alternative heating systems for northern remote …...alternative heating systems for northern...

Alternative heating systems for northern remote communities:

Techno-economic analysis of ground-source heat pumps in

Kuujjuaq, Nunavik, Canada

Evelyn Gunawan

Thesis of 60 ECTS credits

Master of Science (M.Sc.) in Sustainable Energy

April 2019




Evelyn Gunawan

Thesis of 60 ECTS credits submitted to the School of Science and Engineering at

Reykjavík University in partial fulfillment of the requirements for the degree of


April 2019

Supervisor(s):

Jasmin Raymond, Supervisor

Professor, Institut national de la recherche scientifique, Canada

Nicolò Giordano, Supervisor

Postdoctoral Researcher, Institut national de la recherche scientifique, Canada

Páll Jensson, Advisor

Professor, Department Head, Reykjavik University, Iceland

Juliet Newson, Advisor

Director, Reykjavik University, Iceland

Examiner:

Halldór Pálsson, Examiner

Professor, University of Iceland, Iceland

Copyright

Evelyn Gunawan

April 2019




Evelyn Gunawan

April 2019

Abstract

Geothermal energy, through the utilisation of ground source heat pump (GSHP)

has been proposed as a heating alternative to the low efficiency and

environmentally adverse diesel furnaces currently being used to meet residential

heating demand in Nunavik, a cold and remote region covering the northern third

of Québec, Canada. This study describes the application of the G.POT method,

developed by Casasso and Sethi (2016) to create maps of the shallow geothermal

potential in Kuujjuaq, the largest village in Nunavik. Resulting maps show a

relatively high potential for such cold region, ranging between 5.8 MWh/year and

22.9 MWh/year for borehole heat exchanger lengths of 100 m to 300 m. 50-years

life-cycle cost analyses of such geothermal systems reveal that compression GSHP

with electricity derived from solar photovoltaic panels costs as low as

CAD$0.15/kWh and forms the most economically attractive heating option in

Kuujjuaq as compared to the diesel furnace heating currently used at

CAD$0.21/kWh. Studies focusing on the applications of GSHP in subarctic

conditions are currently limited and hence, this work is expected to fill in this gap.

Keywords: renewable energy, geothermal, ground source heat pump, GIS, life

cycle cost

Möguleigir kostir til húsahitunar í afskekktum nyrðri

byggðum: Tæknileg og fjárhagsleg greining á grunnvirkum

varmadælum í Kuujiuaq, Nunavik, Kanada

Evelyn Gunawan

Apríl 2019

Útdráttur

Fram hafa komið tillögur um að nýta jarðhita með varmadælum (GSHP) til

upphitunar í stað díselofna sem nú eru notaðir til að hita íbúðarhúsnæði í Nunavik,

köldu og afskekktu svæði sem nær yfir norðurhluta Quebec í Kanada. Díselofnar

eru bæði óskilvirkir og ekki umhverfisvænir. Þessi ritgerð lýsir notkun G.POT

aðferðarinnar, sem þróuð var af Casasso og Sethi (2016), til að kortleggja jarðhita

á litlu dýpi í Kuujjuaq, stærsta þorpinu í Nunavik. Kortin sýna tiltölulega mikla

möguleika á þessu kalda svæði, á bilinu frá 5,8 MWh/ári og upp í 22,9 MWh/ári

fyrir borholu á bilinu 100 til 300m. 50 ára kostnaðargreining á lífsferli slíkra

jarðhitakerfa sýnir að þjöppun GSHP með rafmagni sem fæst úr sólarsellum kostar

ekki nema CAD$0,15/kWh og er hagkvæmari kyndingarvalkostur fyrir Kuujjuaq

heldur en díselkynding sem nú er notuð og kostar CAD$0,21/kWh. Rannsóknir

sem beinast að notkun varmadæla í heimskautabyggðum hafa verið takmarkaðar

og því er vonast til að með þessu verkefni sé komið til móts við vöntun á þekkingu

á þessu sviði.




Evelyn Gunawan

60 ECTS thesis submitted to the School of Science and Engineering

at Reykjavík University in partial fulfillment

of the requirements for the degree of


April 2019

Student:

___________________________________________

Evelyn Gunawan

Supervisor(s):

___________________________________________

Jasmin Raymond

___________________________________________

Nicolò Giordano

___________________________________________

Páll Jensson

___________________________________________

Juliet Newson

Examiner:

___________________________________________

Halldór Pálsson

The undersigned hereby grants permission to the Reykjavík University Library to reproduce

single copies of this Thesis entitled Alternative heating systems for northern remote

communities: Techno-economic analysis of ground-source heat pumps in Kuujjuaq,

Nunavik, Canada and to lend or sell such copies for private, scholarly or scientific research

purposes only.

The author reserves all other publication and other rights in association with the copyright

in the Thesis, and except as herein before provided, neither the Thesis nor any substantial

portion thereof may be printed or otherwise reproduced in any material form whatsoever

without the author’s prior written permission.

date

Evelyn Gunawan

Master of Science

0 2 / 0 5 / 2 0 1 9

Acknowledgements

I would like to express my sincere gratitude to my supervisor, Dr. Jasmin Raymond,

for providing this memorable opportunity to work on this project at the Institut national de

la recherche scientifique, for his insightful comments and immense knowledge, all the while

supportive of my career goals. I would also like to thank Dr. Nicolò Giordano for his patient

guidance, continuous support and encouragement. Both have shown me, by their examples,

what good scientists and people should be.

Special thank you to Dr. Juliet Newson, who has provided me extensive personal and

professional guidance, and who told me since the very beginning to always “go for it”. My

great appreciation goes to Dr. Páll Jensson for his valuable and constructive suggestions on

the economic aspect of this work. I am indebted to them for their help.

Thank you to the Insitut nordique du Québec for supporting this project financially.

I thank my fellow colleagues at the Iceland School of Energy and Institut national de

la recherche scientifique for being my constant sources of inspiration throughout this

journey.

Finally, I wish to thank my family: my sister, Clarissa Gunawan for her humour and

for enlightening me on the fundamentals of economic analysis, and my parents, Mimi Tjhin

and Hendra Gunawan for their unending support in everything that I pursue. Soli Deo Gloria.

Preface

This dissertation is original work by the author, Evelyn Gunawan.

xix

Contents

Acknowledgements ............................................................................................................ xv

Preface ..............................................................................................................................xvii

Contents ................................................................................................................................ 1

List of Figures ................................................................................................................... xxi

List of Tables .................................................................................................................. xxiii

List of Abbreviations and Acronyms ............................................................................ xxv

List of Symbols ..............................................................................................................xxvii

1 Introduction ...................................................................................................................... 1

2 Methods ............................................................................................................................. 4

2.1 Shallow Geothermal Potential Mapping ................................................................. 4

2.2 Residential Building Heating Load......................................................................... 7

2.2.1 Building Heating Scenarios and Effectiveness .......................................... 9

2.2.2 Building Energy Consumption ................................................................. 10

2.2.3 BHE Drilling Lengths .............................................................................. 10

2.2.4 Solar Panels Quantity ............................................................................... 10

2.3 Life-Cycle Cost Analysis ...................................................................................... 10

2.3.1 Costs of Heating System .......................................................................... 10

2.3.2 Cost of CO2 Emissions ............................................................................. 11

2.3.3 Net Present Cost, Levelised Cost of Energy and Sensitivity Analysis .... 12

2.3.4 Revenue from Selling in the Commodity Market .................................... 12

2.3.5 Economic Scenarios ................................................................................. 13

2.3.6 Assumptions ............................................................................................. 13

3 Results .............................................................................................................................. 15

3.1 Shallow Geothermal Potential Maps .................................................................... 15

3.2 Residential Building Heating Load....................................................................... 17

3.2.1 Building Energy Consumption ................................................................. 18

3.2.2 BHE Drilling Lengths .............................................................................. 18

3.2.3 Solar Panels Quantity ............................................................................... 18

3.3 Life-Cycle Cost Analysis ...................................................................................... 18

3.3.1 Economic Scenario 1 ................................................................................ 19





xx

4 Discussion ........................................................................................................................ 27

5 Conclusions...................................................................................................................... 29

References………………………………………………………………………….....31

A Detailed Steps for Shallow Geothermal Potential Data Processing and Mapping .. 36

A.1 Depths of Unconsolidated Sediments ..................................................................... 36

A.2 Weighted Thermal Conductivity and Heat Capacity .............................................. 38

A.3 G.POT Calculations and Mapping .......................................................................... 39

B SIMEB Calibration ........................................................................................................ 40

B.1 Calibration ............................................................................................................... 40

B.2 DHW Usage Schedule ............................................................................................ 45

B.3 Occupancy Schedule ............................................................................................... 46

B.4 Results of the Calibration ........................................................................................ 47

C Parameter Inputs to Simulate the Heating Load of Residential Building in

Kuujjuaq............................................................................................................................. 49

D COP Calculations........................................................................................................... 53

D.1 COP of Compression Heat Pump (COMP) ............................................................ 53

D.2 COP of Absorption Heat Pump (ABS) ................................................................... 54

E CO2 Emissions ................................................................................................................ 55

F Monthly Heating Load of a Typical Residential Building in Kuujjuaq .................... 56

G NPCs Based on Financial Scenario 5 ........................................................................... 58

xxi

List of Figures

Figure 1.1 Location of Kuujjuaq, the study area, in Canada. .................................................. 1 Figure 2.1 Kuujjuaq bedrock geology (top) and unconsolidated sediments (bottom) with

"Canada Base Map Service-Transportation" map as a background [19,20]. .......................... 6 Figure 2.2 Building heating scenarios. .................................................................................... 9 Figure 3.1 Geothermal potential maps of Kuujjuaq based on three BHE lengths of 100 m

(top), 200 m (center) and 300 m (bottom). X and Y axes represent map coordinates

(NAD83/UTM Zone 19N). .................................................................................................... 16 Figure 3.2 Average daily temperature and heating load profile of a typical residential

building in Kuujjuaq. ............................................................................................................. 17 Figure 3.3 NPC vs. CO2 emissions of different building heating scenarios. ......................... 19

Figure 3.4 Sensitivity analyses of key parameters in all building heating options based on

Economic Scenario 1. ............................................................................................................ 22

Figure 3.5 Range of accumulated NPCs based on worst to best BHE drilling costs compared

to that of business-as-usual heating scenario. ........................................................................ 23 Figure 3.6 Optimisation to determine the best proportion (%) of electricity coming from

solar panels to run a COMP for building heating in Kuujjuaq. ............................................. 26

Figure A1. Maps of bedrock limits (1), point layer of bedrock depths (2) and point layer of

the combined depths of unconsolidated sediments and the extracted bedrock depths (3) .... 36

Figure A2. Interpolations of the depths of unconsolidated sediments in Kuujjuaq with IDW

(left) and TIN (right) methods with 100 x 100 m grid spacings............................................ 37

Figure A3. Surfer maps with contour lines of unconsolidated sediments depths interpolated

with Kriging method using three different grid spacings ...................................................... 38

Figure A4. QGIS point layer of quaternary deposits depth data interpolated with Kriging

and 300 x 300 m grid spacing in Surfer................................................................................. 38

Figure A5. A sample of the .BLN file used to create the study area limits (left) and the

resulting limits viewed in Surfer used to clip the results to show only the study area (left) . 39

Figure B1. DHW usage schedule for Monday-Friday (top), Saturday (middle) and Sunday

(bottom)……………………………………………………………...………………….…..45

Figure B2. Building occupancy schedule for Monday-Friday (top) and Saturday-Sunday

(bottom)…………………………………..…………………………..……………………..46

Figure B3. Comparison of SIMEB calibration results with building heating load profile

from EERE ............................................................................................................................ 48

Figure D1. Graph of ClimateMaster Model TCH/V120 COP vs. EWT ............................... 54

Figure D2. Graph of Robur Model GAHP-WLB GUE vs. EWT .......................................... 55

Figure F1. Typical residential building space heating and domestic hot water load profiles

in Kuujjuaq………………………………………………………………………………….57

Figure G1. Accumulated NPCs of building heating options for home-owner and government

based on Economic Scenario 5 over 50 years LCC…………………….……………...…...59

xxiii

List of Tables

Table 2.1 Thermal conductivity and heat capacity for unconsolidated sediments and bedrock

[16,17]. ......................................................................................................................................5 Table 2.2 Parameters used for mapping the geothermal potential of Kuujjuaq [6]. .................5

