TECHNO-ECONOMIC OPTIMIZATION AND MARKET POTENTIAL STUDY OF SMALL-SCALE PARTICLE HEATING RECEIVER BASED CENTRAL RECEIVER POWER TOWER PLANTS IN MENA REGION
A Dissertation Presented to The Academic Faculty
by
Shakir Shakoor Khatti
In Partial Fulfillment of the Requirements for the Degree Master of Science in the George W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology May 2021
COPYRIGHT © 2021 BY SHAKIR SHAKOOR KHATTI TECHNO-ECONOMIC OPTIMIZATION AND MARKET POTENTIAL STUDY OF SMALL-SCALE PARTICLE HEATING RECEIVER BASED CENTRAL RECEIVER POWER TOWER PLANTS IN MENA REGION
Approved by:
Dr. Sheldon M. Jeter, Advisor G. W. Woodruff School of Mechanical Engineering Georgia Institute of Technology
Dr. Said I. Abdel-Khalik (Co-chair) G. W. Woodruff School of Mechanical Engineering Georgia Institute of Technology
Dr. Hany Al-Ansary Department of Mechanical Engineering King Saud University
Date Approved: April 16, 2021
This thesis work is dedicated to my father, mother, brother, and sisters, for their unending
love and dedicated partnership for success in my life.
ACKNOWLEDGEMENTS
This thesis has been a great journey and also a test of my knowledge, patience, and assiduity. I want to express my deep sense of thanks and gratitude to many who made this journey possible with their help and mentorship.
I would like to thank Almighty God who is omnipotent and omniscient. My parents and my siblings, who have encouraged and supported me in every course of my life. It was their emotional and moral support that made me complete this thesis work while being across the ocean.
I am deeply indebted to my thesis advisor, Dr. Sheldon Jeter, for granting me the opportunity to work with him and his team and providing guidance on my research work through his profound knowledge of the subject. His patience and unparalleled support are highly appreciated.
I would like to express my deepest appreciation to my thesis reading committee for their time and valuable advice. This work would not have been more practical without their important suggestions and comments.
I gratefully acknowledge the insightful comments and suggestions of Dr. Hany Al-
Ansary which helped me in improving the quality of this research work.
I want to extend my sincere thanks to Ms. Courtney Castillo (IIE-Fulbright
Advisor), who helped me during my degree program and research-related problems.
Financial and administrative support of Fulbright Foreign Student Program and United
iv States Educational Foundation in Pakistan (USEFP) towards my master’s degree scholarship is highly appreciated.
Many thanks to my friends in Pakistan and the United States, especially, Mr.
Ammar Osman, Ms. Noor-Us-Saba Khan, Ms. Hira Anis, Mr. Shaarif Sajid, Mr. Abdullah
Ba Gunaid, Mr. Mohid Muneeb Khattak, and Mr. Bilal Mufti who were always a source of support and encouragement.
I cannot leave Georgia Tech without recognizing the assistance of Dr. Kenzo
Repole, Engr. Muhammad Mansoor Sarfaraz, and Mr. Ryan Yeung in this work. Their valuable time, suggestions, and proofreading of this dissertation are recognized and greatly appreciated.
v TABLE OF CONTENTS
ACKNOWLEDGEMENTS iv
LIST OF TABLES viii
LIST OF FIGURES ix
ABBREVIATIONS AND NOMENCLATURE xi
SUMMARY xiv
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. LITERATURE REVIEW 6 2.1 Global Energy Scenario 6 2.1.1 Energy Scenario of MENA region 7 2.2 Concentrated Solar Power 8 2.2.1 Particulate Material CSP Systems 10 2.3 Techno-Economic Analysis of CSP Systems 11 2.3.1 CRPT System Analysis Tools 11 2.3.2 Techno-Economic Analysis of PHR based CSP Systems 14 2.4 Geographic Information System (GIS) 15 2.4.1 GIS Applications in Renewable Energy 16 2.4.2 GIS-Based CSP and PV Site Selection 17
CHAPTER 3. METHODOLOGY AND METHODS 20 3.1 System Description 20 3.2 Techno-Economic Optimization 22 3.2.1 Cost Model for PHR based CSP System 22 3.2.2 System Modelling and Design Inputs 26 3.2.3 Levelized Cost of Electricity 30 3.3 Latitude and DNI Effects on Performance of PHR-CSP System 32 3.3.1 Latitude Dependence 32 3.3.2 DNI Dependence 35 3.4 GIS-Based Market Potential Study 36 3.4.1 Population Density 39 3.4.2 Slope 40 3.4.3 Electric Transmission Network 42 3.4.4 Levelized Savings for Electricity 44
CHAPTER 4. RESULTS AND DISCUSSIONS 46 4.1 Results of Techno-Economic Analysis 46 4.2 Results of Regression Modelling 51 4.3 Results of GIS-based Market Potential Study 52 4.3.1 Algeria 52 4.3.2 Bahrain 54
vi 4.3.3 Egypt 55 4.3.4 Iran 57 4.3.5 Iraq 59 4.3.6 Jordan 60 4.3.7 Kuwait 62 4.3.8 Lebanon 63 4.3.9 Libya 65 4.3.10 Morocco 66 4.3.11 Oman 68 4.3.12 Qatar 70 4.3.13 Saudi Arabia 73 4.3.14 Syria 77 4.3.15 Tunisia 79 4.3.16 United Arab Emirates 81 4.3.17 Yemen 83
CHAPTER 5. CONCLUSION AND FUTURE WORK 85
APPENDIX A: TOWER COST MODEL 92
APPENDIX B: LCOE CALCULATIONS 93
APPENDIX C: REGRESSION ANALYSIS 94
APPENDIX D: INTERPOLATION TECHNIQUE 95
REFERENCES 96
vii LIST OF TABLES
Table 1 – Field optical efficiency matrix...... 28 Table 2 – Optimization variables ...... 29 Table 3 – Constraints, specifications, and financial data [8, 43, 44, 88, 89] ...... 30 Table 4 – Locations chosen for latitude dependence study ...... 34 Table 5 – Locations chosen for DNI dependence study ...... 35 Table 6 – Constraints and criteria embodied in GIS-based analysis [94, 95] ...... 38 Table 7 – Optimum parameters ...... 46 Table 8 – Results of annual performance ...... 49
viii LIST OF FIGURES
Figure 1 – DNI map of MENA region [7] ...... 2 Figure 2 – Normalized summer load profile in Rafha and Sharurah (Data source: [8]) ... 3 Figure 3 – Normalized winter load profile in Rafha and Sharurah (Data source: [8]) ...... 3 Figure 4 – Energy-related CO2 emissions, 1990-2019 (adapted from [10])...... 7 Figure 5 – Comparison of countries in MENA region based on electricity consumption and CO2 emissions [19-21] ...... 8 Figure 6 – Concentrating solar power technologies; (a) paraboloid dish; (b) linear Fresnel collectors; (c) parabolic trough collectors; and (d) central receiver power tower (adapted from [22]) ...... 9 Figure 7 – Visualization of different GIS data layers [61]...... 16 Figure 8 – Overlaying criteria (broken down in sub and sub-sub criteria) layers to obtain final suitability map (adapted from [67]) ...... 17 Figure 9 – Schematic of 1.3 MWe PHR based CSP system ...... 21 Figure 10 – Breakdown of the total capital cost of PHR based CSP system...... 27 Figure 11 – Contour plot of field optical efficiency matrix ...... 27 Figure 12 – Flowchart showing steps involved in techno-economic optimization ...... 33 Figure 13 – Effect of latitude on AEP of PHR-CSP system ...... 35 Figure 14 – Effect of DNI on AEP of PHR-CSP system...... 36 Figure 15 – Raster data representation ...... 37 Figure 16 – Electric transmission network of KSA represented using vector data (lines and points) ...... 38 Figure 17 – Flowchart explaining steps involved in GIS-based study...... 39 Figure 18 – Reclassified slope data with satellite image from Google Earth Pro...... 40 Figure 19 – Comparison between (a) reclassified PD data and (b) night-time ...... 41 Figure 20 – Electric transmission network with 50 km buffers ...... 42 Figure 21 – Rasterized and reclassified transmission network...... 43 Figure 22 – LSFE data interpolated over the whole region ...... 44 Figure 23 – Optimum field layout of 9.75 MWth SHSS (unconstrained)...... 47 Figure 24 – Parametric study by varying SM and storage hours...... 48 Figure 25 – Average daily energy production of 1.3 MWe system in each month ...... 48 Figure 26 – Optimum field layout of 9.75 MWth SHSS (constrained) ...... 49 Figure 27 – LCOE Comparison between constrained and unconstrained SHSS ...... 50 Figure 28 – Locations were chosen in the MENA region for regression modeling ...... 51 Figure 29 – Plot of constructed regression model ...... 52 Figure 30 – Savings per area map of Algeria ...... 53 Figure 31 – Savings per area map of Bahrain ...... 54 Figure 32 – Savings per area map of Egypt...... 56 Figure 33 – Population Density map of Egypt [94] ...... 56 Figure 34 – Savings per area map of Iran...... 58 Figure 35 – Slope map of Iran ...... 58 Figure 36 – Savings per area map of Iraq...... 59 Figure 37 – DNI map of Jordan [7] ...... 60
ix Figure 38 – Savings per area map of Jordan...... 61 Figure 39 – Savings per area map of Kuwait...... 63 Figure 40 – Savings per area map of Lebanon...... 64 Figure 41 – Savings per area map of Libya ...... 66 Figure 42 – Development of electricity consumption in Morocco (Data source: IEA)... 67 Figure 43 – Savings per area map of Morocco ...... 68 Figure 44 – Development of electricity generated from fossil fuel and the electricity consumption in Oman from 1980 to 2017 [117]...... 69 Figure 45 – Savings per area map of Oman ...... 70 Figure 46 – Development of population growth and electricity consumption in Qatar between from 1980 to 2017 from 1990 to 2018 [124, 125] ...... 71 Figure 47 – Savings per area map of Qatar (a) with proximity to electric lines criterion and (b) without proximity to electric lines criterion...... 72 Figure 48 – Map of Qatar showing eight municipalities (Source: Ministry of Municipality and Environment, Qatar) ...... 73 Figure 49 – Electricity production by different resources and electricity consumption of KSA from 2010 to 2018 (Data source: General Authority of Statistics, KSA) ...... 74 Figure 50 – Savings per area map of Saudi Arabia ...... 75 Figure 51 – Savings per area map of Saudi Arabia with potential locales ...... 76 Figure 52 – DNI map of Syria [7]...... 78 Figure 53 – Savings per area map of Syria...... 78 Figure 54 – Electricity production and share of renewables in electricity from the production of Tunisia from 1973 to 2017 [117] ...... 79 Figure 55 – Savings per area map of Tunisia ...... 80 Figure 56 – Electricity production of UAE by fossil fuel and renewables from ...... 81 Figure 57 – Savings per area map of UAE ...... 82 Figure 58 – Satellite photograph of small villages in Emirate of Abu Dhabi...... 83 Figure 59 – Savings per area map of Yemen ...... 83 Figure 60 – Comparison of tower cost models ...... 92 Figure 61 – LCOE calculations performed in Microsoft Excel Sheet ...... 93 Figure 62 –Regression analysis in Microsoft Excel ...... 94 Figure 63 – Illustration of interpolation technique – IDW ...... 95
x ABBREVIATIONS AND NOMENCLATURE
ηconv Conversion efficiency (-)
ϕ Latitude (o)
AEP Annual Energy Production (GWh)
2 Af Area of heliostat field (m )
2 Arec Area of the receiver (m )
BP British Petroleum
CDF Cumulative Distribution Function (%)
CRPT Central Receiver Power Tower
CSP Concentrated Solar Power
DEM Digital Elevation Model
DNI Direct Normal Irradiation
EPC Engineering, Procurement, and Construction
fgross Gross-to-net conversion factor (-)
HTF Heat Transfer Fluid
HTM Heat Transfer Medium
htower Optical height of the tower (m)
GCC Gulf Cooperation Council
GDP Gross Domestic Product
GHG Greenhouse Gases
GIS Geographic Information System
GIT Georgia Institute of Technology
GWh Gigawatt hour
xi HVAC Heating, Ventilation, and Air Conditioning
HX Heat Exchanger
IEA International Energy Administration
KSA Kingdom of Saudi Arabia
KSU King Saud University
ktoe Kilo tons of oil equivalent
kWe Kilowatt – Electric
kWh Kilowatt-hour
kWth Kilowatt – Thermal
LCOE Levelized Cost of Electricity ($/kWh or ¢/kWh)
LSFE Levelized Saving for Electricity ($/kWh or ¢/kWh)
MENA Middle East and North Africa
NREL National Renewable Energy Laboratory
PB Power Block
PD Population Density
PHR Particle Heating Receiver
PT Parabolic Trough
PV Photovoltaic
Design Thermal Power (kW ) Qdes th
2 Sa Savings per area ($/km )
SAM System Advisor Model
SM Solar Multiple (-)
TEA Techno-Economic Analysis
TES Thermal Energy Storage
xii TWh Terawatt Hour
UAE United Arab Emirates
Welec,capita Per capita electricity consumption (kWh/capita)
xiii SUMMARY
Receiver designs using different heat transfer mediums such as salt, gas, and particulate material are under development. High-temperature operation characteristics of particle heating receiver (PHR) based central receiver power tower (CRPT) systems with low-cost thermal energy storage (TES) provide an advantage over the existing salt heating systems. Therefore, this study proposes to investigate and identify the best sites for small- scale PHR based CRPT system deployment to provide electricity to off -grid remote locations in the Middle East and North Africa (MENA) region due to higher direct normal irradiation (DNI) in the region throughout the year. This research aims to develop and apply a methodology for the techno-economic analysis optimization and geographic information system (GIS) based market potential study of PHR based CRPT systems in the region. For sizing the system, optimum solar multiple (SM) and TES hours are found by performing techno-economic optimization in SolarPILOT and SAM. To compare the economic feasibility, a useful economic criterion, Levelized Savings for Electricity (LSFE) is calculated by taking the difference between Levelized Cost of Electricity (LCOE) of PHR based CRPT system and LCOE of stand-alone diesel generators. Suitable regions for PHR-
CRPT sites are identified by excluding regions with higher slopes, extensive urbanization, proximity to electrical transmission lines, and other negative features. Favorable locales within the feasible region are identified by mapping the density of potential consumers, per capita electricity consumption, and LSFE. Overall results of GIS-based multi-criteria analysis are presented as maps showing favorable (higher savings) locales for investment in PHR-CRPT systems.
xiv CHAPTER 1. INTRODUCTION
Issues related to the GHG concentrations in the environment are increasing day by day due to the continuous use of fossil fuels by human beings for commuting from one place to another, HVAC systems in the buildings to maintain thermal comfort, manufacturing of goods used in daily routine, etc. Population increase poses another issue that will further exacerbate the prevailing situation in the future if we continue to use fossil fuels to meet our energy needs. To meet the future energy demand and protect the environment, the energy mix should be diversified by deploying more renewable energy technologies.
Solar energy is one of the widely available renewable energy resources which can be harvested to meet the energy demand. Solar photovoltaic cells convert the sunlight into electricity without any integration with a heat engine [1]. However, the intermittent nature of solar irradiation makes the PV technology less attractive as storage devices have high purchase costs [2]. On the other hand, CSP technologies that concentrate solar irradiation to heat the receiver, provide a better solution in this regard, as the TES in CSP technologies increases energy production and dispatchability [3].
PHR based CRPT system heats the falling curtain of solid particles in the receiver which can attain high temperatures and avoids solidification issues faced in molten salt based CRPT systems [4]. Due to high-temperature operation, overall efficiency for PHR based CRPT system also increases, and cost-effective TES increases the economic value of the overall system by maintaining productivity. Such systems can be used for both off- grid and on-grid electricity generation due to their cost-effective nature and flexibility.
1 CSP systems can operate economically at locations having a DNI of 2000 kWh/m2/year or 5.5 kWh/m2/day [5, 6]. MENA1 region is blessed with not only the oil and gas resources but also receives higher solar irradiation in the world as shown in Figure 1.
