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DEPARTMENT OF MECHANICAL AND CONSTRUCTION ENGINEERING
Improving the Reliability of A Micro-Hydropower Project in Rural Areas of
North Thailand By Stand-Alone Hybrid Renewable Energy Systems
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MSc Renewable and Sustainable Energy Technologies
Advisor: Dr Abhishek Tiwary
By
WUTTIPONG APICHONNABUTR
September 2017
Faculty of Engineering and Environment
Improving the Reliability of A Micro-Hydropower Project in Rural Areas of North Thailand By Stand-Alone
Hybrid Renewable Energy Systems
A Dissertation Submitted in Partial
Fulfilment of the Requirement for the Degree of MSc Renewable and Sustainable Energy Technologies
Advisor: Dr Abhishek Tiwary
By
W16027965
September 2017
DECLARATION FORM I declare the following: 1. That the material contained in my dissertation/research paper is the end result
of my own work and that due acknowledgement has been given in the bibliography and references to ALL sources, be they printed, electronic or personal, using the Cite Them Right Harvard referencing system.
2. The word count of my dissertation/research project is….9,991……..words. 3. That unless my dissertation/research project has been confirmed as confidential
(following approval from the Faculty Research Ethics Director), I agree to an entire electronic copy or sections of my dissertation/research project being placed on the Blackboard, if deemed appropriate, to allow future students and staff the opportunity to see examples of past students’ dissertations/research projects.
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Signed:…………….…...................................................................................................
Date: ……………………..
i
Acknowledgements
This dissertation is formed from my work experience at the Department of
Alternative Energy development and Efficiency, Ministry of Energy, Thailand,
which found the problem of the lack of electricity from micro-hydro power projects
in the summer in the rural and remote areas in north Thailand because of the
limitation of water resources. The lack of electricity is one of the main issues that
affect the development of the quality of life of the population.
I would like to thank all the people who participated in this dissertation.
Firstly, I would like to thank my supervisor, Dr Abhishek Tiwary, for guidance,
suggestions, encouragement and all the help. Secondly, I would also like to thank Dr
Allan Osborne, module leader and all the teachers in Msc Renewable and Sustainable
Energy Technologies programme at Northumbria University, for all their guidance
and help throughout this dissertation and Msc programme. Next, I also thank my
master and workplace colleagues who helped and supported all the data for this
dissertation. Most importantly, I would like to thank my family for their support and
encouragement throughout Msc programme.Finally, I hope my dissertation will be
useful and help develop the quality of life in the rural and remote areas in Thailand
and some developing countries.
Wuttipong Apichonnabutr
September 2017
ii
Abstract
The use of renewable energy is rapidly increasing worldwide. Also,
renewable energy in Thailand has been promoted by the government that planned to
use 30% of RE in 2036. This research focuses on Khun Pang micro hydropower
project, Chaing Mai, Thailand. The electricity production of the project cannot meet
the demand in summer. This paper examined ways of improving the reliability of
Khun Pang micro hydropower project by stand-alone HRES. This research
considered the reliability, stability and cost effectiveness of HRES by using the
HOMER software. This paper simulates two scenarios of HRES modelling. The first
scenario is current load demand (50.83 kW peak). The second scenario is total load
demand in the future (78.51 kW peak) that includes current load and future load. The
results from HOMER simulation showed hybrid Hydro/Diesel/Battery would be
suitable for the first scenario where project and energy cost are $92,441 and $0.0705
(diesel 0.75 $/L). Also, the hybrid Hydro/PV/Diesel/Battery would be suitable for the
second scenario where project and energy cost are $198,435 and $0.0966 (diesel 1.25
$/L). Wind energy will not be suitable for this project because wind speed is too low
and can produce electricity less than 1 kW of electricity.
Keywords: Micro-hydropower project, Stand-alone hybrid renewable energy
systems, HRES, HOMER, Renewable energy, Hybrid Hydro/Diesel/Battery, Hybrid
Hydro/PV/Diesel/Battery.
iii
Contents Page DECLARATION FORM.......................................................................................................... i
Acknowledgements ................................................................................................................... ii
Abstract .................................................................................................................................... iii
List of Figures .......................................................................................................................... vi
List of Tables ......................................................................................................................... viii
List of Abbreviations .............................................................................................................. ix
Chapter 1 Introduction and Brief Background .....................................................................1
1.1 Introduction ...................................................................................................................... 1
1.2 Problem Statement ........................................................................................................... 1
1.3 Research Aims and Objectives ......................................................................................... 2
1.4 Research question/hypothesis ........................................................................................... 3
1.5 Research Method .............................................................................................................. 4
1.6 Structure of the Dissertation ............................................................................................. 4
Chapter 2 Literature Review ...................................................................................................5
2.1 Renewable energy in Thailand ......................................................................................... 5
2.2 Hydro power potential in Thailand................................................................................... 8
2.3 Solar energy potential in Thailand ................................................................................... 9
2.4 Wind energy potential in Thailand ................................................................................. 10
2.5 Related Works ................................................................................................................ 11
Chapter 3 Research Methodology and Method ...................................................................13
3.1 Research Design ............................................................................................................. 13
3.1.1 Study Site at Chaing Mai Province ............................................................................. 14
3.1.2 Study Site of the Khun Pang micro hydropower project ............................................. 14
3.2 Research Approach ......................................................................................................... 17
3.2.1 Modelling Survey in Homer software ......................................................................... 19
3.3 Research procedure ........................................................................................................ 19
3.4 Ethical Considerations .................................................................................................... 20
Chapter 4. Data collection and analysis ................................................................................21
4.1 Data collection ................................................................................................................ 21
4.1.1. Site survey and interview data ................................................................................ 21
4.1.2. Questionnaire .......................................................................................................... 22
iv
4.1.3 Sampling size ........................................................................................................... 22
4.1.4 Internet survey ......................................................................................................... 22
4.1.5 Direct data ................................................................................................................ 23
4.1.6 Hydrology data ........................................................................................................ 23
4.1.7 Solar radiation data .................................................................................................. 23
4.1.8 Wind speed data ....................................................................................................... 24
4.1.9 Specification and prices of RE components ............................................................ 24
4.2. Data analysis .................................................................................................................. 24
4.2.1 Primary load ............................................................................................................. 24
4.2.2 Deferrable load analysis ........................................................................................... 27
4.2.3. Hydrology data analysis.......................................................................................... 28
4.2.4 Solar radiation data analysis .................................................................................... 28
4.2.5 Wind speed data analysis ......................................................................................... 29
4.2.6 Renewable energy component analysis ................................................................... 29
4.2.7 Diesel fuel prices analysis........................................................................................ 29
Chapter 5 Results and Discussions ........................................................................................30
5.1 Results ............................................................................................................................ 30
5.1.1 Monthly Solar radiation ........................................................................................... 30
5.1.2 Monthly Wind Speed ............................................................................................... 31
5.1.3 Monthly Steam Flow ............................................................................................... 31
5.1.4 Primary Current Load Demand................................................................................ 32
5.1.5 Primary Total Future Load Demand ........................................................................ 33
5.1.6 Deferrable Load (kWh/day) ..................................................................................... 35
5.1.7 Hybrid Renewable Energy System in Scenario 1 .................................................... 35
5.1.8 Hybrid Renewable Energy System in Scenario 2 .................................................... 39
5.2 Discussions ..................................................................................................................... 43
Chapter 6 Conclusion and Recommendations .....................................................................49
6.1 Conclusion ...................................................................................................................... 49
6.2 Recommendations .......................................................................................................... 50
6.3 Future work .................................................................................................................... 50
References ................................................................................................................................51
Appendices ...............................................................................................................................59
Appendix A: Site Map .......................................................................................................... 59
v
Appendix B: Quesionnaire Form.......................................................................................... 61
Appendix C: Interview Questions ........................................................................................ 63
Appendix D: Thailand’s Calendar ........................................................................................ 64
Appendix E: SPSS Results ................................................................................................... 65
Appendix F: Total Results of HOMER HRES Modelling ................................................... 66
Appendix G: Theory of HRES ............................................................................................. 67
Appendix H: Research Participant Consent Form ............................................................... 72
Appendix I: Ethics Registration and Approval Form ........................................................... 73
List of Figures
Figure 1 Khun Pang Village location (Mountainous Area) ....................................................... 2
Figure 2 Electricity Consumption and Generation of Thailand in 2015 ................................... 6
Figure 3 Peak Load of Electrical Consuming of Thailand in 2011-2015 ................................ 6
Figure 4 CO2 emission of Thailand in 2015............................................................................... 6
Figure 5 The Schematic of Micro Hydropower Systems ........................................................... 8
Figure 6 Map of Hydropower Plant in Thailand ........................................................................ 9
Figure 7 Map of Annual Solar Radiation of Thailand ........................................................... 10
Figure 8 Map of Summer Solar Radiation of Thailand ........................................................... 10
Figure 9 Annual Wind Map of Thailand ................................................................................. 11
Figure 10 Weir of Khun Pang Micro Hydropower .................................................................. 14
Figure 11 Power House of Khun Pang MHP ......................................................................... 15
Figure 12 Hydro Turbine of Khun Pang MHP......................................................................... 15
Figure 13 Transmission Line of Khun Pang MHP .................................................................. 15
Figure 14 Ban Khun Pang Village ........................................................................................... 16
Figure 15 Ban Khun Pang School ............................................................................................ 16
Figure 16 Wat Khun Pang (Temple) ....................................................................................... 17
Figure 17 Questionnaire Distribution at Khun Pang Village .................................................. 18
Figure 18 Face to Face Interview at Khun Pang Village ......................................................... 18
Figure 19 Researcher Explaining for Research Ethics ............................................................. 20
Figure 20 Mae Pang Stream near Khun Pang MHP ................................................................ 23
Figure 21 OPEC Forecasting of Crude Oil Price ..................................................................... 29
vi
Figure 22 Monthly Average Daily Solar Radiation ................................................................. 30
Figure 23 Monthly Average Wind Speed ................................................................................ 31
Figure 24 Monthly Average Stream Flow ............................................................................... 31
Figure 25 Annual Average Daily Load of Current Load Demand Scenario............................ 32
Figure 26 Daily Load of the Whole Year of Current Load Demand Scenario ........................ 32
Figure 27 Monthly Current Load Demand .............................................................................. 33
Figure 28 Annual Average Daily Load of Total Future Load Demand Scenario .................... 33
Figure 29 Daily Load of the Whole Year of Total Future Load Demand Scenario ................. 34
Figure 30 Monthly Total Future Load Demand ...................................................................... 34
Figure 31 Monthly Deferrable Load ........................................................................................ 35
Figure 32 Schematic of Current Demand Load (Scenario 1) ................................................... 35
Figure 33 Current Demand Load and Hydro/Diesel Power Output ......................................... 37
Figure 34 Monthly Average Electrical Production of Hydro/Diesel/Battery
(Scenario 1) .............................................................................................................................. 38
Figure 35 Cash flow of Hydro/Diesel/Battery (Scenario 1)..................................................... 38
Figure 36 Schematic of Total Future Demand Load (Scenario 2) ........................................... 39