Table 2.3 Main SIMEB parameter inputs to simulate a typical residential building heating

load in Kuujjuaq. .......................................................................................................................8 Table 2.4 Summary of the economic scenarios used to calculate the LCCs. ..........................13 Table 3.1 Energy consumption breakdowns for different heating equipment scenarios. .......18 Table 3.2 Summary of costs, CO2 emissions, NPCs and LCOEs of 50-years LCC for

business-as-usual and alternative heating scenarios. ...............................................................20 Table 3.3 Total 50 years NPCs for home-owner and government based on Economic

Scenario 3. ...............................................................................................................................24

Table 3.4 Total 50 years NPCs for home-owner and government based on Economic

Scenario 4. ...............................................................................................................................24 Table 3.5 Total 50 years NPCs for home-owner and government, and total LCOE based on

Economic Scenario 5. ..............................................................................................................25

Table A1. Comparison of depth interpolation results with Kriging method using three

different grid spacings ............................................................................................................. 38

Table B1. Monthly energy load profile of a typical residential building in Anchorage

obtained from EERE [3] website and modified to assume building heating with electric

equipment ................................................................................................................................ 40

Table B2. SIMEB parameter inputs to simulate a typical residential building heating load in

Anchorage ................................................................................................................................ 41

Table B3. Monthly energy load profile of a typical residential building in Anchorage based

on the calibration results in SIMEB ........................................................................................ 47

Table C1. SIMEB parameter inputs to simulate a typical residential building heating load in

Kuujjuaq .................................................................................................................................. 49

Table D1. Entering water temperatures (EWTs) and their corresponding coefficient of

performances (COPs) [43] ....................................................................................................... 53

Table D2. Entering water temperatures (EWTs) and their corresponding gas utilisation

efficiencies (GUEs) [44] .......................................................................................................... 54

Table E1. CO2 emissions intensity of six heating oil companies in North America ............... 56

xxv

List of Abbreviations and Acronyms

ABS Absorption ground-source heat pump

BHE Borehole heat exchanger

CAD Canadian dollars

CO2 Carbon dioxide

COMP Compression heat pump

COP Coefficient of performance

°C Degree Celcius

DHW Domestic hot water

EWT Entering water temperature

GHG Greenhouse gas

GIS Geographic Information System

GSHP Ground-source heat pump

GUE Gas utilisation efficiency

HDD18 Heating degree days below 18°C

h Hour

K Kelvin

kWh Killowatt-hour

LCC Life-cycle cost

LCCA Life-cycle cost analysis

l Litre

LCOE Levelised cost of electricity

MJ Megajoule

MWh Megawatt-hour

m Metre

NPC Net Present Cost

EERE Office of Energy Efficiency and Renewable Energy

PV Photovoltaic

RBOB Reformulated gasoline blendstock for oxygen blending

SH Space heating

t Tonne

USD United States dollars

W Watts

xxvii

List of Symbols

Symbol Description Value/Units

�̅�BHE Shallow geothermal potential MWh/year

𝑇𝑙𝑖𝑚 Threshold fluid temperature °C

𝐿 Borehole length m

𝑇0 Undisturbed ground temperature °C

𝑟b Borehole radius m

𝑡s Simulated lifetime years

𝑡c Length of the heating season days

𝑅b Borehole thermal resistance Mk/W

𝜆 Thermal conductivity W/mK

𝐶𝑣 Volumetric heat capacity MJ/m3K

𝑢′s Cycle time parameter -

𝑢′c Simulation time parameter -

𝑡′c Operating time ratio -

xxviii

𝐸g available Thermal energy available per metre drilled MWh/year-m

Eg Electricity demand to be met by solar PV

panels per year MWh/year

𝐿drill Total BHE drilling length m

𝑁s Number of solar PV panels -

Es available Electricity generated by each solar PV

panels kWh/year

𝐸s Electricity demand to be met by solar PV

panels kWh/year

r Discount rate %

𝐶t Total cost $

𝐶c Capital cost $

𝐶a Annual cost $

𝐶p Periodic cost $

n Time point Year 0, 1, 2, …

Et Annual energy output kWh/year

1

Chapter 1

1Introduction

Nunavik, home to 14 Inuit villages with a total of 12,300 inhabitants, is a remote

region covering the northern third of Québec province, Canada. These communities are not

connected to the electrical grid and hence, are reliant on diesel power plants and furnaces to

meet their electricity and building heating (space heating (SH) and domestic hot water

(DHW)) demands. However, due to the distance between the location and the closest

transmission lines there is no plan to connect these communities to the grid. In 2018, the

price of fuel oil was $2.03/l, which is heavily subsidised by the local government to $1.63/l

[1]. Such high cost of fuel is partly associated to the additional cost of fuel transportation

from the south to Nunavik. Additionally, diesel is only shipped once a year to these

communities. As a result, they are forced to purchase annual supplies of diesel fuel on the

spot market, making diesel price volatile in this region [2]. Kuujjuaq, the regional capital of

Nunavik, experiences a low annual average temperature of -5.4°C and an annual average of

8,520 heating degree days below 18°C (HDD18), which translates to high building heating

requirements. Furthermore, between 2006 and 2011, the Inuit population in Nunavik

increased by 12% [3]. The combination of high fuel cost, high building heating

requirements, increasing demand and adverse environmental impact of fossil fuel

combustion calls for the development of new approaches, specifically via renewable energy

sources to supply clean and reliable energy in these off-grid communities.

Kuujjuaq

Figure 1.1 Location of Kuujjuaq, the study area, in Canada.

2 CHAPTER 1: INTRODUCTION

In 2011, the government of Québec launched the Plan Nord, a sustainable

development strategy that targets various sectors and aims to provide a platform for

development in Québec north of 49th degree of latitude by 2035. As part of the strategy, one

of the action plans identified in the Priority Actions for 2015-2020 in the Energy Sector is to

support projects in the northern communities that replace fossil fuels with renewable energy

sources [4]. Several options, such as hydro-power and wind generation have been studied to

date. Hydro-Québec [5], Québec’s electric utility company, published a report in 2011 on the

current state and future potential of energy transport and distribution in Québec’s First

Nations territories, which proposed a hybrid of wind and diesel generation in various

communities as a measure to reduce fossil fuel consumption. However, this notion was

rejected by the communities due to various reasons listed in the report. Weis and Llinca [2]

assessed the potential for wind power generation in 89 remote communities in Canada, while

Yan et al. [6] ranked the suitability of waste gasification and combustions of fuel oil, pellets

and natural gas for building heating in Nunavik. However, the potential of geothermal energy

as a possible solution has not been fully assessed.

In this study, geothermal energy, specifically through the adoption of ground-source

heat pump (GSHP) technology is proposed as a viable alternative to the low efficiency and

high greenhouse gas- (GHG-) emitting diesel furnaces currently used for heating buildings.

A GSHP is a highly efficient technology that can provide both cooling and heating to

buildings by taking advantage of Earth’s subsurface maintaining a relatively constant

temperature year-round. Although it is powered by either electricity or a heat source, the main

advantage of this technology lies in its ability to supply more energy than that used to operate

it. During the heating season, the GSHP system extracts heat from the ground via borehole

heat exchanger (BHE) and distributes it to warm the building. During the cooling season, the

system reverses, transferring heat from the building to the ground.

Geng et al. [7] presented a case study on Shenyang, one of the coldest regions in China

with 3,905 HDD18. Since 2006, the municipality has installed 780 GSHPs, representing

36.3% of the country’s total, which resulted in a GHG emission reduction of 2.1 t from 2006

to 2010. Ozyurt and Ekinci [8] conducted a one-year experimental study in 2007 to analyse

the performance of an electric compression GSHP (COMP) with vertical BHE used for space

heating in Erzurum, the coldest city in Turkey with 4,634 HDD18. They found the coefficient

of performance1(COP), which is the measure for GSHP effectiveness, for this entire year to

be favourable, ranging between 2.43 and 3.55. Pike and Whitney [9] reviewed the economic

performances of seven GSHPs with vertical BHEs in Alaska. The authors noted that the

economics of GSHPs depends heavily on the costs of electricity and alternate fuel source,

such as natural gas or heating oil in the location. The Cold Climate Housing Research Centre

installed a GSHP at its Research and Testing Facility in Fairbanks, Alaska with a horizontal

BHE in 2013 and published a report to assess its performance within the first four years of

operation [10]. They found that the GSHP system operated with better-than-expected

performance, ranging between 2.82 to 3.69. Their models suggest that the decline in COP

will plateau in year 5. The cost effectiveness of GSHP however, depends on the cost of oil

and electricity in the area. The lower the cost of oil, the less cost effective the GSHP system

would be compared to the conventional oil furnace heating system [10]. Le Dû et al. [11]

conducted economic analyses of GSHP to meet the cooling and heating demand of a typical

130 m2 residential building in Halifax, Montreal, Toronto and Vancouver in Canada, with

HDD18 of 3,941, 4,363, 3,498 and 2,818, respectively [12]. In Montreal and Halifax, which

1 The effectiveness of GSHP is measured in terms of its COP, which is typically well above 1. For instance, a

COP of 3 indicates that for every 1 unit of electrical or thermal energy input, 3 units of thermal energy would be

delivered.

https://www.researchgate.net/scientific-contributions/2048536937_M_Le_Du

2.1 SHALLOW GEOTHERMAL POTENTIAL MAPPING

3

use electricity and heating oil as their heating energy sources, paybacks for GSHP installation

are expected to be 18.5 years and 11.3 years, respectively. In Toronto and Vancouver, which

use natural gas as their heating source, no payback for GSHP installation are expected due to

the low price of natural gas. Healy and Ugursal [13] conducted a techno-economic analysis

of GSHP with horizontal BHE in Halifax, Canada and concluded that the technology is

economically viable compared to the oil heating system used in the region. Kegel et al. [14]

analysed the application of GSHP for building heating in Whitehorse and Yellowknife,

Canada and showed that in both regions, significant energy, utility cost and GHG emissions

reductions can be achieved with GSHP. The main challenges of operating GSHPs in such

cold climate relate to the low ground temperature near freezing point, lower GSHP COPs,

high building heating needs and the fact that the usage of electricity to run the GSHP is not

advised by Hydro-Québec as electricity in Nunavik is generated by diesel. These studies

established that GSHP has been successfully installed and tested in cold regions around the

world, even though its economic viability may vary according to factors such as the energy

source used to run the GSHP and the cost of that energy. However, none have studied the

application of GSHP in remote and cold region, specifically Kuujjuaq, Nunavik, Canada.

The objective of the present thesis is to quantify the shallow geothermal potential of

Kuujjuaq, by estimating the maximum amount of energy that can be extracted with a GSHP

coupled to vertical BHE installed in shallow subsurface with a relatively cold temperature of

slightly above 0°C, where this system has never been used in such extreme and cold

environment. Additionally, its economical viability will be evaluated.

To achieve this goal, this study is divided into three main parts:

1. Mapping of the shallow geothermal potential of Kuujjuaq using a geographic

information system- (GIS-) based workflow.

2. Simulating the heating load of a typical residential building in Kuujjuaq using

the local weather data.

3. Calculating the 50-years life-cycle costs of business-as-usual heating scenario

of using diesel furnace and four alternative heating scenarios using GSHP.

This work is expected to serve as a basis for future studies focusing on the applications

of GSHP in subarctic conditions, where low ground temperature near the freezing point,

unbalanced heating/cooling loads and remoteness of the communities can significantly

affect its techno-economic feasibility.

4 : METHODS

Chapter 2

2Methods

2.1 Shallow Geothermal Potential Mapping

The G.POT method (Eq. 2.1) is used to estimate the shallow geothermal potential or

the maximum thermal energy that can be sustainably extracted annually by a closed-loop

BHE in a homogeneous subsurface [15]. This method can be used for both cooling and

heating mode. However, geothermal potential of Kuujjuaq is calculated only for heating

mode as there are very low cooling requirements in the study area.

�̅�BHE =0.0701∙(𝑇0−𝑇lim)∙𝜆∙𝐿∙𝑡′

c

−0.629∙𝑡′c∙log(𝑢′

s)+(0.532𝑡′c−0.962)∙log(𝑢′

c)−0.455𝑡′c−1.619+4𝜋𝜆∙𝑅b

(2.1)

The geothermal potential �̅�BHE (MWh/year) is dependent on the maximum possible

temperature difference between the ground and the fluid 𝑇0 − 𝑇lim (°C), the ground thermal

conductivity 𝜆 (W/mK), the borehole length 𝐿 (m), the thermal resistance of the borehole 𝑅b

(mK/W) and the three non-dimensional parameters 𝑢′s, 𝑢′c and 𝑡′c defined by the following

equations:

𝑢′s =𝐶𝑣∙𝑟𝑏

2

4𝜆𝑡s (2.2)

which depends on the ground heat capacity (𝐶𝑣), borehole radius (𝑟𝑏) and simulated

lifetime (𝑡s):

𝑢′c =

𝐶𝑣∙𝑟2b

4𝜆𝑡c (2.3)

which depends on the heating season length (𝑡c), and

𝑡′c =𝑡c

𝑡y (2.4)

which depends on the length of the load cycle (𝑡y).