Due to high DNI values in the region, PHR-CRPT technology can be a promising electricity generation resource in the region for both off-grid and on-grid sites.
Figure 1 – DNI map of MENA region [7]
However, some of the challenges faced in the PHR based CSP systems are related to site selection and adequate sizing. Also, the viability of such systems depends on meeting the power needs. As an example, Figure 2 and Figure 3 show the summer and winter load profiles, respectively, for two potential off-grid locations in KSA (Rafha and
Sharurah). It is evident from figures that capacity factors for these locations in summer and winter range from 72-75% and 61-63% respectively. A PHR based CRPT system should be designed to attain optimum capacity factor as well as minimum LCOE.
1 MENA region in this research includes Algeria, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, and Yemen.
2 1.00
0.95
0.90
0.85
0.80 Normalized load profile load Normalized 0.75
0.70 0 2 4 6 8 10 12 14 16 18 20 22 24 Hour of the day Rafha - Summer Sharurah - Summer
Figure 2 – Normalized summer load profile in Rafha and Sharurah (Data source: [8])
1.00
0.95
0.90
0.85
0.80
0.75
Normalized load profile load Normalized 0.70
0.65
0.60 0 2 4 6 8 10 12 14 16 18 20 22 24 Hour of the day Rafha - Winter Sharurah - Winter
Figure 3 – Normalized winter load profile in Rafha and Sharurah (Data source: [8])
This research work presents a methodology to address the above-cited issues by performing technical and economic analysis of the system to ensure proper sizing of the system with TES. Detailed cost models valid according to current quotations of vendors,
3 manufacturers, and contractors are presented in this research. A regression model is constructed further in this research by analyzing the effects of latitude and DNI on the annual energy production and LCOE. Based on the LCOE calculations performed at different locations, a regression model is then created by setting independent variables as latitude and DNI.
Also, GIS-based methodology to perform the market potential study for PHR based
CRPT system is presented in this thesis. LSFE – a new parameter is introduced in this research to calculate the savings when PHR based CRPT system is used instead of diesel generators. LSFE is then combined with other criteria to perform multi-criteria analysis in the GIS tool. Lastly, saving per area maps are created for each country in the region to identify suitable locales for the deployment of such a system for off-grid electrification.
A detailed literature survey is presented in Chapter 2, which gives a thorough knowledge about the development in PHR based CRPT systems, techno-economic analysis tools, and previous techno-economic analysis and optimization studies. Also, a thorough introduction to GIS applications in renewable energy in general and solar energy sources is presented in that chapter. Detailed cost models based on quotes provided by vendors, suppliers, and manufacturers for subcomponents in the PHR based CRPT system are presented in Chapter 3 on methodology and methods employed in this thesis work. Also, the methodology applied to technical and economic analysis of the system under study is presented.
Chapter 4 presents the results obtained in this thesis and their discussions. Maps presented for the individual country in the region help us in identifying potential locales
4 for the off-grid deployment of the PHR based CRPT system. Conclusions and important takeaway items are presented in Chapter 5 along with a discussion on future work that can be done to improve and refine the results presented in this thesis work.
5 CHAPTER 2. LITERATURE REVIEW
The energy demand around the world has grown due to faster growth in population, industrialization, and urbanization. GHG emissions have caused the rise in sea level, global warming, and threats to the existing ecosystem [9]. These prevailing conditions require the immediate attention of researchers and engineers around the world to increase the contribution of cost-effective renewables in the overall global energy mix. PHR based CSP systems provide cost-effective electricity generation solutions due to the unique properties of the heat transfer medium.
2.1 Global Energy Scenario
The increase in population has also increased the use of transport, heating and cooling systems, and industrial operations around the world which will continue to grow in the future. Around 80% of the world’s total primary energy demand was met using fossil fuels (coal, natural, and oil) in 2019 which has escalated energy-related CO2 emissions by
46% in 2019 compared to the year 2000, with an increase of 10.5 gigatons as shown in
Figure 4 [10].
According to IEA’s World Energy Outlook, global electricity demand will increase
26% by 2030 compared to estimated global electricity demand in 2020 [11]. Increasing energy demand in the world is playing a key role in the global problem of climate change due to the rise of GHG concentrations in the environment. The prevailing issue of climate change has caused serious problems for the growth in the global economy, specifically for developing countries with low flexibility and adaptability [12, 13].
6
Figure 4 – Energy-related CO2 emissions, 1990-2019 (adapted from [10])
2.1.1 Energy Scenario of MENA region
In past, economies in the MENA region have seen slower growth in energy consumption compared to other economies in the world. However, recently the energy consumption in the region has grown swiftly due to growth in the economy, rapid-growing industrialization, high living standards, and population increase [14]. The contribution of the MENA region to the production of oil and gas is higher than other regions in the world due to larger proven reserves [15, 16]. Higher energy subsidies offered in the region [17] and reliance of the region on fossil fuels for socio-economic development have increased the region’s vulnerability to climate change [18]. It can be seen in Figure 5 that some of the countries in the MENA region despite having a smaller population density and area have higher CO2 emissions (energy consumption in all end-uses) and electricity consumption compared to developing countries in Asia, Europe, and North America. It should be noted that the size of the bubble shows the GDP per capita (2019).
7 45 Qatar
40
35
30 Kuwait
25 UAE Saudi Arabia 20 Bahrain Oman
15 Iran Germany
emissions, 2019 (tons per capita) (tonsper 2019 emissions, Iraq 2 2 10 Jordan Libya Singapore United States Canada CO Tunisia Algeria 5 Syria Lebanon Egypt China 0 Yemen Morocco 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 Electricity consumption (kWh/capita) Figure 5 – Comparison of countries in MENA region based on electricity consumption and CO2 emissions [19-21]
With energy transition from high-carbon resources to low-carbon renewable resources will help the region in overcoming the energy demand while contributing towards addressing issues of global warming and sea-level rise in the world.
2.2 Concentrated Solar Power
In CSP technologies, solar energy coming from the sun is concentrated at a point or line using point-focusing or line-focusing concentrating mirrors. Heat transfer medium absorbs thermal energy in the receiver and heats the working fluid which runs the turbine or heat engine to generate mechanical power. An electrical generator coupled to the prime mover generates electricity which can be dispatched to the grid. Thermal energy storage is integrated into the CSP system for continued production of energy when the sun is not shinning.
8 Point focusing technologies like paraboloid dishes (Figure 6a) concentrate solar energy at a single point to run the Stirling engine and generate power. Linear Fresnel collectors and parabolic trough collectors as shown in Figure 6b and Figure 6c respectively are line focusing technologies that concentrate the rays at the receiver pipe to heat the HTF flowing in it.
(a) (b)
(c) (d) Figure 6 – Concentrating solar power technologies; (a) paraboloid dish; (b) linear Fresnel collectors; (c) parabolic trough collectors; and (d) central receiver power tower (adapted from [22])
Central tower surrounded by heliostats is another point focusing technology, which concentrates solar energy at the receiver to heat the working fluid as shown in Figure 6d.
Currently, three technologies in the central receiver power tower are under the research and development phase. Those three technologies are categorized as molten salt, particulate material, and gaseous substances based on CSP systems [23].
9 2.2.1 Particulate Material CSP Systems
Several shortcomings of molten salt based CSP systems are; solidification of salt at temperatures below 230℃, relatively higher cost of material, lower operating temperature range 250-620℃, and corrosive characteristics of salt [24-26]. These drawbacks can be avoided when particulate material instead of salt is used in CRPT systems which enable high-temperature operation [27], add more flexibility to the system, and use high-efficiency cycles like sCO2 [28].
Ho et al. studied the recent advancements in particle heating receiver designs, which show higher-temperature operations and cost-effective TES due to the chemically benign nature of solid particles compared to current state-of-art molten salt CRPT systems
[29]. Two receiver designs, namely, falling curtain and impeded flow receiver for heating the particulate material in CRPT have been proposed and analyzed [30, 31]. The experimental and numerical study was also carried out for granular flow mesh screen design (impeded flow characteristics) for particle heating receiver applications and flow properties of solid particles were analyzed [32].
On-sun testing was performed to evaluate the performance of 1.0 MW high- temperature particle heating receivers which showed that obstructed flow design has better performance than free-falling particle receiver [33]. Later, on-sun testing was performed for falling particle receiver with automated flow and temperature control which add to the overall performance in commercial designs [34]. In 2018, on-sun testing was performed for a 300 kWth PHR based CRPT system in Riyadh, Saudi Arabia to run a 100 kWe microturbine. The on-sun testing shown encouraging results and recorded high
10 temperatures for particles show that the overall efficiency of the system increases and cost- effective subcomponents can reduce the LCOE [4].
Previous research shows that PHR based CRPT system can be economical and efficient than molten salt based CSP systems which makes PHR-CRPT systems a viable option for on-grid and off-grid electricity generation.
2.3 Techno-Economic Analysis of CSP Systems
Techno-economic analysis (TEA) is performed to evaluate the system performance and economics of the system. Techno-economic analyses of two CSP technologies using different TES were carried out by identifying net present value (NPV) as the suitable economic indicator in the spot electricity market. The results obtained by authors showed that two-tank direct sensible heat storage (SHS) presents an economically viable option in comparison to currently used two-tank indirect SHS [35]. Trabelsi et al. carried out the techno-economic analysis of the 50 MWe PTC CSP plant at Tataouine, Tunisia using SAM.
They compared the dry-cooled and wet-cooled systems based on their impact on annual energy production, LCOE, and water consumption. The results obtained in techno- economic assessment suggested that dry-cooled system has lower efficiency and comparatively lesser energy production which can be compensated by higher DNI values in the region [36].
2.3.1 CRPT System Analysis Tools
CRPT technologies’ modeling tools are categorized into two main categories; tools performing optimization of the system and tools developed for detailed optical
11 performance [37]. Different tools like University of Houston Codes (UHC – known as
RCELL) [38], DELSOL3 [39], HFLCAL [40], and TieSOL [41] have been developed in past to generate and optimize field geometry. SolTrace [42] on the other hand simulates the optical performance of the field but field geometry is not optimized using this tool.
SolarPILOT – a tool developed by NREL, combines the field layout generation and optimization capabilities with optical performance calculations using both analytical series approximation and ray-tracing algorithm (used in SolTrace) to perform better modeling of the receiver, tower, and heliostat field [43]. The field efficiency matrix obtained from field characterization tools can be combined with economical and system parameters to evaluate annual performance and LCOE calculations in SAM [44].
Abaza et al. carried out sizing analysis and techno-economic optimization of stand- alone 10 MWe CRPT system in Aswan, Egypt. They compared different power block designs, namely, open gas cycle, organic Rankine cycle, and steam Rankine cycle integrated to open volumetric receiver based CRPT having 4 hours of TES (thermocline single tank). Results suggested in the study showed that the steam Rankine cycle in power block design has a minimum LCOE of 0.1 $/kWh which makes the design feasible for decentralized applications [45]. Hirbodi et al. investigated technical, economical, and environmental aspects of CSP technologies in the south-central part of Iran. Two CSP technologies; PT, and molten salt CRPT were chosen for this study, and optimum SM and
TES hours were found by maximizing the annual electricity production and solar-to- electric efficiency in Shiraz, Iran. The authors suggested that SM of 3 and 14 hours of TES with dry-cooling option give optimum results for 100 MWe system. Environmental
12 evaluation results revealed that 399 kilotons of CO2 can be reduced by deployment of the afore-mentioned 100 MWe system [46].
Abdelhady performed a techno-economic assessment of 50 MWe solar dish technology in Aswan, Egypt. Technical and economic analysis was carried out in SAM and estimated an LCOE of 0.13 $/kWh which is comparable to LCOEs estimated for the same technology in other regions and MENA region. The sensitivity analysis carried out by the author revealed a strong correlation between collectors' cost and LCOE/NPV [47].
Sultan et al. performed the techno-economic analysis of 50 MWe PT-CSP plant by taking
AndaSol-1 [48] in Spain as a reference plant. The authors focused on arid climate conditions and water scarcity in Kuwait and compared dry-cooling and wet-cooling options in the plant. Techno-economic optimization carried out to find optimum SM and TES full load hours to minimize LCOE revealed that SM of 3.3 and 16 hours of TES gives a cost- competitive solution. Minimum LCOE was reported as 0.15 $/kWh with an annual electricity generation of 249 GWh with TES [49].
Praveen R.P. performed techno-economic optimization of 100 MWe CSP tower plant at three different locations in the KSA, namely, Yanbu, Abha, and Dawadmi. CRPT system was optimized for maximum annual energy production and minimum LCOE using the parametric study of solar multiple and full load hours of storage in SAM and reported that SM of 2.4 with 8 hours of storage gives a maximum CF of 61% for Yanbu (western province) [50]. In a techno-economic study carried out by Azouzoute et al., 62 locations in the whole MENA region were chosen. The sizing was done for a 50 MWe wet-cooled PT-
CSP system with two storage tanks at an individual location to find optimum SM and TES full load hours. Authors used Greenius software [51] developed by the German Aerospace
13 Center (DLR) to perform the study which revealed that the MENA region has good potential of harvesting solar energy using CSP systems and show LCOE in the range of
0.14 $/kWh to 0.27 $/kWh [52]. Benmakhlouf carried out the techno-economic and market potential study for 13 kWe solar dish small-scale CSP for mainly industrial applications in the MENA region. The authors chose five representative countries in the MENA region and performed technical and economic optimization of the system based on the energy needs of industrial customers. Ranking process was used to identify and rank the potential industrial customers in chosen countries based on multi-criteria analysis (MCA) [53].
A thorough survey of current literature on the techno-economic analysis presented in this section suggests that proper sizing of the system by choosing optimum SM and TES hours is a necessary step in the feasibility studies and techno-economic optimization of the
CSP system. It also provided a thorough knowledge about techniques and tools used in techno-economic analysis and optimization.
2.3.2 Techno-Economic Analysis of PHR based CSP Systems
Buck and Giuliano performed optimization of the particle heating CRPT integrated to sCO2 power cycle for varying outlet temperatures of particles to find minimum LCOE, the analysis showed that higher outlet temperature of particles reduces the LCOE [28]. Ma et al. studied the particle heating CSP system integrated into the fluidized bed (FB) technology for heat transfer applications and thermal energy storage of particulate materials. The techno-economic study presented in the paper suggested that a 20% reduction in LCOE is seen for FB-CSP in comparison to molten salt based CSP systems
[54]. Cathy et al. carried out techno-economic optimization of scaled-up CentRec® [55]
14 receiver based CSP system to find the minimum Levelized Cost of Heat (LCOH) with TES and without TES. Minimum LCOH was found to be 0.02 $/kWth and 0.018 $/kWth for two cases respectively [56].
In [57], Albrecht et al. created an EES model for a falling-particle receiver based
CRPT system. Presented model coupled the performance and economic evaluation of the system involving the effects of changes in operating parameters of subcomponents; particle-to-fluid heat exchanger, power cycle, TES bins, and particulate material. LCOE was calculated using tailored cost models and baseline system costs were compared to
SunShot targets of the U.S. Department of Energy [58]. González-Portillo et al. improved on the techno-economic model presented in [57] to increase the fidelity and accuracy of the model. Off-design conditions were added to the system and the receiver model was refined by incorporating results of CFD simulations. Results presented in the study show that particle loss should be less than 0.01% to achieve SunShot target LCOE. It was also shown that a minimum LCOE of 0.06 $/kWh can be achieved for a 100 MWe system by having a solar multiple of 3, with 16 hours of TES, and a concentration ratio of 2500 [59].
Various researchers have performed the techno-economic optimization of the PHR-
CRPT system and important characteristics are identified as solar multiple and TES full load hours. One thing of substantial interest is that optimum solar multiple is in the range of 2.4-3.1 and optimum TES range from 14 to 16 hours in the presented literature survey.
2.4 Geographic Information System (GIS)
According to the definition given by United States Geological Survey [60], a
Geographic Information System (GIS) is a computer-based system that displays and
15 analyzes the geographically referenced information. Every data point is unique to a particular location in GIS. Different layers containing a specific type of data (e.g., topographic data, water bodies data, demographic data, etc.) can be combined as shown in
Figure 7 to generate a map that helps scientific research and engineering.