Figure 37 Current Demand Load and Hydro/PV/Diesel Power Output .................................. 41
Figure 38 Monthly Average Electrical Production of Hydro/PV/Diesel/Battery
(Scenario 2) .............................................................................................................................. 42
Figure 39 Cash flow of Hydro/PV/Diesel/Battery (Scenario 2) .............................................. 42
Figure 40 Monthly Electrical Demand in Khun Pang Village ................................................. 43
Figure 41 Annual Electrical Demand ....................................................................................... 44
Figure 42 Sensitivity Analysis of Solar Radiation and Diesel Fuel Price of
Hydro/Diesel/Battery (scenario 1) ........................................................................................... 45
Figure 43 Sensitivity Analysis of Wind Speed and Diesel Fuel Price of
Hydro/Diesel/Battery (scenario 1) ........................................................................................... 46
Figure 44 Sensitivity Analysis of SR and DFP of Hydro/PV/Diesel/Battery
(scenario 2) ............................................................................................................................... 46
Figure 45 Sensitivity Analysis of WF and DFP of Hydro/PV/Diesel/Battery
(Scenario 2) .............................................................................................................................. 47
Figure 46 Wind Turbine Power Cuve (HOMER, 2016) .......................................................... 48
Figure 47 Chaing Mai Province (Project Site) ......................................................................... 58
Figure 48 Khun Pang Village, Chaing Mai (Project Site) ....................................................... 59
Figure 49 Quesionnaire Form 1 (Jone and Lomas, 2016) ........................................................ 60
vii
Figure 50 Quesionnaire Form 2 (Jone and Lomas, 2016) ........................................................ 61
Figure 51 Interview Questions (Kooijman van Dijk and Claney, 2010) ................................ 62
Figure 52 Thailand’s Calendar ................................................................................................. 63
Figure 53 Thailand’s Holiday List ........................................................................................... 63
Figure 54 Theory and Formula of HRES 1 .............................................................................. 67
Figure 55 Theory and Formula of HRES 2 .............................................................................. 68
Figure 56 Theory and Formula of HRES 3 .............................................................................. 69
Figure 57 Theory and Formula of HRES 4 .............................................................................. 70
Figure 58 Theory and Formula of HRES 5 .............................................................................. 71
List of Tables
Table 1 Renewable Energy Consumption Target of Thailand in 2036 ........................ 7
Table 2 Electrical Energy Consumption Target of Thailand in 2036 .......................... 7
Table 3 Monthly Current Load Demand Consumption (kW) for Weekdays............ 25
Table 4 Monthly Current Load Demand Consumption (kW) for Weekends ............ 26
Table 5 Monthly Total Future Load Demand Consumption (kW) for Weekdays ..... 26
Table 6 Monthly Total Future Load Demand Consumption (kW) for Weekends ..... 27
Table 7 Deferrable Load ............................................................................................ 28
Table 8 Mae Pang Stream Flow Rate ......................................................................... 28
Table 9 Solar Radiation of Project ............................................................................. 28
Table 10 Wind Speed of Project ................................................................................ 29
Table 11 RE Component Prices and Specification .................................................... 29
Table 12 Overall of Simulation in Diesel Price 0.75 to 1.5 $/L (Scenario 1) ............ 36
Table 13 Overall of Simulation in Diesel Price 0.75 $/L (Scenario 1) ...................... 37
Table 14 Annual Electrical Production of Hydro/Diesel/Battery (Scenario 1).......... 38
Table 15 Emission of Hydro/Diesel/Battery (Scenario 1) ......................................... 39
Table 16 Overall of Simulation in Diesel Price 0.75 to 1.5 $/L (Scenario 2) ............ 40
Table 17 Overall of Simulation in Diesel Price 1.25 $/L (Scenario 2) ...................... 41
Table 18 Annual Electrical Production of Hydro/PV/Diesel/Battery (Scenario 2) ... 42
Table 19 Emission of Hydro/PV/Diesel/Battery (Scenario 2) ................................... 43
viii
Table 20 Average Current Appliance Capacity by Items (IBM SPSS Statistics, 2013)
.................................................................................................................................... 65
Table 21 Average Appliance Capacity of Future Demand by Items (IBM SPSS
Statistics, 2013) .......................................................................................................... 65
Table 22 Total Results of HOMER HRES Modelling 1 ............................................ 66
Table 23 Total Results of HOMER HRES Modelling 2 ............................................ 67
List of Abbreviations
AADL - Annual Average Daily Load
ADL - Average Daily Load
AEP - Annual Electrical Production
AVSR - Annual Average Solar Radiation
CC - Capital Cost
CLD - Current Load Demand
CM - Chaing Mai
CO - Carbon Monoxide
CO2 - Carbon Dioxide
COE - Levelised Cost of Energy
DEDE - Department of Alternative Energy
Development and Efficiency
DFP - Diesel Fuel Prices
DG - Diesel Generator
DINP - Doi Intranon National Park
DL - Daily Load
EGAT - The Electricity Generating Authority
Of Thailand
EPL - Electrical Peak Load
GDP - Gross Domestic Product
HC - Hydropower Cost
HOMER - The Hybrid Optimization of Multiple
Energy Resource
ix
HP - Hydropower
HPP - Hydropower Production
HRES - Hybrid Renewable Energy Systems
KP - Khun Pang
KP-MHP - Khun Pang Micro Hydropower Project
KPV - Khun Pang Village
ktoe - The Kilotonne of Oil Equivalent
MAEP - Monthly Average Electrical Production
MAXL - Maximum Load
MH - Micro Hydropower
MINL - Minimum Load
NASA - The National Aeronautics
and Space Administration
NOx - Nitrogen Dioxide
NPC - Net Present Cost
OPEC - The Organization of the Petroleum
Exporting Countries
PD - Phrao District
PEA - The Provincial Electricity Authority
PL - Peak Load
RC - Replacement Cost
RE - Renewable Energy
REC - Renewable Energy Component
RER - Renewable Energy Resources
RF - Renewable Fraction
SC - Salvage Cost
SF - Stream Flow
SHP - Small Hydropower
SLNP - Sri Lanna National Park
SO2 - Sulfur Dioxide
SPV - Solar Photovoltaics
SR - Solar Radiation
TAEP - Total Annual Electrical Production
TCML - Tea Leaf Cutting Machine
x
TFLD - Total Future Load Demand
TL - Thailand
WE - Wind Energy
WF - Water Flow
WP - Water Pumping
WS - WindSpeed
xi
1
Chapter 1 Introduction and Brief Background
1.1 Introduction
Climate change, increasing fossil fuel and energy demand influence the use
renewable energy (Kolhe et al., 2015; Sharafi and Elmekkawy, 2014). As a result,
the use of renewable energy (RE) is rapidly increasing worldwide because it reduces
emissions (Javadi at el, 2011). RE in Thailand (TL) has been promoted by the
government. The Thai government planned to use 30% of RE in 2036. At present,
the MHP in rural areas of northern TL has some problems in the summer because
stream water is limited and cannot support the demand for electricity in local villages
(Kruangpradit and Tayati, 1996). The hybrid Renewable Energy Systems (HRES)
can combine micro hydropower (MH) with other renewable technologies such as
solar and wind energy (Kruangpradit and Tayati, 1996). Kaldellis (2010) states that
HRES can reduce the cost of diesel production. Moreover, TL has large RE potential
such as solar and biomass energy (Uddin et al., 2010). Therefore, HRES can improve
the reliability of MH and use it to develop the quality of life in rural areas.
This research focuses on the Khun Pang micro hydropower project (KP-
MHP), in the Chaing Mai (CM) province, TL. The electricity production of the
project cannot meet the demand in the summer. This paper examined ways of
improving the reliability of KP-MHP by stand-alone HRES. This research considered
the reliability, stability and cost effectiveness of HRES by using the Hybrid
Optimization of Multiple Energy Resource (HOMER) software that is widely
acknowledged and the professional HRES modelling tool. This research used the
mixed method approach which is the qualitative and quantitative approach for data
collection. This paper collected data from questionnaires and interviews at the site.
This paper simulates two scenarios of HRES modelling. The first scenario is current
load demand (CLD). The second scenario is total load demand in the future that
includes current load and future load. In this paper, total load demand in the future is
called “total future load demand (TFLD)”.
1.2 Problem Statement
An MHP in Khun Pang Village (KPV), TL (Appendix A) which is located in
the rural and mountainous area (Figure 1), has the same problem in the summer. The
2
water volume in the stream is the lowest in the summer. It affects the electricity
demand and the water supply. The electricity production from KP-MHP cannot meet
the high demand. Some villagers cannot use some appliances such as water heaters,
in the peak time of summer. Moreover, the village highly demands electricity in the
hot season because the temperature is high. They need to turn on the electrical fans
day and night. Also, the village plans to store water supply by tank, and needs an
electrical pump. However, the electricity production from MHP cannot support the
electrical pumped load.
The Khun Pang (KP) primary school in the village has used electricity from
MHP. The appliances needed in the school such as computers and rice cooker, are
used for education and meals for young students. Also, the school plans to store
water supply by tank, and it needs an electrical pump. The electricity production
from MHP cannot support all the demands of the school in summer.
The Khun Pang temple also has used electricity from MHP. The temple needs
to use it especially for high demand Buddhism activities such as Buddhism day,
when many Buddhists go to the temple and listen to Dharma in the sermon hall in a
monastery where they need to turn on many electrical fans in summer.
Therefore, this research investigates how to improve the reliability of a KP-
MHP to support all electricity demand in the village, which can develop villagers’
quality of life and quality of education for the young students.
Figure 1 Khun Pang Village Location (Mountainous Area)
1.3 Research Aims and Objectives
This research considered the problem statement and investigated problem-
solving. From the literature review in chapter 2, this research found HRES can be
3
used to make the system stable and reliable (Kolhe at el, 2015; Sharafi and
Elmekkawy, 2014; Nejad et al., 2012 and Jahromi et al., 2013). Moreover, this paper
found that HOMER software is a professional and widespread tool (Sinha and
Chandel, 2014). Therefore, HRES can complement the KP-MHP to produce more
electricity in the summer.
The aim of this research is
“To investigate improving the reliability of a micro-hydropower project in
Khun Pang Village, Chaing Mai, Thailand by stand-alone hybrid renewable
energy systems.”
The following objectives of the research are examined.
- To improve the reliability of stand-alone HRES based on an MH.
- To optimise size and cost of SPV/wind turbine/diesel generator/battery
required to complement MH.
- To optimise the cost-effectiveness of HRES.
- To design HRES for the most effective and efficient use of the potential of
existing systems.
- To design HRES to support the 24-hour demand for electricity in the local
villages.
- To design HRES to balance the use of renewable energy resources (RER) in
the summer, winter and rainy seasons.
- To apply HOMER modelling programs in research.
1.4 Research question/hypothesis
From the literature review, this research found that the key theoretical
concepts are HRES optimisation, balancing method and the relationship between
existing MH and HRES. Also, a lot of research discovered that HOMER is a
reasonable program for HRES. As a result, this research constructs the following
research question:
“ How can the size and cost of HRES be designed and optimised to
complement a micro-hydropower in Khun Pang Village, Chaing Mai, Thailand by
using a professional tool (HOMER)? “
4
1.5 Research Method
This research is engineering research that collects data, analyses and
summarises the results. The approach adopted during the research is mix method
approaches which are quantitative and qualitative research. For the quantitative
research, this study collects numerical data and views the relationship between
research and theory which is deductive and a natural science approach (Bryman,
2015). This approach can collect, analyse data in a fixed format using scientific
methods that are redundant accurate because it uses statistical methods (Bryman,
2015). For the qualitative research, this paper collects observations and findings to
develop a theory which is inductive and interpretive epistemological (Bryman,
2015). This approach can collect data in word, texts, pictures and stories gained
from interviews and ethnography (Bryman, 2015). This approach is suitable to gain
experience and find about the problems from the participants. It actually is
information (Creswell, 2013).
1.6 Structure of the Dissertation
This research comprises six chapters as follows:
Introduction
This chapter presents the project background, problem statement,
research aim and objectives, research question/hypothesis and research
method.
Literature Review
This chapter presents the situation of RE in TL and related works
Research Methodology and Method
This chapter presents the research design and approach which include
social research method survey, CM information, KP-MHP, HOMER software
and ethical consideration.
Data Collection and Analysis
This chapter presents data collection and analysis, including raw data,
calculation and analysis of electrical load, RER and HRES cost.
Results and Discussion
5
This chapter presents results and discussions from HOMER
modelling of the electrical load, HRES capacity and cost and it evaluates and
compares the results.
Conclusions and Recommendation
This chapter presents conclusion, recommendation and future work.
The conclusion will summarise the main objective, research approach and the
results of this project.
References
Appendices
Chapter 2 Literature Review
The journals, books and information on the website are considered in this
literature review that is shown in the following section.
2.1 Renewable energy in Thailand
TL is a tropical and developing country in South East Asia (Khedari, 2002). It
has three seasons, winter, monsoon and summer. The climate is hot and humid
(Wongtes, 2000). Summer starts in March and lasts until May. The monsoon lasts
from June to November. The winter is December to February. The population is
approximately 68 million citizens. Buddhism is the main religion, accounting for
95% (Tourismthailand, 2017). The Gross domestic product (GDP) is approximately
3.2 in 2016 (Bank of Thailand, 2017a).
Thai economy has slightly grown and the energy policy should be concerned
with environmental issues. The fossil fuels which are natural gas and coal are the
main resource of electrical production in Thailand (Shrestha et al., 2007). TL has
large renewable energy potential such as solar and biomass energy (Uddin, 2010)
because it is an agricultural country that has large waste from the crop (Tanatvanit et
al., 2003).
The RE situation of TL in 2015 is shown in Figure 2. Total electricity
generation consumption is 192,189 GWh that used natural gas, coal, RE and large
hydro 67%, 18%, 6% and 2% (EPPO, 2016b). For electrical consumption, the peak
load (PL) in 2014 and 2015 are 26,942 MW (April) and 27,346 MW in June (Figure
6
3). The CO2 emission of power generation, transportation, industry and other are
38%, 27%, 27% and 8% relatively (Figure 4).
Figure 2 Electricity Consumption and Generation in Thailand in 2015
Figure 3 Peak Load of Electrical Consuming of Thailand in 2011-2015
Figure 4 CO2 Emissions in Thailand in 2015
7
The Ministry of Energy of TL plans to promote and increase the use of RE
(EPPO, 2016a). The target of RE consumption in 2036 is 30%; that accounts for
39,388.67 ktoe (Table 1). Also, total electrical energy from RE in 2036 is 20%,
accounting for 19,684 MW (Table 2). Table 1 Renewable Energy Consumption Target of Thailand in 2036
Table 2 Electrical Energy Consumption Target of Thailand in 2036
8
2.2 Hydro power potential in Thailand
Hydropower (HP) is the largest RE technology sector in the world (Wagner
and Mathur, 2011; Dent, 2014). Murni et al. (2013) state that in tropical countries,
the MH output is low in the dry season and it has been affected from global climate
change. The schematics of MH systems is shown in Figure 5 (Elbratan et al., 2015).
Figure 5 The Schematics of Micro Hydropower Systems
TL has potential for HP. The large HP is managed by Electricity Generating
Authority of Thailand (EGAT) that has 14 large HP systems, meaning 2,952.40 MW
(EGAT, 2017). The small hydropower (SHP) systems are managed by the
Department of Alternative Energy Development and Efficiency (DEDE) that has 22
SHP systems, meaning 46.04 MW (DEDE, 2017a). The map of the HP plant in TL is
illustrated in Figure 6.
