and electrical energy demand of the prototype
model, and as such five Excel files are obtained for five different locations. These Excel files formed the basis of
energy analysis for the two methods discussed previously. A full discussion of the methodology is in chapter 3.
Figure 6 - 1: Methodology
77
For energy exchange analysis, only the cooling and the heating loads are taken into consideration. Three prototypical
days are selected to analyze the cooling and heating load profile, and understand the time period where there is an
overlap between the two energy loads. The process was performed for all the different cities and it gave an idea on
the feasibility of building energy between the buildings in the prototype model for each of the different cities.
To analyze the cogeneration system, all the three energy load profiles are taken into consideration. The waste heat
generated from electricity production is the main factor that drives this method. The amount of waste heat that can be
recovered was calculated and was compared against the cooling and the heating energy demand. All these calculations
are performed for the prototype model, and then a conclusion was formed on the feasibility of a cogeneration plant in
each of the five different cities. The overall process gave a standard idea of the energy requirements of these buildings
type and how it varied in different locations.
6.1.1. Energy exchange
The hourly result helped to analyze the energy load profile and understand the energy requirements of each of the
buildings. It showed the possibilities of reusing the waste energy from one building to meet the energy demand of
other building within the prototype model. But several other factors were not taken into consideration, like the
temperature or quality of the waste heat or the maximum distance between the buildings for which the transfer of
energy would be efficient. A general overview was achieved that defines the overlap in the cooling and the heating
energy demand. It is clear about the requirement of both cooling and heating energy of the prototype model, but it
would be really helpful to understand, in general, the quality of the waste heat from a particular type of building, and
also the process of transferring it to other buildings.
The results suggested that Los Angeles and New York could possibly be a good location for the purpose of building
energy exchange. Both the location showed a relatively high value for both the cooling and the heating energy demand
falling in the period of thermal overlap, and New York was better than Los Angeles in one of the days. But for Chicago,
the values were relatively low in both the days. So, considering the three prototypical days analyzed for cooling and
heating load profile, Los Angeles and New York could be good options to consider reusing of waste energy through
the process of building energy exchange, whereas Chicago is a bad option for the same purpose. But, considering the
overall energy demand (Figure 6-3) throughout a year, Chicago seems to have a better chance for building energy
exchange than Los Angeles and New York. This is because the difference in total demand for cooling and heating
energy load for a year is less for Chicago and high for Los Angeles, New York lies in the middle of the two cities.
Since this is the overall energy demand for a year, and does not indicate whether there is any overlap between the two
energy loads (cooling and heating), it would not be fair to reach a conclusion on the basis of just energy load demand.
The energy load profile needs to be evaluated and hence was done for the three prototypical days. But as there is no
guarantee that the energy load profile for the whole year cannot be predicted on the basis of the three prototypical
days, it would be better to analyze the energy load profile for all the days of a year to reach a better conclusion for
building energy exchange.
The cooling and heating energy analysis for Seattle showed huge variation in the duration of thermal overlap for the
two potentially best days analyzed. In one of the days, both the cooling and the heating energy demand has low values
(28% and 26% respectively) falling in the period of thermal overlap, whereas for the other day, the values were
relatively high (69% and 68% respectively). This suggests that the potential days selected for analysis do not provide
a fair conclusion for the feasibility of building energy exchange, and more detailed analysis needs to be performed to
understand the importance of building energy exchange in Seattle. Also, the difference in the total cooling and heating
energy load (Figure 6-3) for a year in Seattle is lowest among all the cities, and hence the analysis for all the days of
a year would provide a better conclusion for the feasibility of building energy exchange in Seattle.
From the different locations analyzed, Miami was a very different case. The cooling energy for Miami was always
higher than the heating energy, except for few handful of hours for an entire year. The difference in the total cooling
and heating energy demand for a year was highest for Miami (Figure 6-3) among all the cities, but yet there was some
possibility for building energy exchange for the two potential days analyzed. (Figure 6-2). This shows that both the
78
graphs (Figure 6-2 and 6-3) aren’t sufficient enough to predict the best solution for feasibility of building energy
exchange. Hence, like other cities, all the days should be analyzed for Miami as well.