Figure 7 – Visualization of different GIS data layers [61]
GIS tools process the geospatial data presented in raster or vector format. Raster data help in performing mathematical operations like addition, multiplication, etc. Vector data can also be processed to create shapes, boundaries, etc. Also, other processing tools such as Digital Elevation Model (DEM), interpolation, geostatistics, Geospatial Data
Abstraction Library (GDAL) in GIS software are useful in GIS-based analyses.
2.4.1 GIS Applications in Renewable Energy
In the area of renewable energy, GIS is often used in the process of site selection for the deployment of renewable energy generation. This literature survey shows that
16 previously GIS has been used for site selection of wind farms [62-64], solar power plants
[65-68], hybrid renewable energy systems (HRES) [69], and biomass [70]. Multi-Criteria
Decision Making (MCDM) process is often combined with GIS tools to help in solving decision-related problems [65]. Multiple layers are overlaid based on weights of a particular criterion found using Analytical Hierarchy Process (AHP) [71] and suitability metrics are generated based on GIS results for the final map. Figure 8 shows the process of overlaying different criteria layers to obtain the suitability map (wind and solar farms in this case) in GIS software.
Figure 8 – Overlaying criteria (broken down in sub and sub-sub criteria) layers to obtain final suitability map (adapted from [67])
2.4.2 GIS-Based CSP and PV Site Selection
Studies have been carried out in past to evaluate the suitability of available land in the region for solar energy generation based on suitable criteria. Gerbo et al. carried out a
GIS-AHP study to generate a suitability map for grid-connected solar PV farms in East
Shewa Zone, Ethiopia, based on technical, economic, and environmental criteria and constraints including potential solar power. Their study showed that 2.2 TW electric power can be fed into the electric grid by utilizing the available area of 1129 km2 for harvesting
17 solar energy [68]. Yushchenko et al. carried out a technical and geographical potential study of on-grid (CSP and PV) and off-grid (PV) to identify suitable locations considering the topological, social, and economical constraints. They did the case study for the West
Africa region and estimated the potential of 700-1800 TWh/year and 900-3200 TWh/year for CSP and PV technologies respectively for on-grid and 81 TWh/year for off-grid rural electrification [66]. Garni and Awasthi performed the site suitability analysis for utility- scale solar PV farms using GIS and MCDM. KSA was taken as a case study region with different constraints; climatic (solar irradiation and average temperature), economical (land cost and construction cost), infrastructural (distance to roads, distance to power lines, etc.), topographic (slope, aspect, etc.) and environmental constraints (vegetation, etc.). The analysis revealed that “highly suitable” areas are in the Northern and Northwestern parts of the Kingdom [72].
This literature survey suggests that many GIS-based site selection studies have been done for the MENA region to generate suitability maps for CSP plants [73-77]. Ibarra et al. performed the GIS-based site suitability analysis for the 50 MWe PT-CSP plant in Saudi
Arabia. They overlaid restricted land map (classified based on land use, slope, and water bodies), geographic value map (accessibility to roads and transmission lines), and dust risk map (wind, relative humidity, etc.) with PTC performance map to arrive at suitability map for KSA. The resulting map showed that the Northwestern region of the country is highly suitable due to higher solar irradiations and hence the performance of the plant [73].
Merrouni et al, performed site-suitability analysis for the Eastern region of
Morocco using GIS-based exclusion criteria. In the analysis, the authors excluded areas
18 with vegetation, roads, water bodies, and transmission lines. The final suitability map was created by combining all layers generated after the exclusion of “non-suitable” areas which showed that the eastern region of Morocco has 65% of land which is suitable for deployment of a CSP facility [75]. Site suitability study carried out by Gherboudj and
Ghedira for the UAE showed that 9.7% and 1.0% of UAE’s land area is available for deployment of PV and CSP electricity generation systems when all geographical, economical, and environmental constraints are combined in GIS-based map [77].
Researchers in the past have performed GIS-based suitability analysis for CSP and solar PV technology by considering multiple factors and criteria in their analysis. Most of the criteria considered in those studies are irradiation, slope, land cover (vegetation, water bodies, etc.), infrastructure (proximity to roads and electrical transmission grid), and protected areas.
An important factor called Levelized Savings for Electricity is introduced in this research work to quantify the economic value of using PHR based CRPT system instead of diesel generators in remote areas. This important factor presented in this thesis will help the currently available literature on GIS-based site suitability analysis. Also, in the current study, population density – an important demographic variable – is included which was not looked upon in previous research.
19 CHAPTER 3. METHODOLOGY AND METHODS
This chapter presents the methodology and methods used to perform the techno- economic and market potential study of the 1.3 MWe PHR based CRPT system for off-grid application. The capacity of 1.3 MWe is chosen as the high-efficiency turbine of this capacity is readily available, which can be easily integrated into the PHR-CRPT system.
Description of the system is presented which identifies the main components of the system and their working principles. Cost models and design inputs of the system are presented in detail along with the optimization methodology used in current research to maximize the energy output of the system. The methodology employed for the GIS-based market potential assessment of the PHR based CSP system is also presented in this chapter identifying the constraints and criteria set in this study. Lastly, GIS model validations are presented in this chapter using other satellite data and sources.
3.1 System Description
A schematic of the 1.3 MWe PHR based CRPT system is shown in Figure 9. Given
PHR-CSP system consists of a cavity receiver mounted at the top of the power tower. The power tower is surrounded by the heliostat field which concentrates the solar energy at the aperture of the cavity receiver. The particle heating receiver is made of discrete structures
(a type of obstructed flow PHR) to limit the velocity of particles and increase the residence time to maximize absorbed solar flux in the receiver [8]. Heated particles are transferred to a hot TES bin, those heated particles are transferred to the particle-to-working fluid heat exchanger (PWFHX). After the heat exchange process, heated working fluid runs the air-
Brayton gas turbine. Cold particles after the heat exchange process are stored in a cold TES
20 bin and transferred back to the top of the tower using a skip hoist. A charging bin is also constructed to compensate for any lost particles during operation.
Legend BDP – Back Draft Preventer PHR – Particle Heating Receiver CB – Charging Bin PS – Pre-Skip DNI – Direct Normal Irradiance PWF – Particle-to-Working Fluid FC – Flow Controller PWR – Power FT – Flow Transmitter SG – Slide Gate HT – High Temperature SH – Skip Hoist HX – Heat Exchanger TC -Temperature Controller LI – Level Indicator TES – Thermal Energy Storage LT – Low Temperature UH – Upper Hopper PBC – Power Block Controller WS – Weigh Station PF – Particle Filter
Figure 9 – Schematic of 1.3 MWe PHR based CSP system
21 3.2 Techno-Economic Optimization
This section presents the methods used in techno-economic optimization of the 1.3
MWe PHR-CSP system shown in Figure 9. First, cost models of subcomponents in the CSP system are discussed, which are constructed based on vendor quotes from suppliers and manufacturers, proprietary data, and cost engineering tools. Model and design inputs in techno-economic tools are discussed next. Finally, a methodology to calculate LCOE is presented and discussed.
The Generic CSP Model in SAM [44] is used to perform techno-economic analysis as suggested by [57]. Solar Multiple and TES full load hours of storage are chosen as optimization variables (see Table 2) to maximize the annual energy production for optimum field size.
3.2.1 Cost Model for PHR based CSP System
The cost model for PHR based CSP system is divided into three parts i.e., the cost model for Solar Heat Supply System (SHSS), the cost model for power block and TES
(PB-TES), and the cost model for the balance of system (BOS), which together give total capital cost (TCC) of PHR-CRPT system.
3.2.1.1 Cost Model for Solar Heat Supply System
The cost model equations for SHSS are tuned to best fit the data obtained from contractors, manufacturers, and cost engineering tools. Cost models for an individual subcomponent of the SHSS are presented below:
22 ▪ Tower cost
Tower cost is calculated using fixed tower cost, tower cost scaling exponent, and tower optical height2. By incorporating quotations from contractors and manufacturers for the recent 47.1 m tower to be built in Turaif, KSA, and power tower of larger-scale 26.5 MWe
PHR-CRPT system [8], cost model given by Equation (1) is developed (see Appendix A).
The below-given equation shows that tower cost is a function of tower optical height.
1 0.035htower m m (1) Costtower = 172,000 $ e
▪ Receiver cost
Discrete Structure (DS) PHR [31] is used in the current system under study. These discrete structures are made of low-cost ceramic materials which can operate at high temperature. DS-PHR is lower in cost than receivers currently used in molten salt CSP towers. The tool used for field optimization incorporates the receiver cost model for molten salt CSP systems. For the current study, receiver cost parameters (fixed receiver cost and scaling exponent) are adjusted using quotations from suppliers and manufacturers for recent receiver design for the 27 MWe system as given in [8]. Equation (2) calculates receiver cost as follows, it shows that receiver cost is a function of receiver area:
0.7 2 Arec m Cost10,300,000rec = $ (2) 1571 m2 ▪ Total heliostat field cost
The heliostat field consists of heliostat structures arranged around the tower using the radial-staggered method. The community goal is to achieve $75/m2 for the cost of the heliostat [78]. However, several opportunities for cost reduction do not seem to be fully
2 Optical height of tower is defined as the distance from pivot/center point of heliostat structure to the center of receiver.
23 exploited such as wireless control described in [79], which is essentially conventional.
Furthermore, low profile designs such as described by [80, 81] along with wireless and possibly autonomous control and other available technology improvement opportunities appear capable of achieving the $50/m2 goal, especially, when implemented in larger-scale regional or local production. Heliostats are modular units, the modular cost of heliostat by adding $10/m2 for site improvements cost is given by Equation (3).
$$ 22 Cost= 50 AA m + 10 m field,tot22 f f (3) mm
The total direct capital cost of SHSS can then be calculated using Equation (4).
Cost=Cost+Cost+CostSHSStowerrecfield,tot (4)
3.2.1.2 Cost Model for Power Block and TES
Power block of 1.3 MWe PHR-CRPT system consists of first of its kind A1300 turbine manufactured by Aurelia (Finnish turbine company) [82]. A1300 is an efficient turbine that incorporates compressor intercooling and recuperation of turbine exhaust. This turbine is chosen for the current system due to its easy integration with PWFHX and ability to operate in hybrid mode. The cost of the power block can be found using Equation (5) which is modeled based on the private conversation with the manufacturer as given in [8].
$ Cost= 1,300W kW PB elec e (5) kWe
TES is constructed from masonry and ceramic materials which reduce TES cost than currently used two-tank TES [52] in molten salt based CSP systems. Equation (6) can be used to calculate the cost associated with TES. Given cost model for TES is modeled based on the cost estimates for storage medium and construction materials and cost [8].
24 $ Welec Cost5.0kWTEShTESthhours= (6) kWh thconv
3.2.1.3 Cost Model for Balance of System
Balance of system cost can be calculated by summing costs related to PWFHX, high-temperature particle lift system, and power piping that are adopted from vendor and supplier quotes.
Two heat exchanger types, namely, conventional shell and tube (ST) and direct contact (DC) [83] heat exchanger are used in this system, and results are compared for both cases. Equation (7) and Equation (8) can be used to calculate the cost of PWFHX for both cases, respectively. The cost model for the first-of-its-kind shell and tube heat exchanger is constructed based on quotations from manufacturers as given in [8]. For direct contact heat exchanger, the cost model given by Equation (8) is constructed based on cost estimates of the developer of this technology as provided in [84].
$ Cost1,870kWPWFHX,STelece=W (7) kWe
$ Cost170kWPWFHX,DCelece=W (8) kWe
The cost of particle lift system [85] and power piping [8] can be calculated as given by Equation (9) and Equation (10). Equation (11) calculates the BOS cost in total.
$ Cost80kWPLelece=W (9) kWe $ CostPP= 50W elec kW e (10) kWe
25 Cost=Cost+Cost+CostBOSPWFHXPLPP (11)
The total installed cost of the PHR based CSP system can be calculated by Equation
(12) which incorporates the indirect costs related to contingency and EPC.
Cost=Cost+Cost+Cost+Cost+Cost+CostPHR,CSPSHSSPBTESBOScontEPC (12)
Costs related to contingency and EPC are calculated as given below,
Cost=Cost=0.1(Cost+Cost+Cost+Cost+Cost)contEPCtowerrecPBTESBOS (13)
It should be noted that tax and land costs are not incorporated in the current study as they would be minimal even if existing in the MENA region. It is also projected that contingency costs will be reduced for future CSP projects as this study assumes serial production due to opportunities existing in several sites as many as a hundred.
3.2.2 System Modelling and Design Inputs
3.2.2.1 Field Optical Efficiency Matrix
After the cost modeling of the system is done, the field layout for the SHSS is generated and optimized for maximum annual energy output while minimizing the cost.
Tower height and receiver dimensions play an important role in the optimization of the whole CSP system because heliostat field arrangement depends on these parameters. Also, the cost share of SHSS in the total capital cost of the CSP system is highest (as shown in
Figure 10) which makes the optimization of SHSS an important step in techno-economic optimization of the overall CSP system.
26 It should be noted that values in the pie chart show the cost-share percentage of the individual subsystem. BOS has the second-highest cost in the overall PHR-CSP system and by reducing this cost, LCOE can be reduced.
Figure 10 – Breakdown of the total capital cost of PHR based CSP system
SHSS is optimized in SolarPILOT [43] which has been used previously by other authors [4, 50, 59]. Once the SHSS is optimized, the field optical efficiency matrix is generated by varying the solar azimuth and solar zenith angles. A contour plot of the field efficiency matrix is shown in Figure 11. Also, field optical efficiency is listed in Table 1 for a range of solar zenith and solar azimuth angels.
Figure 11 – Contour plot of field optical efficiency matrix
27 Table 1 – Field optical efficiency matrix Solar Azimuth (o)
-120 -90 -60 -30 0 30 60 90 120
10 58.6 60.3 61.9 63.2 63.6 63.2 61.9 60.3 58.6
)
o (
20 56.3 59.6 62.9 65.3 66.2 65.3 62.9 59.6 56.3 30 53.9 58.9 63.7 67.1 68.3 67.1 63.7 58.9 53.9 40 51.5 58.1 64.3 68.5 70.0 68.5 64.3 58.1 51.5 50 49.0 57.1 64.4 69.3 71.0 69.3 64.4 57.1 49.0 60 46.0 55.1 63.1 68.4 69.9 68.4 63.1 55.1 46.0 Solar Zenith Solar 70 40.3 49.5 57.1 63.2 64.2 63.2 57.1 49.5 40.3 80 29.4 36.3 39.6 44.6 43.2 44.6 39.6 36.3 29.4
3.2.2.2 Weather data
Weather data is most important in field layout generation and evaluating the annual performance of the system. In this analysis Typical Meteorological Year (TMY) data is used as a weather data file which is acquired from EU PVGIS – A European Commission’s
GIS-based database, which provides the information about solar radiation and PV system performance [86]. The weather data file contains information about latitude, longitude, solar irradiation, dry-bulb and wet-bulb temperatures, relative humidity, pressure, wind direction, and wind speed at hourly intervals for 8760 hours of the year. The atmospheric attenuation model is selected as DELSOL3 clear day model [39].
3.2.2.3 Sizing Heliostat Field
The field layout is generated based on the design thermal power, which quantifies the amount of thermal power absorbed by the HTM to convert into electricity. Design thermal power can be calculated using Equation (14).
WeleckW e Qdes =SM (14) conv
28 The radial stagger layout method is used in this research with no-blocking dense
(radial spacing method) and azimuthal spacing factor which defines the azimuthal spacing between two heliostats in first ring of a group is taken as 2 (twice the heliostat width) [87].
Design point DNI is chosen as value at 50% of CDF. Tower height and receiver dimensions are important parameters in field layout optimization, which are given in Table 2.
Table 2 – Optimization variables Variable Unit SolarPILOT Tower optical height m Receiver width m Receiver height m SAM Full load hours of TES hour SM [-]
3.2.2.4 Heliostat and Receiver Structures
Single panel small heliostats are used in this study. This analysis assumes a cavity receiver; however, a flat plate type receiver is chosen due to the software’s limitations.