9
Figure 6 Map of Hydropower Plant in Thailand
2.3 Solar energy potential in Thailand
Solar photovoltaic (SPV) is the solar technology that can convert solar
radiation (SR) into electricity (Häberlin, 2012). It forms from many solar cells on a
10
solar panel. Solar cells absorb the SR from the sun to generate electricity (Wenham,
2012). The SPV has rapidly grown from 30% to 85% since 1997 (Häberlin, 2012).
The annual average solar radiation (AVSR) is 19.2 MJ/m2/day (DEDE, 2017d). The
map of annual SR of TL is shown in Figure 7. The highest SR is in the summer, with
20-24 MJ/m2/day (DEDE, 2017d). The map of summer SR of TL is shown in Figure
8. Total SPV installation in 2015 in TL is approximately 4,700 kW (DEDE, 2016).
Figure 7 Map of Annual Solar Radiation in TL Figure 8 Map of Summer Solar Radiation in TL
2.4 Wind energy potential in Thailand
Wind energy (WE) has been used Europe since the last century, and it is a
rapidly growing resource (Promsen et al., 2012). Wind energy is RER that is clean
energy and a free resource (Chinggulpitak and Wongwises, 2014; Chaichana and
Chaitep, 2010 and Werapun at el, 2014). Chaichana and Chaitep (2010) state that
average wind speed (WS) in CM is 5.7 m/s. The annual average wind speed (AAWS)
at 50-meter height is 6.4 m/s (DEDE, 2017e). The map of annual wind map in TL is
shown in Figure 9. The high WS is in southern TL and ranges from 3.6 to 7 m/s and
the WS in northern TL is 2.8 – 4.4 m/s (DEDE, 2017e). Total wind turbine
installation in 2015 in TL is approximately 3,800 kW (DEDE, 2016).
11
Figure 9 Annual Wind Map of Thailand
2.5 Related Works
Greacen, et al. (2007) investigate RE options on Thai islands in the Andaman
Sea where the Koh Pu and Koh Po islands are located. The project studies how to use
alternative energy for the most effective and efficient use of the potential of existing
systems and how to produce electricity to support the 24-hour demand of the islands.
The researcher collects and evaluates the data such as population, current electricity
demand, future electricity demand forecasts and electricity production from existing
systems, which are SPV and diesel generator (DG). Moreover, this research uses the
HOMER modelling program to optimise the size and cost of HRES because
HOMER can simulate energy balance calculations for each system and also estimate
12
the cost for each system. This research found that SPV/diesel/batteries/inverter is the
best option because the electricity cost ($0.422/kWh) is lower than with the addition
of a wind turbine. Greacen, et al. (2007) state that HRES can reduce the electricity
cost for the islands because the electricity cost of existing systems is high, at about
$0.659/kWh. In contrast, HRES are not only cost-effective but can also increase the
reliability of the systems (Karakoulidis, et al., 2011). Also, the Matlab simulations
program can be used to optimise the HRES (Maleki, et al., 2016) in the same way as
HOMER.
Kruangpradit and Tayati (1996) analyse the Provincial Electricity Authority
(PEA) program in HRES in the remote village of TL. The program considers
designing, implementing and evaluating HRES. The PEA has existing MH/diesel
systems at Kun Pae village in CM. The capacity of MH is 90 kW. However, it can
only support the demand for nine months. In the summer, MH can only produce for 5
hours per day because of the water limitations in the streams. Kruangpradit and
Tayati (1996) state that MH is the main alternative in the rainy season and SPV is a
key selection in summer. This paper proposed using PV/MH/diesel/battery HRES
because SPV can support the demand in the summer.
Phuangpornpitak and Kumar (2007) state that SPV hybrid systems are
suitable in the rural and remote areas in TL because it is a tropical country.
Phuangpornpitak and Kumar (2007) present a summary of ten SPV hybrid projects in
TL. Their total capacity is 285 kW. However, the barrier to SPV development in TL
is the high cost of PV systems. The study shows two PV/wind/diesel hybrid projects
which are in Tarutao and Phu Kradung national parks and use HOMER for cost
analysis of HRES. This paper shows that diesel generator (DG) can increase the
reliability of HRES because renewable resources are not stable, as they depend on
time and season.
Bekele and Tadesse (2012) study the feasibility of small hydro/PV/wind in
six remote areas of Dejen district, Ethiopia. These regions lack electricity but have
the potential for HP because there are close to the streams. This study uses the
electrical load from community demand, hydro, solar and wind data, and simulates
HRES by using the HOMER program. The final result shows energy cost is less than
$0.16/kWh. This project studies HRES because it is cheaper and cleaner than the
13
biomass and oil that are used in the village. This paper used HOMER because it is
the simplest method of HRES optimisation. Conversely, this study did not
demonstrate which the best condition in HRES is because it can help decision-
making regarding suitable HRES for the project (Fung, et al., 2002). This paper did
not collect the solar and wind data on site although it is more accurate (Gomaa et al.,
1995) than estimates from The National Aeronautics and Space Administration
(NASA) data. Tadesse’s thesis (2011) is considered, which is the same study as
Bekele and Tadesse’s paper (2012) but in more detail. Tadesse (2012) states that in
future work, SR and WS data should be measured on site, the load prediction in the
future should be considered because population and economy at the site will grow.
Kenfack, et al. (2009) studied MH/PV in rural areas of Batocha, Cameroon.
HP is highly cost-effective but cannot support the power demand through the year
because the flow rate of the stream is low during the dry season. Therefore, SPV can
supplement the electricity demand because the solar potential is high in summer.
Moreover, this paper considers DG for back-up systems because it is cheap, widely
used technologically and useful. Kenfack, et al. (2009) analyse the MH/PV hybrid
system by HOMER, the Levelized cost of which is $0.28/kWh. This paper
considered PV and hydro because they have high potential in Cameroon and used the
HOMER program because it can model a combination of all the components.
Chapter 3 Research Methodology and Method
This research use mix method approaches which are quantitative and
qualitative research because this study needs realistic data to build a realistic, reliable
and sustainable project.
3.1 Research Design
From the Bekele and Tadesse’s study and Tadesse’s thesis in the literature
review, this paper found the gap of research that did not collect HRES data such as
SR and WS at the site. Moreover, Bekele and Tadesse’s study assumed the current
load demand but did not survey villagers’ demand and did not predict the electrical
demands in the future. Therefore, this research considered the research objective and
used mix method approaches, quantitative research that used questionnaires to gauge
14
current and future electrical demand and qualitative research that used interviews to
gather other load and information such as water pumping (WP) and tea leaf cutting
machine (TLCM) load because this study needs a sustainable project which can
support all electrical demands in the future. This project needs RER, load demand,
appliance type and size, renewable energy component (REC) price, KPV and MHP
data.
3.1.1 Study Site at Chaing Mai Province
Chaing Mai province is located in the north of TL. The climate in winter is
quite cold (Panuwet, 2008). The average temperature is 25.4 o C, rainfall is 100 to
120 cm, and humidity is 72%. It has three seasons, summer, winter and monsoon
(Pudpong, 2011). It is located in the flat and mountainous area. The people of the
mountains are poor and lack water and electricity. They farm rice, tea,
chrysanthemums, grapes, strawberries, flowers (Grabowsky, 1995).
3.1.2 Study Site of the Khun Pang micro hydropower project
DEDE has 63 MHP and is located in north and west of TL (DEDE, 2017a).
KP-MHP is the MHP at KPV, Phrao District (PD), CM Province, TL . It is located at
19°11.0'N, 99°17.0'E. KP-MHP is a stand-alone RE system project because the site
is located in a mountainous area that cannot be connected to the national grid. It was
established in 2011 by DEDE. It supports 48 households, a primary school and a
temple. The turbine type is a cross flow with a capacity of 37 kW (Figure 12). The
capacity of the synchronous generator is 35 kW and the net head is 54.79 m. The
headrace diameter and length are 400 mm and 800 m in order. The penstock
diameter and length are 300 mm and 150 m in order. Weir height and length are 1.5
m and 12 m respectively (Figure 10). The length of the high transmission line is 1 km
(Figure 13). The length of the low transmission line is 1 km (DEDE, 2017f).
15
Figure 10 Weir of Khun Pang MHP
Figure 11 Power House of Khun Pang MHP Figure 12 Hydro Turbine of Khun Pang MHP
Figure 13 Transmission Line of Khun Pang MHP
KPV (Figure 14) is a local village in PD, CM Province, TL, 95 km from CM.
It is a mountainous area that is in Sri Lanna National Park which manages 1,400 km2
of mountains, forests and wildlife (Mychiangmaitour, 2017). KPV has a primary
16
school (Figure 15) called “Ban Khun Pang School” with about 20 students and a
teacher (Gofundme, 2017). It also has a temple (Figure 16), “Wat Khun
Pang“(Mbendi, 2017).
Figure 14 Khun Pang Village
17
Figure 15 Ban Khun Pang School
Figure 16 Wat Khun Pang (Temple)
3.2 Research Approach
This research considered the research objective and used mix method
approaches which are quantitative and qualitative research. This project needs RER,
load demand, appliance type and size, RE component price, KPV and MHP data. The
direct data collection is used in this quantitative research. The hydro, solar and wind
18
data are collected from the Thai government by direct contact because it is accurate
data.
The type of the questionnaire used is a combination of closed-ended and
open-ended questionnaire (Thomas, 2013) because this research needs to be fixed
and with variable number data. The closed-ended questions are used with ticking
boxes and scale ranking (Dawson, 2009). In contrast, the open-ended ones are used
for questions such as “How much?” and “How many?” (Dawson, 2009). The
questionnaire is the quantitative research (Bryman, 2015).The electricity demand is
collected by questionnaire survey since questionnaires are cheaper, quicker and more
convenience for respondents (Bryman, 2015; Sapford and Jupp, 2006).
This research used face to face interviews; it is semi-structured because that
is the most common type of interview (Dawson, 2009) and the researcher can gather
specific information and more significant information that will emerge during the
interview (Dawson, 2009). Current and future load demand data was collected from
villagers, school and temple in KPV by a questionnaire (Figure 17).
Figure 17 Questionnaire Distribution at Khun Pang Village
Future load demand such as WP, TLCM load and other information from
KPV, school, and temple were collected by face to face interviews. This research
interviewed the village chief, teacher leader and monk chairman (Figure 18).
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Figure 18 Face to Face Interview at Khun Pang Village
A hybrid energy system modelling was developed for current load and future
load prediction scenarios. More description of the data and analysis is given in
Chapter 4. Also, the modelling formulation for power, cost and reliability of HRES is
presented in Appendix G.
3.2.1 Modelling Survey in Homer software
HOMER is a widely RE system design program because it is easy to use and
rapidly simulates in optimisation and sensitive analysis of HRES (Sinha and
Chandel, 2014). It was established from the National Renewable Energy Laboratory
of United State of America and has been used in over 193 countries (Sinha and
Chandel, 2014). HOMER requires RES, electrical load demand, emission data and
REC cost and specification to input in the simulation. The software models 8,760
hours covered a year. The results of the simulation are shown in graphs, tables and a
schematic which represent a net present cost, cost of energy, amount of REC, a
renewable fraction (RF), electrical production of HRES, fuel consumption and
emissions (HOMER, 2017).
3.3 Research procedure
This research studies the optimisation of suitable size and cost of HRES by
HOMER energy program to complement MH in the dry season. First, this research
collects and evaluates the data such as population, current electricity demand, future
electricity demand forecasts and electricity production of the existing system which
is MH, and also the SR and WS data. Second, this research considers and
understands the current limitations of the stand-alone MH systems. Then it applies
HRES techniques using an energy system tool (HOMER).
20
Steps of the research
- Develop a research proposal
- Develop the research tools
- Collect data on demand profiles of seasonal resources in hydro, wind and
solar.
- Use data to quantify the level of demand and supply of electricity for KPV,
CM, TL.
- Data analysis
- Design cost-effective, reliable HRES using the energy outputs to cover the
demand shortfall
- Develop the research report
3.4 Ethical Considerations
This research investigates university research ethics and deals with ethical
issues. The respondents; data will be protected and will not be used in other projects.
This research investigates HRES to develop the quality of life in rural areas, and the
MH is government-owned. The research is not business-related.
Before this research started to survey the site and collect data, it explains the
research project information and benefits to villagers (Figure 19). This research
allowed the villagers to ask questions about the project. This research protects their
data. They will not be applied to other research.
Figure 19 Researcher Explaining Research Ethics
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Chapter 4. Data collection and analysis
4.1 Data collection
Then the researcher obtained the research purpose and question which uses
the HOMER software. This paper investigates HOMER data requirement on
simulations in HOMER user manual and help function (HOMER, 2016a; HOMER,
2016b), which needs electrical load demand, RER data and size and cost of REC.
The RER data and technical data of KP-MHP can be collected from DEDE. The size
and cost of REC can be collected from REC dealer companies. This research
considers the approach to collect electrical load demand at the site, then selects the
questionnaire, site survey and interview approach to survey and collect the data
because the questionnaire is suitable for this project. The site interview is suitable for
this project because the participant can provide historical and new information
(Creswell, 2013). Afterwards, the questionnaire and interview questions are
constructed. This research translated the questionnaire and interview questions in the
Thai language before the survey because the villagers cannot speak or understand the
English language. After collecting the Thai feedback, it was interpreted to English
and analysed.