The threshold fluid temperature (𝑇lim) is the minimum average fluid temperature in

the BHE and is a design parameter that depends on the BHE length and the temperature

difference between the ground and the fluid flowing through the BHE. In this paper, 𝑇lim is

assumed to be -5°C. A field campaign was previously conducted over the study area to

collect data related to quaternary sediments depths, as well as thermal conductivity and heat

capacity of unconsolidated sediments and host rock (Table 2.1) [16,17]. The typical BHE

length for residential usage is usually around 100 m. However, due to the constraints of the

study area pertaining to low underground temperature and high building heating

2.1 SHALLOW GEOTHERMAL POTENTIAL MAPPING

5

requirements, deeper BHEs at 200 m and 300 m were also considered. The undisturbed

ground temperature 𝑇0, is estimated to be 1.0°C, 1.75°C and 2.75°C over the first 100 m,

200 m and 300 m, respectively. These values were obtained for the corresponding BHE

lengths by Della Valentina et al. [18]. Data on borehole characteristics were defined based

on the possible diameters that can be installed by drilling firms providing services for mining

exploration companies in Kuujjuaq. The input parameters used to map the geothermal

potential are summarised in Table 2.2.

Table 2.1 Thermal conductivity and heat capacity for unconsolidated sediments and

bedrock [16,17].

Table 2.2 Parameters used for mapping the geothermal potential of Kuujjuaq [6].

Parameter Symbol Values Unit

Threshold fluid temperature 𝑇𝑙𝑖𝑚 -5 °C

Borehole length 𝐿 100/200/300 m

Undisturbed ground temperature 𝑇0 1.0/1.75/2.75 °C

Borehole radius 𝑟b 0.038 m

Simulated lifetime 𝑡s 50 years

Length of the heating season 𝑡c 270 days

Borehole thermal resistance 𝑅b 0.1 mK/W

The “Canada Base Map Service-Transportation” that is available online as a Web Map

S ervice was used as a background map [19]. This map exists as a raster and was displayed

at a scale of 1:150,000 and projected in NAD83/UTM Zone 19N. Shapefiles of

unconsolidated sediments and bedrock geology of the study area were also used [20]

(Fig. 2.1). The QGIS 2.18.21 (QGIS) mapping software [21] was used for this procedure.

Types λ saturated

(W/mK)

𝑪𝒗 saturated

(MJ/m3K)

Bedrock Lithology

Paragneiss 2.7 2.4

Diorites 3.0 2.4

Granites 2.9 2.3

Gabbros 3.0 2.4

Tonalites 3.4 2.3

Unconsolidated

Sediments

Marine 1.5 3.0

Alluvial 1.4 3.2

Glacial Till 1.6 3.0

Outcrops 0 0

6 CHAPTER 2: METHODS

Using both QGIS and Surfer® 9 (Surfer) software [22], a depth layer consisting of

existing data of depths of unconsolidated sediments obtained from the field study was

created and interpolated with the Kriging method using a 300 x 300 m grid spacing to cover

the entire study area. The ground thermal conductivity and heat capacity values for both

unconsolidated sediments and bedrock geology were also incorporated to the layer. The

weighted thermal conductivity and heat capacity were calculated for 100 m, 200 m and

300 m BHE lengths scenarios. A sample formula used to calculate the weighted thermal

conductivity at 100 m BHE length is given as follow:

𝜆𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 = (𝐷𝑒𝑝𝑡ℎ𝑢𝑛𝑐𝑜𝑛𝑠𝑜𝑙𝑖𝑑𝑎𝑡𝑒𝑑

100∙ 𝜆𝑢𝑛𝑐𝑜𝑛𝑠𝑜𝑙𝑖𝑑𝑎𝑡𝑒𝑑) + (

100−𝐷𝑒𝑝𝑡ℎ𝑢𝑛𝑐𝑜𝑛𝑠𝑜𝑙𝑖𝑑𝑎𝑡𝑒𝑑

100∙ 𝜆𝑏𝑒𝑑𝑟𝑜𝑐𝑘) (2.5)

The shallow geothermal potential of Kuujjuaq was calculated for each BHE length

scenario by applying Equation 2.1 in Microsoft Excel and then visualised in Surfer. An in-

depth description of the mapping procedure is provided in Appendix A.

Kuujjuaq Unconsolidated Sediments

Kuujjuaq Bedrock Geology

Figure 2.1 Kuujjuaq bedrock geology (top) and unconsolidated sediments (bottom) with

"Canada Base Map Service-Transportation" map as a background [19,20].

2.2 RESIDENTIAL BUILDING HEATING LOAD

7

2.2 Residential Building Heating Load

The heating load of a 252 m2, one-floor residential house with 5 occupants was

modeled with SIMEB using weather data for Kuujjuaq and known parameters on residential

buildings [23,24]. SIMEB is a software program that simulates building energy

consumption, which allows the estimation of hour-by-hour building energy usage given

certain inputs, such as architectural data, thermal envelope, occupancy and mechanical

systems, such as lighting, ventilation and heating [25]. This tool provides a simplified

interface for the DOE-2 and EnergyPlus calculation engines that were developed to perform

building energy simulation. DOE-2 was developed by the Lawrence Berkeley National

Laboratory and funded by the US Department of Energy in late 1970s. EnergyPlus was

developed in 1996 based on the systems algorithms of DOE-2 [25]. In this paper, the DOE-2

algorithm was chosen to model the heating load of the house as it remains one of the most

widely-used building energy modeling programs.

Since current data on building energy usage in Kuujjuaq is limited, hourly load profile

data for a typical residential building in Anchorage, Alaska, US was initially used to

calibrate the inputs specified in SIMEB (Appendix B) [26]. Anchorage was chosen as both

Anchorage and Kuujjuaq have a subarctic climate, with the Alaskan capital showing 7,500

HDD18. The building occupancy and usage schedule were adjusted until similar heating load

profiles were achieved (Appendix B) [27].

Table 2.3 lists down some of the important parameter inputs that were used in SIMEB

to model the building heating load in Kuujjuaq. The full list of inputs, their sources and

calculations can be found in Appendix C. Due to its cold weather, buildings in Kuujjuaq are

regulated by the Société d’habitation du Québec to meet the minimum insulation in order to

minimise heat loss [28]. The RSI-value (m2K/W) is a way to measure insulation and depends

on the thermal resistance of the material. U-value (W/m2K) measures heat loss through a

structure, while SHGC (unitless) represents the amount of solar radiation through a window.


Table 2.3 Main SIMEB parameter inputs to simulate a typical residential building

heating load in Kuujjuaq.

Parameter Values

Thermal Envelope

Roof insulation 9 RSI

Wall insulation 5.11 RSI

Fenestration U: 2.16 W/m2 K

SHGC: 0.5

DHW

Water heater Electrical

Efficiency 100%

Maximum load 20.7 W/m2

Central HVAC System

Type Single zone: single

supply duct system

Heating Electrical

Heating equipment

efficiency 100%

Cooling None

Regulation

Minimum temperature 21.1 °C

Maximum temperature 24.4 °C

Perimeter heating Hydronic baseboard

Occupation

Sensible heat 64.5 W/occupant

Latent heat 48.1 W/occupant


9

2.2.1 Building Heating Scenarios and Effectiveness

The building heating systems considered in this study are summarised in Figure 2.2.

Both COMP and ABS can provide heating and cooling in building applications.

However, there are fundamental differences between the two. COMP runs on electricity to

extract geothermal energy. ABS runs on thermal input, most commonly natural gas.

However, since diesel is readily available in Kuujjuaq, it is assumed the ABS described in

Case 3 will be customised to run on diesel. The effectiveness of COMP and ABS are

measured in coefficient of performance (COP) and gas utilisation efficiency (GUE),

respectively, and depend on the entering water temperature (EWT), which is defined as the

temperature of fluid entering the heat pump. In turn, the EWT depends on both the BHE

configuration and 𝑇0. In this paper, the EWT was assumed to be equal to 𝑇lim at -5°C. Both

COP and GUE measure the ratio of the heating supplied to the building, to the electrical

energy or in the case of ABS, gas consumed by the thermal compressor (Eq. 2.6). For

instance, a COP of 3 indicates that for every 1 unit of electrical or thermal energy input, 3

units of thermal energy would be delivered. For simplicity, the term COP will be used in

this paper to refer to the effectiveness of both COMP and ABS. The COP ratings of COMP

are typically higher than ABS [29]. The COMP selected for this paper has a COP of 3.1,

while the ABS has a COP of 1.2 at the selected EWT. Refer to Appendix D for detailed

calculations. These values were calculated based on the product specifications provided by

the manufacturers. The COP of a heat pump is measured by:

𝐶𝑂𝑃 =𝑈𝑠𝑒𝑓𝑢𝑙 ℎ𝑒𝑎𝑡

𝑊𝑜𝑟𝑘 𝑒𝑥𝑝𝑒𝑛𝑑𝑒𝑑 (2.6)

Electricity, which is produced by the local diesel power plant is not advised to be used

for building heating in Kuujjuaq since it is generated from a diesel power plant at an

efficiency of roughly 33.2% [30]. Therefore, Cases 2A and 2B consider the generation of

electricity from solar photovoltaic (PV) panels.

Sizing a GSHP to provide all the heating required by a house is not normally

Building Heating Scenario

Case 1: Business-as-usual with diesel

furnace

Case 2: Compression heat pump (COMP)

A. 70% of electricity by solar PV panels,

30% from diesel power plant

B. 100% of electricity by solar PV panels

C. 100% of electricity by diesel power plant

Case 3: Absorption heat pump (ABS)

Figure 2.2 Building heating scenarios.


recommended. The occasional peak heating load during severe weather conditions are

usually met by a secondary heating system. Hence, for both Cases 2 and 3, the GSHP is

sized to meet 50% of the peak load. The remaining load will be covered with diesel furnace,

which has an efficiency of 78%.

2.2.2 Building Energy Consumption

Based on the simulated total annual building heating load in Kuujjuaq (Table 3.1), the

energy consumptions for each heating scenario and for different heating equipment were

calculated according to the efficiency for diesel furnace and COPs for heat pump, as well

ass energy densities or calorific values [31] as follow:

1. 1 kWh electricity = 0.0036 GJ

2. 1 l diesel oil = 0.0387 GJ

2.2.3 BHE Drilling Lengths

Based on the average geothermal potential in Kuujjuaq (�̅�BHE), the thermal energy

available per meter drilled (Eg available) were calculated for Cases 2 and 3 based on the three

BHE lengths (L) considered in the G.POT calculation.

𝐸g available =�̅�BHE

𝐿 (2.7)

Based on the total ground load (Eg), which is the ground thermal energy required to

meet the building load with the GSHP system, the total drilling length necessary (𝐿drill) were

calculated as follows:

𝐿drill =𝐸g ∙ 𝐿

�̅�BHE (2.8)

2.2.4 Solar Panels Quantity

The number of solar PV panels required (Ns) for Cases 2A and 2B were calculated by

dividing the electricity demand to be met by the solar PV panels (Es) by the energy generated

from each panel (Es available). Es available was calculated by multiplying the solar PV panel

rating, which was assumed at 0.3 kW with 1,033 kWh/kW/year, the average annual solar

PV potential in Kuujjuaq [32].

𝑁s =𝐸s

𝐸s available (2.9)

2.3 Life-Cycle Cost Analysis

2.3.1 Costs of Heating System

All costs in this study are in Canadian dollars (CAD), unless otherwise specified. For

2.3 LIFE-CYCLE COST ANALYSIS 11

prices involving the United States dollars (USD), the conversion rate 1 USD = 1.272 CAD

on November 6, 2018 was considered [33]. A 14.98% Québec sales tax was applied to all

costs. The total cost (Ct) was divided into capital costs (Cc), annual costs (Ca), and periodic

costs (Cp) (Eq. 2.10). Capital costs include the cost of equipment, installation or labour and

shipping. Annual costs were divided to the costs of energy (diesel and/or electricity),

maintenance and GHG or carbon dioxide (CO2) emissions. Periodic costs include the cost

of equipment to be replaced at the end of its lifetime, installation and shipping.