Dimensions of the receiver and heliostat structure are listed in Table 3. Net thermal power absorbed by HTM is given by Equation (15).
kW QQqA= −kWm th 2 HTMincthlossrec 2 (15) m
Where qloss accounts for thermal losses and α is absorptivity of the receiver.
3.2.2.5 Power Block and TES
Power block parameters use conversion efficiency and gross-to-net conversion factor which accounts for parasitic loads to calculate the net electrical power available.
29 Equation (16) can be used to calculate electric power generated by the system (without accounting for parasitic losses). TES hours of storage can be used to calculate maximum thermal capacity as given by Equation (17).
WWfnetelecegross=kW (16)
WelecekW U TEShours=TESh (17) conv
Table 3 – Constraints, specifications, and financial data [8, 43, 44, 88, 89] Constraints SolarPILOT Variable Value Allowable peak flux G ≤ 10 MW/m2 Minimum field radius rmin ≥ 34.9 m Maximum field radius rmax ≤ 350 m SAM
Full load hours of TES 2 ≤ TEShours ≤ 20 hour Specifications SolarPILOT Design power 1.3 MWe Conversion efficiency 0.4 [-] Heliostat width 1.2 m Heliostat height 1.2 m Heliostat reflectivity 0.95 [-] Heliostat focusing type At slant Receiver horizontal acceptance angle 110o Receiver thermal absorptance 0.9 [-] Financial Parameters SAM Fixed operating cost 1% of TCC Internal rate of return, d 6% The inflation rate, i 2.5% Analysis period, n 30 years
3.2.3 Levelized Cost of Electricity
Total capital cost and annual energy production of the system can be incorporated with fixed operation and maintenance (O&M) cost, variable O&M cost, and other financial
30 parameters listed in Table 3 to calculate the LCOE using Equation (18) or Equation (19).
Fixed O&M GSPWF FCRTCC + LCOEVOC=+USPWF (18) Annual Energy TCC + (Fixed O&M GSPWF)
LCOEVOC=+USPWF (19) Annual Energy
In the above-given equations, FCR is the fixed charge rate (reciprocal of USPWF as defined below) and VOC is the variable operating cost.
GSPWF is the geometric series present worth factor, which represents the present worth of future costs of fixed O&M adjusted for inflation as given by Equation (20).
n 11+ i GSPWF1=− (20) did−+1
USPWF is the uniform series present worth factor calculated using Equation (21), which represents the present worth of future cost without accounting for inflation.
(11+−d )n USPFW = (21) dd(1+ )n
The formula for LCOE in SAM [44] was compared with the formula used in this thesis work and it was seen that fixed O&M costs are not accounted for their economic average over the lifetime of the system (as presented in Equation (22).) which significantly affect the LCOE calculation of system. These costs should be represented as economic average costs over the lifetime of the system. In the current study, hybrid mode operation is not assumed, therefore, variable O&M costs (VOC) are not appreciable, and we assume a fixed amount of O&M costs (see Table 3) regardless of annual energy production. LCOE
31 calculations performed using a Microsoft Excel Sheet based model are presented in
Appendix B.
FCR TCC + Fixed O&M LCOE=+ VOC (22) Annual Energy
A flowchart presented in Figure 12 shows the steps involved in techno-economic analysis and optimization of the system. It should be noted that red dotted lines show steps involved in SolarPILOT (SHSS optimization) and green dotted lines represent optimization steps involved in SAM.
3.3 Latitude and DNI Effects on Performance of PHR-CSP System
The performance of CSP systems depends on the weather conditions and solar angles. Solar angles change with the latitude of a particular location which increases or decreases the performance of the CSP system at a given time of the day. DNI, on the other hand, is categorized as the most significant parameter among meteorological variables, which also varies with location. This section proposes the methodology to construct the regression model for LCOE calculation given as a function of latitude and DNI value. A linear regression model is constructed based on the simulated data obtained for the optimum 1.3 MWe PHR-CRPT system at 35 different locations in the whole MENA region.
3.3.1 Latitude Dependence
Platzer generated the artificial weather data to separate the latitude effects from DNI effects. Author studied the effects of varying latitude on the performance of different CSP technologies i.e. linear Fresnel, PT, and CRPT. It was concluded based on presented results
32
Start
Constraints, specifications, and TMY weather cost model (see Table 3) data Data Entry
Generate guesses for optimization variables (see Table 2)
Generate tentative heliostat field
Simulation
No Check for optimum
Yes
Optimum SHSS
Generate optical efficiency matrix for optimum SHSS
TMY Design specifications and weather data Data Entry cost model (see Table 3)
Generate guesses for optimization variables (see Table 2)
Simulation
No Check for optimum
Yes
Annual performance and LCOE data
Finish
Figure 12 – Flowchart showing steps involved in techno-economic optimization
33 at higher latitudes, the seasonal performance of the CSP system varies largely relative to smaller latitudes. It is because variations in season are due to variations in solar angles throughout the year [90]. Chhatbar and Meyer studied the influence of meteorological parameters on the performance of CSP systems. They chose PT and CRPT systems in their analysis and found that a newly defined parameter A EP decreases when latitude is DNI increased for both technologies [91].
To better study the effects of variation in latitude, the annual DNI average was adjusted to the same baseline value for five locations at different latitudes (see Table 4)
Table 4 – Locations chosen for latitude dependence study Adjusted DNI Latitude (o) Region AEP (GWh) (kWh/m2/day) 44.22 Mongolia 6.53 2.45 34.35 Afghanistan 6.53 7.08 31.6 KSA 6.53 9.27 24.3 KSA 6.53 8.95 18.3 KSA 6.53 8.41
In this study, it was found that AEP increases with an increase in latitude but it decreases sharply with further increase beyond certain latitude which in the current study is found as 32o approximately as shown in Figure 13.
The effect of latitude variation presented in the current study aligns with the trends presented in [90], which suggested that as the latitude increases CSP systems get more vulnerable to seasonal changes due to variation in solar angles at higher latitudes. At higher latitudes, the sun is at a lower position which increases angular losses. In winters, at higher latitudes, days are shorter in length, which significantly decreases the annual energy production of CSP systems.
34 10 7 AEP 9 DNI
8 6.5
7 /day) 2
6 6 AEP (GWh) AEP
5 DNI (kWh/m DNI
4 5.5
3
2 5 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 Latitude (degree)
Figure 13 – Effect of latitude on AEP of PHR-CSP system
3.3.2 DNI Dependence
It has been shown in various studies presented in Section 2.3 that the annual performance of CSP systems increases with an increase in solar irradiation. In the current study, we chose different locations having the same latitude and different DNI values to see the effect on AEP. Selected locations are listed in Table 5. It was seen that AEP increases with an increase in DNI as shown in Figure 14.
Table 5 – Locations chosen for DNI dependence study DNI AEP Latitude (o) Region (kWh/m2/day) (GWh) 24.6 KSA 6.43 8.3 24.6 KSA 6.47 8.9 24.6 KSA 6.89 9.1
35 10.5
10
9.5
9
8.5
8 AEP (GWh) AEP 7.5
7
6.5
6 6.43 6.47 6.89 2 DNI (kWh/m /day) Figure 14 – Effect of DNI on AEP of PHR-CSP system
Based on the above-discussed results, the general linear regression model for LCOE is given below by Equation (23). Here ϕ is in degrees and DNI has a unit of kWh/m2/day.
LCOE,(DNIccc) =++ DNI123 (23)
3.4 GIS-Based Market Potential Study
Many applications of GIS in site selection for renewable energy resources were discussed in the literature survey presented in Chapter 2. In the current research, GIS tools are used to perform the market potential study of a 1.3 MWe PHR based CRPT system for off-grid applications in the MENA region. Different constraints and criteria are defined which play an important role in the identification of off-grid sites and their potential. Geo- referenced data in both raster and vector form is used in this study to overlay multiple layers and generate final maps.
36 Raster data is in matrix form which is arranged in rows and columns and each grid cell/pixel has information stored in it e.g., temperature, elevation, DNI, etc. as shown in
Figure 15.
Figure 15 – Raster data representation
Vector data is represented by points, lines, and polygons. Vector data is composed of discrete geometric locations (latitude, longitude) or (x,y) which combined represent the shape of spatial objects. E.g., roads, rail tracks, electric transmission networks, buildings, etc. can be represented using vector data in the GIS tool. As an example, Figure 16 shows the vector data; lines (electric transmission network of KSA) and points (transmission hubs) at specific latitude and longitude.
Different criteria and constraints used in this GIS-based market potential study are listed in Table 6. Shapefiles of countries are downloaded from the Stanford Digital
Repository and OCHA ROMENA [92, 93] which provide first-level (provincial)
37 administrative divisions of the country. A flowchart explaining the methodology followed in the GIS-based market potential study is presented in Figure 17.
Figure 16 – Electric transmission network of KSA represented using vector data (lines and points)
Table 6 – Constraints and criteria embodied in GIS-based analysis [94, 95] Exclusion Data Type Constraints criteria Population density PD ≥ 150 Urban Urban areas (PD) [capita/km2] PD < 150 Rural
Slope ≥ 3 Not-suitable Not-suitable areas Slope [%] Slope < 3 Suitable for CSP Electric Regions in a buffer Proximity Transmission zone Regions with Network (with 50 Regions away from proximity Remoteness km buffers) a buffer zone
38
Figure 17 – Flowchart explaining steps involved in GIS-based study 3.4.1 Population Density
Population density (PD) is an important demographic variable that gives us information about population distribution in a region or country. Based on the population density one can identify the urban, semi-urban, or rural areas, which help in the policy- making and decision-making process in a region.
In this research population density data obtained from [96] is used to classify the urban and rural areas which help in identifying potential locations for off-grid PHR-CRPT sites. Based on PD data, urban areas and rural areas are classified, further urban areas are excluded (given a pixel value of 0) as those are not considered as potential sites with off- grid electricity consumers. PD data is classified based on the suggestions mentioned in
[94], which are also listed in Table 6.
39 To further validate the classification made in this work, reclassified PD data are compared with night-time satellite images acquired from [97]. It can be seen in Figure 19b that densely populated areas (light density very high in night-time satellite image) are also classified as urban areas shown in Figure 19a.
3.4.2 Slope
In site selection of CSP systems, the slope is considered as an important factor because in areas with higher slope, costs of leveling the terrain will be higher which affect the overall plant cost. The literature review has suggested that slopes in range of 2-4% are used in multiple studies of CSP systems [73-76]. In this study slopes higher than or equal to 3% are considered as non-suitable for CSP sites (see Figure 18) as discussed in [95].
Figure 18 – Reclassified slope data with satellite image from Google Earth Pro
40
(a)
(b) Figure 19 – Comparison between (a) reclassified PD data and (b) night-time satellite image
41 Digital Elevation data is obtained from GMTED2010 in [98]. Further, the slope is calculated using the DEM in QGIS [99]. Once the slope data is calculated, reclassification of slope raster data is carried out to identify suitable and non-suitable regions. Reclassified data is also compared with satellite images acquired from Google Earth Pro as shown in
Figure 18. The image shows that terrain is too rugged for the construction of CSP facility.
3.4.3 Electric Transmission Network
Distance to the electric transmission grid is an important factor in the site selection of CSP systems. Off-grid locations are located away from the transmission grid due to which transmission costs increase with increasing distance. Due to heavy costs related to transmission, diesel generators are used for generating electricity in remote areas.
Figure 20 – Electric transmission network with 50 km buffers
42 In this study, electric transmission network data (updated till 2017) is obtained from the database of The World Bank [100]. Different resources reviewed in the literature survey used distances from the grid in the range of 30-80 km [66, 73, 76], in this study 50 km buffers (radius) are drawn around the transmission lines using “Geoprocessing Tools” in
QGIS software as shown in Figure 20. Once, the buffers are drawn, this vector data is rasterized in software and processed to generate final classified raster data (see Figure 21) identifying places that can be provided via the grid (pixel value of 0) and potential off-grid areas (pixel value of 1).
Figure 21 – Rasterized and reclassified transmission network
It should be noted that reclassification shown in Figure 21 can be reversed for on- grid sites by assigning 0 values to the pixels in remote areas and values of 1 to pixel in the proximity of the electric transmission grid.
43 3.4.4 Levelized Savings for Electricity
LSFE shows the savings when PHR based CRPT system is used instead of diesel generators at off-grid locations. LSFE plays an important role in identifying the potential locations for off-grid applications as it depends on the LCOE at a location that is a function of latitude and DNI as discussed in Section 3.3. LSFE can be calculated as given in
Equation (24) where the LCOE for diesel generator is taken from [101]. It should be noted that LCOE calculations in [101] assume unsubsidized costs for fuel.
Once the LSFE is calculated for multiple locations in the country, it can be spatially interpolated over the whole country region in GIS software using inverse distance weighted interpolation technique (see Appendix D) as shown in Figure 22.
LSFE = LCOELCOEdieselPHR-CRPT− (24)
Figure 22 – LSFE data interpolated over the whole region
44 LSFE interpolated over the whole country is in raster data form which can be multiplied with other raster data discussed earlier and electricity consumption per capita
[21] to calculate the saving per area given by Equation (25).
capitakWh$ SPDW= ××LSFE (25) a 2 elec,capita kmcapitakWh
System description was presented in this chapter to understand different subcomponents and their working principles. Detailed cost models were presented along with the methodology to perform the techno-economic optimization of the system. Latitude and DNI effects helped in creating a regression model to find the LCOE of the PHR based
CRPT system at a location. Lastly, methodology and different data used in the GIS-based market potential study were presented, which will help in creating the maps that can be used by investors, government organizations, and policy-makers for future deployment.
The methods explained in this chapter are extended to the whole MENA region and the results are discussed in the following chapter.
45 CHAPTER 4. RESULTS AND DISCUSSIONS
First, techno-economic analysis of 1.3 MWe PHR based CRPT system for off-grid electrification is carried out using the methodology presented in Chapter 3. SM and TES full load hours are chosen as the optimization variables in TEA to find an optimum configuration with minimum LCOE. Thirty-five locations are then chosen in the whole
MENA region and optimum fields are created and simulated. Annual performance and
LCOE data of those locations are further used to generate a regression model. LSFE data is generated for all countries in the region using a regression model to interpolate and incorporate in the GIS tool to generate final savings maps of the countries.
4.1 Results of Techno-Economic Analysis
Wa’ad Al-Shamaal located in the north of KSA is chosen as the base case to perform the techno-economic analysis in SAM and SolarPILOT to find the optimum configuration of a small-scale standalone system. This location is chosen as the base case because a CSP facility already exists at the site and a pre-commercial PHR based CRPT system will be built at the above-mentioned location [8]. Table 7 lists the optimum values of the chosen optimization variables.
Table 7 – Optimum parameters Variable Value Unit SolarPILOT Tower optical height 48.8 m Receiver width 4.89 m Receiver height 3.73 m SAM Full load hours of TES 14 hour SM 3 [-]
46 The optimum field layout for a 1.3 MWe system with an SM of 3 and 14 hours of storage in Wa’ad Al-Shamaal is shown in Figure 23, the field consists of 15,252 heliostats.
Figure 23 – Optimum field layout of 9.75 MWth SHSS (unconstrained)
Solar multiple and TES full load hours are varied to find the optimum configuration. SM is varied from 2 to 4 and TES full hours are varied from 2 to 20 as shown in Figure 24, it can be seen that optimum SM is found to be 3 as LCOE decreases from SM of 2 to 3 but increases significantly when SM is increased from 3 to 4. The plot shows that
SM of 2 has a weak minimum of around 13 hours of TES. In contrast, SM of 3 does not even show a weak minimum and appears to have an asymptotic value somewhat beyond
15 hours of TES. The trend shows that further investment will be substantially expensive beyond 15 hours of TES and LCOE is not significantly reduced; therefore, in this research
14 hours of storage and SM of 3 are chosen as optimum.