4.1.1. Site survey and interview data
This project surveyed the geology, economy, society, traditions, education
and village problems in KPV by using interviews. This paper requires the village
background information because it relates to electricity demand and limitation of
resources. This research interviewed the village chief, teacher leader and monk
chairman. From the interviews (Appendix C), the researcher knew the amount of
population in KPV, 110 citizens, 48 households, 20 students in school and 5 monks
in the temple.
KPV has 48 households. The number of citizens is 110. Villagers are poor
and normally farm rice, corn and tea leaves, namely Camellia Sinensis Tea. Some
villagers have small chicken and pig farms, which are the main economy of the
village. Also, the religion of the villagers is Buddhist. Every household has an
electrical metre. The electricity price is approximately 0.12 US dollar per unit (4.00
Thai Bath). The village made electricity use community group and has had an
22
electricity-board for management such as electricity bill and maintenance. The
electricity use community group had 744 US dollar (25,000 Thai Bath) net income in
2016. The village has a problem with the electricity demand. The electrical
production cannot support the demand because the volume of water in the stream is
very low in summer and winter. The lowest water flow rate is in summer, less than
50 litres per second. Some villagers cannot use some appliances such as water heater,
in the peak time of summer. For the primary school, the problems are the same as for
KPV.
4.1.2. Questionnaire
Before simulating the HRES, this research considered the purpose and data
needed for the project. Therefore, this study constructed the questionnaire that
surveys village electricity current and future demand. This paper used paper
questionnaires because the villagers do not have an internet connection in the village.
The questionnaire should be short, simple and easy to understand (Dawson, 2009).
The questionnaire form is shown in Appendix B.
4.1.3 Sampling size
From interviews and site survey, this research knew the amount of population
in KPV, 110 citizens, 48 households, a school and a temple. The targets of this
questionnaire are the households, the school and the temple because the electrical
load should be measured in a house per Watts. This paper needs more than 70%
questionnaire feedback to ensure the reliability of the questionnaire.
4.1.4 Internet survey
This research surveys a lot of online information and websites which relate to
the project such as appliance type and capacity which is important to calculate
electricity demand load.
Appliance type and capacity (Watts) are collected from the internet. Thailand
has many appliance companies which are from Japan, China, Korea, Turkey,
Netherlands, Sweden, USA, and Thailand. This research collected appliance type and
capacity from appliance companies website (Samsung Thailand, 2017; Panasonic
Thailand, 2017; Sharp Thai, 2017; Toshiba Thailand, 2017; Electrolux Thailand,
23
2017; Philips Electronics Thailand, 2017; Beko Thailand, 2017; Hewlett-Packard
Thailand, 2017; Lenovo Thailand, 2017; Imarflex Thailand, 2017; Hanabishi
Thailand, 2017; Mitsubishi Electric Thailand, 2017 and Hatari, 2017) and Thai
shopping store website (Powerbuy, 2017; Homepro, 2017 and Central, 2017)
4.1.5 Direct data
This paper directly contacted DEDE to collect raw data from the site, on
hydrology, SR, WS data and technical data for KP-MHP.
4.1.6 Hydrology data
KP-MHP is near the Mae Pang stream (Figure 20) which has a different
water flow (WF) in during the year. It has high WF in monsoon season and low WF
in summer. The hydrology data is collected from the electricity production data of
KP-MHP shown in data analysis section.
Figure 20 Mae Pang Stream near Khun Pang MHP
4.1.7 Solar radiation data
This research collected the SR data from DEDE at Doi Inthanon National
Park (DINP), Chom Thong District, CM, northern TL, because KPV did not have an
instrument that can measure the SR data. However, the geography of DINP is
similar to Sri Lanna National Park (SLNP) where KPV is located. The SR data was
collected by pyranometres and pyrheliomete (Trachow, 2015) at the site. It was
collected in hourly per day in terms of MJ/m2 when had sunlight. This paper used
the average daily data.
24
4.1.8 Wind speed data
The WS data was collected from DEDE at DINP, because KPV did not have
an instrument to measure. However, the geography of DINP is similar to SLNP. The
site used 3-cup anemometres on steel truss tower to measure WS (Waewsak et al.,
2011). It was collected in hourly per day in terms of m/s. This paper used the average
daily data.
4.1.9 Specification and prices of RE components
This research investigated specification and prices of REC in TL from
company websites. However, the information on the website does not cover all REC.
Therefore, the researcher directly contacted REC Sale Company and dealer in TL to
gather all REC information especially REC prices. The SPV, battery and inverter
data are collected from Solaris Green Energy Company and Supersolarz Company
(Solaris Green Energy, 2017a; Supersolarz, 2017). The wind turbine data is collected
from Solaris Green Energy Company and Siam Green Engineer Company (Solaris
Green Energy, 2017b; Siam Green Engineer, 2017). The DG data is collected from
THAI-GENERATOR Sale and Service Company Namsang Chakkol Company (Thai
Generator Sale and Service, 2017; Namsang Chakkol, 2017).
4.2. Data analysis
From data collection section, this research gained all the raw HRES data
needed. This paper analysed all the data such as primary and deferrable load, RER
and component and diesel fuel prices (DFP) to prepare for modelling. The DFP
analysis is used in the sensitivity case that will be presented in the next chapter.
4.2.1 Primary load
First, the feedback from the questionnaire came from a school and a temple
and 36 households. This is 75% of total households (48 households). This paper used
the average value (mean by SPSS that is shown in Appendix E) of villager behaviour
of appliance use from feedback to represent another household which did not answer.
This research knew the type and capacity of appliances used by the villager. Also,
this paper knew the time and amount of hours when villagers used electricity.
Second, this paper found the appliances type and capacity presently on sale in TL.
25
Next, from the literature review in chapter 2, this paper knew CM season that is
winter, monsoon and, summer which have different electrical usage. Therefore, this
paper assumed the weekdays and weekends times of villagers’ electrical usage
calculated from TL’s calendar and list of holidays (Appendix D). Then, this paper
assumed the times of appliance use. For example, an electric rice cooker is used for 2
hours in the morning and evening on weekdays because villagers will take lunch
made in the morning to their work place. This behaviour is surveyed from the
interview. Moreover, an electric rice cooker is used for 2 hours in morning, afternoon
and evening on weekends. Finally, this research calculates the CLD (First scenario)
and TFLD (Second scenario).
The monthly CLD consumption (kW) is shown in tables 3 and 4, and it is
high in summer and low in winter. The monthly TFLD consumption (kW) is shown
in tables 5 and 6 and it is high in summer and low in monsoon. Table 3 Monthly Current Load Demand Consumption (kW) for Weekdays
26
Table 4 Monthly Current Load Demand Consumption (kW) for Weekends
Table 5 Monthly Total Future Load Demand Consumption (kW) for Weekdays
27
Table 6 Monthly Total Future Load Demand Consumption (kW) for Weekends
4.2.2 Deferrable load analysis
The KPV community such as the village, the school and the temple need to
use WP for water storage and village supply and irrigation. Therefore, this research
calculates WF in the pipeline from equation 5 and 6 in Appendix F.
Then this paper got the number and capacity of WP, and explored the
specification and WP sales company (Grundfos, 2017). As a result, this paper
selected two WP with 1 kW per pump and assumed the water was used three times a
day in the morning, afternoon and evening. However, in the summer, the weather is
very hot and people use water to take a shower at night before sleep. Therefore, in
the summer, the pumped water load is more than in winter and in the rainy season.
This load is shown in Table 7.
The KPV community needs to use TLCM to produce tea because the tea crop
is one of the villagers’ main occupations. It can increase value added of tea leaf that
will rise farmers’ income. Therefore, this research explored the capacity and
specification of TLCM (Surrimachine, 2017) and assumed the capacity of TLCM is 1
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kW. Then, this paper assumed TLCM is used two hours per day because the machine
can cut 200 kg per hours. The TLCM load is shown in table 7. Table 7 Deferrable Load
4.2.3. Hydrology data analysis
DEDE only has monthly electricity production data. Therefore, this research
calculates WF by data converting. The WF data is converted from monthly electricity
production that refers to the power of turbine and generator operation. The power
output of MH can be converted to calculate WF because it depends on the WF in the
stream as shown in equation 4 in Appendix G.
The flow rate is high during, monsoon, which is June to November. The
highest flow is in July. In contrast, in March, April and May which is summer, the
flow rate is low. The lowest flow is in April. The Mae Pang stream hydrology data is
shown in Table 8. Table 8 Mae Pang Stream Flow Rate
4.2.4 Solar radiation data analysis
The SR data is collected from a site in term of hourly data and MJ/m2 unit.
This research calculated SR hourly data to monthly data and converted SR data in
term of MJ/m2 unit to kWh/m2 unit because the HOMER requires the monthly data
in term of kWh/m2 unit (HOMER, 2017b). The SR data is shown in Table 9. Table 9 Solar Radiation of Project
Month January February March April May June July August September October November December
Water Pump Power (kWh/day) 6.000 6.000 8.000 8.000 8.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000Tea Leaf Cutting Machine Power (kWh/day) 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000 2.000Total (kWh/day) 8.000 8.000 10.000 10.000 10.000 8.000 8.000 8.000 8.000 8.000 8.000 8.000
Month January February March April May June July August September October November DecemberFlow Rate (L/s) 83.356 77.104 79.188 45.846 62.517 101.277 102.111 100.027 98.777 92.108 70.852 64.601
Month January February March April May June July August September October November DecemberSolar Radiation (kWh/m2/day) 4.780 6.031 6.583 6.617 5.168 3.668 3.131 2.938 3.182 3.164 4.344 4.950
29
4.2.5 Wind speed data analysis
The WS data is collected from a site in term of hourly data and m/s unit. This
research calculated WS hourly data to monthly data. The HOMER requires the
monthly data in term of m/s unit (HOMER, 2016b). The WS data is shown in Table
10. Table 10 Wind Speed of Project
4.2.6 Renewable energy component analysis
This research knew the specification and prices of REC in TL from the REC
Sale Company and dealer.Then, this research calculated REC price by average prices
of three manufactures. For specification, this paper used the data which is the same
type of REC. For example, SPV specification will be used from multi-crystalline cell
type from three manufacturers. The REC data is shown in Table 11. Table 11 RE Component Prices and Specification
4.2.7 Diesel fuel prices analysis
This research needs to build a sustainable project so the project can support
every case in the future. Therefore, this project considered the sensitivity of DFP
because The Organization of the Petroleum Exporting Countries (OPEC) forecasted
crude oil price (OPEC, 2016) will increase (Figure 21).
Month January February March April May June July August September October November DecemberWind Speed (m/s) 3.449 4.445 4.651 3.254 3.758 6.331 5.991 5.771 3.747 3.526 3.133 3.040
RE Component Type/Model Capacity Capital ($) Replacement cost ($)
O&M cost ($/years)
Life time (years)
Efficiency (%)
Solar PV Generic Flat Plate PV 1 kW 781.00 781.00 2.58 20 years 15.98 Wind Turbine Generic 1 kW 4,038.00 4,038.00 40.38 20 years - Hydro Turbine Natel FreeJet 32 kW - - - 30 years 60.00 Diesel Generator Autosize Genset 1 kW 588.00 588.00 0.040 $/hours 15,000 hours - Battery Generic 1 kWh Li-Ion (ASM) 1 kWh 641.00 641.00 10.65 5 years - Converter System Converter 1 kW 686.00 686.00 - 15 years 90.00
30
Figure 21 OPEC Forecasting of Crude Oil Price
Next, this paper compared the average crude oil prices from OPEC and TL in
2016 (PTT, 2017, Shell Thailand, 2017). Then, it calculated TL’s crude oil prices
forecasting and converted it to diesel prices forecasting from 2016 to 2040 by using
the Thai Bath exchange rate in US dollars (Bank of Thailand, 2017b). Finally, this
paper assumed DFP range for HOMER modelling which is 0.75, 1.0, 1.25 and 1.50
$/L.
Chapter 5 Results and Discussions
5.1 Results
In this research, the main objective is to design and optimise HRES to
complement an MH in KPV by using HOMER software. HOMER is used to
simulate two scenarios which are CLD and TFLD. This project also modelled in the
sensitivity of DFP which are 0.75, 1.0, 1.25 and 1.50 $/L. After inputting all the data
to HOMER simulation, this research gained the results (Appendix F) that are shown
in the following section.
5.1.1 Monthly Solar radiation
The SR that is collected at the site has its highest value in the summer, the
second ranking is in winter and the lowest is in monsoon because this season is the
rainy season. The monthly average daily SR is shown in Figure 22.
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Figure 22 Monthly Average Daily Solar Radiation
5.1.2 Monthly Wind Speed
The WS that is collected at the site has the highest value in monsoon because
this season is the rainy season and has strong winds; the second ranking is in winter
and the lowest in summer. The monthly average WS is shown in Figure 23.
Figure 23 Monthly Average Wind Speed
5.1.3 Monthly Steam Flow
The stream flow (SF) that is collected at the site, has its highest value in
monsoon because this season is the rainy season; the second ranking is in winter and
the lowest in summer because this season is the dry season. The monthly average SF
is shown in Figure 24.
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Figure 24 Monthly Average Stream Flow
5.1.4 Primary Current Load Demand
The primary CLD is collected and calculated from the questionnaire survey.