𝐶t = 𝐶c + 𝐶a + 𝐶p (2.10)

Price of fuel: Diesel price in Kuujjuaq is $2.03/l before and $1.63/l after the subsidy

[1]. The cost of electricity production by diesel power plant in Kuujjuaq is $0.86/kWh [34].

With subsidies, the base rate for electricity if 40.64c/day, and $5.40/month in summer and

$6.21/month in winter, while the variable rate is 5.91c/kWh for the first 10,950 kWh per

annum and 41.05c/kWh thereafter [35].

Price and lifetime of equipment: The price of oil tank is $666.92, which has an

expected lifetime of 25 years [36]. The price of boiler is $3,248.30 and a lifetime of 15 years

[37]. The price of both COMP and ABS were assumed to be the same at USD$7,000 for

35 kW, which can cover the heating needs of three houses. The lifetime of heat pump is

expected to be 20 years. The cost of drilling sums to $344.94/m, which includes labour and

u-pipe heat exchanger [38]. The lifetime of the heat exchanger is assumed to be 50 years.

The cost of solar PV panel installation, which includes both labour and equipment is

assumed to be at a higher end at $5.0/W in Kuujjuaq, which was inferred from the average

installation cost in Québec at $2-3.5/W [39]. The lifetime of solar PV panel is assumed to

be 20 years.

Labour wage and installation time: The average wage for 13 maintenance and

technician jobs in Kuujjuaq was $26.32/hour [40]. It takes two working days for boiler

installation and one working day for tank installation. Due to the difference in expertise

required, the average wage for heat pump installation is assumed to be $35.00/hour. Heat

pump installation takes two working days.

Maintenance: Maintenance for all heating scenario is assumed to be conducted

annually at $3.87/m2 for diesel furnace system and $1.81/m2 for both heat pump systems

[41]. Since in Cases 2 and 3 diesel furnaces is only used to meet 50% of the peak heating

demand, the maintenance cost for oil furnace in these cases were halved and added to the

heat pump maintenance cost.

Shipping: Shipping of oil tank, oil furnace and heat pumps from Québec City is

provided by NEAS cargo shipping company at approximately $1.15/kg, which includes tax

and fuel surcharge [42].

Equipment weight: A 275-gallon oil tank weighs 127 kg. The average weight of

seven oil furnaces is 255 kg. The weight of COMP is 316.6 kg [43]. The weight of ABS is

300 kg [44]. The weight of wooden pallet packaging for each equipment was assumed at

15 kg. The weight of solar PV panel was assumed at 15 kg/m2, while a typical size of a solar

PV panel is 1.64 m2.

2.3.2 Cost of CO2 Emissions

The CO2 emissions per MJ of product of six heating oil companies in North America

were averaged and multiplied by the annual diesel consumption to determine the annual

carbon dioxide emissions for each heating scenario (Appendix E) [45].


The CO2 emissions for each scenario was multiplied with $19.40/t, the estimated price

of carbon in Québec’s carbon market in 2020, to obtain the cost of CO2 emissions associated

with each heating option [46].

2.3.3 Net Present Cost, Levelised Cost of Energy and Sensitivity Analysis

A net present cost (NPC) and levelised cost of energy (LCOE) approaches were

chosen to compare the 50-years life-cycle costs (LCCs) of the heating alternatives. It is

important to note that life-cycle cost analysis (LCCA) cannot be used for budget allocation.

However, LCCA is especially useful to compare project alternatives that fulfill similar

function, which in this study is for building heating in Kuujjuaq, and to select the most cost-

efficient option. The NPC formula converts or discounts costs incurred at different time

point (n) during the project life-cycle, at the discount rate (r) to a common point in time,

which in this study is 2020. NPC calculations were applied to obtain the LCC for both home-

owner and government.

𝑁𝑃𝐶 = ∑𝐶t,n

(1+𝑟)n𝑁𝑛=0 (2.11)

The LCOE is an additional way to rank project alternatives. Compared to the NPC

method, LCOE considers both the total LCC, as well as the total amount of energy

consumed, both of which are discounted over the project’s lifetime. It indicates the minimum

cost per unit of energy that will recover the lifetime costs of the system and is measured by

dividing the NPC of the heating system by its total lifetime energy output. The annual energy

output (Et) is the total energy consumption for each heating scenario (Table 3.2).

𝐿𝐶𝑂𝐸 =𝑁𝑃𝐶

∑𝐸t,n

(1+𝑟)n𝑁𝑛=1

(2.12)

To address the uncertainty in predicting these costs and to identify critical parameters,

sensitivity analyses were conducted to measure the effect on the NPC of variations in the

key input variables. The key inputs that were subject to sensitivity analysis were capital cost,

energy cost, maintenance cost, and periodic costs for heat pump, oil boiler, oil tank and solar

PV panels. Each of the key inputs was changed by 30% in 10% increments above and below

their original values. Sensitivity graphs were plotted for each heating scenario to visualise

the results of the sensitivity analyses (Fig. 3.4). The gradients of the lines indicate how

sensitive the NPC is to changes in each of the inputs. A steeper slope indicates a more crucial

variable that has more effect on the NPC.

2.3.4 Revenue from Selling in the Commodity Market

Switching from business-as-usual heating scenario in Case 1 to GSHP heating systems

in Case 2 and 3 cuts the consumption of diesel. This opportunity benefit is defined as the

revenue gained from selling surplus diesel in the commodity market and the avoided cost

for not shipping and selling to Kuujjuaq. These costs were considered when calculating the

NPCs of Cases 2 and 3. The cost of diesel was assumed to be USD$1.41/gal or $0.47/l based

on the price of RBOB gasoline in the commodity market on January 5, 2019 [47]. The cost

of shipping diesel was assumed to be the cost of diesel production before subsidy minus the

cost after subsidy at $0.40/l.


2.3.5 Economic Scenarios

To be able to propose recommendation and/or identify areas to be considered for

future improvement, the LCCAs were applied for various economic scenarios, where one to

several variables were varied, while the others were held constant (Table 2.4). First, an

LCCA based on the current condition and the values assumed above was created. Second,

LCCAs to show the uncertainties resulting from best ($50/m), moderate ($175/m) and worst

($300/m) BHE drilling costs were created. The best drilling cost was assumed based on the

typical BHE drilling cost in the south. Third, a scenario was analysed in which the

government covers 50% of worst BHE drilling cost ($300/m) and GSHP and/or solar PV

panels costs, while all subsidies on electricity and diesel remain. Fourth, the government still

covers 50% of BHE drilling cost and GSHP and/or solar PV panels costs, but there would

be no more subsidies on electricity and diesel for the home-owner. Fifth, the government

covers 50% of GSHP and/or solar PV panels costs, but there would be no more subsidies on

electricity and diesel for the home-owner. In this last scenario, the home-owner are fully

responsible for the cost of drilling at $50/m. From the third economic scenario onwards, the

effect of government incentive to the NPC as well as the effect of such incentive to the

distribution of cost between home-owner and government can be observed, whereas the first

and second economic scenarios are expected to provide an overview of the total costs of the

project.

Table 2.4 Summary of the economic scenarios used to calculate the LCCs.

2.3.6 Assumptions

In addition to the costs and economic scenarios stated above, the following technical

assumptions were made:

1. Solar PV panels were installed south facing at an angle equal to the latitude, with no

shade, such as from buildings, trees and snow.

2. Cost of solar energy storage was not considered.

3. To limit the scope of the economic analysis, the cost of heating distribution was not

considered.

4. Tools and parts, such as bolts and screws were considered negligible and not

included.

5. Roof replacement costs incurred when solar PV panels are replaced were not

considered as roof has an expected lifetime of 20 years and hence, need to be

replaced regardless in all cases.

The following economic assumptions were made:

Economic

Scenario

Drilling Energy (diesel

and electricity)

Subsidy

GSHP and Solar

PV Panel Costs

Covered by the

Government

Cost ($/m) Cost Covered

by the

Government

1 300 no yes no

2 300, 175 and 50 no yes no

3 300 50% yes 50%

4 300 50% no 50%

5 50 no no 50%


1. Discount rate = 6% [48].

2. Annual energy and maintenance costs escalation rates = 0%.

3. Project lifetime = 50 years. Project starts in 2020 and ends in 2069.

4. No sudden fluctuation in the costs of electricity and diesel throughout the project life-

cycle.

5. Depreciation rates of heating equipment not considered.

1. In the third, fourth and fifth economic scenarios, the government is assumed to bear

the cost of CO2 emissions.

3.1 SHALLOW GEOTHERMAL POTENTIAL MAPS

15

Chapter 3

3Results

3.1 Shallow Geothermal Potential Maps

With the input parameters described in Tables 2.1 and 2.2, the shallow geothermal

potential, �̅�𝐵𝐻𝐸 was calculated using the G.POT equation (Eq. 2.1). Figure 3.1 presents the

resulting geothermal potential maps based on three BHE lengths in Kuujjuaq.

16 CHAPTER 3: RESULTS

Figure 3.1 Geothermal potential maps of Kuujjuaq based on three BHE lengths of

100 m (top), 200 m (center) and 300 m (bottom). X and Y axes represent map

coordinates (NAD83/UTM Zone 19N).


17

The geothermal potential at 100 m BHE length ranged 5.2-6.6 MWh/year and

averaged 5.8 MWh/year. At 200 m, the geothermal potential ranged 12.2-14.9 MWh/year

and averaged 13.3 MWh/year. At 300 m, the geothermal potential ranged

21.3-25.6 MWh/year and averaged 22.9 MWh/year. Thus, the geothermal potential

increases supralinearly with borehole lengths due to higher temperature at greater depths

and lower thermal conductivity of shallow quaternary deposits.

Geologically-accurate geothermal potential maps were successfully produced by

applying the steps outlined in the methodology. In areas where the dominating bedrock

lithology has lower thermal conductivity, there is generally lower geothermal potential in

the area, and vice versa. For instance, in the area overlying paragneiss bedrock, which has

an average thermal conductivity of 2.7 W/mK, there is lower geothermal potential. While in

area that overlies the tonalites, which has an average thermal conductivity of 3.4 W/mK,

there is higher geothermal potential (Table 2.1).

3.2 Residential Building Heating Load

Based on Kuujjuaq’s weather data and the building parameters described previously,

the annual heating load of a 252 m2 residential building in Kuujjuaq is approximately

71,300 kWh (Appendix F). Figure 3.2 shows the daily heating load profile modelled using

SIMEB. Apart from the main input parameters (Table 2.3), the building heating load is

heavily influenced by the outdoor air temperature data. During warmer summer months,

from June-August, the average outside daily temperature reaches an hourly average of

18.5°C and the total heating load is 39.4 kWh. During winter months, it gets as cold as -

36.3°C and the total heating load is predicted to be as high as 272.1 kWh.

Figure 3.2 Average daily temperature and heating load profile of a typical residential

building in Kuujjuaq.


3.2.1 Building Energy Consumption

The energy consumptions for each heating equipment scenario (Fig. 2.2) were

calculated based on the building heating load, the effectiveness or COP of the heating

equipment and the energy densities (Table 3.1). The efficiency of the diesel furnace was

assumed to be 78%, while the COPs for COMP and ABS were 3.1 and 1.2, respectively. For

Cases 2 and 3, 50% of the heating load was allocated to the secondary system, which is

diesel furnace. For the business-as-usual heating scenario (Case 1), this translates to an

annual energy consumption of 8,174.7 l or 32.4 l/m2 (Table 3.1). This value is comparable

with the reported annual average energy consumption of 3,100 l diesel for a 110.9 m2 house

in Kuujjuaq, which translates to an energy consumption of 28.0 l/m2 [6].

Table 3.1 Energy consumption breakdowns for different heating equipment scenarios.

Heating

Scenario

For diesel

furnace For GSHP

Diesel (l)

Electricity from

solar PV panels,

Es (kWh)

Diesel for

GSHP (l)

Ground

thermal energy,

Eg (kWh)

Electricity from

diesel power

plant (kWh)

1 8,174.7

4,253.1

4,253.1

4,253.1

4,253.1

0 0 0 0

2A 8,054.9 0 24,164.6 3,452.1

2B 11,506.9 0 24,164.6 0

2C 0 0 24,164.6 11,506.9

3 0 2,764.5 5,945.3 0

3.2.2 BHE Drilling Lengths

Based on the average geothermal potential in Kuujjuaq at different BHE lengths, the

annual thermal energy that can be extracted (Eg available) were 58.4 kWh/m for 100 m BHE,

66.3 kWh/m for 200 m BHE and 76.3 kWh/m for 300 m BHE. The drilling lengths (Ldrill)

for each type of heat pump were then calculated according to the required thermal energy

from the ground (Eg). For Case 2, the Eg available from a 300 m BHE was considered due to

the high Eg, while the Eg available from a 100 m BHE was used in Case 3. Based on this, the

drilling lengths required in Cases 2 and 3 were 316.5 m and 101.8 m, respectively.