47 21.0 SM = 2 SM = 3 19.0 SM = 4
17.0
15.0
/kWh) ¢
13.0 LCOE LCOE ( 11.0
9.0
7.0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
TES full load hours (hour) Figure 24 – Parametric study by varying SM and storage hours
31000
29000
27000
25000
23000
21000
19000
17000 Average Daily Energy Production Production Daily (kWh) Energy Average
15000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Figure 25 – Average daily energy production of 1.3 MWe system in each month
48 Capacity factor, annual energy production, and LCOE for the optimum system are listed in Table 8. A capacity factor of 81.3% can be achieved with SM of 3 and 14 hours of TES, which is higher than capacity factors calculated for Rafha and Sharurah using normalized load profiles given in [8]. Monthly energy production is shown in Figure 25.
Table 8 – Results of annual performance Variable Unit Annual energy 9,258,000 kWh Capacity factor 81.3% LCOE (Conventional shell and tube) 8.1 ¢/kWh
It was found that 30% (approx.) reduction in LCOE can be achieved when advanced
(direct contact) HX is used instead of shell and tube HX. The LCOE of 5.62 ¢/kWh achieved using direct contact HX is lower than SunShot LCOE targets – 6 ¢/kWh [23].
Figure 26 – Optimum field layout of 9.75 MWth SHSS (constrained)
49 A comparison was made between field layouts with and without land constraints to see the effect of land constraints on the cost of the PHR based CRPT system. The field layout of the SHSS of 1.3 MWe system with land constraints is shown in Figure 26. Results for the aforementioned cases are plotted in Figure 27. It was found that a 1.3 MWe PHR based CSP system with 14 hours of storage has a difference of 0.9 ¢/kWh in LCOE for two cases under study. This difference between LCOE values can be attributed to the higher tower optical height required in the case of constrained land.
19.0 Bounded land Unbounded land 17.0
15.0
/kWh) ¢
13.0 LCOE ( LCOE
11.0
9.0
7.0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 TES (hours)
Figure 27 – LCOE Comparison between constrained and unconstrained SHSS for PHR-CRPT
Relatively arid locations are chosen most of the time for the construction of CSP plants, which makes it favorable to choose an unconstrained field layout for SHSS, making the overall system economically viable.
50 4.2 Results of Regression Modelling
Thirty-five different locations were selected in the MENA region as shown in
Figure 28 to construct the regression model. A linear regression model is constructed based on the LCOE data generated for those locations setting latitude and DNI as governing variables. Latitude and DNI are chosen as dependent variables because optical efficiency is the function of latitude and annual energy production depends on DNI value.
Calculations of the regression modeling are presented in Appendix C.
Figure 28 – Locations were chosen in the MENA region for regression modeling
The result of the regression modeling is presented by Equation (26) for which the
R2 value (coefficient of determination) was found to be 0.782.
LCOE( ,21.242DNIDNI) =−− 0.062 1.58 (26)
A 3D plot showing the relation between LCOE, latitude, and DNI is plotted as shown in Figure 29. The plot shows that most of the training data points lie on the
51 hyperplane representing the above-defined equation. It can also be seen that some outliers should be investigated in future research. This regression model can calculate the LCOE
2 of 1.3 MWe PHR based CSP system at a location given the DNI (kWh/m /day) and latitude
(degrees). LCOE calculated using Equation (26) can be used to calculate LSFE.
Figure 29 – Plot of constructed regression model
4.3 Results of GIS-based Market Potential Study
4.3.1 Algeria
According to the framework of the national renewable energy and energy efficiency plan, 22000 MW renewable electricity generation systems will be deployed in the country from 2011 to 2030 and CSP technologies represent 70% of the generation mix [102].
52 Due to the ambitious plans of the country, it is essential to locate suitable sites for both on-grid and off-grid generation. Figure 30 presents the savings per area map generated for an off-grid small-scale system using the methodology presented in section 3.4. It should be noted that areas with no savings are represented as white-colored pixels.
Figure 30 – Savings per area map of Algeria
The map shows that savings per area are more in the Northern region compared to the southern part of the country due to the higher population density in the northern part.
Also, considerable savings can be seen in some parts of Adrar province. It can be concluded from the presented results that an on-grid larger-scale PHR based CRPT system in the
53 northern part of the country will be beneficial due to proximity to transmission lines and a higher number of consumers.
4.3.2 Bahrain
Figure 31 – Savings per area map of Bahrain
Bahrain is the smallest country in GCC countries with its economy heavily dependent on oil and gas reserves. However, the country has set the target to generate 5% of electricity using renewables in 2025 which will increase to 10% in 2030 [18]. To assess the market potential of an off-grid PHR based CRPT system, savings per map is generated
(as shown in Figure 31) by incorporating all criteria mentioned in section 3.4 except proximity to the electric transmission network. This criterion is not incorporated for
54 Bahrain as the smaller area of the country makes it feasible to provide on-grid power to the areas located within a 50 km radius.
Figure 31 shows the savings per area map for Bahrain, all savings are concentrated in the southern province of the country, as the Northern region is densely populated. Given the smaller area of the country, opportunities for an on-grid larger-scale PHR based CRPT system should be assessed in future studies. On-grid systems will help in fulfilling the energy demand of the country and reduce the CO2 emissions of the region.
4.3.3 Egypt
Egypt is an oil and gas producing country. The country has faced serious socio- economic challenges due to an increase in population growth and, hence, electricity demand at the rate of 6% per year [103]. To increase the contribution of renewable energy systems in the overall energy generation mix of the country, the government has proposed a new strategy. According to the New & Renewable Energy Authority (NREA), a novel strategy proposed by the government will achieve 20% electricity generation by renewables in 2022 with the deployment of 2550 MW of CSP systems [104].
As the country receives an ample amount of solar irradiation, opportunities for deployment of small-scale CSP should be assessed to help the government, policymakers, and stakeholders in the decision-making process. Figure 32 presents the savings per area map created using the GIS tool. It can be seen in the map that a larger area of the country is not suitable for deployment of small-scale PHR based CRPT systems due to small or no savings (represented by white pixels). Only Al Wadi al Jadid (New Valley) and Matrouh provinces have few opportunities for the deployment of the PHR-CRPT system.
55
Figure 32 – Savings per area map of Egypt
Figure 33 – Population Density map of Egypt [96]
56 This can be explained by the population density map presented in Figure 33 which shows that population growth is restricted to the Nile Valley and Delta region and the
Western Desert region near the Libyan border is not populated at all. Due to unique population density distribution, opportunities for a larger-scale PHR based CSP system should be assessed to provide electricity to consumers in Nile Valley and Delta region.
Asyut, Luxor, and Aswan are the potential regions for an on-grid PHR based larger
CSP system because of gird connectivity and yearly average DNI of higher than 2200 kWh/m2. LCOE can be further minimized in those regions by local manufacturing of heliostats and other equipment to increase the feasibility of the plant.
4.3.4 Iran
Iran is blessed with huge fossil fuel reserves; the country is ranked to have the second-largest gas reserves and fifth-largest proven oil reserves in the world [105, 106].
Despite having vast fossil fuel reserves, population growth and increasing electricity demand in the country require special attention. To date, 11.5 MW is generated by hydro- power, 117 MW is generated by wind, and only 32 MW from solar energy technologies
(both PV and CSP) according to the report published by IRENA [107] in 2017.
The Central and Southern regions of Iran receive higher DNI ranging from 2100 to
2400 kWh/m2, which makes it feasible for the deployment of CSP facilities. To evaluate the market potential in the country, savings per area map is created using the methodology presented earlier in this thesis. It should be noted that this map is created without incorporating the criterion of proximity to electric transmission lines due to the unavailability of data as also advised in [108].
57
Figure 34 – Savings per area map of Iran
Figure 35 – Slope map of Iran
58 Figure 34 shows the savings per area map of Iran, opportunities for the deployment of small-scale PHR based CSP systems exist in the provinces of Khuzestan and Sistan &
Baluchestan. Also, the province of Kerman presents few opportunities to utilize off-grid power produced by the PHR-CSP system. It should be noted that much of the area in the country is not suitable for the construction of CSP facilities due to slopes higher than or equal to 3% as shown in Figure 35.
4.3.5 Iraq
Iraq has rich fossil fuel resources which makes it one of the fossil fuel supplying countries in the world [109]. Due to the presence of fossil fuels in the country, the government and people of Iraq have not identified the true importance of renewable energy systems to generate electricity [110].
Figure 36 – Savings per area map of Iraq
59 Most region of the country receives the daily average DNI in the range of 5.8 to 6.6 kWh/m2 which makes it feasible for a CSP facility. Off-grid opportunities for PHR based
CSP systems are quantified as savings per area and the generated map is shown in Figure
36. It is clear on the map that higher savings, and, hence, more opportunities are in Nineveh
(Ninawa) province of Iraq. Al Anbar province on the other hand offers smaller savings per area despite being the biggest in the area. It is due to the desert climate of Al Anbar province as some part of it is in the Syrian Desert.
4.3.6 Jordan
The country has limited fossil fuel reserves and relies on imported fossil fuels.
According to the annual report published in 2016 by the Ministry of Energy and Mineral
Resources, 2978 kilotons of crude oil along with other fossil fuels (diesel, gasoline, liquefied gas) were imported into the country to meet the energy demand [111].
Figure 37 – DNI map of Jordan [7]
60 As the country receives a good amount of solar irradiation as shown in Figure 37, potential sites in the region can be located for the deployment of a PHR based CRPT system. Figure 38 shows the savings per area map of Jordan. Due to grid connectivity in most of the country, few opportunities in the province of Ma’an Governorate can be witnessed.
Figure 38 – Savings per area map of Jordan
To increase the contribution of solar energy technologies in the electricity generation mix of Jordan, more feasibility and GIS-based site selection studies should be carried out. Especially, GIS-based site selection study of larger-scale facility incorporating road networks, grid connections, and other useful criteria will help stakeholders and government to invest at suitable sites and provide energy to more consumers.
61 4.3.7 Kuwait
Kuwait is a GCC country with hot arid weather, long summers, and short winters.
Summers bring extremely hot temperatures and the region is known for frequent sandstorms [112]. The country has rich reserves of oil, it was the tenth-largest producer of petroleum products in 2015 [113]. Kuwait Ministry of Electricity and Water estimated that
342,850 barrels of oil were used in 2016 to produce electricity [114]. Due to the heavy reliance of the country on fossil fuels for electricity generation, it is estimated that oil consumption will reach 1 million barrels per day by 2030 [49].
According to [49], the State of Kuwait has set the goal of meeting 15% of the electricity demand using renewable energy systems by 2030. In the pursuit of increasing share of solar energy technologies in the overall generation mix of the country, techno- economic optimization and GIS-based site selection study of the small-scale system is carried out for the State of Kuwait.
Figure 39 shows the savings per area map of Kuwait, it is clear in the map that more opportunities (shown by red-colored pixels) for off-grid PHR based CRPT system are in
Al Jahrah Governorate which is also the largest governorate among the six in the country.
Abdali region located in the north of Al Jahrah governorate is not grid-connected and makes it a potential site for the deployment of a small-scale off-grid PHR-CSP system.
Also, some of the opportunities exist in the southern part of Al Jahrah governorate near the
Saudi Arabian border.
A larger population of the country is concentrated on the eastern coastal region which makes the on-grid larger scale feasible to generate electricity and supply to the
62 consumers in the urban or densely populated areas. An on-grid PHR based CRPT system will offer good savings or cost-effective electricity as the country has the highest per capita electricity consumption in the whole MENA region as shown in Figure 5.
Figure 39 – Savings per area map of Kuwait
4.3.8 Lebanon
Lebanon is a country with an area of 10,452 km2 in the Mediterranean Basin.
According to the World Bank Data, population growth of 29.7% is seen in the region from
2009 to 2019. Increasing population growth and a decrease in the fossil fuel reserves of the country have posed many socio-economic issues. Lebanon imported 7054 ktoe of oil in
2016 to fulfill the electricity demand and other energy needs [115].
63 To increase sustainability in the region, more feasibility studies should be carried out to demonstrate the potential of renewable energy technologies in the region. In this study, an optimized PHR based CRPT system with 14 hours of storage and SM if 3 is chosen. Based on the LSFE results interpolated for the whole country, savings per area map created in the GIS tool is given in Figure 40.
Figure 40 – Savings per area map of Lebanon
It should be noted that given the smaller area of the country, the criterion of proximity to the electric transmission network is not taken into account. According to the given map, comparatively larger savings per area are offered in Baalbek-Hermel
Governorate. It was found in a GIS-based study that given the smaller area of the country; all areas can be provided using the main transmission network also shown in Figure 40.
This increases the market opportunities for larger-scale PHR based CSP systems which can
64 take the advantage of economies of scale and provide low-carbon electricity to a larger number of people.
4.3.9 Libya
Libya is an oil-producing country that has proven oil reserves of 48.4 billion barrels as of 2018 [15]. The country is the fourth largest by area in Africa with a total population of 6.77 million people in 2019 according to World Bank Data.
According to IEA, the Libyan government launched a renewable energy strategic plan in 2013 to generate 7% of electricity using renewable energy technologies in 2020 which will increase to 10% by 2025.
A potential study performed by Belgasim et al. showed that the deployment of CSP technologies in Libya can provide cost-effective energy generation due to higher irradiation throughout the year [116]. In this thesis, we present the savings per area map to identify the potential markets for an off-grid PHR based CRPT system. Savings per area map is given in Figure 41 shows that Libya has good potential for off-grid electricity generation using the PHR-CSP system chosen in this thesis. Specifically, Tazirbu Village
(25o39′52″N, 21o2′42″E) and Al-Jawf Town (24o12′14″N, 23o17′37″E) in Al Kufrah
District are promising locales for the deployment of small-scale PHR based CRPT system due to higher savings.
Also, other scattered potential locales can be seen in the districts; Al Wahat,
Murzuq, Wadi al Hayat, and Al Jabal al Gharbi. Savings per area map provided in Figure
41 can be a helpful preliminary resource for government, stakeholders, and policymakers for future deployment of off-grid systems. The opportunities for investment seen in the
65 map will be attractive when further improvement in the policy framework of the country will take place [17].
Figure 41 – Savings per area map of Libya
4.3.10 Morocco
Morocco lacks natural resources like oil and gas, and imports 97% of the energy resources to meet the demand. The country’s total primary energy supply (TPES) increased from 7.6 Mtoe to 18.98 Mtoe in 1990-2014 [52]. Figure 42 shows the development of electricity consumption in the Kingdom of Morocco from 1990 to 2019. It is evident in the below-given plot that electricity consumption has increased approximately 31% from 2009
66 to 2019. According to the World Bank Data, the population has increased from 31.92 million to 36.47 million in the above-said time frame.
40
35
30
25
20
15
10 Electricity Consumption (TWh) Consumption Electricity 5
0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Figure 42 – Development of electricity consumption in Morocco (Data source: IEA)
Until 2016, the country’s largest renewable electricity generation resource was hydropower with a cumulative capacity of 1770 MW [107]. Other renewable energy technologies like the wind (797.7 MW installed capacity) and solar (204 MW- both CSP and PV) diversified the overall generation mix of the country. Morocco is the country with the largest deployment of CSP facilities in the whole MENA region. The biggest solar project is referred to as Noor Ouarzazate having a cumulative capacity of 580 MW [52].
To assess the opportunities of small-scale standalone PHR-CSP system, savings per area map is created as shown in Figure 43. The savings per area map shows that good opportunities for the off-grid PHR based CRPT system exist in the Souss-Massa-Draa region near the Algerian border. It is due to the small villages; Tagounite, Lblida, Gourgir,
Ksar El Kebir and others are in that region.
67
Figure 43 – Savings per area map of Morocco
Also, other regions; Chaouia-Ouardigha, Fes-Boulemane, and Meknes-Tafilalet have the potential for an off-grid generation. Locale near the city of Sijilmassa
(31°16'28"N, 4°16'28"W) in Meknes-Tafilalet region is of interest due to higher savings.
4.3.11 Oman
Sultanate of Oman is one of the oil and gas producing countries in GCC. The main source of electricity generation in the country is fossil fuel as shown in Figure 44.
Consumption of electricity has also increased over the years due to population growth in
Sultanate – 1.15 million to 4.66 million – from 1980-2017 as given by World Bank Data.