After that this research loaded the data to HOMER modelling. The results showed
the annual average daily load (AADL) is 277.98 kWh/day. LF is 0.23. The graph
illustrates the AADL (Figure 25). Overall, it can be seen that the electrical load is
high in the morning, evening and before bed time. Moreover, the electrical peak load
(EPL) is in the evening because this is when electrical users light bulbs and turn on
many appliances such as rice cooker, TV and electric fans. The average daily load
(ADL) is 11.58 kW.
Figure 25 Annual Average Daily Load of Current Load Demand Scenario
In detail, the daily load (DL) of the whole year in Figure 26 showed the Peak
load (PL) is 50.83 kW. The minimum load accounted for approximately 17 kW.
33
Figure 26 Daily Load of the Whole Year of Current Load Demand Scenario
The bar chart of monthly current load (kWh), in Figure 27 showed the load
demand is high in summer and low in winter because TL is a hot country that uses a
lot of electricity in the summer. The maximum load (MAXL) of monthly
consumption is in May, 8,800 kWh and the minimum load (MINL) of monthly
consumption is in February, 7,858 kWh.
Figure 27 Monthly Current Load Demand
5.1.5 Primary Total Future Load Demand
The primary TFLD is collected and calculated from the questionnaire survey.
The results showed the AADL is 426.94 kWh/day. LF is 0.26. The bar chart shows
the AADL (Figure 28). Overall, it can be seen that the electrical load is high in the
34
morning, evening and before bed time. Moreover, the EPL is in the morning because
this time electrical users turn on many appliances such as rice cookers, TVs, electric
fans, electric kettles, water heaters and light bulbs because in winter and monsoon
sunrise it is late. The ADL is 17.71 kW.
Figure 28 Annual Average Daily Load of Total Future Load Demand Scenario
In detail, the DL of the whole year in Figure 29 shows PL is 68.51 kW. The
MINL accounts for approximately 22 kW.
Figure 29 Daily Load of the Whole Year of Total Future Load Demand Scenario
The graph of monthly total future load demand (kWh) in Figure 30 shows the
load demand is high in summer and low in monsoon because TL is a hot country that
uses large amounts of electricity in the summer and winter. The weather in CM, is
cold and that means villagers use water heaters. The MAXL of monthly consumption
35
is in May, accounting for 13,759 kWh, and the MINL of monthly consumption is in
February meaning 12,256 kWh.
Figure 30 Monthly Total Future Load Demand
5.1.6 Deferrable Load (kWh/day)
The results from HOMER simulation shows the AADL is 8.5 kWh/day. PL is
10.00 kW. Storage capacity is 20 kWh. The bar chart represents the monthly
deferrable load (Figure 31). Overall, it can be seen that the highest load is in summer,
10.0 kWh/day, for March, April and May. In winter and monsoon it is lower, 8.0
kWh/day.
Figure 31 Monthly Deferrable Load
5.1.7 Hybrid Renewable Energy System in Scenario 1
The results of HOMER simulation illustrate HRES schematic, tables and
graphs which are shown below. The HRES schematic shows the image of the system
36
that can gain the idea of simulation (Figure 32). The schematic represents REC and
an electrical load in AC and DC electrical bus bar.
Figure 32 Schematic of Current Demand Load (Scenario 1)
Table 12 shows the overall simulation in the sensitivity case where DFP
range is 0.75 to 1.5 $/L. The simulation prioritises the results HRES. The top ranking
is a cost effective condition. Overall, the hybrid Hydro/Diesel/Battery is cost
effective for the DFP range 0.75 to 1.25 $/L. In contrast, hybrid
Hydro/PV/Diesel/Battery is cost effective for the DFP range 1.5 $/L. Table 12 Overall of Simulation in Diesel Price 0.75 to 1.5 $/L (Scenario 1)
For scenario 1 (CDL), this research chose the current DFP that is 0.75 $/L to
present in this paper as represented in Table 13. Overall, the table shows the ranking
of cost effectiveness from the cost of energy (COE) and net present cost (NPC). The
first ranking, the lowest COE, is $0.0705. NPC that is $92,441 is
Hydro/Diesel/Battery. The second is Hydro/PV/Diesel/Battery. The third is
Hydro/Wind/Diesel/Battery. The last ranking is Hydro/PV/Wind/Diesel/Battery.
This research only considers the case that unmet load value is zero because it
demonstrates the reliability of the system that is present in Appendix G.
37
Table 13 Overall of Simulation in Diesel Price 0.75 $/L (Scenario 1)
This research chose to present the Hydro/Diesel/Battery case in this paper
because it is the lowest COE and NPC and reliable system. This research illustrates
the load demand and HRES power output in April because this month represents
summer time when there is high demand. The graph represented in Figure 33 shows
CDL and Hydro/Diesel power output. Overall, it can be seen that hydro, and diesel
power output, which are the red and blue lines can relatively support the load
demand that is the green line. In detail, the hydro power output is a limit that
accounts for 13 kW. It cannot meet the load demand that is 46 kW PL. However, the
diesel power output that accounts for 36 PL will complement the hydro power to
support the demand.
Figure 33 Current Demand Load and Hydro/Diesel Power Output
The annual electrical production (AEP) of the Hydro/Diesel/Battery case is
demonstrated in Table 14. The total annual electrical production (TAEP) is 199,597
38
kWh/year. The HP production is 98%, accounting for 195,520 kWh/year, and the
diesel generator is about 2%, meaning 4,077 kWh/year. Table 14 Annual Electrical Production of Hydro/Diesel/Battery (Scenario 1)
The bar chart of monthly average electrical production (MAEP) is
represented in Figure 34. The hydro power production (HPP) shown in the orange
bar is the main HRES system that has low and high potential in summer and
monsoon relatively. However, the DG shown in the green bar will complement hydro
to support the demand in summer (April and May) and November and December.
Figure 34 Monthly Average Electrical Production of Hydro/Diesel/Battery (Scenario 1)
The bar chart of cash flow is represented in Figure 35. The DG, battery and
converter cost are blue, light blue and red relatively. The capital cost (CC) is in the
first year when the project started. The replacement cost (RC) is at year 15 and
salvage cost (SC) is the end of the project. However, the hydropower cost (HC) is not
in cash flow because it is an existing component.
39
Figure 35 Cash flow of Hydro/Diesel/Battery (Scenario 1)
The emission of hybrid Hydro/Diesel/Battery case is demonstrated in Table
15. The carbon dioxide (CO2) is 3,961 kg/year, the carbon monoxide (CO) is 25
kg/year, the nitrogen oxides (NOX) are 23.5 kg/year, and the sulfur dioxide (SO2) is
9.7 kg/year. Table 15 Emission of Hydro/Diesel/Battery (Scenario 1)
5.1.8 Hybrid Renewable Energy System in Scenario 2
The results of HOMER simulation illustrates the HRES schematic, tables and
graphs which are shown below. The HRES schematic shows the image of the system
that can gain the idea of simulation (Figure 36).
40
Figure 36 Schematic of Total Future Demand Load (Scenario 2)
Table 16 shows the overall simulation in the sensitivity case that DFP range
0.75 to 1.5 $/L. The simulation prioritises the results HRES. The top ranking is a cost
effective condition. Overall, the Hydro/PV/Diesel/Battery is cost effective for a DFP
range 0.75 to 1.5 $/L. The COE range is 0.0875 to 0.0997 $/L and the NPC range
179,741 to 204,816 $/L. Table 16 Overall of Simulation in Diesel Price 0.75 to 1.5 $/L (Scenario 2)
For scenario 2 (TFDL), in Chapter 4 data analysis, the maximum DFP that is
forecasted, is 1.5 $/L. However, the forecast of the Petroleum Institute of Thailand
(2016) for crude oil prices states it will slowly increase because the world and Thai
economy have grown slowly and alternative energy usage in TL will rise. Therefore,
in scenario 2, this research focuses especially on DFP 1.25 $/L to present in this
paper what is represented in Table 17. Overall, the table shows the ranking of cost
effective of the COE and NPC. The first ranking that is the lowest COE is $0.0966
and NPC is $198,435 is Hydro/PV/Diesel/Battery. The second is
Hydro/PV/Wind/Diesel/Battery. The third is Hydro/Diesel/Battery. The last ranking
is Hydro/Wind/Diesel/Battery.
41
Table 17 Overall of Simulation in Diesel Price 1.25 $/L (Scenario 2)
This research chose to present the Hydro/PV/Diesel/Battery case in this paper
because it is the lowest COE and NPC and reliable system. This research illustrates
the load demand and HRES power output in May because this month represents
summer time where there is high demand. The graph represented in Figure 37 shows
TFDL and Hydro/PV/Diesel power output. Overall, it can be seen that RE (hydro and
PV) and diesel power output which are the brown and red lines relatively, can
support the load demand that is the green line. In detail, the RE power output is 28
kW peak; that cannot meet the load demand that is at 50 kW PL. However, the diesel
power output that accounted for 22 PL will complement the RE power to support the
demand.
Figure 37 Total Future Demand Load and Hydro/PV/Diesel Power Output
The AEP of hybrid Hydro/PV/Diesel/Battery case is demonstrated in table
18. The TAEP is 223,402 kWh/year. The HPP is 87.5%, meaning 195,520 kWh/year,
the SPV production is approximately 10%, that is 22,283 kWh/year, and the DG is
about 2.5 % or 5,600 kWh/year.
42
Table 18 Annual Electrical Production of Hydro/PV/Diesel/Battery (Scenario 2)
The bar chart of MAEP is represented in figure 38. The HP production shown
in the orange bar is the main HRES system that has low and high potential in
summer and monsoon relatively. However, the DG shown in the green bar will
complement hydro to support demand in summer (April and May), and November
and December. The SPV shown in the brown and green bar will complement hydro
to support demand during the whole year but it is a high support in summer and
winter because sunlight is low in monsoon (rainy season).
Figure 38 Monthly Average Electrical Production of Hydro/PV/Diesel/Battery (Scenario 2)
The bar chart of cash flow is represented in figure 39. The SPV, DG, battery
and converter cost are red, blue, light blue and yellow relatively. The CC is in the
first year when the project started. The RC is at year 15 and SC is at the end of the
project. However, the HC is not in cash flow because it is an existing component.
43
Figure 39 Cash flow of Hydro/PV/Diesel/Battery (Scenario 2)
The emission of hybrid Hydro/PV/Diesel/Battery case is demonstrated in
table 19. The CO2 is 5,625 kg/year, the CO is 35.5 kg/year, the NOX is 33.3 kg/year,
and the SO2 is 13.8 kg/year. Table 19 Emission of Hydro/PV/Diesel/Battery (Scenario 2)
5.2 Discussions
This research obtained the results of load demand in scenario 1 and 2. It built
the bar chart to compare them as shown in Figure 40 and 41. The bar chart represents
the amount of monthly electrical demand in KPV (Figure 40). Overall, the CLD is
less than TFLD because, as seen from the questionnaire, the villagers will need to
use more appliances such as water heaters, washing machines and electric kettles in
the future if the electrical production can support their need. In detail, the load
demand is high in summer for two scenarios because TL is a hot country that uses
large amounts of electricity in the summer. In contrast, the load demand is low in
winter in scenario 1 and in a monsoon in scenario 2 because, in winter, the weather
in CM is cold and that means villagers use water heaters. The MAXL of monthly
consumption is in May, that is 8,800 kWh (scenario 1) and 13,759 kWh (scenario 2).
44
The MINL of monthly consumption is in February, that is 7,858 kWh (scenario 1)
and 12,256 kWh (scenario 2).
Figure 40 Monthly Electrical Demand in Khun Pang Village
The bar chart illustrates the amount of annual electrical demand between
CLD and TFLD (Figure 41). The trend of electrical load slightly increases from
current to future. The CLD would be 102 MWh, and the TFLD would be 156 MWh.
Figure 41 Annual Electrical Demand
For scenario 1, this research chose the current DFP that is 0.75 $/L at present
in this paper. For scenario 2, in Chapter 4 data analysis, the maximum DFP that is
chosen for the future case is 1.25 $/L.
From table 12, scenario 1, the first ranking (case 1) that is the lowest COE
($0.0705) and NPC ($92,441), is Hydro/Diesel/Battery. The second (case 2) is
Hydro/PV/Diesel/Battery. The third (case 3) is Hydro/Wind/Diesel/Battery. The last
45
ranking (case 4) is Hydro/PV/Wind/Diesel/Battery. In renewable fraction (RF)
subject, all cases use RE, about 96%. In total fuel and CO2 subject, case 1 is 1,513
L/year and 3,961 kg/year relatively; that is the highest value. In contrast, case 2 has
the lowest total fuel and CO2 which accounts for 1,463 L/year and 3,831 kg/year
relatively. However, in case 2 (Hydro/PV/Diesel/Battery), the amount of SPV from
the simulation is tiny, about 3 W. It is impossible to use this capacity because the
SPV panel capacity in TL starts at 20 W (Supersolarz, 2017). Therefore,
Hydro/Diesel/Battery will be the best case for scenario 1.