Figure 6 - 2: Energy exchange possibility
Figure 6 - 3: Total energy demand (kBTU/sq.ft.) per year
79
6.1.2. Cogeneration
Suitable location of a cogeneration system can be very effective in the early planning stage. From the results it can be
concluded that for these building types, Seattle proved to be an effective place for the location of a CHP plant, and
Miami is not at all suitable for placing a CHP plant. The other cities- Los Angeles, Chicago and New York are partly
suitable for a cogeneration plant. But both the studies (energy exchange and cogeneration) were performed
independently of each other. It would be really great to see how much energy can this prototype model save if the
synergy between energy exchange and cogeneration systems is considered.
6.2. Limitations and energy modeling consideration
Getting the correct inputs in an energy model is one of the very important steps to get results close to accurate. Initially
the results obtained were obviously not correct, and then some modifications were done to bring the results close to
accurate.
A few things that were considered to verify the accuracy of the results are the following:
• The results were verified from the BEPS report and were checked that the number of hours outside the
throttling range are not more than 300.
• The EUI of the of each of the buildings were in close range with the site energy.
The things that were modified to bring the result closer to accurate are
• Heating and the cooling sizing ratio
• Airflow
• Occupancy schedule, lighting schedule and equipment schedule
An important point that was observed in this process was that when the system sizing ratio was reduced, the number
of hours outside the throttling range increased. But increasing sizing ratio (to bring hours outside the throttling range
under 300) also contributes to high total site energy (EUI). For every model the sizing ratio was balanced with
throttling range hours and the EUI. A more detailed analysis should be done to address this issue for each of the
building types for getting a more accurate result and at the same time using an efficient system.
6.3. Future work
The option of integrating building energy exchange with a CHP plant would be great research to work on. Reusing of
waste energy was analyzed separately in the two methods – building energy exchange and a cogeneration plant. But
what if building energy exchange is considered first and then check the amount of extra energy required for cooling
and heating energy loads. Can these remaining extra energy needs be met using a CHP plant? And that too for a place
like Miami, which showed negative result for both the methods. If not, is it still feasible to make use of renewables or
intermittent energy resources to meet the energy demands of the prototype model in Miami, instead of generating
energy from scratch. Combining synergies of different energy saving methods can help to cut the demand of generating
energy by using fossil fuels. The result analyzed for New York and Chicago showed that CHP plant can be partially
effective in these places, whereas building energy exchange in both Los Angeles and New York are good options. So,
combining both the methods would be an effective way to deal with maximum utilization of waste energy.
6.3.1. Other methods
There are two other methods which can be analyzed for the purpose of building energy exchange. One of these is to
analyze the building energy exchange for every minute or second. Although this is not practical as energy can’t be
transferred instantaneously, but it would be great to see what other outcomes does this method generates. The second
method is to analyze the option of storage for waste energy. More research could be done to determine the practical
80
length of time for storing energy. This could also include loss of energy with respect to storage, transfer between
modes, and other real-life issues.
Apart from it further research can also be performed to ease the energy exchange procedure, like a computer program
could be developed that could automatically merge all the excel files into one (for a particular location) and then add
all the energy loads as required. This could save a huge amount of time and also could possible save any error caused
due to manual calculation.
6.3.2. Improvement in Building Energy Exchange
The method used for building energy exchange was theoretical, and possible synergies and constraints were not taken
into account. These could have a huge impact when implementing the process in the real world. For example, the
quality of the waste heat generated was not taken into account. This could have a huge impact on the graphs analyzed
for the potential days. The overlap period might have waste heat that could possibly be not reused. So, determining
the quality and temperature of the waste energy is one of the prime consideration for the feasibility of building energy
exchange. Energy storage is another factor. This could play an important role in storing waste energy and using it
later. Determining the length and feasibility of energy storage could have good positive impact on energy exchange
and would increase the efficiency of the overall process as well. A possible synergy could be combining the building
energy exchange with the cogeneration system. The cogeneration system is helpful for using the waste energy
(generated during electricity production), to meet the cooling and the heating energy demand. Combining it with
building energy exchange could possibly give a better idea of the actual supplemental energy demand.