68 35000
30000
25000
20000 GWh 15000
10000
5000
0 1980 1990 2000 2005 2010 2016 2017 Electricity generation from fossil fuels Consumption of Electricity
Figure 44 – Development of electricity generated from fossil fuel and the electricity consumption in Oman from 1980 to 2017 [117]
Studies in the past have shown that country has good potential of generating electricity using both solar and wind energy resources [118, 119]. Due to higher irradiation in the region, a CSP facility can perform better and provide electricity to the customers even when the sun is not shinning. Also, it has been shown in previous research that potential sites for both small-scale and large-scale solar energy systems are available in the country [120, 121]. In this research, an assessment of potential sites for deployment of small-scale PHR-CRPT system is given by saving per area map shown in Figure 45.
Savings per area map shows the Sultanate of Oman has greater potential for off- grid solar electricity generation using the PHR-CSP system. One of the higher savings regions is Mirbat (16°59′19″N 54°41′32″E) which is a coastal town in Dhofar governorate.
Another potential locale is Dumq which is a port town on the Arabian Sea in Al Wusta
69 governorate. Other potential locales can be seen in Al Dhahira and Ad Dakhliyah governorates.
Figure 45 – Savings per area map of Oman
4.3.12 Qatar
Qatar is blessed with vast oil and gas resources. The country is the fourth-largest producer of gas [122]. Figure 46 shows the development in population and energy consumption of the country. It is evident from the below-given plot that the country has seen rapid growth in electricity consumption in recent years. According to IRENA’s report total renewable energy capacity of 43 MW is installed in the country as of 2019 [123]. To diversify the energy mix and reduce the consumption of fossil fuels for electricity
70 generation, a strategic plan called Qatar Vision 2030 is put forward by the government.
According to Qatar Vision 2030, 1800 MW of renewable energy technologies will be installed in the whole country [17].
3 50 45 2.5 40 35 2 30 1.5 25
20 TWh 1 Million People Million 15 10 0.5 5 0 0 1990 1995 2000 2005 2010 2015 2018 Population Electricity Consumption
Figure 46 – Development of population growth and electricity consumption in Qatar between from 1980 to 2017 from 1990 to 2018 [124, 125]
To identify the potential of off-grid electricity generation using PHR-CSP technology, savings per area maps are generated as shown in Figure 47. Figure 47b shows the savings per area map by excluding proximity to electric transmission network criterion.
Qatar has an area of 11,581 km2, given the smaller area, most of the state can be provided on-grid power except the locales in the Eastern coastal region. Potential sites are located in the Al Rayyan municipality near Dukhan coastal city (25°25′10″N 50°47′32″E) as shown in Figure 47a. Figure 47b shows that savings per area increase in almost all municipalities when the criterion of proximity to electric transmission lines is not taken into account. Significant savings can be seen in the northern and middle parts of the country.
71
(a)
(b) Figure 47 – Savings per area map of Qatar (a) with proximity to electric lines criterion and (b) without proximity to electric lines criterion
72 It should be noted that the first-level administrative map (shapefile) obtained from
[92] does not show Al-Shahaniya municipality as shown in Figure 48.
Figure 48 – Map of Qatar showing eight municipalities (Source: Ministry of Municipality and Environment, Qatar)
To conclude, the State of Qatar has good potential for on-grid PHR-CSP electricity generation, also the country has considerable potential for off-grid power generation in remote villages.
4.3.13 Saudi Arabia
Kingdom of Saudi Arabia is the largest country in GCC and hosts a significant position in the world oil market. KSA has enormous oil reserves which make it the second- largest producer of oil in the world preceded by Venezuela. According to BP’s report, KSA
73 has proven oil reserves of 297.7 billion barrels as of 2018 [15]. Also, the country holds the world’s sixth-largest gas reserves [18].
0.6 350000
0.5 300000
250000 0.4 200000
0.3
GWh [%] 150000 0.2 100000
0.1 50000
0 0 2010 2011 2012 2013 2014 2015 2016 2017 2018
Natural gas Crude Oil Heavy fuel oil Diesel Electricity consumption
Figure 49 – Electricity production by different resources and electricity consumption of KSA from 2010 to 2018 (Data source: General Authority of Statistics, KSA)
Figure 49 shows the percentage of different fossil fuels used for electricity production and development of electricity consumption in the country from 2010 to 2018.
The electricity consumption rate has increased at the rate of 4.1% per year. The increase in electricity consumption is due to the population growth in the region, fuel subsidies, and increased industrialization [126]. It is also evident in the above-given plot that all the electricity generated in the Kingdom is through fossil fuels. According to the Arab Future
Energy Index report of 2019, less than 100 MW of solar PV capacity and 50 MW of CSP capacity are installed in Saudi Arabia as of 2018 [17]. As of 2019, the total installed capacity of renewable energy technologies in KSA is 397 MW [123].
74 Saudi Arabia has good DNI resources for CSP technologies especially in the
Northwestern region [18]. Saudi Arabia has announced ambitious plans of increasing the contribution of renewables to 30% in the total generation mix. These RE targets are the largest in the region with Egypt being second [17]. The assessment of market opportunities for small-scale standalone PHR based CRPT system is done by performing a techno- economic study of system followed by GIS-based multi-criteria analysis based on specific criteria defined earlier. Savings per area map created for the KSA is shown in Figure 50.
Figure 50 – Savings per area map of Saudi Arabia
Savings per area map shows that country has good potential for off-grid electricity generation. The white pixels show zero savings per area as those are excluded by incorporating the criteria in GIS-based analysis. It can be seen that many opportunities are
75 located in the middle and north of the country. To identify some of the potential locations in the country, savings per area map with potential locales is created.
Figure 51 – Savings per area map of Saudi Arabia with potential locales
Figure 51 shows the potential locales with higher savings per area. Sharurah, Wadi ad-Dawasir, Layla, Afif, Al Bijadyah, Rafha, Al Uwayqilah, and Tubarjal are among the potential locales where a 1.3 MWe standalone PHR based CRPT facility can help in achieving higher savings while providing electricity to the communities living in those areas.
Apart from the identified potential locales, KSA has a higher potential for off-grid
PHR-CSP deployment than other countries in the MENA region given the larger area of
76 the country and scattered population. Sharurah and Rafha are identified as potential locales for off-grid electricity generation by Saudi Electricity Company (SEC), in the current study those locations are also identified as potential sites for the construction of small-scale PHR-
CSP system as shown in Figure 51.
KSA has good potential for off-grid electricity generation, also a similar GIS-based can be carried out to identify potential sites for on-grid larger-scale electricity generation using the PHR-CRPT system. To assess the potential for on-grid systems, proximity to roads, protected areas, and areas with vegetation should be considered in greater detail to increase the feasibility of the system.
4.3.14 Syria
Syria has good solar potential ranging from 5.2 to 7.2 kWh/m2/day. The
Southwestern part of the country has higher DNI as shown in Figure 52 which makes that part of the country more feasible for deployment of CSP facilities to generate electricity.
Figure 53 shows the savings per area map created for Syria; it can be seen that appreciably potential areas with relatively higher savings per area are in the Deir Ez-Zor governorate near the Iraqi border. Higher savings per area are in Deir Ez-Zor governorate because the Euphrates flows in that region and population is observed on both sides of it.
Near the Turkish border, in Ar Raqqa and Aleppo governorates of the country, there are potential locales for the deployment of the PHR based CSP.
Given the country’s poor market structure and policy framework related to renewables energy generation, deployment of PHR-CSP system for off-grid and on-grid will take considerable time [17].
77
Figure 52 – DNI map of Syria [7]
Figure 53 – Savings per area map of Syria
78 4.3.15 Tunisia
Tunisia is not a country with larger oil and gas reservoirs. The country imported
3,756 kilotons of oil in 2017 to meet energy needs [117]. It can be seen in Figure 54 that
Tunisia has a comparatively good share of renewable energy technologies in the overall generation mix. In fact, among total renewable energy (without hydro) capacities, Tunisia has fifth place in the region [17].
25000 7.0
6.0 20000 5.0
15000
4.0
[%] GWh 3.0 10000
2.0 5000 1.0
0 0.0 1973 1980 1990 2000 2005 2010 2016 2017 Total production Renewable Energy
Figure 54 – Electricity production and share of renewables in electricity from the production of Tunisia from 1973 to 2017 [117]
According to the National Renewable Energy Action Plan 2018 of Tunisia, the country will install 1,755 MW, 1,510 MW, and 450 MW of wind, solar PV, and CSP respectively by 2030 [17]. To increase the share of renewables in general and CSP in particular, the potential assessment of a small-scale standalone PHR-CSP system is done in this thesis work and savings per area map created in the GIS tool is represented in Figure
55. The savings per area map shows that most of the areas in governorates of Tataouine,
79 Kebili, Tozeur, and Kasserineare not grid-connected which can be suitable for an off-grid generation.
Figure 55 – Savings per area map of Tunisia
Favorable locales can be seen in the governorate of Kebili where small towns like
Jamnah and Qibili and villages like Zaafrane and Fatnassa are located. In these small villages or towns, small-scale PHR based CSP of 1.3 MWe or higher (≤ 5 MWe) can generate electricity to provide to the community while being independent of the main grid.
Similarly, Gafsa and Kasserine governorates have significant potential for off-grid electricity generation using PHR-CSP due to remote villages and towns.
80 4.3.16 United Arab Emirates
UAE is a country blessed with oil and gas resources. The country is the third-largest producer of oil in the Middle East with proven reserves of 97.8 billion barrels as of 2018.
Also, UAE is the fourth-largest producer of natural gas with proves reserves of 5.9 trillion cubic meters [15]. Being a country with rich oil and gas reserves, it has relied on electricity generation through fossil fuels mainly natural gas as shown in Figure 56.
140000
120000
100000
80000 GWh 60000
40000
20000
0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Oil Natural gas Solar PV CSP
Figure 56 – Electricity production of UAE by fossil fuel and renewables from 1990 to 2018 [125]
Figure 56 shows that in 2009, the country made the progress in diversifying the energy generation mix through solar PV. In 2013, CSP was also added to the overall generation mix to decrease the carbon footprint and reliance on fossil fuels. UAE has also put forward plans to increase the capacity of renewable energy technologies to represent
44% of the total electricity generation mix by the year 2050 [18].
81 This thesis presents the savings per area map shown in Figure 57 to recognize the potential of a small-scale PHR-CSP system for off-grid electricity generation in the UAE.
It can be seen that potential sites are in the Emirate of Abu Dhabi and also few are located in the Emirate of Sharjah and Emirate of Dubai.
Figure 57 – Savings per area map of UAE
Small villages in the Emirate of Abu Dhabi (marked by an ellipse) are the potential locations for deployment of small-scale CSP system, these locales are also shown in Figure
58 using the satellite photograph from Google Earth Pro. In Sharjah and Dubai, small villages like Murqquab and Madam are the potential locations for off -grid electricity generation using PHR-CSP. It should be noted that savings per area are generally higher in comparison to the other countries in the region due to higher per capita electricity consumption.
82
Figure 58 – Satellite photograph of small villages in Emirate of Abu Dhabi
4.3.17 Yemen
Yemen has good potential for off-grid electricity generation as 20% of rural areas are not connected to the grid [127]. In this study, savings per area map is created for Yemen based on the techno-economic and GIS-based analyses as shown in Figure 59.
Figure 59 – Savings per area map of Yemen
83 The map shows that more potential areas for off-grid PHR-CSP deployment are in
Hadramaut, Al Jawf, and Al Bayda governorates. It can be seen that communities in the
Hadramaut governorate are not grid-connected, therefore, in this governorate, there are potential areas for deployment of small-scale to medium-scale PHR-CSP systems.
In general, savings are lower than other countries in the region because per capita consumption is the lowest for Yemen in the whole MENA region as shown in Figure 5.
Due to the poor economic situation of the country, on-grid larger systems can be deployed which can provide electricity to a larger population while being cost-effective.
84 CHAPTER 5. CONCLUSION AND FUTURE WORK
To conclude, this thesis work presented the methodology to perform the techno- economic analysis and optimization of particle heating receiver based central power tower system for off-grid electrification. Also, a methodology was presented to assess the potential of an off-grid PHR based CRPT system using multi-criteria analysis in GIS tool.
Key parameters which play an important role in SHSS were identified and cost models for the subcomponents of SHSS were developed based on the quotes of vendors, manufacturers, contractors, and cost engineering tools. Techno-economic optimization was performed based on optimum SHSS results by varying SM and TES full load hours. A regression model was created by performing techno-economic analysis for 35 different locations in the MENA region. The regression model constructed in this research work showed that latitude and DNI play an important role in the annual power production of
CSP specifically power tower technology. LCOE calculations were performed using constructed regression model, and a new parameter introduced in this research – Levelized
Savings for Electricity – was calculated.
LSFE results were then interpolated and combined with other criteria; proximity to electric transmission network, slope, population density, and per capita electricity consumption, to generate final savings per area map in GIS software. Based on savings per area results, potential regions or locales in all MENA countries were identified. Key findings of this research work are listed below:
1. Results of techno-economic optimization showed that SM of 3 and 14 hours of
85 storage help in achieving minimum LCOE and maximum electricity production.
The results of this techno-economic optimization are in good agreement with the
results obtained by [8, 57, 59].
2. Choosing an unconstrained field for the construction of SHSS can help in reducing
LCOE as the tower and receiver costs are dependent on the arrangement of
heliostats in the field.
3. In previous research, it was found that at higher latitudes, the performance of CSP
systems is affected due to changes in sun angles, which is also confirmed in this
research. Additionally, in this research, it was found that annual energy production
first increased with increasing latitude and then decreased sharply when the latitude
is increased beyond 32o roughly. This behavior contradicts the findings in [91],
where A EP decreases with increasing latitude. DNI
4. The regression model presented in this thesis work calculates LCOE of 1.3 MWe
PHR based CSP system given latitude (in the northern hemisphere) and DNI.
5. Algeria, being the largest country in Africa shows a smaller potential for off-grid
electricity generation using PHR based CSP technologies as most of the population
lives in the Northern part of the country. Therefore, for the country of Algeria, the
potential of larger-scale grid-connected systems should be assessed.
6. Given the smaller area of Bahrain, the country’s population can be supplied power
via grid connection. This opportunity for grid connectivity can be utilized for
larger-scale systems to help in achieving the country’s future goals [18].
7. The population density map of Egypt provided in Figure 33 showed that most of
the population settlement is in Nile Valley and Nile Delta region. Regions like
86 Aswan, Asyut, and Firan receive higher DNI and also are grid-connected, which
makes them potential sites for deployment of larger-scale PHR-CRPT plant.
8. Iran has significant potential for off-grid electricity generation using PHR-CRPT.
Due to higher slopes in most of the country’s region, deployment of CSP is limited.
9. Nineveh and Al Najaf governorates in Iraq have good potential for deployment of
standalone PHR-CSP system but more opportunities for deployment of the larger
capacity system should be investigated using GIS-based analysis.
10. Jordan has lesser potential for off-grid PHR-CRPT deployment due to smaller
savings per area. Therefore, the option of grid-connected larger PHR-CSP with
good storage will be more beneficial as the country receives higher DNI throughout
the year.
11. Kuwait has good potential for off-grid electricity generation using the PHR-CRPT
system. The potential of a larger PHR-CRPT system should be assessed as the
prices of electricity are higher in the region which makes it very promising [49].
12. Lebanon has a smaller area and shows that the whole country can be provided
power using grid connections like Bahrain. Therefore, the country’s potential for
on-grid generation should be investigated in future research.
13. Savings per area map showed that Libya has good potential for the deployment of
an off-grid PHR-CSP system. Further potential assessment should be carried out in
future research on district-level to help policymakers and government in the
decision-making process.
14. Deployment opportunities for off-grid PHR-CSP are higher in Morocco which
makes it attractive for future investment. As the country has the highest deployment
87 of commercial-scale CSP in the whole MENA region, savings per area map
generated in this thesis work will help as a preliminary resource for recognizing
potential sites for off-grid plants.
15. Many suitable locales for the deployment of standalone PHR-CSP systems in the
country of Oman can be spotted. The country has higher potential in the region due
to grid connection limited to the Eastern coastal region.