From table 16, scenario 2, the first ranking (case 1) that is the lowest COE
($0.0966) and NPC (198,435), is Hydro/PV/Diesel/Battery. The second (case 2) is
Hydro/PV/Wind/Diesel/Battery. The third (case 3) is Hydro/Diesel/Battery. The last
ranking (case 4) is Hydro/Wind/Diesel/Battery. In RF subject, case 1 and 2 use RE
about 96%. Case 3 and 4 use RE about 93%. In total fuel and CO2 subject, case1 is
2,149 L/year and 5,625 kg/year relatively; that is the lowest value. Therefore,
Hydro/PV/Diesel/Battery will be the best case for scenario 2.
This research considers adding the sensitivity analysis from HOMER
simulation to confirm the best case for the two scenarios because HOMER can
present the variety of DFP with RER (Vargas, 2013; Khan and lqbal, 2005).
Therefore, this paper modelled the sensitivity analysis by DFP range 0.75,
1.0,1.25,1.5 $/L, SR range 3,4,5,6,7 kWh/m2/day and WS range 3,4,5,6,7 m/s. This
paper does not focus on hydro because it is fixed capacity and production.
For scenario 1, the graph shows the sensitivity analysis of SR and DFP
(Figure 42). Overall, Hydro/Diesel/Battery is suitable for all ranges of SR when DFP
is less than 1.4 $/L. In contrast, Hydro/PV/Diesel/Battery is suitable for an SR range
of 3.5 to 4.2 kWh/m2/day when DFP is more than 1.4 $/L.
46
Figure 42 Sensitivity Analysis of Solar Radiation and Diesel Fuel Price of Hydro/Diesel/Battery
(Scenario 1)
For scenario 1, the graph shows the sensitivity of WS and DFP (Figure 43).
Overall, Hydro/Diesel/Battery is suitable for any range of WS when DFP is less than
1.47 $/L. In contrast, Hydro/PV/Diesel/Battery is suitable for any WS range when
DFP is more than 1.47 $/L.
Figure 43 Sensitivity Analysis of Wind Speed and Diesel Fuel Price of Hydro/Diesel/Battery
(Scenario 1)
47
For scenario 2, the graph shows the sensitivity of SR and DFP (Figure 44).
Overall, Hydro/PV/Diesel/Battery is suitable for any range of SR and DFP.
Figure 44 Sensitivity Analysis of SR and DFP of Hydro/PV/Diesel/Battery (Scenario 2)
For scenario 2, the graph shows the sensitivity of WS and DFP (Figure 45).
Overall, Hydro/PV/Diesel/Battery is suitable for any range of WS and DFP.
Figure 45 Sensitivity Analysis of WF and DFP of Hydro/PV/Diesel/Battery (scenario 2)
The AEP of Hydro/Diesel/Battery case in scenario 1 that used hydro to
produce power is 98% or 195,520 kWh/year and the DG is about 2% or 4,077
48
kWh/year (Table 14). The HP is the main HRES system that has low and high
potential in summer and monsoon relatively (Figure 34). However, the DG will
complement hydro to support the demand in summer.
The AEP of hybrid Hydro/PV/Diesel/Battery case in scenario 2 that used
hydro to produce power is 87.5%, that means 195,520 kWh/year; the SPV production
is approximately 10%, meaning 22,283 kWh/year and the DG is about 2.5 % or
5,600 kWh/year (Table 18). The HP is the main HRES system that has low and high
potential in summer, and monsoon relatively (Figure 38). However, the DG will
complement hydro to support the demand in summer and the SPV will complement
hydro to support the demand during the whole year, but it is high support in summer
and winter.
The bar chart of the cash flow of scenario 1 (Figure 35) and 2 (Figure 39)
have the same pattern which is represented in figure1 and 2. The CC is in the first
year when project started. The RC is at year 15 and SC, at the end of the project.
However, the HC is not in cash flow because it is an existing component.
The emission of Hydro/Diesel/Battery case in scenario 1 (Table 15) is less
than Hydro/PV/Diesel/Battery case in scenario2 (Table 19). The CO2 is the
maximum emission, and the minimum emission is the particulate matter. In
Hydro/Diesel/Battery case, the CO2 is 3,961 kg/year, the CO is 25 kg/year, the NOX
is 23.5 kg/year, and the SO2 is 9.7 kg/year. In Hydro/PV/Diesel/Battery case, the
CO2 is 5,625 kg/year, the CO is 35.5 kg/year, the NOX is 33.3 kg/year, and the SO2 is
13.8 kg/year.
In WE case, WE is not suitable for this project because maximum, and
average WS are low, with 6.331 and 4.26 m/s relatively (Table 10). It is very low for
the WE needed (HOMER, 2016) for 1 kW as shown in Figure 46.
49
Figure 46 Wind Turbine Power Curve
To summarise, according to the results of HOMER modelling and
sensitivity analysis, the Hydro/Diesel/Battery will be the best case for scenario 1 that
designed Diesel 56 kW, Battery 27 kWh, Converter 16.8 kW and was based on
existing Hydro 32 kW. The Hydro/PV/Diesel/Battery will be the best case for
scenario 2 that designed PV capacity 16.7 kW, Diesel 77 kW, Battery 69 kWh,
Converter 31.7 kW and was based on existing Hydro 32 kW. In contrast, wind
energy is not suitable for this project because it has low speed.
Chapter 6 Conclusion and Recommendations
6.1 Conclusion
KP-MHP is researched because the electricity production in summer cannot
meet the demand. Therefore, this research simulated HRES by HOMER and
considered the PV/Wind/Diesel/Battery component to complement Hydro. This
research used mixed method approach which is quantitative (questionnaire survey)
and qualitative (semi-structured interview) approach for data collection. This paper
simulates two scenarios of HRES modelling. The first scenario is CLD (50.83 kW
peak). The second scenario is TFLD (78.51 kW peak) that includes current load and
future load. The results from HOMER simulation and sensitivity analysis showed
Hydro/Diesel/Battery would be suitable for the first scenario where project and
energy cost are $92,441 and $0.0705 for a current DFP of 0.75 $/L. The
Hydro/PV/Diesel/Battery would be suitable for the second scenario where project
and energy cost are $198,435 and $0.0966 for a future DFP of 1.25 $/L. Also, WE
will be not suitable for this project because WS is low and it can produce less than 1
kW of electricity. The 100% of RE usage is not suitable for this project because it is
not a reliable system where with RER has fluctuations (Budischak at al, 2013).
50
However, the project should plan to build a sustainable project. Wagner and Mathur
(2011) state that designers should consider social, environmental and project cycle
condition when designing and planning RE projects. It means the project should be
built so as to meet future electrical demands because the population growth is
slightly increasing. Therefore, Hydro/PV/Diesel/Battery will be suitable for this
project with a design PV capacity 16.7 kW, Diesel 77 kW, Battery 69 kWh,
Converter 31.7 kW and based on existing Hydro 32 kW. Furthermore, HP can reduce
its production during the first period (CLD) for lifetime saving.
6.2 Recommendations
SR and WS are measured at DINP. Although, the geography of DINP is
similar to SLNP where KPV is located, it is not the exact site. Therefore, the RER
measured instrument should be installed at KPV to collect the data. Wenham (2012)
states that WP is important for water storage and supply for the village and SPV can
be used for WP. Kaldellis (2010) states that the WE can be used for water storage
and village supply and irrigation. Solar energy and diesel can also use for WP.
Therefore, the deferrable load that is from WP and TLCM might be considered only
to use TLCM because WP can use SPV in a stand-alone system.
6.3 Future work
From literature review in chapter 2, this research knew many software
programs could be used to model HRES except HOMER. Therefore, next research
should compare HRES modelling in HOMER software with other HRES software
such as Matlab and RETScreen. Sinha and Chandei (2014) states that RETScreen is
HRES software that is a free to download. It has a worldwide climate database over
with 6,000 measuring stations and can connect to NASA characteristic weather
database. Also, this research knew the main occupation of KPV is farmers. It has a
high potential in biomass energy because they cultivate rice, corn and tea. Therefore,
the next study can consider biomass in HRES modelling. Moreover, the next
research can design in detail civil work, the electrical and mechanical design of
HRES component of this project and can consider separating the WP load to design
in a PV stand-alone system for pumping.
51
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Appendices
Appendix A: Site Map
Figure 47 Chaing Mai Province (Project Site)
60
Figure 48 Khun Pang Village, Chaing Mai (Project Site)
Khun Pang Village
61
Appendix B: Quesionnaire Form
Figure 49 Quesionnaire Form 1 (Jones and Lomas, 2016)
62
Figure 50 Quesionnaire Form 2 (Jones and Lomas, 2016)
63
Appendix C: Interview Questions
Figure 51 Interview Questions (Kooijman-van Dijk and Clancy, 2010)
64
Appendix D: Thailand’s Calendar
Figure 52 Thailand’s Calendar
Figure 53 Thailand’s Holiday List
65
Appendix E: SPSS Results
Table 20 Average Current Appliance Capacity by Items (IBM SPSS Statistics, 2013)
Table 21 Average Appliance Capacity of Future Demand by Items (IBM SPSS Statistics, 2013)
66
Appendix F: Total Results of HOMER HRES Modelling
Table 22 Total Results of HOMER HRES Modelling 1
Sensi
tivity
/Dies
el Fu
el Pr
ice
($/L)
Arch
itectu
re/PV
(kW)
Arch
itectu
re/G
1
Arch
itectu
re/G
en (k
W)
Arch
itectu
re/LI
ASM
Arch
itectu
re/Na
tel49
(kW)
Arch
itectu
re/Co
nvert
er (kW
)
Arch
itectu
re/Di
spatch
Cost/
COE
($)
Cost