Only four building types were analyzed. Other types of buildings (industries) that require huge amounts of heating
energy for its operation should be included in the prototype model for Miami to check for the balance of cooling and
heating energy load. Also, building types like Industrial building that generate a lot of pollution, should be analyzed
for its location in the prototype model before being considered for analysis. Including more type of buildings will give
a different result and that might help to balance the cooling and heating energy load of the prototype model.
6.3.3. Improvement in cogeneration system
The cogeneration system analyzed uses gas to generate electricity. So, it still uses fossil fuel to generate electricity
and is not a good option from the perspective of sustainability. So, moving out from a gas fired cogeneration plant
should be taken into consideration for the betterment of our planet. Possible options need to be evaluated. One option
could be to use an Electricity powered heat recovery systems as used in Stanford University. They shifted from a gas-
fired cogeneration plant to an electricity powered energy facility and achieved an overall on-site efficiency of more
than 100% (Office of Sustainability, 2015). They used grid plus on-site PV for electricity generation and a heat
recovery system. So, this could be a possible positive checklist for further research on implementing a cogeneration
plant. Other options would be to select the fuel used in a cogeneration plant and evaluate it on terms of GHG emission,
and implementing intermittent renewable energy sources in a district energy system (like wind turbine).
6.4. Summary
Two types of methods have been discussed to increase the efficiency of energy use for the built environment. These
are solely based on the techniques of reusing waste energy or rejected energy. However, there were many
considerations adopted during each of the methods, which can make a huge difference when applied in the real world.
This was just an initial analysis conducted on an urban scale (prototype model) to make optimum use of energy.
Though the results do not provide a complete and detailed plan for reusing waste energy, further study of the
techniques (building energy exchange and cogeneration) will bring the results close to perfect and can be a starting
point on framing regulations for sustainable urban planning (from the perspective of energy efficiency).
81
Bibliography
(2017, October 1). Retrieved from Wikipedia:
https://en.wikipedia.org/wiki/New_York_City_steam_system
Architecture 2030. (2018, February 12). Architecture 2030 (about us). Retrieved from Architecture 2030:
http://architecture2030.org/about/
ASHRAE. (2013). Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta.
ASHRAE. (2016). DISTRICT HEATING AND COOLING. In ASHRAE Handbook (pp. 12.1-12.49). Atlanta.
ASHRAE Standard 90.1-2013. (2013). Energy Standard for Buildings Except Low-Rise Residential
Buildings. Atlanta.
Boardman, T. (2018, February 17). Retrieved from St. Paul Real Estate Blog:
http://www.stpaulrealestateblog.com/2016/03/what-is-district-energy.html
Borlase, S., & Stuart Contributer, B. (2012). Smart Grids Infrastructure, Technology, and solutions.
Hoboken : Taylor and Francis.
Cooper, L., & Rajkovich, N. (2012). An Evaluation of District Energy Systems in North America: Lessons
Learned from Four Heating Dominated Cities in the U.S. and Canada. ACEEE, 11-62.
Cornell University. (2018, February 15). Infrastructure Properties and Planning. Retrieved from
Combined Heat and Power Plant:
https://energyandsustainability.fs.cornell.edu/util/heating/production/cep.cfm
District Energy, S. P. (2018, February 16). Combined Heat and Power. Retrieved from District Energy, St.
Paul: http://www.districtenergy.com/technologies/combined-heat-and-power/
District Energy, S. P. (2018, February 16). District cooling. Retrieved from District Energy, St. Paul:
http://www.districtenergy.com/technologies/district-cooling/
District energy, S. P. (2018, Febraury 16). District heating. Retrieved from District Energy, St. Paul:
http://www.districtenergy.com/technologies/district-heating/
DOE. (2017, August 28). Demand Response. Retrieved from US DOE:
https://energy.gov/oe/activities/technology-development/grid-modernization-and-smart-
grid/demand-response
DOE. (2017, November 24). EnergyPlus. Retrieved from EnergyPlus: https://energyplus.net/
Dominkovic, D., Bacekovic, I., Sveinbjornsson, D., Pedersen, A., & Krajacic, G. (2017). On the way
towards smart energy supply in cities: The impact of interconnecting geographically distributed
district heating grids on the energy system. Energy.