16. Qatar has the third-highest per capita electricity consumption rate in the region. As
the country is smaller in area than other countries like Saudi Arabia and Egypt,
most of the population is grid-connected. Keeping that in mind it will be beneficial
to explore the potential of on-grid energy generation.
17. Saudi Arabia has vast potential for off-grid electricity generation using the PHR-
CRPT system. Apart from the potential locales that were identified in the savings
per area map (see Figure 51), the country presents many opportunities for the
deployment of small-scale systems as the population is not limited to urban cities
like Riyadh, Jeddah, or Dammam. Potential locales like Sharurah, Rafha, and Wadi
Ad-Dawasir showed savings of 18.3 M$, 17.9 M$, and 45.2 M$ respectively. Other
than the opportunities for off-grid deployment, on-grid electricity generation using
PHR-CRPT system wall also help the country in achieving future goals [18] and
reducing carbon footprint.
18. Syria also has good potential for off-grid electricity generation specifically in the
Deir-Ez-Zor governorate, where both sides of the Euphrates river are populated.
19. Gafsa, Kasserine, Kebili, and Tozeur governorates in Tunisia have greater potential
for deployment of off-grid PHR-CSP system. The country’s appreciable efforts in
88 the deployment of renewable energy technologies will also help in taking advantage
of this low-cost and relatively reliable technology.
20. Abu Dhabi is the largest emirate in UAE by area, therefore, most of the favorable
locales are in the Emirate of Abu Dhabi. In recent years, UAE has made good
progress in the deployment of renewable energy technologies, specifically, solar
PV and CSP. Savings per area map generated in this research will provide useful
insights for future investment in off-grid CSP electricity generation.
21. Yemen has potential for deployment of standalone PHR-CSP system in some
governorates, like Hadramaut, Al Jawf, and Al Bayda. As per capita electricity
consumption is lower in the country, savings per area are lowest in the whole
MENA region. This can pose challenges in investments to deploy the PHR-CSP
system.
Methods presented in this work can be utilized in the analysis of other CSP systems to perform techno-economic optimization or multi-criteria GIS-based analysis. The results of this research are stepping stones to the GIS-based suitability studies and techno- economic optimization of PHR based CRPT systems.
This research work has shown that PHR-CRPT technology is a cost-effective CSP technology that can be integrated into low-cost TES to provide power when the sun is not shinning. Further application of PHR based small-scale CRPT system for off-grid application in MENA region showed that there is good potential for the deployment of standalone PHR-CRPT system. This potential of off-grid generation will not only decrease the carbon emissions of the region but also provide an opportunity to the countries to gain sustainability in the future.
89 Savings per area maps generated in this research are fundamental to the GIS-based site selection process for PHR based CRPT systems. These maps will help the stakeholders, policymakers, and government or private organizations in making decisions about investing in PHR based technology at suitable locations. Maps presented here can be compared with other maps generated for other CSP or solar PV technologies to further help in the decision-making process. These maps can also be enhanced by incorporating renewable energy portfolios and investment tax credit in individual countries.
In the future, a similar study should be performed for grid-connected larger-scale
PHR-CRPT systems in the MENA region or other favorable places in the world having higher DNI. Important factor and criteria should be identified in GIS-based market potential study of larger-scale systems which can be proximity to the electric grid, costs related to grid connection, savings per area when PHR-CRPT system is used instead of natural gas or coal-fired system, proximity to road networks, urban or semi-urban areas, slope, vegetation, protected and sensitive areas, etc.
Future work would also require a detailed analysis of annual performance using in- house Microsoft Excel Visual Basic for Applications (VBA) based code tailored to the
PHR-CSP system to confirm and maybe improve the results obtained using commercial tools already available in the academia or industry. It will also require improved and more robust cost models for tower and receiver based on the most recent data acquired from contractors, manufacturers, and cost engineering tools. The bulk of this research work has assumed $50/m2 heliostat cost due to innovative cost-effective design along with local and regional production. However, it will be worthwhile to repeat the techno-economic analysis and optimization using a community goal of $75/m2 [78] for heliostats.
90 In future research, the dependency of energy production of CSP technology on latitude and DNI distribution over the year should be carefully and critically analyzed and compared. Also, the regression model presented in this research work should be improved by incorporating more data and other useful variables which affect LCOE or annual energy production.
GIS-based analysis should be improved in the future by incorporating more criteria such as land cover, vegetation, water bodies, road networks, most recent electric transmission network, and protected areas. Also, in future research, more data should be generated to improve the interpolation of LSFE or LCOE results. Apart from savings per area maps, a suitability map (with the ranking of sites) should be created using the analytical hierarchy process (AHP) as discussed in [66, 74] and the weighted-sum method as discussed in [73]. As the MENA region is mostly arid, dust is more common, which affects the performance of CSP facilities by soiling mirrors and other structures. Therefore, in future research, dust deposition risk maps [73] should be incorporated.
91 APPENDIX A: TOWER COST MODEL
The cost model presented in this research is based on quotes from contractors, vendors, and manufacturers. Parameters for two different cost models presented by
Equation (A1) and Equation (A2) were calculated based on quotations for 47.1 m and 120 m towers for 1.3 MWe and 26.5 MWe systems in Engineering Equation solver (EES) [128].
A comparison between the two models is presented in Figure 60.
1 0.0245mhtower m (A1) Cost356,792$Tower, KSU-VE = e
1 0.0352mhtower m (A2) Cost172,000Tower, GIT-HE = $ e
x 106 44 Cost ModelGIT,HE 40 Cost ModelKSU,VE
36 Training DataGIT,HE
Training DataKSU,VE
] 32
$
[
t 28
s
o C
24
r e
w 20 o T 16
12
8
4
0 25 45 65 85 105 125 145 165
htower [m] Figure 60 – Comparison of tower cost models
92 APPENDIX B: LCOE CALCULATIONS
LCOE calculations were performed in a Microsoft Excel sheet to compare with the
LCOE calculations performed in SAM. Figure 61 shows the screenshot of the LCOE calculation in the Microsoft Excel sheet.
Figure 61 – LCOE calculations performed in Microsoft Excel Sheet
93 APPENDIX C: REGRESSION ANALYSIS
Regression Analysis is carried out in a Microsoft Excel sheet using built-in Data
Analysis tools. Figure 62 shows the screenshot of the regression analysis performed in
Microsoft Excel. It should be noted that R2 value (coefficient of determination) is 0.782 as shown in the below-given figure.
Figure 62 –Regression analysis in Microsoft Excel
94 APPENDIX D: INTERPOLATION TECHNIQUE
The inverse distance weighting (IDW) interpolation technique is used in this research work. The basic algorithm for this interpolation technique can be explained using the schematic given in Figure 63. In IDW, every known data point has a weight (λi) associated with it which is inversely proportional to distance (di) between the known data point and unknown data point.
Unknown, x*
i 1.0
di 0.7
0.4
0.1
Figure 63 – Illustration of interpolation technique – IDW
Using Equation (D1), the weight of the individual data point can be found. Here p is the power and usually taken the value in the range of 1 to 2. Unknown data points can be found using Equation (D2) by incorporating weights of all known data points.
1 i = (D1) d p i
* 1x 1+ 2 x 2 +... + ii x x = (D2) + +... + 12 i
95 REFERENCES
[1] A. Caillé et al., "2007 survey of energy resources," World Energy Council, vol. 2007, 2007.
[2] M. Bruch and M. Müller, "Calculation of the Cost-effectiveness of a PV Battery System," Energy Procedia, vol. 46, pp. 262-270, 2014.
[3] S. Vergura and V. Lameira, "Technical-financial comparison between a PV plant and a CSP plant," Revista Eletrônica Sistemas & Gestão, vol. 6, no. 2, pp. 210-220, 2011.
[4] H. Al-Ansary et al., "Design features of the world’s first commercial concentrating solar power plant using the particle heating receiver concept," in ASME 2019 13th International Conference on Energy Sustainability collocated with the ASME 2019 Heat Transfer Summer Conference, 2019: American Society of Mechanical Engineers Digital Collection.
[5] K. Lovegrove and W. Stein, "Concentrating solar power technology: principles, developments and applications," 2012.
[6] H. Müller-Steinhagen and F. Trieb, "Concentrating solar power," A review of the technology. Ingenia Inform QR Acad Eng, vol. 18, pp. 43-50, 2004.
[7] SOLARGIS. Solar resource maps of Middle East and North Africa, Global Solar Atlas 2.0. [Online]. Available: https://solargis.com/
[8] H. Al-Ansary et al., "G3P3-KSA Final Report," in "Generation 3 Particle Pilot Plant Project," Kind Saud University, Riyadh, KSA, January 2021.
[9] T. Stocker et al., "Intergovernmental Panel on Climate Change. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change," Cambridge University Press, vol. 1111, pp. 96-102, 2013. [Online]. Available: http://www.ipcc.ch/report/ar5/wg1/
[10] IEA, "Global Energy Review 2019," Paris, 2019. [Online]. Available: https://www.iea.org/reports/global-energy-review-2019
[11] IEA, "World Energy Outlook 2020," Paris, 2020. [Online]. Available: https://www.iea.org/reports/world-energy-outlook-2020
[12] B. K. Sovacool and M. A. Brown, Energy and American Society–Thirteen Myths. Springer, 2007.
96 [13] N. Stern, "The economics of climate change," American Economic Review, vol. 98, no. 2, pp. 1-37, 2008.
[14] IRENA, "Renewable Energy Market Analysis: GCC 2016," Abu Dhabi, 2016. [Online]. Available: https://irena.org/publications/2016/Jan/Renewable-Energy- Market-Analysis-The-GCC-Region
[15] B. Looney, "BP Statistical Review of World Energy 2020," BP, 2020.
[16] U. S. Energy Information Administration, "International Energy Outlook 2019," Washington, DC, 2019. [Online]. Available: https://www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf
[17] RCREEE, "Arab Future Energy Index (AFEX) 2019," Cairo, Egypt, 2019. [Online]. Available: https://www.rcreee.org/content/arab-future-energy-index- renewable-energy-2019-report
[18] IRENA, "Renewable Energy Market Analysis: GCC 2019," Abu Dhabi, 2019. [Online]. Available: https://www.irena.org/publications/2019/Jan/Renewable- Energy-Market-Analysis-GCC-2019
[19] World Development Indicators (2019). GDP per capita, The World Bank. [Online]. Available: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
[20] H. Ritchie and M. Roser, "CO₂ and Greenhouse Gas Emissions," Our World in Data, 2017. [Online]. Available: https://ourworldindata.org/co2-and-other- greenhouse-gas-emissions.
[21] H. Ritchie and M. Roser, "Energy," Our World in Data, 2014. [Online]. Available: https://ourworldindata.org/energy.
[22] U. S. DOE. "Concentrating Solar-Thermal Power Basics." https://www.energy.gov/eere/solar/concentrating-solar-thermal-power-basics (accessed 03 March, 2021).
[23] M. Mehos et al., "Concentrating solar power Gen3 demonstration roadmap," National Renewable Energy Lab.(NREL), Golden, CO (United States), 2017.
[24] H. Zhang, J. Baeyens, J. Degrève, and G. Cacères, "Concentrated solar power plants: Review and design methodology," Renewable and sustainable energy reviews, vol. 22, pp. 466-481, 2013.
[25] A. Skumanich, "CSP: Developments in heat transfer and storage materials," Renewable Energy Focus, vol. 11, no. 5, pp. 40-43, 2010.
[26] G. J. Kolb, C. K. Ho, T. R. Mancini, and J. A. Gary, "Power tower technology roadmap and cost reduction plan," SAND2011-2419, Sandia National Laboratories, Albuquerque, NM, vol. 7, 2011.
97 [27] R. Knott, D. Sadowski, S. Jeter, S. Abdel-Khalik, H. Al-Ansary, and A. El-Leathy, "High temperature durability of solid particles for use in particle heating concentrator solar power systems," in Energy Sustainability, 2014, vol. 45868: American Society of Mechanical Engineers, p. V001T02A041.
[28] R. Buck and S. Giuliano, "Impact of Solar Tower Design Parameters on xCO2- based Solar Tower Plants," 2018.
[29] C. Ho et al., "Technology advancements for next generation falling particle receivers," Energy Procedia, vol. 49, pp. 398-407, 2014.
[30] B. Gobereit, L. Amsbeck, R. Buck, R. Pitz-Paal, M. Röger, and H. Müller- Steinhagen, "Assessment of a falling solid particle receiver with numerical simulation," Solar Energy, vol. 115, pp. 505-517, 2015.
[31] H. Al-Ansary et al., "Solid particle receiver with porous structure for flow regulation and enhancement of heat transfer," ed: Google Patents, 2017.
[32] M. Sandlin and S. Abdel-Khalik, "A study of granular flow through horizontal wire mesh screens for concentrated solar power particle heating receiver applications– Part II: Parametric model predictions," Solar Energy, vol. 174, pp. 1252-1262, 2018.
[33] C. K. Ho et al., "On-sun performance evaluation of alternative high-temperature falling particle receiver designs," Journal of Solar Energy Engineering, vol. 141, no. 1, 2019.
[34] C. K. Ho, G. Peacock, J. M. Christian, K. Albrecht, J. E. Yellowhair, and D. Ray, "On-sun testing of a 1 MWt particle receiver with automated particle mass-flow and temperature control," in AIP Conference Proceedings, 2019, vol. 2126, no. 1: AIP Publishing LLC, p. 030027.
[35] I. Khamlich et al., "Technical and economic assessment of thermal energy storage in concentrated solar power plants within a spot electricity market," Renewable and Sustainable Energy Reviews, p. 110583, 2020.
[36] S. E. Trabelsi, R. Chargui, L. Qoaider, A. Liqreina, and A. Guizani, "Techno- economic performance of concentrating solar power plants under the climatic conditions of the southern region of Tunisia," Energy Conversion and Management, vol. 119, pp. 203-214, 2016.
[37] P. Garcia, A. Ferriere, and J.-J. Bezian, "Codes for solar flux calculation dedicated to central receiver system applications: A comparative review," Solar Energy, vol. 82, no. 3, pp. 189-197, 2008.
[38] F. Lipps and L. L. Vant-Hull, "A cellwise method for the optimization of large central receiver systems," Solar Energy, vol. 20, no. 6, pp. 505-516, 1978.
98 [39] B. L. Kistler, "A user's manual for DELSOL3: a computer code for calculating the optical performance and optimal system design for solar thermal central receiver plants," Sandia National Lab.(SNL-CA), Livermore, CA (United States), 1986.
[40] M. Kiera, "Description of the Computing Code System HFLCAL," I. Report, GASTIAS-BT-200000-075, Interatom Report, 1986.
[41] M. Izygon, P. Armstrong, C. Nilsson, and N. Vu, "TieSOL–a GPU-based suite of software for central receiver solar power plants," Proceedings of SolarPACES, 2011.
[42] T. Wendelin, "SolTRACE: a new optical modeling tool for concentrating solar optics," in International Solar Energy Conference, 2003, vol. 36762, pp. 253-260.
[43] M. J. Wagner and T. Wendelin, "SolarPILOT: A power tower solar field layout and characterization tool," Solar Energy, vol. 171, pp. 185-196, 2018.
[44] System Advisor Model Version 2020.11.29. (2020). National Renewable Energy Laboratory, Golden, CO. Accessed: December 27, 2020. [Online]. Available: https://sam.nrel.gov
[45] M. A. Abaza, W. M. El-Maghlany, M. Hassab, and F. Abulfotuh, "10 MW concentrated solar power (CSP) plant operated by 100% solar energy: Sizing and techno-economic optimization," Alexandria Engineering Journal, vol. 59, no. 1, pp. 39-47, 2020.
[46] K. Hirbodi, M. Enjavi-Arsanjani, and M. Yaghoubi, "Techno-economic assessment and environmental impact of concentrating solar power plants in Iran," Renewable and Sustainable Energy Reviews, vol. 120, p. 109642, 2020.
[47] S. Abdelhady, "Performance and cost evaluation of solar dish power plant: sensitivity analysis of levelized cost of electricity (LCOE) and net present value (NPV)," Renewable Energy, vol. 168, pp. 332-342, 2021.