/NPC
($)
Cost/
Opera
ting
cost
($)
Cost
/Initia
l cap
ital ($
)
Syste
m/Re
n Frac
(%
)
Gen
/Hou
rs Ge
n/Prod
uctio
n (kW
h)
Gen
/Fuel
(L)
Gen
/O&M
Co
st ($)
G
en/Fu
el Co
st ($)
PV/C
apita
l Co
st ($)
PV/Pr
oduc
tion
(kWh)
G1/C
apita
l Co
st ($)
G1/P
roduc
tion
(kWh)
G1/O
&M
Cost
($)
LI A
SM/
Auton
omy
(hr)
LI
ASM/
Annu
al Th
rough
put
(kWh)
Conv
erter/
Recti
fier
Mean
Ou
tput
(kW)
Conv
erter/
Invert
er Me
an
Outpu
t (kW
)
Natel
49/
Mean
Ou
tput
(kW)
0.75
56.00
27
.00
32.01
16
.84
LF0.0
7
92,44
0.98
2,3
71.00
61
,789.8
1
95.98
24
2.00
4,076
.72
1,513
.25
542.0
8
1,1
34.94
1.6
7
6,069
.12
0.7
3
0.63
22
.32
0.75
0.00
56
.00
28.00
32
.01
16.84
LF
0.07
92
,552.5
9
2,330
.32
62,42
7.32
96
.11
233.0
0
3,9
50.69
1,4
63.40
52
1.92
1,097
.55
2.38
4.0
7
1.7
3
6,142
.47
0.7
3
0.63
22
.32
0.75
1.00
56
.00
27.00
32
.01
16.84
LF
0.07
97
,430.7
6
2,444
.63
65,82
7.81
96
.00
241.0
0
4,0
56.10
1,5
06.05
53
9.84
1,129
.54
4,038
.00
873.0
8
40
.38
1.6
7
6,001
.74
0.7
1
0.63
22
.32
0.75
0.45
1.0
0
56.00
27
.00
32.01
16
.86
LF0.0
7
97,60
5.73
2,4
30.31
66
,187.8
5
96.03
23
9.00
4,023
.14
1,493
.73
535.3
6
1,1
20.30
34
9.38
596.6
6
4,038
.00
873.0
8
40
.38
1.6
7
5,975
.39
0.7
0
0.63
22
.32
0.75
18.34
82
.00
32.01
30
.59
CC0.1
0
125,0
98.40
2,8
80.03
87
,866.8
4
100.0
0
14,32
2.20
24
,459.1
5
5.0
6
8,845
.68
0.8
4
1.05
22
.32
0.75
18.53
1.0
0
82.00
32
.01
30.30
CC
0.10
13
0,109
.60
2,959
.08
91,85
6.04
10
0.00
14
,471.5
9
24,71
4.28
4,038
.00
873.0
8
40
.38
5.0
6
8,758
.67
0.8
2
1.05
22
.32
0.75
56.00
32
.01
CC0.1
5
197,9
46.30
12
,764.8
9
32
,928.0
0
76.84
1,6
29.00
23,49
4.79
9,194
.76
3,648
.96
6,896
.07
22.32
0.7
5
0.0
7
56.00
32
.01
0.05
CC0.1
5
198,0
46.60
12
,765.7
8
33
,016.7
3
76.85
1,6
29.00
23,49
3.98
9,194
.56
3,648
.96
6,895
.92
56.43
96.37
-
0.00
22
.32
0.75
1.00
56
.00
32.01
0.0
8
CC
0.15
20
2,690
.00
12,81
5.47
37,01
7.86
76
.90
1,625
.00
23
,434.7
9
9,1
71.60
3,6
40.00
6,8
78.70
4,0
38.00
87
3.08
40.38
-
0.00
22
.32
0.75
0.37
1.0
0
56.00
32
.01
0.39
CC0.1
5
203,1
59.60
12
,812.5
3
37
,525.3
7
76.92
1,6
24.00
23,41
4.29
9,164
.43
3,637
.76
6,873
.32
289.6
2
49
4.61
4,0
38.00
87
3.08
40.38
-
0.00
22
.32
0.75
20.00
16
0.00
32.01
35
.90
CC0.2
3
295,4
63.10
6,7
69.49
20
7,950
.40
100.0
0
80,76
0.00
17
,461.5
1
80
7.60
9.8
7
8,860
.03
0.9
5
1.05
22
.32
0.75
387.0
0
32
.01
41.42
CC
0.32
42
4,990
.80
11,48
7.74
276,4
82.80
10
0.00
23
.88
10
,084.9
4
1.20
1.0
5
22.32
1.0
0
56
.00
28.00
32
.01
16.84
LF
0.07
97
,172.0
6
2,687
.50
62,42
9.41
96
.12
232.0
0
3,9
36.72
1,4
57.87
51
9.68
1,457
.87
1.73
6,1
57.20
0.74
0.6
4
22.32
1.0
0
0.0
1
56.00
28
.00
32.01
16
.78
LF0.0
7
97,24
1.48
2,6
95.21
62
,399.1
3
96.11
23
3.00
3,950
.58
1,463
.37
521.9
2
1,4
63.37
10
.27
17
.54
1.7
3
6,141
.59
0.7
3
0.63
22
.32
1.00
1.00
56
.00
28.00
32
.01
16.85
LF
0.08
10
2,277
.60
2,769
.78
66,47
1.30
96
.13
232.0
0
3,9
30.10
1,4
56.21
51
9.68
1,456
.21
4,038
.00
873.0
8
40
.38
1.7
3
6,074
.54
0.7
2
0.64
22
.32
1.00
0.01
1.0
0
56.00
28
.00
32.01
16
.92
LF0.0
8
102,3
47.20
2,7
71.02
66
,524.7
9
96.13
23
2.00
3,930
.00
1,456
.18
519.6
8
1,4
56.18
6.6
3
11.31
4,038
.00
873.0
8
40
.38
1.7
3
6,073
.82
0.7
2
0.64
22
.32
1.00
18.34
82
.00
32.01
30
.59
CC0.1
0
125,0
98.40
2,8
80.03
87
,866.8
4
100.0
0
14,32
2.20
24
,459.1
5
5.0
6
8,845
.68
0.8
4
1.05
22
.32
1.00
18.53
1.0
0
82.00
32
.01
30.30
CC
0.10
13
0,109
.60
2,959
.08
91,85
6.04
10
0.00
14
,471.5
9
24,71
4.28
4,038
.00
873.0
8
40
.38
5.0
6
8,758
.67
0.8
2
1.05
22
.32
1.00
56.00
32
.01
CC0.1
7
227,6
62.70
15
,063.5
8
32
,928.0
0
76.84
1,6
29.00
23,49
4.79
9,194
.76
3,648
.96
9,194
.76
22.32
1.0
0
0.3
6
56.00
32
.01
0.10
CC0.1
7
228,0
42.00
15
,065.7
4
33
,279.4
8
76.85
1,6
29.00
23,49
2.36
9,194
.15
3,648
.96
9,194
.15
282.7
1
48
2.80
-
0.0
0
22.32
1.0
0
1.0
0
56.00
32
.01
0.08
CC0.1
8
232,3
31.50
15
,108.3
7
37
,017.8
6
76.90
1,6
25.00
23,43
4.79
9,171
.60
3,640
.00
9,171
.60
4,038
.00
873.0
8
40
.38
-
0.0
0
22.32
1.0
0
0.3
7
1.00
56
.00
32.01
0.3
9
CC
0.18
23
2,777
.90
15,10
3.64
37,52
5.37
76
.92
1,624
.00
23
,414.2
9
9,1
64.43
3,6
37.76
9,1
64.43
28
9.62
494.6
1
4,038
.00
873.0
8
40
.38
-
0.0
0
22.32
1.0
0
20
.00
160.0
0
32
.01
35.90
CC
0.23
29
5,463
.10
6,769
.49
207,9
50.40
10
0.00
80
,760.0
0
17,46
1.51
807.6
0
9.87
8,8
60.03
0.95
1.0
5
22.32
1.0
0
38
7.00
32.01
41
.42
CC0.3
2
424,9
90.80
11
,487.7
4
27
6,482
.80
100.0
0
23.88
10,08
4.94
1.2
0
1.05
22
.32
1.25
56.00
34
.00
32.01
18
.80
LF0.0
8
101,4
17.00
2,6
14.61
67
,616.6
2
97.35
18
9.00
2,688
.08
1,057
.29
423.3
6
1,3
21.61
2.1
0
7,614
.82
0.9
1
0.79
22
.32
1.25
0.01
56
.00
33.00
32
.01
18.76
LF
0.08
10
1,547
.60
2,675
.80
66,95
6.26
97
.24
197.0
0
2,8
05.40
1,1
02.93
44
1.28
1,378
.67
5.43
9.2
7
2.0
4
7,510
.73
0.9
0
0.78
22
.32
1.25
1.00
56
.00
33.00
32
.01
18.83
LF
0.08
10
6,502
.00
2,743
.24
71,03
8.78
97
.25
196.0
0
2,7
90.45
1,0
97.16
43
9.04
1,371
.45
4,038
.00
873.0
8
40
.38
2.0
4
7,453
.50
0.8
8
0.78
22
.32
1.25
0.50
1.0
0
56.00
33
.00
32.01
18
.75
LF0.0
8
106,5
85.70
2,7
23.75
71
,374.4
2
97.28
19
4.00
2,757
.08
1,084
.73
434.5
6
1,3
55.92
39
0.54
666.9
6
4,038
.00
873.0
8
40
.38
2.0
4
7,401
.85
0.8
6
0.78
22
.32
1.25
18.34
82
.00
32.01
30
.59
CC0.1
0
125,0
98.40
2,8
80.03
87
,866.8
4
100.0
0
14,32
2.20
24
,459.1
5
5.0
6
8,845
.68
0.8
4
1.05
22
.32
1.25
18.53
1.0
0
82.00
32
.01
30.30
CC
0.10
13
0,109
.60
2,959
.08
91,85
6.04
10
0.00
14
,471.5
9
24,71
4.28
4,038
.00
873.0
8
40
.38
5.0
6
8,758
.67
0.8
2
1.05
22
.32
1.25
56.00
32
.01
CC0.2
0
257,3
79.10
17
,362.2
7
32
,928.0
0
76.84
1,6
29.00
23,49
4.79
9,194
.76
3,648
.96
11,49
3.45
22
.32
1.25
0.36
56
.00
32.01
0.1
0
CC
0.20
25
7,756
.40
17,36
4.27
33,27
9.48
76
.85
1,629
.00
23
,492.3
6
9,1
94.15
3,6
48.96
11
,492.6
9
282.7
1
48
2.80
-
0.0
0
22.32
1.2
5
1.0
0
56.00
32
.01
0.08
CC0.2
0
261,9
73.10
17
,401.2
7
37
,017.8
6
76.90
1,6
25.00
23,43
4.79
9,171
.60
3,640
.00
11,46
4.50
4,0
38.00
87
3.08
40.38
-
0.00
22
.32
1.25
0.37
1.0
0
56.00
32
.01
0.39
CC0.2
0
262,3
96.30
17
,394.7
5
37
,525.3
7
76.92
1,6
24.00
23,41
4.29
9,164
.43
3,637
.76
11,45
5.54
28
9.62
494.6
1
4,038
.00
873.0
8
40
.38
-
0.0
0
22.32
1.2
5
20
.00
160.0
0
32
.01
35.90
CC
0.23
29
5,463
.10
6,769
.49
207,9
50.40
10
0.00
80
,760.0
0
17,46
1.51
807.6
0
9.87
8,8
60.03
0.95
1.0
5
22.32
1.2
5
38
7.00
32.01
41
.42
CC0.3
2
424,9
90.80
11
,487.7
4
27
6,482
.80
100.0
0
23.88
10,08
4.94
1.2
0
1.05
22
.32
1.50
0.14
56
.00
37.00
32
.01
21.78
LF
0.08
10
4,743
.00
2,556
.80
71,68
9.84
97
.91
150.0
0
2,1
18.32
83
5.33
33
6.00
1,253
.00
106.1
9
18
1.34
2.2
8
8,173
.67
0.9
7
0.85
22
.32
1.50
56.00
37
.00
32.01
21
.68
LF0.0
8
104,7
57.20
2,5
71.11
71
,519.0
7
97.90
15
1.00
2,134
.28
841.3
6
338.2
4
1,2
62.05
2.2
8
8,172
.53
0.9
8
0.84
22
.32
1.50
1.00
56
.00
37.00
32
.01
21.45
LF
0.08
10
9,604
.90
2,646
.23
75,39
5.73
97
.90
151.0
0
2,1
32.62
84
0.95
33
8.24
1,261
.42
4,038
.00
873.0
8
40
.38
2.2
8
8,085
.27
0.9
6
0.84
22
.32
1.50
0.36
1.0
0
56.00
36
.00
32.01
21
.70
LF0.0
8
109,9
02.70
2,6
83.52
75
,211.4
5
97.83
15
6.00
2,203
.86
868.9
5
349.4
4
1,3
03.42
28
4.04
485.0
8
4,038
.00
873.0
8
40
.38
2.2
2
7,960
.83
0.9
3
0.84
22
.32
1.50
18.34
82
.00
32.01
30
.59
CC0.1
0
125,0
98.40
2,8
80.03
87
,866.8
4
100.0
0
14,32
2.20
24
,459.1
5
5.0
6
8,845
.68
0.8
4
1.05
22
.32
1.50
18.53
1.0
0
82.00
32
.01
30.30
CC
0.10
13
0,109
.60
2,959
.08
91,85
6.04
10
0.00
14
,471.5
9
24,71
4.28
4,038
.00
873.0
8
40
.38
5.0
6
8,758
.67
0.8
2
1.05
22
.32
1.50
56.00
32
.01
CC0.2
2
287,0
95.40
19
,660.9
6
32
,928.0
0
76.84
1,6
29.00
23,49
4.79
9,194
.76
3,648
.96
13,79
2.14
22
.32
1.50
0.36
56
.00
32.01
0.1
0
CC
0.22
28
7,470
.80
19,66
2.81
33,27
9.48
76
.85
1,629
.00
23
,492.3
6
9,1
94.15
3,6
48.96
13
,791.2
2
282.7
1
48
2.80
-
0.0
0
22.32
1.5
0
1.0
0
56.00
32
.01
0.08
CC0.2
2
291,6
14.60
19
,694.1
7
37
,017.8
6
76.90
1,6
25.00
23,43
4.79
9,171
.60
3,640
.00
13,75
7.41
4,0
38.00
87
3.08
40.38
-
0.00
22
.32
1.50
0.37
1.0
0
56.00
32
.01
0.39
CC0.2
2
292,0
14.60
19
,685.8
5
37
,525.3
7
76.92
1,6
24.00
23,41
4.29
9,164
.43
3,637
.76
13,74
6.65
28
9.62
494.6
1
4,038
.00
873.0
8
40
.38
-
0.0
0
22.32
1.5
0
20
.00
160.0
0
32
.01
35.90
CC
0.23
29
5,463
.10
6,769
.49
207,9
50.40
10
0.00
80
,760.0
0
17,46
1.51
807.6
0
9.87
8,8
60.03
0.95
1.0
5
22.32
1.5
0
38
7.00
32.01
41
.42
CC0.3
2
424,9
90.80
11
,487.7
4
27
6,482
.80
100.0
0
23.88
10,08
4.94
1.2
0
1.05
22
.32
67
Table 23 Total Results of HOMER HRES Modelling 2
Sen
sitivity
/Diese
l Fue
l Price
($/L
)
Archit
ecture
/PV (k
W)
Archit
ecture
/G1
Archit
ecture
/Gen (
kW)
Archit
ecture
/LI
ASM
Archit
ecture
/Na
tel49 (k
W) Arc
hitect
ure/
Conve
rter
(kW)
Archit
ecture
/Dis
patch
Cost/C
OE
($)
Cost/N
PC ($)
Cost/O
peratin
g cos
t ($)
Cost/I
nitial
capital
($)
Syste
m/
Ren F
rac
(%)
Gen/H
ours Ge
n/Prod
uction
(kW
h)
Gen/F
uel
(L)
Gen/O
&M
Cost (
$)
Gen/F
uel
Cost (
$)
PV/Ca
pital
Cost (
$)
PV/Pr
oductio
n (kW
h)
G1/Ca
pital
Cost (
$)
G1/Pr
oductio
n (kW
h)
G1/O&
M Co
st ($)
LI
ASM/
Auton
omy
(hr)
LI
ASM/
Annua
l Th
roughp
ut (kW
h)
Conve
rter/R
ectifie
r Me
an Ou
tput (k
W) Co
nverte
r/Inver
ter
Mean
Outpu
t (kW)
Natel4
9/Mean
Ou
tput (k
W)
0.75
14.22
77.00
64.00
32.
01
29.55
LF
0.09
179,74
0.90
4,8
01.32
117,67
1.70
95.
50
298.00
7,150.
72
2,564.
45
917.84
1,9
23.33
11,
102.91
18,
961.31
2.52
18,346
.80
1.6
8
2.26
22.
32
0.75
13.52
1.00
77.
00
64.
00
32.01
29.
38
LF0.0
9
184
,322.6
0
4,894.