82
Email exchange with Peter Simmonds, Subject (ASHRAE conference in Chicago) (USC email December 7,
2017).
FMS, U. (2018, 5 1). Thermal energy storage. Retrieved from
http://facilities.usc.edu/leftsidebar.asp?ItemID=462
Harbinger, W. (2014, November 21). Early Energy Modeling with an Energy Conservation Measures
Toolkit. Retrieved from Wood Harbinger: https://www.woodharbinger.com/early-energy-
modeling-energy-conservation-measures-toolkit/
Howard, B., Parshal, L., Thompson, J., Hammer, S., Dickinson, J., & Modi, V. (2011). Spatial distribution of
urban building energy consumption by end use. Energy and Buildings, 141-151.
International District Energy Association. (2005). The District Energy Industry. Westborough, MA.
Janis, R. R., & Tao, W. K. (2014). Mechanical and Eectrical Systems in Buildings. Pearson Education.
Lund, H., Østergaard, P., Connolly, D., & Mathiesen, B. (2017). Smart energy and smart energy systems.
Energy.
Lund, H., Werner, S., Wiltshire, R., Svendsen, S., Thorsen, J. E., Hvelplund, F., & Mathiesen, B. V. (2014).
4th Generation District Heating (4GDH) 4th Generation District Heating (4GDH) energy systems.
Energy.
Mindset, T. E. (2018, February 26). Absorption Chiller, How it works - working principle. Retrieved from
You Tube: https://www.youtube.com/watch?v=0R84hLprO5s&t=307s
NRG. (2018, February 17). NRG Energy Center Minneapolis. Retrieved from
http://www.nrg.com/business/large-business/thermal/projects/minneapolis/
Office of Sustainability, S. U. (2015). Stanford University Energy and Climate Plan. Stanford: Stanford
University.
Pass, R., Wetter, M., & Piette, M. (2016). A Tale of Three District Energy Systems: Metrics and Future
Opportunities. ACEEE Summer Study on Energy Efficiency in Buildings.
Sehrawat, P., & Kensek, K. (2014). URBAN ENERGY MODELING: GIS as an alternative to BIM. Building
Simulation Conference.
Smart energy systems and 4th generation district heating. (2016). Energy, 1-4.
Woodcock, R. D. (2018, January 17). Energy Smart blog. Retrieved from Understanding Peak Demand
Charges: Understanding Peak Demand Charges
Wujek, J. B., & Dagostino, F. R. (2010). Mechanical and Electrical Systems in Architecture, Engineering
and construction. Columbus: Pearson.
83
84
Appendix A
Figure A1: General information
Figure A2: Building footprint
85
Figure A3: Building envelope and constructions
Figure A4: Building interior constructions
86
Figure A5: Doors
Figure A6: Exterior windows
87
Figure A7: Window shades and blinds
Figure A8: Roof skylights
88
Figure A9: Season definitions
Figure A10: Building operation schedule
89
Figure A11: Activity areas allocation
Figure A12: Non-HVAC end uses
90
Figure A13: Interior lighting load and profiles
Figure A14: Cooking loads and profiles
91
Figure A15: Refrigeration loads
Figure A16: Miscellaneous loads
92
Figure A17: Exterior lighting loads
Figure A18: Domestic Water heating hourly profiles
93
Figure A19: HVAC system definitions
Figure A20: HVAC zones
94
Appendix 2
Residential Hotel Office Data Center
Construction Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c. Metal Frame, 24 in. o.c.