3.2.3 Solar Panels Quantity

The energy generated by each solar PV panel (Es available) was calculated to be

309.9 kWh/year. The number of solar PV panels required (Ns) for Case 2A is 26 panels and

for Case 2B is 37 panels.

3.3 Life-Cycle Cost Analysis

The NPCs and LCOEs for all heating scenario were calculated to determine the most

viable alternative, if any, to building heating in Kuujjuaq that reduces both costs and CO2

emissions. Other factors that affect the NPC, such as cost of CO2 emission, payback period

and sensitivity analysis are also presented.


The average CO2 emission considered and determined from six heating oil companies

is 0.0902 tCO2/GJ (Appendix E). The cost of emission was then obtained by multiplying

this value with the price of carbon and included in the NPC calculations.

3.3.1 Economic Scenario 1

The results of the 50-years LCCA based on the current condition and values outlined

in the methodology are shown in Table 3.2. In this economic scenario, it is interesting to

note that the two options that emits the least CO2 have the lowest NPCs and LCOEs

(Fig. 3.3). A linear trend between CO2 emissions and NPC could also be observed; the

heating option emitting higher CO2 has higher 50-years total NPC (Fig. 3.3). Despite the

high capital costs incurred in Cases 2A and 2B, the low annual costs combined with the high

annual opportunity benefit make COMP with solar PV panels an economically attractive

building heating solution that also reduces CO2 emissions. Cases 2A and 2B are expected to

have a payback period comparable to the business-as-usual scenario within 11 and 12 years,

respectively, which can be considered fast for such major investment.

1

2A

2B

2C3

0.0

5.0

10.0

15.0

20.0

25.0

30.0

150K 170K 190K 210K 230K 250K 270K 290K

CO

2E

mis

sions

(t)

NPC ($)

Figure 3.3 NPC vs. CO2 emissions of different building heating scenarios.


Table 3.2 Summary of costs, CO2 emissions, NPCs and LCOEs of 50-years LCC for business-as-usual and alternative heating scenarios.

Heating

Scenario

Capital

Cost ($)

Annual Costs ($) Periodic

Cost ($)

Parts

Replaced

Annual

Opportunity

Benefit ($)

CO2

Emissions

(t)

Annual Cost

of Emission

($)

Total

NPC ($)

LCOE

($/kWh) Energy Maintenance

1 5,063 16,595 1,059 1,041 Diesel tank

0 28.5 554 276,875 0.21 4,022 Diesel furnace

2A 158,324 8,634 849

4,354 Heat pump

9,819 18.0 350 203,153 0.13 1,041 Diesel tank

4,022 Diesel furnace

39,723 Solar PV panel

2B 175,348 8,634 849

4,354 Heat pump

9,819 14.8 288 179,433 0.15 1,041 Diesel tank


56,747 Solar PV panel

2C 118,601 18,530 849

4,354 Heat pump

9,819 25.5 495 258,500 0.21 1,041 Diesel tank


3 44,484 14,246 849

4,335 Heat pump

2,897 24.5 475 231,459 0.19 1,041 Diesel tank



Sensitivity analyses of key inputs revealed that the most sensitive cost item for all

heating equipment were either the energy cost or capital cost (Fig. 3.4). Variations on the

periodic costs and maintenance cost appear to have little effect on the NPC of the LCC of

the heating options. The energy cost is more sensitive than the capital cost for heating

options that rely heavily on diesel fuel (Cases 2C and 3). For these heating options, the high

energy cost, which is heavily influenced by the transportation cost to the north, affects the

NPC more than the capital cost, which includes the cost of the heating equipment and BHE

drilling in the case of GSHP heating.

K

100K

200K

300K

400K

-30% -20% -10% 0% 10% 20% 30%

NP

C (

$)

Variation in Parameter

Case 1: Sensitivity Analysis

K

50K

100K

150K

200K

250K

-30% -20% -10% 0% 10% 20% 30%

NP

C (

$)


Case 2A: Sensitivity Analysis


Figure 3.4 Sensitivity analyses of key parameters in all building heating options

based on Economic Scenario 1.

K

50K

100K

150K

200K

250K

-30% -20% -10% 0% 10% 20% 30%

NP

C (

$)


Case 2B: Sensitivity Analysis

K

100K

200K

300K

400K

-30% -20% -10% 0% 10% 20% 30%

NP

C (

$)


Case 2C: Sensitivity Analysis

K

100K

200K

300K

400K

-30% -20% -10% 0% 10% 20% 30%

NP

C (

$)


Case 3: Sensitivity Analysis


Figure 3.5 Range of accumulated NPCs based on worst to best BHE drilling costs

compared to that of business-as-usual heating scenario.


As for many development projects, there is typically an initial need to develop

industrial policies that promote supporting businesses in the area. The purpose of this

economic scenario is to reveal whether there is a need to support the northern drilling

industry to make the cost of drilling more economical. LCCs for the heating scenarios were

calculated based on best ($50/m), moderate ($175/m) and worst ($300/m) drilling costs

(Fig. 3.5). Figure 3 shows that regardless of the drilling costs, switching to any type of GSHP

is always more economically attractive and will payback within 50 years in respect to the

business-as-usual scenario. Cases 2A and 2B present the largest savings from the business-

as-usual scenario. However, with the best drilling cost at $50/m, the paybacks for these two

cases are expected to significantly decrease to within 3 and 4 years, respectively. Thus, a

policy to support the growth of drilling industry to lower drilling cost in the north could be

beneficial, especially when considering a COMP as an alternative heating system in

Kuujjuaq.

Compression heat pump: 70% electricity from solar panels

Compression heat pump: 100% electricity from solar panels

Absorption heat pump: runs of diesel

Compression heat pump: 100% from diesel power plant



This economic scenario analyses an incentive scheme in which the government covers

half of the current drilling cost ($300/m), as well as the GSHP and/or solar PV panels costs,

while subsidy on electricity and diesel remain. The purpose of this analysis is to calculate a

scenario in which the home-owner still has the option to use a diesel furnace, although a

GSHP heating system has been introduced. Additionally, while the total NPCs remain the

same for all cases as in ‘Economic Scenario 1’, this scenario helps in analysing the

breakdown of burden on home-owner and government.

In this scenario, Case 3 presents savings for both home-owner and government

compared to business-as-usual heating (Table 3.3). However, when analysing the total NPC,

Cases 2A and 2B present optimum options that cut costs from the business-as-usual Case 1

and distribute these costs most evenly between home-owner and government.


Scenario 3.

Heating

Scenario

Total NPC for

Home-Owner ($)

Total NPC

Government ($)

1 220,022 56,854

2A 110,123 93,029

2B 114,499 64,934

2C 95,205 163,294

3 179,924 51,534


Economic scenario 4 was modified from ‘Economic Scenario 3’, the only difference

being that the subsidy on electricity and diesel were eliminated, such that home-owners will

pay the unsubsidised cost of diesel fuel and/or electricity. The purpose of this analysis is to

analyse the role of subsidy in the distribution of the costs of building heating between home-

owner and government.

When a heating option consumes more fossil fuel, the distribution of costs between

home-owner and government becomes more uneven, with a higher proportion of the burden

falling on the hands of the home-owner (Table 3.4). Therefore, as long as the costs of energy

remain high in Kuujjuaq, any heating option that consume more fossil fuel and emit more

CO2 will result in higher annual costs and hence, become less economically attractive.


Scenario 4.

Heating Scenario Total NPC for

Home-Owner ($)

Total NPC for

Government ($)

1 268,646 8,232


2A 149,941 53,211

2B 116,471 62,962

2C 228,039 30,460

3 214,783 16,676


This economic scenario analyses a government incentive scheme after the northern

drilling industry has been developed such that the cost of drilling is the same as that in the

south at $50/m. In this scenario, the government still covers 50% of GSHP and/or solar PV

panels costs, although the home-owner is fully responsible for the cost of drilling. Subsidies

on electricity and diesel are also eliminated to encourage the switch to a cleaner heating

alternative. Previous sensitivity analyses (Fig. 3.4) have shown energy and capital to be the

most sensitive cost items in Cases 2A and 2B. Thus, the purpose of this economic scenario

is to analyse the effect of eliminating subsidy on diesel and electricity and lower BHE

drilling cost in the north and shed light on the potential of GSHP as an optimum building

heating solution in northern remote communities that can reduce both costs and CO2

emissions.

Although Case 2C brings profit to the government, the cost to the home-owner is

relatively high (Table 3.5). Business-as-usual and Case 3 are not the most viable due to the

high costs incurred to the home-owners and the high LCOEs. Again, Cases 2A and 2B have

the lowest LCOEs and total NPCs and hence, are more economically attractive compared to

the business-as-usual and other heating options (Appendix G). Additionally, this economic

scenario results in lower total NPCs for Cases 2 and 3 as compared to Economic Scenario 1,

which analyses NPCs based on the current conditions. This means that the development of

northern drilling industry and such government incentive are predicted to be efficient in

reducing total LCCs for any GSHP systems listed in this paper.

Table 3.5 Total 50 years NPCs for home-owner and government, and total LCOE

based on Economic Scenario 5.

Heating Scenario Total NPC for

Home-Owner ($)

Total NPC for

Government ($)

LCOE

($/kWh)

1 268,646 8,232 0.21

2A 117,550 4,625 0.10

2B 84,080 14,375 0.08

2C 195,648 -18,126 0.15

3 204,369 1,055 0.17

Both Cases 2A and 2B utilises the COMP as the main heating equipment, the only

difference being the proportion of electricity that comes from solar PV panels. The optimal


proportion of electricity coming from solar PV panels is illustrated in Figure 3.6. Increasing

the proportion of electricity coming from solar PV panels reduces the cost of heating for the

home-owner more than it increases for the government. Additionally, when all electricity

required for the COMP comes from solar PV panels, the total NPC and CO2 emissions

become lower than other combinations. Below 56%, the government would have a negative

total NPC in 50 years of its lifetime, which means positive cashflow or revenue through

selling surplus diesel in the commodity market, but higher total NPC for the home-owner.

At 56% the government breaks even. However, for COMP to be an economically more

attractive option for the government than the business-as-usual heating scenario, the

proportion of electricity coming from solar PV panels needs to be below 80% (Fig. 3.6).

Figure 3.6 Optimisation to determine the best proportion (%) of electricity coming

from solar panels to run a COMP for building heating in Kuujjuaq.

0

5

10

15

20

25

30

-50K

K

50K

100K

150K

200K

250K

300K

0 10 20 30 40 50 60 70 80 90 100

CO

2 E

mis

sions

(t)

Tota

l N

PC

($)

% Electricity from Solar PV Panels

COMP, Home-Owner COMP, Government

Diesel Furnace, Home-Owner Diesel Furnace, Government

CO2 Emissions


Chapter 4

4Discussion

One of the limitations of this study arose from the assumption that the COPs of the

GSHP systems considered remain constant throughout all seasons and years. This COP

assumption is conservative since it is based on the minimum water temperature leaving the

BHE for the 50-years period considered in the G.POT calculation. It was also assumed that

the GSHPs operate only in heating mode and there is no cooling requirement in the region

[15]. Thus, further study on the actual performances of COMP and ABS operating in the

area would be required to more accurately predict the project’s viability. Additionally, the

average annual solar PV potential in Kuujjuaq was used in calculating the number of solar

panels required in the COMP heating scenario as a simplification, as a detailed solar analysis

was not within the scope of this study. Future study could therefore focus on the economics

of using battery storage versus sizing the solar panels according to the monthly solar PV

potential in Kuujjuaq. Moreover, although the LCCA is not a useful tool for budget

allocation, it is a straightforward way to compare the profitability or for capital budgeting.

Social acceptability and level of implementation effort, such as those measured in Yan et

al.’s study was not considered and could be a subject of future research [6].

In 2012, Majorowicz and Grasby [49] conducted an initial assessment of the potential

of geothermal energy development in northern Canadian communities, showing that there

is enough energy to heat northern communities at competitive cost in Mackenzie Corridor

areas and Yukon for 3-5 km and 6 km wells depths. Specifically, their study concerns high-

temperature or deep geothermal resources, which is defined as heat obtained from

geothermal fluid with temperatures above 150°C. Deep geothermal resources are commonly

utilized to drive turbines in geothermal power plants to generate electricity. Low-

temperature or shallow geothermal resources on the other hand, deals with geothermal fluid

temperatures of 150°C and less. Shallow geothermal resources are typically used in direct-

use applications, such as for heating, greenhouses and fish-drying facilities. Thus, this study

attempted to further Majorowicz and Grasby’s work [49] by investigating the shallow

geothermal potential of Kuujjuaq, another northern Canadian community in Nunavik.