38
121
,050.5
0
95.50
298
.00
7,1
49.35
2,5
64.10
917
.84
1,923.
08
10,555
.29
18,026
.11
4,0
38.00
873.08
40.
38
2.5
2
18,
271.57
1.66
2.2
6
22.32
0.7
5
77.
00
61.
00
32.01
28.
11
LF0.0
9
189
,503.9
0
6,640.
50
103
,658.8
0
92.95
489
.00
11,
201.45
4,074.
35
1,506.
12
3,055.
76
2.40
18,349
.81
2.1
9
1.90
22.
32
0.75
1.00
77.
00
61.
00
32.01
28.
29
LF0.0
9
194
,195.5
0
6,681.
35
107
,822.3
0
92.98
487
.00
11,
156.90
4,058.
00
1,499.
96
3,043.
50
4,038.
00
873
.08
40.38
2.40
18,148
.02
2.1
4
1.90
22.
32
0.75
74.86
171.00
32.01
59.
80
CC0.1
4
287
,023.0
0
6,027.
57
209
,101.5
0
100.00
58,
467.01
99,
848.72
6.74
21,479
.25
1.4
1
3.02
22.
32
0.75
66.36
1.00
177
.00
32.
01
60.76
CC
0.14
292,10
0.50
6,2
73.37
211,00
1.40
100
.00
51,824
.76
88,505
.23
4,0
38.00
873.08
40.
38
6.9
7
21,
568.86
1.42
3.0
2
22.32
0.7
5
42.
99
77.
00
32.
01
14.11
LF
0.21
438,01
7.70
27,
034.10
88,533
.94
69.
65
2,422.
00
48,231
.04
18,
359.06
7,4
59.76
13,
769.30
33,
578.90
57,
345.33
-
0.27
22.
32
0.75
39.46
1.00
77.
00
32.
01
13.88
LF
0.22
442,81
8.40
27,
318.85
89,653
.51
69.
44
2,440.
00
48,575
.86
18,
492.08
7,5
15.20
13,
869.06
30,
820.65
52,
634.85
4,038.
00
873
.08
40.38
-
0.26
22.
32
0.75
77.00
32.01
LF
0.24
495,16
3.90
34,
800.80
45,276
.00
61.
09
3,109.
00
61,834
.17
23,
547.12
9,5
75.72
17,
660.34
22.
32
0.75
1.00
77.
00
32.
01
0.27
LF
0.24
499,58
2.10
34,
815.97
49,498
.00
61.
17
3,103.
00
61,705
.41
23,
499.30
9,5
57.24
17,
624.48
4,0
38.00
873.08
40.
38
-
0.0
0
22.32
0.7
5
345
.00
607
.00
32.
01
75.49
CC
1.19
2,443,
334.00
47,
135.73
1,833,
986.00
100
.00
1,393,
110.00
301
,211.1
0
13,931
.10
23.92
15,066
.16
0.8
4
3.04
22.
32
1.00
13.70
77.00
65.00
32.
01
32.03
LF
0.09
187,73
8.10
5,2
69.45
119,61
7.20
95.
85
274.00
6,594.
12
2,362.
76
843.92
2,3
62.76
10,
702.80
18,
278.02
2.56
19,066
.05
1.7
3
2.33
22.
32
1.00
14.34
1.00
77.
00
66.
00
32.01
31.
58
LF0.0
9
192
,345.1
0
5,249.
69
124
,479.7
0
95.93
269
.00
6,4
63.54
2,3
17.07
828
.52
2,317.
07
11,196
.66
19,121
.42
4,0
38.00
873.08
40.
38
2.6
0
18,
910.59
1.69
2.3
4
22.32
1.0
0
77.
00
62.
00
32.01
29.
06
LF0.1
0
202
,527.3
0
7,547.
88
104
,952.0
0
93.19
474
.00
10,
829.41
3,942.
22
1,459.
92
3,942.
22
2.44
18,751
.72
2.2
4
1.94
22.
32
1.00
1.00
77.
00
62.
00
32.01
29.
04
LF0.1
0
206
,659.9
0
7,556.
22
108
,976.8
0
93.20
473
.00
10,
805.09
3,933.
54
1,456.
84
3,933.
54
4,038.
00
873
.08
40.38
2.44
18,545
.26
2.1
8
1.94
22.
32
1.00
74.86
171.00
32.01
59.
80
CC0.1
4
287
,023.0
0
6,027.
57
209
,101.5
0
100.00
58,
467.01
99,
848.72
6.74
21,479
.25
1.4
1
3.02
22.
32
1.00
66.36
1.00
177
.00
32.
01
60.76
CC
0.14
292,10
0.50
6,2
73.37
211,00
1.40
100
.00
51,824
.76
88,505
.23
4,0
38.00
873.08
40.
38
6.9
7
21,
568.86
1.42
3.0
2
22.32
1.0
0
46.
93
77.
00
32.
01
14.50
LF
0.24
497,67
8.80
31,
390.50
91,877
.63
69.
88
2,404.
00
47,874
.68
18,
223.14
7,4
04.32
18,
223.14
36,
652.79
62,
594.86
-
0.29
22.
32
1.00
45.79
1.00
77.
00
32.
01
13.73
LF
0.24
502,38
6.00
31,
552.32
94,492
.89
69.
80
2,411.
00
48,003
.43
18,
273.53
7,4
25.88
18,
273.53
35,
760.60
61,
071.19
4,038.
00
873
.08
40.38
-
0.28
22.
32
1.00
77.00
32.01
LF
0.28
571,26
5.30
40,
687.58
45,276
.00
61.
09
3,109.
00
61,834
.17
23,
547.12
9,5
75.72
23,
547.12
22.
32
1.00
1.00
77.
00
32.
01
0.27
LF
0.28
575,52
8.90
40,
690.80
49,498
.00
61.
17
3,103.
00
61,705
.41
23,
499.30
9,5
57.24
23,
499.30
4,0
38.00
873.08
40.
38
-
0.0
0
22.32
1.0
0
345
.00
607
.00
32.
01
75.49
CC
1.19
2,443,
334.00
47,
135.73
1,833,
986.00
100
.00
1,393,
110.00
301
,211.1
0
13,931
.10
23.92
15,066
.16
0.8
4
3.04
22.
32
1.25
16.71
77.00
69.00
32.
01
31.65
LF
0.10
198,43
4.80
5,7
37.14
124,26
7.80
96.
48
288.00
5,599.
85
2,149.
09
887.04
2,6
86.36
13,
047.65
22,
282.50
2.72
19,949
.09
1.7
5
2.47
22.
32
1.25
15.15
1.00
77.
00
68.
00
32.01
32.
09
LF0.1
0
202
,934.3
0
5,893.
18
126
,750.1
0
96.44
292
.00
5,6
63.60
2,1
75.41
899
.36
2,719.
27
11,834
.62
20,210
.92
4,0
38.00
873.08
40.
38
2.6
8
19,
973.57
1.75
2.4
6
22.32
1.2
5
77.
00
68.
00
32.01
29.
49
LF0.1
1
221
,134.2
0
8,666.
97
109
,091.8
0
93.69
510
.00
10,
021.71
3,832.
13
1,570.
80
4,790.
16
2.68
20,417
.13
2.4
3
2.11
22.
32
1.25
1.00
77.
00
67.
00
32.01
29.
50
LF0.1
1
224
,406.5
0
8,656.
76
112
,496.1
0
93.76
505
.00
9,9
17.57
3,7
93.08
1,5
55.40
4,7
41.35
4,0
38.00
873.08
40.
38
2.6
4
20,
199.16
2.38
2.1
1
22.32
1.2
5
74.
86
171
.00
32.
01
59.80
CC
0.14
287,02
3.00
6,0
27.57
209,10
1.50
100
.00
58,467
.01
99,848
.72
6.7
4
21,
479.25
1.41
3.0
2
22.32
1.2
5
66.
36
1.0
0
177.00
32.01
60.
76
CC0.1
4
292
,100.5
0
6,273.
37
211
,001.4
0
100.00
51,
824.76
88,
505.23
4,038.
00
873
.08
40.38
6.97
21,568
.86
1.4
2
3.02
22.
32
1.25
46.70
77.00
32.01
15.
69
LF0.2
7
556
,280.8
0
35,874
.28
92,
515.45
69.95
2,3
98.00
47,
758.50
18,178
.49
7,385.
84
22,723
.12
36,475
.14
62,291
.47
-
0.3
0
22.32
1.2
5
45.
79
1.0
0
77.00
32.01
13.
73
LF0.2
7
561
,443.8
0
36,120
.70
94,
492.89
69.80
2,4
11.00
48,
003.43
18,273
.53
7,425.
88
22,841
.91
35,760
.60
61,071
.19
4,0
38.00
873.08
40.
38
-
0.2
8
22.32
1.2
5
77.
00
32.
01
LF0.3
2
647
,366.8
0
46,574
.36
45,
276.00
61.09
3,1
09.00
61,
834.17
23,547
.12
9,575.
72
29,433
.89
22.32
1.2
5
1.0
0
77.00
32.01
0.2
7
LF0.3
2
651
,475.9
0
46,565
.62
49,
498.00
61.17
3,1
03.00
61,
705.41
23,499
.30
9,557.
24
29,374
.13
4,038.
00
873
.08
40.38
-
0.00
22.
32
1.25
345.00
607.00
32.01
75.
49
CC1.1
9
2,4
43,334
.00
47,135
.73
1,8
33,986
.00
100.00
1,3
93,110
.00
301,21
1.10
13,
931.10
23.
92
15,
066.16
0.84
3.0
4
22.32
1.5
0
20.
68
77.
00
71.
00
32.01
33.
26
LF0.1
0
204
,815.7
0
5,806.
13
129
,756.9
0
96.90
254
.00
4,9
32.11
1,8
93.71
782
.32
2,840.
56
16,153
.28
27,586
.22
2.8
0
20,
082.29
1.71
2.5
5
22.32
1.5
0
20.
68
1.0
0
77.00
71.00
32.
01
32.93
LF
0.10
209,65
5.30
5,8
85.70
133,56
7.80
96.
90
254.00
4,926.
24
1,892.
23
782.32
2,8
38.35
16,
153.28
27,
586.22
4,038.
00
873
.08
40.38
2.80
19,869
.23
1.6
7
2.54
22.
32
1.50
77.00
70.00
32.
01
29.66
LF
0.11
233,75
5.20
9,5
34.64
110,49
6.00
93.
81
500.00
9,833.
72
3,759.
13
1,540.
00
5,638.
69
2.76
20,483
.32
2.4
4
2.12
22.
32
1.50
1.00
77.
00
67.
00
32.01
29.
50
LF0.1
2
236
,672.1
0
9,605.
17
112
,501.1
0
93.76
505
.00
9,9
17.57
3,7
93.08
1,5
55.40
5,6
89.62
4,0
38.00
873.08
40.
38
2.6
4
20,
199.16
2.38
2.1
1
22.32
1.5
0
74.
86
171
.00
32.
01
59.80
CC
0.14
287,02
3.00
6,0
27.57
209,10
1.50
100
.00
58,467
.01
99,848
.72
6.7
4
21,
479.25
1.41
3.0
2
22.32
1.5
0
66.
36
1.0
0
177.00
32.01
60.
76
CC0.1
4
292
,100.5
0
6,273.
37
211
,001.4
0
100.00
51,
824.76
88,
505.23
4,038.
00
873
.08
40.38
6.97
21,568
.86
1.4
2
3.02
22.
32
1.50
47.70
77.00
32.01
15.
46
LF0.3
0
614
,734.3
0
40,348
.39
93,
129.84
70.00
2,3
94.00
47,
679.53
18,148
.35
7,373.
52
27,222
.52
37,250
.55
63,615
.70
-
0.3
0
22.32
1.5
0
41.
93
1.0
0
77.00
32.01
17.
38
LF0.3
0
621
,420.4
0
40,799
.89
93,
979.07
69.76
2,4
14.00
48,
065.40
18,296
.83
7,435.
12
27,445
.25
32,743
.56
55,918
.77
4,0
38.00
873.08
40.
38
-
0.2
9
22.32
1.5
0
77.
00
32.
01
LF0.3
5
723
,468.2
0
52,461
.13
45,
276.00
61.09
3,1
09.00
61,
834.17
23,547
.12
9,575.
72
35,320
.67
22.32
1.5
0
1.0
0
77.00
32.01
0.2
7
LF0.3
5
727
,422.8
0
52,440
.45
49,
498.00
61.17
3,1
03.00
61,
705.41
23,499
.30
9,557.
24
35,248
.96
4,038.
00
873
.08
40.38
-
0.00
22.
32
1.50
345.00
607.00
32.01
75.
49
CC1.1
9
2,4
43,334
.00
47,135
.73
1,8
33,986
.00
100.00
1,3
93,110
.00
301,21
1.10
13,
931.10
23.
92
15,
066.16
0.84
3.0
4
22.32
67
Appendix G: Theory of HRES
Figure 54 Theory and Formula of HRES 1
68
Figure 55 Theory and Formula of HRES 2
69
Figure 56 Theory and Formula of HRES 3
70
Figure 57 Theory and Formula of HRES 4
71
Figure 58 Theory and Formula of HRES 5
72
Appendix H: Research Participant Consent Form
73
Appendix I: Ethics Registration and Approval Form
74
75
76