Ext. Finish / Color Roof, built-up Roof, built-up Roof, built-up Roof, built-up
Ext Insulation 2 in. polyurethane (R-12) 2 in. polyurethane (R-12) 2 in. polyurethane (R-12) 2 in. polyurethane (R-12)
Add Insulation R-19 batt, no rad barrier R-30 batt, no rad barrier R-19 batt, no rad barrier R-19 batt, no rad barrier
Construction Metal Frame, 2x6, 16 in. o.c. Metal Frame, 2x6, 24 in. o.c. Metal Frame, 2x6, 24 in. o.c. Metal Frame, 2x6, 24 in. o.c.
Ext. Finish / Color Aluminium Aluminium Aluminium Aluminium
Add Insulation 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5) 1 1/2 in. polyisocyanurate (R-10.5)
Exposure Earth Contact Earth Contact Earth Contact Earth Contact
Construction 6 in. Concrete 6 in. Concrete 6 in. Concrete 6 in. Concrete
Ext Insulation no perimeter insulation no perimeter insulation no perimeter insulation no perimeter insulation
Interior Finish no surface finish Carpet with rubber pad no surface finish no surface finish
Int. Finish Lay-In Acoustic Tile Lay-In Acoustic Tile Lay-In Acoustic Tile Lay-In Acoustic Tile
Batt Insulation no ceiling insulation no ceiling insulation no ceiling insulation no ceiling insulation
Int. Finish Carpet with fiber pad Carpet with rubber pad Carpet with rubber pad Carpet with rubber pad
Construction 4 in. Concrete 4 in. Concrete 4 in. Concrete 4 in. Concrete
Concrete Cap 1.25 in. LW Concrete 1.25 in. LW Concrete 1.25 in. LW Concrete 1.25 in. LW Concrete
Rigid Insulation 1 in. polystyrene (R-4) 1 in. polystyrene (R-4) 1 in. polystyrene (R-4) 1 in. polystyrene (R-4)
Door type 1 Construction Steel Hollow core w/o Brk Steel Hollow core w/o Brk Steel Hollow core w/o Brk Steel Hollow core w/o Brk
Door type 2 Construction Double Viracon Double Viracon Double Viracon NA
Glass Category Single PPG Single PPG Single PPG Single PPG
Glass type Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9) Starphire 6mm (U=1.03, SHGC=0.9)
Frame type Aluminium, Fixed Aluminium, Fixed Aluminium, Fixed Aluminium, Fixed
Window
Roof Surfaces
Above Grade
Walls
Ground Floor
Ceilings
Floors
Abstract (if available)
Abstract
Singular building scale energy efficiency may not be enough to combat larger issues of climate change. Instead, at a district scale, understanding the dynamics of energy usage including heating, cooling, and electricity by different buildings through the day and year could save overall energy. A district energy model was developed based on collections of different building types (e.g. offices, residential, data centers, etc.) to transfer any surplus or wasted energy among buildings and reduce the need for energy generation for building operation. The model analyzes five districts that are composed of four specific building types in five different climate zones in the United States to understand the dynamics of energy demand between the buildings and then suggests the possibility of having building energy exchange and using a cogeneration plant, in those five locations. A district energy strategy could help to minimize the use of fossil fuels to generate energy for building operation and maintenance. A simple prototype model composed of offices, residential buildings, datacenters, and hotels was created with specifications from ASHRAE guidelines to obtain the heating, cooling, and electricity demand for each building for every hour throughout the year. The buildings were modelled in eQUEST to generate heating, cooling and electrical loads. The results indicated that building energy exchange is not feasible in all the cities, but there are possible options to reuse the waste produced from electricity generation over the course of a day. This provided a guideline for selecting building types for achieving maximum energy efficiency in the five climate zones.
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Asset Metadata
Creator
Kundu, Debjit
(author)
Core Title
District energy systems: Studying building types at an urban scale to understand building energy consumption and waste energy generation
School
School of Architecture
Degree
Master of Building Science
Degree Program
Building Science
Publication Date
07/29/2018
Defense Date
04/26/2018
Publisher
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Tag
co-generation,district cooling,district energy systems,district heating,energy transfer,OAI-PMH Harvest
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Tags
co-generation
district cooling
district energy systems
district heating
energy transfer