As far as the author’s knowledge and apart from this study, only Yan et al. [6] has

investigated alternative heating systems for Kuujjuaq. Their study disqualified geothermal

technology due to climate limitations, electricity production and economical issues. Instead,

they analysed waste gasification and combustions of fuel oil, wood pellets and natural gas,

with wood pellets ranking first in their analysis. However, since there is no local supply for

wood pellets, they need to be imported to the area [6]. In addition to this present study

showing a relatively high shallow geothermal potential in the area, as well as the economic

viability of the GSHP system, the Cold Climate Housing Research Centre [10] installed a

pilot GSHP system with horizontal BHE in 2013 in Fairbanks, Alaska, which also has a

similar, subarctic climate as Kuujjuaq and demonstrated that GSHP operation is feasible in

such climate. Therefore, GSHP as a heating alternative should not be quickly dismissed.

Consistent with the results from Pike and Whitney’s [9] study on the costs and

performance of seven GSHPs currently installed in Alaska, heat pumps are viable heating

28 CHAPTER 4: DISCUSSION

technology for colder climates. The economic benefit of GSHP system, however, depends

heavily on the costs of energy (fuel and electricity) in the area. Similar conclusion was

achieved in this study; sensitivity analyses for various GSHP heating options in Kuujjuaq

demonstrated that the costs of energy form one of the most critical factor that influences the

system’s economic viability. In Alaska, although the costs of fuel oil and natural gas are

relatively high, the cost of electricity is low [9]. For Kuujjuaq, a combination of solutions

was considered and COMP with electricity from solar PV panels was found to be the most

economically attractive option, as the costs of both fuel and electricity are high in this region.

One of the challenges of operating GSHPs in cold regions pertain to the soil thermal

imbalances. You et al.’s study [50] proposed solutions addressing each of these challenges

through modifications on BHE, system design and operation design. Additionally, although

previous studies have proven successful utilisation of GSHP technology in various cold

regions worldwide, none have studied its application and economic feasibility in remote

subarctic region [7-14]. This study attempted to address this gap. Furthermore, the G.POT

method [15], which considered a sustainable resource extraction was successfully applied to

estimate the shallow geothermal potential in Kuujjuaq, enabling a long-term prediction of

GSHP economic performance in such climate and community. Finally, this study proposed

a viable alternative to building heating in Kuujjuaq of using COMP with electricity derived

from solar PV panels, thereby providing a solution to help this community achieve energy

security and independence using a locally-generated and sustainable resource.


Chapter 5

Conclusion

Presently, Nunavik‘s remote northern communities are heavily dependent on fossil

fuel to meet their heating demands, which incurs high costs, energy dependence and net CO2

emissions. This study focused on the economic attractiveness and emissions reduction

potential of ground-source heat pump (GSHP) as an alternative heating source. The heating

options analysed in this study were:

1. Case 1: Business-as-usual using diesel furnace

2. Case 2A: Compression GSHP with 70% of electricity derived from solar photovoltaic

(PV) panels and 30% from diesel power plant

3. Case 2B: Compression GSHP with 100% of electricity derived from solar PV panels

4. Case 2C: Compression GSHP with 100% of electricity derived from diesel power

plant

5. Case 3: Absorption GSHP customised to run on diesel

Maps of the shallow geothermal potential of Kuujjuaq were created based on

laboratory measurements of the subsurface thermal conductivity samples, field measurement

of the subsurface temperature and using a GIS-based workflow to estimate the maximum

amount of energy that can be extracted with a GSHP system operating in cold temperatures.

The resulting maps show that the average geothermal potential in Kuujjuaq is relatively high

for such cold region, ranging between 5.8 MWh/year and 22.9 MWh/year, and that it

increases more than linearly with borehole depths. These maps provide a useful tool for

planners to identify the most suitable location for future GSHP installations and serve as a

crucial first-step towards calculating the total drilling costs for the borehole heat exchanger

(BHE). The annual heating load of a typical residential building in Kuujjuaq was then

modeled using local weather data to determine the energy consumptions for each heating

options considered.

50-years life-cycle cost analysis (LCCA) based on current costs and conditions

(Economic Scenario 1) revealed that all GSHP heating options are economically more

attractive compared to the diesel furnace heating currently being used. However,

compression GSHP with electricity derived from solar PV panels (Cases 2A and 2B)

presents the most environmentally friendly and economically attractive heating option. Such

outcome was partially driven by the fact that surplus diesel obtained from switching to

GSHP system can now be sold to the commodity market, serving as a government revenue.

Cases 2A and 2B save up to $97,442 in total net present cost (NPC) and have a maximum

payback within 12 years –depending on the proportion of electricity derived from solar PV

panels– when compared to the business-as-usual heating scenario. A positive linear trend

between CO2 emissions and NPCs of the heating options further indicates the long-term

viability of GSHP technology in reducing emissions.

However, consistent with the results of previous studies, energy and capital costs form

the most sensitive cost items for all heating options, implying that in addition to the high

30 CHAPTER 5: CONCLUSION

capital costs incurred from BHE drilling in the case of GSHP heating and the cost of

equipment itself, the economic feasibility of any heating system in Kuujjuaq depends

heavily on the source and cost of energy used in the area [9,10,11]. Varying the drilling cost

from the current price in Kuujjuaq at $300/m to the current price in the south at $50/m

revealed a significant reduction in total NPC and payback periods for Cases 2A and 2B

heating options. This shows that without the appropriate government policy to supports the

drilling industry in the north or the government incentive to alleviate the cost burden from

the hands of the home-owner, it would be challenging to initiate such project.

The best economic outcome was thus obtained in Economic Scenario 5 when the cost

of BHE drilling was reduced to $50/m and government provides incentive by covering 50%

of GSHP and/or solar PV panels costs, but subsidies on energy are eliminated and the home-

owner is fully responsible for the drilling cost. In this scenario, Cases 2A and 2B present the

lowest levelised cost of energy (LCOE) and total NPC for both government and home-owner

alike, compared to all other economic scenarios. This scenario results in lower LCOEs at

$0.10/kWh and $0.08/kWh for Cases 2A and 2B, as compared to those of economic

scenario 1 at $0.13/kWh and $0.15/kWh. In comparison, the LCOE for business-as-usual

diesel furnace heating for all economic scenario is $0.21/kWh. In terms of total NPCs for

both government and home-owner, Cases 2A and 2B save up to $178,423 compared to diesel

furnace heating.

Finally, higher proportion of electricity derived from solar PV panels will result in

lower total NPCs and LCOEs for compression GSHP heating option. However, 80% was

determined to be the maximum cut-off for this technology as an economically more

attractive heating solution for the government compared to the diesel furnace. The optimum

proportion depends on factors such as governmental budget, availability of grants and

capital. In any case, compression GSHP with a proportion of electricity derived from solar

PV panels remains the most economically attractive option that offsets CO2 emissions

compared to a diesel furnace heating based on the conditions listed in this study.

Chapter 5

31

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34

[41] Bloomquist, R. G. (2001). The Economics of Geothermal Heat Pump Systems for

Commercial and Institutional Buildings. In International Summer School on Direct

Application of Geothermal Energy

[42] NEAS (2018). Nunavik Sealift Rates. Retrieved from https://neas.ca/rates/

[43] ClimateMaster. (2016). Tranquility Compact Belt Drive (TC) Series Submittal Data

Models TCH072-120 TCV-072-300 60Hz-HFC410A. Retrieved from

https://www.climatemaster.com/download/18.274be999165850ccd5b5c48/1535543869128/l

c517-climatemaster-commercial-tranquility-compact-belt-drive-tchv-series-water-source-

heat-pump-submittal-set.pdf

[44] Robur. (2010). Submittal Data GAHP Line W LB Series. Retrieved from

https://www.roburcorp.com/heat_pumps/water_to_water_gas_absorption_heat_pump_gahp_

w_lb

[45] Oil-Climate Index (2015). Viewing Total Emissions. Retrieved from

https://oci.carnegieendowment.org/#total-

emissions?ratioSelect=perMJ&regionSelect=North%20America&oiltypeSelect=Light

[46] Tasker, J. P. (2016, October 03). Retrieved from

https://www.cbc.ca/news/politics/provinces-with-carbon-pricing-1.3789174

[47] Business Insider. (2019). RBOB Gasoline. Retrieved from

https://markets.businessinsider.com/commodities/rbob-gasoline

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2017 (Rep.). Retrieved https://www.grantthornton.ie/globalassets/1.-member-

firms/ireland/insights/publications/grant-thornton---renewable-energy-discount-rate-survey-

2017.pdf

[49] Majorowicz, J., & Grasby, S. E. (2014). Geothermal energy for northern Canada: Is it

economical? Natural Resources Research, 23(1), 159-173. doi:10.1007/s11053-013-9199-3

[50] You, T., Wu, W., Shi, W., Wang, B., & Li, X. (2016). An overview of the problems and

solutions of soil thermal imbalance of ground-coupled heat pumps in cold regions. Applied

Energy, 177, 515-536. doi:10.1016/j.apenergy.2016.05.115

[51] OpenEI. (2017). Building Characteristics for Residential Hourly Load Data (Rep.).

Retrieved September 26, 2018, from National Renewable Energy Laboratory website:

https://openei.org/doe-opendata/dataset/eadfbd10-67a2-4f64-a394-

3176c7b686c1/resource/cd6704ba-3f53-4632-8d08-

c9597842fde3/download/buildingcharacteristicsforresidentialhourlyloaddata.pdf

[52] BC Hydro. (n.d.). Reference Guide for Lighting Calculator Version 2.7 (Rep.). Retrieved

September 27, 2018, from BC Hydro website:

https://www.bchydro.com/content/dam/hydro/medialib/internet/documents/psbusiness/pdf/ps

_business_-_hpb-eeld.pdf

[53] Goldner, F. S., & Price, D. C. (1994). Domestic Hot Water Loads, System Sizing and

Selection for Multifamily Buildings. American Council for an Energy Efficient Economy

https://neas.ca/rates/

https://www.climatemaster.com/download/18.274be999165850ccd5b5c48/1535543869128/lc517-climatemaster-commercial-tranquility-compact-belt-drive-tchv-series-water-source-heat-pump-submittal-set.pdf



https://www.roburcorp.com/heat_pumps/water_to_water_gas_absorption_heat_pump_gahp_w_lb

https://www.roburcorp.com/heat_pumps/water_to_water_gas_absorption_heat_pump_gahp_w_lb

https://oci.carnegieendowment.org/#total-emissions?ratioSelect=perMJ&regionSelect=North%20America&oiltypeSelect=Light

https://oci.carnegieendowment.org/#total-emissions?ratioSelect=perMJ&regionSelect=North%20America&oiltypeSelect=Light

https://www.cbc.ca/news/politics/provinces-with-carbon-pricing-1.3789174

https://markets.businessinsider.com/commodities/rbob-gasoline

https://www.grantthornton.ie/globalassets/1.-member-firms/ireland/insights/publications/grant-thornton---renewable-energy-discount-rate-survey-2017.pdf



https://openei.org/doe-opendata/dataset/eadfbd10-67a2-4f64-a394-3176c7b686c1/resource/cd6704ba-3f53-4632-8d08-c9597842fde3/download/buildingcharacteristicsforresidentialhourlyloaddata.pdf



https://www.bchydro.com/content/dam/hydro/medialib/internet/documents/psbusiness/pdf/ps_business_-_hpb-eeld.pdf

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35

Summer Study Proceedings,2. Retrieved September 27, 2018, from

https://aceee.org/files/proceedings/1994/data/papers/SS94_Panel2_Paper12.pdf

[54] ASHRAE. (2013). ASHRAE Standard: Ventilation for Acceptable Indoor Air Quality

(Rep.). Retrieved September 27, 2018, from American Society of Heating, Refrigerating and

Air-Conditioning Engineers, Inc website: http://arco-hvac.ir/wp-

content/uploads/2016/04/ASHRAE-62_1-2010.pdf

[55] RDH Building Engineering Ltd. (2013). Air Leakage Control in Multi-Unit Residential

Buildings: Development of Testing and Measurement Strategies to Quantify Air Leakage in

MURBS (Rep. No. 5314). Retrieved September 27, 2018, from RDH Building Engineering

Ltd website: https://rdh.com/wp-content/uploads/2014/04/Air-Leakage-Control-in-Multi-

Unit-Residential-Buildings.pdf

[56] Déry, S., & Zoungrana, H. (2009, December). The Housing Situation in Nunavik: A

public health priority (Rep.). Retrieved September 27, 2018, from Nunavik Regional Board of

Health and Social Services website:

http://www.krg.ca/images/stories/docs/Parnasimautik/Annexes/ENG/Annex 4 The housing

situation in Nunavik a public health priority eng.pdf

https://aceee.org/files/proceedings/1994/data/papers/SS94_Panel2_Paper12.pdf

http://arco-hvac.ir/wp-content/uploads/2016/04/ASHRAE-62_1-2010.pdf

http://arco-hvac.ir/wp-content/uploads/2016/04/ASHRAE-62_1-2010.pdf

https://rdh.com/wp-content/uploads/2014/04/Air-Leakage-Control-in-Multi-Unit-Residential-Buildings.pdf

https://rdh.com/wp-content/uploads/2014/04/Air-Leakage-Control-in-Multi-Unit-Residential-Buildings.pdf

36

APPENDIX A

Detailed Steps for Shallow Geothermal

Potential Data Processing and Mapping

A.1 Depths of Unconsolidated Sediments

The outcrops of bedrock from the unconsolidated sediments map was first extracted

to create a new polygon layer that showed only the outcrops. Using the unconsolidated

sediments shapefile, the “extract nodes” vector geometry tool was used to draw points along

the outline of the outcrops. These points were assigned depth values of zero, referring to the

absence of unconsolidated sediments in areas where there are outcrops. The resulting point

vector layer was then combined with the data of depths of unconsolidated sediments

obtained from the field study to produce a new layer (Fig. A1).