[48] U. Herrmann, M. Geyer, and R. Kistner, "The AndaSol Project," ed, 2002, p. https://p2infohouse.org/ref/46/45523.pdf.
[49] A. J. Sultan, K. J. Hughes, D. B. Ingham, L. Ma, and M. Pourkashanian, "Techno- economic competitiveness of 50 MW concentrating solar power plants for electricity generation under Kuwait climatic conditions," Renewable and Sustainable Energy Reviews, vol. 134, p. 110342, 2020.
[50] P. RP, "Performance analysis and optimization of central receiver solar thermal power plants for utility scale power generation," Sustainability, vol. 12, no. 1, p. 127, 2020.
[51] V. Quaschning, W. Ortmanns, R. Kistner, and M. Geyer, "Greenius: a new simulation environment for technical and economical analysis of renewable
99 independent power projects," in International Solar Energy Conference, 2001, vol. 16702: American Society of Mechanical Engineers, pp. 413-417.
[52] A. Azouzoute, A. A. Merrouni, and S. Touili, "Overview of the integration of CSP as an alternative energy source in the MENA region," Energy Strategy Reviews, vol. 29, p. 100493, 2020.
[53] Y. Benmakhlouf Andaloussi, "On the Market Potential of Modular Stirling CSP Systems With Storage in the MENA," ed, 2018.
[54] Z. Ma, M. Mehos, G. Glatzmaier, and B. Sakadjian, "Development of a concentrating solar power system using fluidized-bed technology for thermal energy conversion and solid particles for thermal energy storage," Energy Procedia, vol. 69, pp. 1349-1359, 2015.
[55] M. Ebert et al., "Operational experience of a centrifugal particle receiver prototype," in AIP Conference Proceedings, 2019, vol. 2126, no. 1: AIP Publishing LLC, p. 030018.
[56] C. Frantz, R. Buck, and L. Amsbeck, "Design and Cost Study of Improved Scaled- Up Centrifugal Particle Receiver Based on Simulation," in Energy Sustainability, 2020, vol. 83631: American Society of Mechanical Engineers, p. V001T02A005.
[57] K. J. Albrecht, M. L. Bauer, and C. K. Ho, "Parametric Analysis of Particle CSP System Performance and Cost to Intrinsic Particle Properties and Operating Conditions," in ASME 2019 13th International Conference on Energy Sustainability collocated with the ASME 2019 Heat Transfer Summer Conference, 2019: American Society of Mechanical Engineers Digital Collection.
[58] M. Mehos, C. Turchi, J. Jorgenson, P. Denholm, C. Ho, and K. Armijo, "On the path to SunShot-advancing concentrating solar power technology, performance, and dispatchability," EERE Publication and Product Library, 2016.
[59] L. F. González-Portillo, K. Albrecht, and C. K. Ho, "Techno-Economic Optimization of CSP Plants with Free-Falling Particle Receivers," Entropy, vol. 23, no. 1, p. 76, 2021.
[60] USGS. "What is a geographic information system (GIS)?" https://www.usgs.gov/faqs/what-a-geographic-information-system-gis? (accessed 03 March, 2021).
[61] USGS. "GIS data layers visualization." https://www.usgs.gov/media/images/gis- data-layers-visualization (accessed 03 March, 2021).
[62] S. Rezaian and S. A. Jozi, "Application of multi criteria decision-making technique in site selection of wind farm-a case study of Northwestern Iran," Journal of the Indian Society of Remote Sensing, vol. 44, no. 5, pp. 803-809, 2016.
100 [63] G. Villacreses, G. Gaona, J. Martínez-Gómez, and D. J. Jijón, "Wind farms suitability location using geographical information system (GIS), based on multi- criteria decision making (MCDM) methods: The case of continental Ecuador," Renewable energy, vol. 109, pp. 275-286, 2017.
[64] Y. Xu et al., "Site selection of wind farms using GIS and multi-criteria decision making method in Wafangdian, China," Energy, vol. 207, p. 118222, 2020.
[65] J. R. Doorga, S. D. Rughooputh, and R. Boojhawon, "Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in Mauritius," Renewable energy, vol. 133, pp. 1201-1219, 2019.
[66] A. Yushchenko, A. De Bono, B. Chatenoux, M. K. Patel, and N. Ray, "GIS-based assessment of photovoltaic (PV) and concentrated solar power (CSP) generation potential in West Africa," Renewable and Sustainable Energy Reviews, vol. 81, pp. 2088-2103, 2018.
[67] S. Ali, J. Taweekun, K. Techato, J. Waewsak, and S. Gyawali, "GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand," Renewable Energy, vol. 132, pp. 1360-1372, 2019.
[68] A. Gerbo, K. V. Suryabhagavan, and T. Kumar Raghuvanshi, "GIS-based approach for modeling grid-connected solar power potential sites: a case study of East Shewa Zone, Ethiopia," Geology, Ecology, and Landscapes, pp. 1-15, 2020.
[69] J. Waewsak, S. Ali, W. Natee, C. Kongruang, C. Chancham, and Y. Gagnon, "Assessment of hybrid, firm renewable energy-based power plants: Application in the southernmost region of Thailand," Renewable and Sustainable Energy Reviews, vol. 130, p. 109953, 2020.
[70] C. Perpiña, J. C. Martínez-Llario, and Á. Pérez-Navarro, "Multicriteria assessment in GIS environments for siting biomass plants," Land Use Policy, vol. 31, pp. 326- 335, 2013.
[71] T. L. Saaty, "A scaling method for priorities in hierarchical structures," Journal of mathematical psychology, vol. 15, no. 3, pp. 234-281, 1977.
[72] H. Z. Al Garni and A. Awasthi, "Solar PV power plant site selection using a GIS- AHP based approach with application in Saudi Arabia," Applied energy, vol. 206, pp. 1225-1240, 2017.
[73] M. Ibarra, I. Gherboudj, A. Al Rished, and H. Ghedira, "Geospatial assessment of solar energy potential for utility scale parabolic trough collector power plant in Saudi Arabia," in AIP Conference Proceedings, 2017, vol. 1850, no. 1: AIP Publishing LLC, p. 160015.
[74] A. A. Merrouni, F. E. Elalaoui, A. Ghennioui, A. Mezrhab, and A. Mezrhab, "A GIS-AHP combination for the sites assessment of large-scale CSP plants with dry
101 and wet cooling systems. Case study: Eastern Morocco," Solar Energy, vol. 166, pp. 2-12, 2018.
[75] A. A. Merrouni, A. Mezrhab, and A. Mezrhab, "CSP sites suitability analysis in the Eastern region of Morocco," Energy Procedia, vol. 49, pp. 2270-2279, 2014.
[76] M. B. Alqaderi, W. Emar, and O. A. Saraereh, "Concentrated solar power site suitability using GIS-MCDM technique taken UAE as a case study," energy, vol. 1009, 2018.
[77] I. Gherboudj and H. Ghedira, "Assessment of solar energy potential over the United Arab Emirates using remote sensing and weather forecast data," Renewable and Sustainable Energy Reviews, vol. 55, pp. 1210-1224, 2016.
[78] E. Home, I. General, S. Business, N. F. Act, and W. Protection, "2014 SunShot Initiative Portfolio Book: Tackling Challenges in Solar Energy."
[79] S. Kubisch, M. Randt, R. Buck, A. Pfahl, and S. Unterschütz, "Wireless heliostat and control system for large self-powered heliostat fields," Proceedings SolarPACES 2011 Granada, 2011.
[80] A. Pfahl et al., "Heliostat Innovation in Detail to Reach Challenging Cost Target," 2020.
[81] A. Pfahl, R. Buck, P.-J. LIEDKE, and F. GROSS, "Solar device," 2016.
[82] Aurelia. "The most efficient small gas turbines in the world." https://aureliaturbines.com/ (accessed April 8, 2021).
[83] S. Alaqel et al., "A novel particle-to-fluid direct-contact counter-flow heat exchanger for CSP power generation applications: Design features and experimental testing," Renewable Energy, vol. 170, pp. 905-926, 2021.
[84] S. Alaqel, "An Experimental Investigation of Particle-to-Working Fluid Counter- Flow Direct-contact Heat Exchanger (Ph.D Thesis)," King Saud University, KSA, Under Publication, 2021.
[85] K. Repole and S. Jeter, "The Development of a Modular Level Cost Model and its Application in Conceptual Design Particle Lift System for a Commercial Scale Solid Particle Concentrated Solar Power Plant," in Proceedings of SolarPACES 2020 (A full manuscript was submitted and will be published in the proceedings), Albuquerque, New Mexico, 2020.
[86] T. Huld, R. Müller, and A. Gambardella, "A new solar radiation database for estimating PV performance in Europe and Africa," Solar Energy, vol. 86, no. 6, pp. 1803-1815, 2012.
102 [87] F. Siala and M. Elayeb, "Mathematical formulation of a graphical method for a no- blocking heliostat field layout," Renewable energy, vol. 23, no. 1, pp. 77-92, 2001.
[88] U. S. Energy Information Administration, "Cost and Performance Characteristics of New Generating Technologies, Annual Energy Outlook 2016," Technical Report, 2016. [Online]. Available: https://www.eia.gov/outlooks/aeo/assumptions/pdf/table_8.2.pdf.
[89] C. Turchi, M. Mehos, C. K. Ho, and G. J. Kolb, "Current and future costs for parabolic trough and power tower systems in the US market," National Renewable Energy Lab.(NREL), Golden, CO (United States), 2010.
[90] W. J. Platzer, "Separation of latitude-dependent and climate-dependent effects of CSP power plant performance," in AIP Conference Proceedings, 2019, vol. 2126, no. 1: AIP Publishing LLC, p. 100005.
[91] K. Chhatbar and R. Meyer, "The influence of meteorological parameters on the energy yield of solar thermal plants," 2011.
[92] R. Hijmans and University of California Berkeley Museum of Vertebrate Zoology, "First-level Administrative Divisions, 2015," UC Berkeley, Museum of Vertebrate Zoology, 2015. [Online]. Available: https://purl.stanford.edu/gy362tj5856.
[93] OCHA ROMENA. Subnational Administrative Boundaries [Online] Available: https://data.humdata.org/organization/ae50792c-b87d-46bb-b42f-3887264ce019
[94] O. O. R. Outlook, "Productive regions for inclusive societies," ed: Paris: OECD Publishing, 2016.
[95] A. Lopez, B. Roberts, D. Heimiller, N. Blair, and G. Porro, "US Renewable Energy Technical Potentials. A GIS-Based Analysis," National Renewable Energy Lab.(NREL), Golden, CO (United States), 2012.
[96] WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018) - Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). [Online]. Available: https://dx.doi.org/10.5258/SOTON/WP00670
[97] NASA. NASA Earth Observatory images, NASA. [Online]. Available: https://earthobservatory.nasa.gov/features/NightLights/page3.php
[98] E. E. Interface and P. Documentation, "Earth Explorer," 2000.
[99] QGIS Geographic Information System. (2009). [Online]. Available: http://qgis.org
103 [100] World Bank Group. Electricity Transmission Network, The World Bank. [Online]. Available: https://datacatalog.worldbank.org/search/type/dataset
[101] LAZARD, "Lazard’s Levelized Cost of Energy Analysis - Version 11.0," in "LAZARD," New York, NY, USA, 2017.
[102] M. Abbas, Z. Belgroun, H. Aburidah, and N. K. Merzouk, "Assessment of a solar parabolic trough power plant for electricity generation under Mediterranean and arid climate conditions in Algeria," Energy Procedia, vol. 42, pp. 93-102, 2013.
[103] E. R. Shouman and N. Khattab, "Future economic of concentrating solar power (CSP) for electricity generation in Egypt," Renewable and Sustainable Energy Reviews, vol. 41, pp. 1119-1127, 2015.
[104] K. D. Patlitzianas, "Solar energy in Egypt: Significant business opportunities," Renewable energy, vol. 36, no. 9, pp. 2305-2311, 2011.
[105] S. Moshiri and S. Lechtenböhmer, "Sustainable energy strategy for Iran," in "Wuppertal Spezial," Wuppertal Institut für Klima, Umwelt, Energie, Wuppertal, 2015. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:wup4-opus- 61757
[106] A. A. Tofigh and M. Abedian, "Analysis of energy status in Iran for designing sustainable energy roadmap," Renewable and Sustainable Energy Reviews, vol. 57, pp. 1296-1306, 2016.
[107] IRENA, "Renewable Energy Statistics 2017," The International Renewable Energy Agency, Abu Dhabi, 2017. [Online]. Available: https://www.irena.org/- /media/Files/IRENA/Agency/Publication/2017/Jul/IRENA_Renewable_Energy_S tatistics_2017.pdf
[108] K. Mohammadi and H. Khorasanizadeh, "The potential and deployment viability of concentrated solar power (CSP) in Iran," Energy Strategy Reviews, vol. 24, pp. 358-369, 2019.
[109] F. M. Abed, Y. Al-Douri, and G. M. Al-Shahery, "Review on the energy and renewable energy status in Iraq: The outlooks," Renewable and Sustainable Energy Reviews, vol. 39, pp. 816-827, 2014.
[110] H. H. Al-Kayiem and S. T. Mohammad, "Potential of renewable energy resources with an emphasis on solar power in Iraq: An outlook," Resources, vol. 8, no. 1, p. 42, 2019.
[111] The Ministry of Energy and Mineral Resources, "Annual Report," Jordan, 2016.
[112] A. Al-Dousari et al., "Solar and wind energy: challenges and solutions in desert regions," Energy, vol. 176, pp. 184-194, 2019.
104 [113] U. S. Energy Information Administration, "Kuwait was the 10th-largest producer of petroleum and other liquids in 2015," in Kuwait, U. S. EIA, Ed., ed, 2016.
[114] Ministry of Electricity and Water, "Statistical Year Book," in "Annual Statistics," Kuwait, 2017.
[115] IEA, "World Energy Statistics 2016," Paris, 2016. [Online]. Available: https://doi.org/10.1787/9789264263079-en
[116] B. Belgasim, Y. Aldali, M. J. Abdunnabi, G. Hashem, and K. Hossin, "The potential of concentrating solar power (CSP) for electricity generation in Libya," Renewable and sustainable energy reviews, vol. 90, pp. 1-15, 2018.
[117] IEA, "World Energy Statistics 2019," Paris, 2019. [Online]. Available: https://doi.org/10.1787/9789264263079-en
[118] A. Saket and A. Etemad-Shahidi, "Wave energy potential along the northern coasts of the Gulf of Oman, Iran," Renewable Energy, vol. 40, no. 1, pp. 90-97, 2012.
[119] Authority for Electricity Regulation, "Study on renewable energy sources," Oman, 2008.
[120] Y. Charabi and A. Gastli, "PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation," Renewable Energy, vol. 36, no. 9, pp. 2554-2561, 2011.
[121] Y. Charabi and A. Gastli, "GIS assessment of large CSP plant in Duqum, Oman," Renewable and Sustainable Energy Reviews, vol. 14, no. 2, pp. 835-841, 2010.
[122] B. Looney, "BP Statistical Review of World Energy 2018," BP, 2018.
[123] IRENA, "Renewable Energy Statistics 2020," The International Renewable Energy Agency, Abu Dhabi, 2020. [Online]. Available: https://www.irena.org/publications/2020/Jul/Renewable-energy-statistics-2020
[124] World Development Indicators (2019). Population The World Bank. [Online]. Available: https://data.worldbank.org/indicator/SP.POP.TOTL
[125] IEA, "Key Energy Statistics 2018," Paris, 2018.
[126] M. Groissböck and M. J. Pickl, "An analysis of the power market in Saudi Arabia: Retrospective cost and environmental optimization," Applied energy, vol. 165, pp. 548-558, 2016.
[127] D. Ansari, C. Kemfert, and H. Al-Kuhlani, Yemen's solar revolution: Developments, challenges, opportunities (no. 142). DIW Berlin: Politikberatung kompakt, 2019.
105 [128] S. Klein, "EES - Engineering Equation Solver (V10.990-3D)," ed: F-Chart Software, 2020.
106