Figure A1. Bedrock limits (1), point layer of bedrock depths (2) and point layer of the

combined depths of unconsolidated sediments and the extracted bedrock depths (3).

The combined depths data, along with their respective coordinates were then extracted

from the attribute table, saved in Microsoft Excel (2016) .CSV format and imported to create

a grid file using the Surfer® 9 (Surfer) software [22]. Surfer contains an internal algorithm

that takes the irregularly spaced XYZ data and uses it to create an interpolated, regularly

37

spaced grid file. Each point will have its respective XY location and has a Z value or in this

case, depth value associated with it.

The Inverse Distance Weighting (IDW) interpolation method with 100 x 100 m grid

spacing was first tested. This interpolation method assumes that nodes that are close by are

more similar than those further apart. Therefore, the nodes closest to the unknown point have

more weight on the point than those further away. As a result, shadows of the data points

can be seen on the contour map and the resulting map does not best represent the real

environment due to the presence of the shadows (Fig. A2). Hence, the IDW interpolation

method was not chosen.

Next, the Triangulation with Linear Interpolation (TIN) method with 100 x 100 m grid

spacing was tested. This method draws lines between data points to create triangles, with no

triangles intersecting each other. Although the resulting map appeared smoother than the

IDW interpolated map, this method was also not chosen as the resulting maps contain jagged

lines that again, do no best represent the real environment. Additionally, this algorithm

produced a maximum outlier value of 1.70 x 1023 m, even after the values were limited to

the study area.

Figure A2. Interpolated depths of unconsolidated sediments in Kuujjuaq with IDW (left)

and TIN (right) methods at 100 x 100 m grid spacing.

The Kriging method was finally chosen to interpolate the depths of unconsolidated

sediments. This method can compensate for clustered data as it gives less weight to the

cluster during interpolation. Additionally, each grid point is calculated based on the known

data points of neighbouring node and is weighted by its distance away from the node.

Therefore, points that are further from the node will also have less weight in the node

estimation. Three grid spacing options, 100 x 100 m, 300 x 300 m and 400 x 400 m were

tested, and the results compared with each other. Visually, the 100 x 100 m spacing contour

map produced the smoothest contour lines, while the 400 x 400 m spacing map resulted in

more jagged lines (Fig. A3). The interpolated results were then filtered to show only values

that fall within the study area and compared to each other (Table A1).

38

Figure A3. Surfer maps with contour lines of unconsolidated sediments depths

interpolated with Kriging method using three grid spacing options.

Table A1. Comparison of depth interpolation results with Kriging method using three

different grid spacings.

Comparison 100 x 100 m 300 x 300 m 400 x 400 m

Lowest interpolated value -4.00 -1.80 -3.22

# Negative data points 930 86 40

# Total data points 4,995 536 309

Negative values (%) 18.6 16.0 12.9

The 300 x 300 m grid spacing interpolation method produced the least negative outlier

(Table A1). Although it still has a larger percentage of negative values than the 400 x 400

m grid spacing interpolation, the resulting contour map using the 300 x 300 m grid spacing

has smoother contour lines. Therefore, the Kriging method with 300 x 300 m grid spacing

was selected. The Kriging results were then imported back as a point layer in QGIS 2.18.21

(QGIS) (Fig. A4) [21].

Figure A4. QGIS point layer of quaternary deposits depth data interpolated with Kriging

and 300 x 300 m grid spacing in Surfer.

A.2 Weighted Thermal Conductivity and Heat Capacity

The geological and unconsolidated sediments map layers were modified to include the

thermal conductivity and heat capacity data from Table 2.1. The point layer as shown in

39

Figure A4 was then duplicated. The “Point Sampling Tool” plugin in QGIS was used to

apply the thermal conductivity and heat capacity values from the bedrock geology and

unconsolidated sediments maps to the corresponding points in the duplicated layer. Thus,

four new point layers, such as in Figure A4 were generated for: 1) Unconsolidated sediments

thermal conductivity, 2) unconsolidated sediments heat capacity, 3) bedrock geology

thermal conductivity, 4) and bedrock geology heat capacity.

The ground thermal properties, along with their respective coordinates value were

imported from the attribute table and viewed in an Excel document. The weighted thermal

conductivity and heat capacity values were calculated for 100 m, 200 m and 300 m BHE

lengths scenarios based on Equation 2.5.

A.3 G.POT Calculations and Mapping

The shallow geothermal potential of Kuujjuaq is calculated for each BHE length

scenario by applying Equation 2.1 in Excel. The calculated G.POT values for each scenario,

along with their respective coordinates where then opened in Surfer as contour maps. The

Kriging interpolation option with 100 x 100 m grid spacing was selected to image the

geothermal potential of the area.

With Kriging interpolation in Surfer, the resulting maps are always extrapolated

beyond the study area. To limit the results to the study area, a .BLN file, which records the

boundary coordinates of the study area was created (Fig. A5). The “Blank” function in Surfer

is then used to crop the maps according to the boundary recorded in the .BLN file. The

contours property and colours were then adjusted for visualisation purposes.

Figure A5. A sample of the .BLN file used to create the study area limits (left) and the

resulting limits viewed in Surfer used to clip the results to show only the study area (left).

Kuujjuaq

Koksoak River

40

APPENDIX B

5SIMEB Calibration

B.1 Calibration

To calibrate the parameter input for SIMEB, an Excel dataset containing hourly energy

load profile for a typical residential building in Anchorage, Alaska, US was downloaded

from the Office of Energy Efficiency and Renewable Energy (EERE) website [51]. This data

represents the current, annual energy usage for a typical house in Anchorage, in which SH

and DHW requirements are met with natural gas [51]. According to the “2014 Building

America House Simulation Protocol”, the efficiency of natural gas heating equipment is

78%. To obtain the heating load for the building and for future comparison purposes, the

data was modified to assume building heating with electric equipment, which has an

efficiency of 100% (Table B1).


obtained from EERE [3] website and modified to assume building heating with electric

equipment.

Month Interior

Lights

Outdoor

Lights Heating Equipment

HVAC

Fans DHW Total

(in kWh)

January 210 46 9,607 525 206 822 11,415

February 165 36 8,566 476 184 757 10,185

March 155 34 7,857 488 169 827 9,530

April 122 27 5,236 473 112 773 6,743

May 110 24 3,168 477 68 691 4,538

June 99 21 1,487 433 32 662 2,734

July 105 23 892 448 19 641 2,128

August 117 26 0 424 0 535 1,101

September 136 30 1,859 434 40 613 3,112

October 170 37 4,984 476 107 669 6,442

November 195 42 7,358 459 158 697 8,909

December 216 47 8,855 512 190 705 10,525

Total 1,799 392 59,868 5,625 1,286 8,392 77,361

Two documents, 1) “2014 Building America House Simulation Protocols”, which

describes simulation protocols for various building types and 2) List of key parameters used

by the authors to create the Anchorage residential building energy profile were used to obtain

the parameters inputs required to replicate similar energy profile in SIMEB [26,27]. Table

B2 records the parameters inputs to simulate residential building energy load in Anchorage

41

using SIMEB. The building is a 252 m2, one-floor residential house with an approximate

dimension of 21 m x 12 m and wall height of 2.5 m. An unfinished basement with wall

height of 2.4 m, with the same area as the main dwelling. The building occupancy and usage

schedule were then adjusted until similar energy load profiles were achieved.

Table B2. SIMEB parameter inputs to simulate a typical residential building heating

load in Anchorage.

Parameter Values Source Remarks/Calculation

Building azimuth North, 0°

Type of construction Medium

Thermal envelope

Uniform roof insulation 8.63 RSI [27]

Uniform wall insulation 3.87 RSI [27]

Uniform basement wall

insulation

2.4 m (8 ft) RSI

2.64 (R-15) ext.,

Concrete

[27]

Fenestration

Other (U: 1.99

W/m2K; SHGC:

0.44)

[27]

Outdoor lighting capacity 0.1 kW [52]

From [52], canopies and overhangs

= 1.3 W/ft2 = 13.99 W/m2

Assuming 0.3 m overhangs on all

sides and only 2 sides have lights,

surface area of overhangs = (21 ×

0.3) + (12 × 0.3) = 9.9 m2

Hence, lighting on overhang =

13.99 W/m2 × 9.9 m2 = 138.501 W

= 0.1 kW

DHW

Type of water heater Electrical

Typical residential buildings in

Anchorage uses natural gas.

Electricity input chosen to obtain

maximum energy demand

Efficiency 100%

Maximum load 6.21 W/m2 [52,27]

From [52], medium DHW usage in

peak hour = 5 gal/person

From [27], number of occupants =

0.59 × Nbedroom + 0.87 = 0.59 × 3 +

0.87 = 3 occupants

Hence, maximum load = 15

gal/hour = 1.58 x 10-5 m3/s

42

From [27], water heater set off =

125°F = 51.7°C

Specific heat capacity, 𝑄 =𝑚𝐶∆𝑇 = 𝜌𝑉𝐶∆𝑇. Hence, Q = 997

kg/m3 x 1.58 ×10-5 m3/s × 4185.5

J/kg°C × (51.7 – 4) °C = 3145 J/s =

3145 W

Dividing by the total floor area,

maximum DHW load = 3145 W ÷

504 m2 = 6.21 W/m2

Central HVAC system


supply duct system

Preheating None

Heating Electricity

Typical residential buildings in

Anchorage uses natural gas.

Electricity input chosen to obtain

maximum energy demand

Heating coil capacity Autosized Default

SIMEB

Heating equipment

efficiency 100%

Cooling None

Humidification None

Ventilation

Supply flow 154 l/s [54]

From [54], minimum ventilation

rate in residential dwelling unit =

0.06 CFM/ft2. Hence, flow = 0.06

CFM/ft2 x 5425 ft2 = 326 CFM =

154 l/s

Static pressure 0.32 kPa Default

SIMEB

Regulation

Minimum supply

temperature 21.1°C [27]

Maximum supply


Envelope (Basement)

Exterior walls infiltration 0.25 l/s/m2 Default

SIMEB

Default is chosen to reflect the low

basement occupancy.

Type of contact with ground Slab on ground Houses in Kuujjuaq have crawl

space foundation

Envelope (Dwelling unit)

43

Exterior walls infiltration 2.92 l/s/m2 [55]

Anchorage is in climate zone 7.

From source, residential buildings in

climate zone 7 ACH50 = 2.75

Volume of house = 12 m x 21 m x

2.5 m = 630 m3 = 22248.2 ft3

ACH = 60 CFM ÷ Volume. Hence,

CFM = 1019.71 = 481.25 l/s

Surface area of exterior walls = (2 x

12 m x 2.5 m) + (2 x 21 m x 2.5 m)

= 165 m2. Hence, specific

infiltration = 481.25 l/s ÷ 165 m2 =

2.92 l/s/m2

Type of contact with ground Basement

Lighting and plug loads (Basement)

Lighting density 2.15 W/m2 [52]

Plug loads 0.00 W/m2 Unfinished basement

Lighting and plug loads (Dwelling unit)


Plug loads 4.95 W/m2 Default

SIMEB

Processes (Basement)

Power 0.0 kW Unfinished basement

Energy source -

Sensible heat -

Latent heat -

Processes (Dwelling unit)

Power 0.4 kW [27]

Assuming the house is equipped

with refrigerator, clothes washer

with 3.2 ft3 drum, electric cooking

range and miscellaneous equipment,

and there are no dryer and

dishwasher

From [27], electricity usage for:

Refrigerator = 434 kWh/year

Cooking range = 499 kWh/year

Clothes washer = 77.5 kWh/year

Miscellaneous = 2590.65 kWh/year

Total = 3601.245 kwh/year = 0.4

kW

44

Energy source Electrical

Sensible heat 86.20% [27]

From [27], sensible load fraction

for:

Refrigerator = 1.00

Cooking range = 0.40

Clothes washer = 0.80

Miscellaneous = 0.93

Total sensible load = (434 x 1.00) +

(499 x 0.4) + (77.5 x 0.80) +

(2590.65 x 0.93) + 3104.90

kWh/year

% sensible heat = (3104.90

kWh/year ÷ 3601.245 kwh/year) x

100% = 86.2%

Latent heat 5.60% [27]

From [27], latent load fraction for:

Refrigerator = 0.00

Cooking range = 0.30

Clothes washer = 0.00

Miscellaneous = 0.02

Total latent load = 201.51 kWh/year

% latent load = (201.51 kWh/year ÷

3601.245 kwh/year) x 100% = 5.6%

HVAC (Basement)

Central HVAC None

Perimeter Heating Electric baseboard

HVAC (Dwelling unit)

Central HVAC Yes

Perimeter heating Electric baseboard

Occupation

Occupation density 84.01 m2/occupant

As determined in previous equation,

number of occupants = 3 people.

Hence, occupation density = 252 m2

÷ 3 occupants = 84 m2/occupant

Sensible heat 64.5 W/occupant [27]

Latent heat 48.1 W/occupant [27]

Outside air (basement) 0.300 l/s/m2 Default

SIMEB

Per area unit default is chosen to

reflect the low basement occupancy

Outside air (dwelling unit) 2.360 l/s/occupant [54]

45

B.2 DHW Usage Schedule

46

Figure B1. DHW usage schedule for Monday-Friday (top), Saturday (middle) and

Sunday (bottom).

B.3 Occupancy Schedule

47

Figure B2. Building occupancy schedule for Monday-Friday (top) and Saturday-

Sunday (bottom).

B.4 Results of the Calibration

Using these parameters inputs, a total annual energy load of 77,734 kWh was obtained

(compared to the total annual energy load of 77,361 kWh from the original simulation done

by EERE). Table B3 shows the calibration results in SIMEB. Figure B3 shows the

comparison between the calibration results and the data obtained from EERE.


based on the calibration results in SIMEB.

Month Interior

Lights

Outdoor

Lights Heating

Plug loads

and Process Fans Pumps DHW Total

(in kWh)

January 190 46 8,553 545 87 37 719 10,177

February 172 36 7,887 492 78 34 648 9,348

March 192 37 8,623 545 87 37 722 10,242

April 185 30 5,113 528 84 36 690 6,664

May 101 25 2,483 545 87 23 719 3,983

June 99 24 1,113 528 84 5 698 2,550

July 102 25 562 545 87 1 713 2,035

August 101 31 724 545 87 3 719 2,210

September 100 33 1,567 528 84 13 692 3,016

October 190 40 6,231 545 87 37 719 7,850

November 184 42 7,659 528 84 36 696 9,228

December 192 50 8,805 545 87 37 716 10,431

Total 1,808 419 59,320 6,419 1,023 299 8,451 77,734

48

Figure B3. Comparison of SIMEB calibration results with building heating load profile

from EERE.

0

100

200

300

400

500

600

700

800

900

Load

(kW

h)

Month

Domestic Hot Water

SIMEB Simulation EERE Simulation

0

2000

4000

6000

8000

10000

12000

Load

(kW

h)

Month

Space Heating

0

2000

4000

6000

8000

10000

12000

Load

(kW

h)

Month

Total Heating Load

49

APPENDIX C

6Parameter Inputs to Simulate the Heating

Load of Residential Building in Kuujjuaq

Table C1. SIMEB parameter inputs to simulate a typical residential building heating

load in Kuujjuaq.

Parameter Values Source Remarks/Calculation

Building azimuth North, 0°

Type of construction Medium

Thermal Envelope

Uniform roof insulation 9 RSI [28]

Uniform wall insulation 5.11 RSI [28]

Fenestration

Double clear efficient

with argon - low-E (U:

2.16 W/m2K; SHGC:

0.5)

[28]

Outdoor lighting

capacity 0.1 kW [52] Same as calibration

DHW

Type of water heater Electrical [28]

Efficiency 100% [27]

Maximum load 20.69 W/m2 [53,56]

From [27], medium DHW

usage in peak hour = 5

gal/person

From [56], average occupants

of houses in Kuujjuaq = 5

people

Hence, maximum load = 25

gal/hour = 2.63 x 10-5 m3/s

From [53], water heater set

off = 125°F = 51.7°C

50

Specific heat capacity, 𝑄 =𝑚𝐶∆𝑇 = 𝜌𝑉𝐶∆𝑇. Hence, Q =

997 kg/m3 x 2.63 ×10-5 m3/s ×

4185.5 J/kg°C × (51.7 – 4) °C

= 5235 J/s = 5235 W

Dividing by the total floor

area, maximum DHW load =

5235 W ÷ 252 m2 = 20.69

W/m2

Central HVAC system


supply duct system

Preheating None

Heating Electricity SIMEB has no option for oil

heating equipment

Heating coil capacity Autosized Default

SIMEB

Heating equipment

efficiency 100%

Cooling None

Humidification None

Ventilation

Supply flow 77 l/s [53]

From [53], minimum

ventilation rate in residential

dwelling unit = 0.06 CFM/ft2.

Hence, flow = 0.06 CFM/ft2 x

2712.5 ft2 = 163 CFM = 77 l/s

Static pressure 0.32 kPa Default

SIMEB

Regulation

Minimum supply


Maximum supply


Envelope

Exterior walls

infiltration 2.12 l/s/m2 [55]

Kuujjuaq is in climate zone 7.

From source, residential

buildings in climate zone 8

ACH50 = 2

Volume of house = 12 m x 21

m x 2.5 m = 630 m3 =

22248.2 ft3

51

ACH = 60 CFM ÷ Volume

Hence, CFM = 741.61 = 350

l/s

Surface area of exterior walls

= (2 x 12 m x 2.5 m) + (2 x 21

m x 2.5 m) = 165 m2. Hence,

specific infiltration = 350 l/s ÷

165 m2 = 2.12 l/s/m2

Type of contact with

ground No contact Houses in Kuujjuaq have

crawl space foundation

Lighting and plug loads


Plug loads 4.95 W/m2 Default

SIMEB

Processes

Power 0.4 kW [27] Same as calibration

Energy source Electrical

Sensible heat 86.20% [27] Same as calibration

Latent heat 5.60% [27] Same as calibration

HVAC

Central HVAC Yes

Perimeter heating Hydronic

baseboard/radiator

Occupation

Occupation density 50.4 m2/occupant

As determined previously,

number of occupants = 5

people. Hence, occupation

density = 252 m2 ÷ 5

occupants = 50.4 m2/occupant

Sensible heat 64.5 W/occupant [27]

Latent heat 48.1 W/occupant [27]

Outside air 2.360 l/s/occupant [27]

Plant: Hot water loop

Type Boiler

Energy source Oil, with burner

modulation

Capacity Autosized Default

SIMEB

Efficiency 78% [27]

Temperature control Fixed temperature [27]

52

(setpoint: 51.7°C)

Pump flow control None

53

APPENDIX D

7COP Calculations

D.1 COP of Compression Heat Pump (COMP)

The ClimateMaster Model TCH/V120 was selected for the analysis [43]. An EWT vs.

COP table was provided in the product specification (Table D1).

Table D1. Entering water temperatures (EWTs) and their corresponding coefficient of

performances (COPs) [43].

EWT (°F) EWT (°C) COP

20 -6.7 3

30 -1.1 3.3

40 4.4 3.6

50 10.0 4

60 15.6 4.3

70 21.1 4.6

80 26.7 4.9

85 29.4 5

90 32.2 5.1

The values in Table D1 were graphed in Figure D1. Based on the graph, the COP of

this COMP operating in Kuujjuaq is 3.1.

Figure D1. Graph of ClimateMaster Model TCH/V120 COP vs. EWT.

y = 0.0554x + 3.3889

0

1

2

3

4

5

6

-10.0 0.0 10.0 20.0 30.0 40.0

CO

P

EWT (°C)

54

D.2 COP of Absorption Heat Pump (ABS)

The Robur Model GAHP-WLB was selected for the analysis [44]. The effectiveness

of an ABS is measured by its gas utilisation efficiency (GUE), which is the ratio of the

heating supplied to the building to the energy consumed by the compressor. Although an

EWT vs. GUE table was not provided in the product specification, the heating mode

capacity, gas input and chilled water temperature, which is the same as EWT are provided

(Table D2). The GUEs were calculated by dividing the heating mode capacity by the gas

input.

Table D2. Entering water temperatures (EWTs) and their corresponding gas

utilisation efficiencies (GUEs) [44].

Gas input: 95,500 BTU/h (28.0 Kw)

Chilled water inlet

temperature (°F)

Chilled water inlet

temperature (°C)

Heating Mode

Capacity

(BTU/h)

Heating Mode

Capacity (kW) GUE

32 0 119,400 35.0 1.3

41 5 124,600 36.5 1.3

50 10 128,400 37.6 1.3

59 15 131,000 38.4 1.4

68 20 132,400 38.8 1.4

77 25 132,900 38.9 1.4

The values in Table D2 were graphed in Figure D2. Based on the graph, the GUE of

this COMP operating in Kuujjuaq is 1.2.

Figure D2. Graph of Robur Model GAHP-WLB GUE vs. EWT.

y = -0.0002x2 + 0.011x + 1.2549

1.15

1.2

1.25

1.3

1.35

1.4

1.45

-10 -5 0 5 10 15 20 25 30

GU

E

Chilled water temperature (°C)

55

APPENDIX E

8CO2 Emissions

Table E1. CO2 emissions intensity of six heating oil companies in North America.

Company Emissions (in gCO2/MJ) Emissions (in tCO2/GJ)

US Wyoming WC 86 0.086

US Bakken No Flare 87 0.087

Canada Hibernia 88 0.088

US Texas Spraberry 90 0.09

US Texas Eagle Ford

Black Oil Zone 91 0.091

US Bakken Flare 99 0.099

Average 90.2 0.0902

56

APPENDIX F

9Monthly Heating Load of a Typical

Residential Building in Kuujjuaq

0100020003000400050006000700080009000

Lo

ad (

kW

h)

Month

Space Heating

0100020003000400050006000700080009000

10000

Load

(kW

h)

Month

Total Heating Load

57

Figure F1. Typical residential building space heating and domestic hot water load

profiles in Kuujjuaq.

100010201040106010801100112011401160118012001220

Lo

ad (

kW

h)

Month

Domestic Hot Water

58

APPENDIX G

10NPCs Based on Financial Scenario 5

Figure G1. Accumulated NPCs of building heating options for home-owner and

government based on Economic Scenario 5 over 50 years LCC.

K

50K

100K

150K

200K

250K

300K

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

2052

2054

2056

2058

2060

2062

2064

2066

2068

Acc

um

ula

ted

NP

C (

$)

Year

Home-Owners

-25K

-20K

-15K

-10K

-5K

K

5K

10K

15K

20K

25K

30K

20

18

20

20

20

22

20

24

20

26

20

28

20

30

2032

2034

20

36

20

38

20

40

20

42

20

44

20

46

20

48

20

50

20

52

20

54

20

56

20

58

20

60

20

62

20

64

20

66

20

68

Acc

um

ula

ted N

PC

($)

Year

Government

Case 1 Case 2A Case 2B Case 2C Case 3

Eau Terre Environnement Research Centre

Institut national de la recherche scientifique

490 Rue de la Couronne

Québec City, Québec G1K 9A9, Canada

www.inrs.ca

School of Science and Engineering

Reykjavik University

Menntavegur 1

101 Reykjavik, Iceland

Tel. +354 599 6200

www.ru.is

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