Therefore, the main objective of this study is to develop a model for optimal sourcing of LNG by optimising the cost as well as ensuring energy security for the destination / imported co
Global energy scenario
Energy is the essential driving force behind a nation's economic progress, with modern civilization heavily dependent on efficient energy utilization for development As a country's economy grows, its power consumption steadily increases, highlighting the need for sustainable energy sources Historically, hydrocarbons like oil and coal have dominated global energy consumption, accounting for nearly 80% of total usage, peaking in the 1970s (BP, 2019; U.S Energy Information Administration) Transitioning to renewable energy is crucial for ensuring long-term economic stability and environmental sustainability.
Figure 1.1: Primary direct energy consumption by source [adapted from (BP, 2019) and (U.S
However, the demand has been steadily decreasing due to political (1973 oil embargo) and environmental issues as shown in Figure 1.2 The need for energy security and cleaner fuel source, as hydrocarbons produce carbon dioxide and other greenhouse gases which have a detrimental effect on global warming, has led to developed countries to diversify their energy
OilGasCoalSolarHydropowerNuclearWindOther renewablesBiofuels
The share of sustainable energy sources such as solar, wind, biofuels, and nuclear energy has increased globally, as shown in Figure 1.2, reflecting a shift towards renewable energy Meanwhile, global oil and gas consumption has steadily declined since the 1970s, with oil decreasing from nearly 47% to about 39% in the 2000s, indicating a move away from fossil fuels Conversely, nuclear energy consumption has experienced significant growth, increasing nearly 49 times from 0.13% in the 1970s to 6.73% in the 1990s Gas consumption has also seen a steady rise, growing from 17% in the 1970s to 22% in the 2000s, highlighting a gradual transition toward alternative and cleaner energy sources.
Figure 1.2: Percentage of total world energy consumptions from 1970 to 2019 [adapted from
(BP, 2019) and (U.S Energy Information Administration, 2019)]
During the 2000s and 2010s, the share of coal in global energy consumption increased from 25% to nearly 30%, primarily due to the demand for affordable energy from developing countries like China and India While coal provides a cheap energy source to support economic growth, it also results in high carbon dioxide and greenhouse gas emissions, contributing to global warming The ongoing challenge has been balancing economic development with environmental protection, driving the shift toward cleaner energy sources Recent advancements in technology and cost-effective alternatives are facilitating developing countries' transition to a low-carbon energy economy Since the 2010s, there has been a notable rise in renewable energy consumption worldwide, with countries increasingly moving away from high-emission sources such as oil and coal to adopt greener energy solutions.
P er ce n tag e o f to tal w o rld en er g y co n su m p tio n
OilGasCoalSolarHydropowerNuclearWindOther renewablesBiofuels
5 and sustainable energy such as renewables and gas Another driver is the Paris Agreement where countries pledged to combat climate change by keeping global temperature rise below
2 degree Celsius above pre-industrial levels (Rogelj et al., 2016)
Hydrocarbons continue to dominate the global energy mix, accounting for the largest share of primary energy sources, with oil at 33.1%, natural gas at 24.2%, and coal at 27% However, renewable and sustainable energy sources, including wind, solar, and hydro power, are steadily increasing and currently represent approximately 11.4% of the global energy share.
Figure 1.3: Current fuel shares of primary energy in 2019 [adapted from (BP, 2019)]
Global energy consumption has been steadily increasing, primarily driven by rising demand from Asia, particularly China and India Since the 2000s, Asia's energy consumption has grown by approximately 1.3%, followed by the Middle East at 1.24%, Africa at 0.73%, Latin America at 0.42%, CIS regions at 0.22%, and North America at 0.02% Interestingly, energy consumption in Europe has slightly decreased by around 0.04%, indicating regional variations in energy demand trends.
Figure 1.4: Energy consumption trend over 1991 to 2019 [adapted from (BP, 2019)]
Energy consumption has declined in most regions over the past decade compared to the 2000s, with North America being an exception The Asia Pacific region experienced the largest decrease at 0.75%, followed by the Middle East (0.72%), Africa (0.4%), Latin America (0.3%), CIS regions (0.11%), and Europe (0.01%) Advancements in technology leading to more energy-efficient solutions are a key factor contributing to these reductions, according to the Council (2019).
The total world proved oil reserves are around 1733.9 thousand million barrels at the end of
2019 (BP, 2019) The top ten countries with highest oil reserves are shown in Figure 1.5 Venezuela has the highest oil reserves at 303.8 thousand million barrels which are around 17.5% of the total world reserves It is followed by Saudi Arabia, Canada, Iran, Russian Federation, Kuwait, UAE, US and Libya with the total around 297.6, 169.7, 155.6, 145, 107.2, 101.5, 97.8, 68.9 and 48.4 thousand million barrels respectively
Millio n to n n e o f o il e q u iv alen t
Middle East Africa Asia Latin America North America CIS
Figure 1.5: Energy consumption trend over 1991 to 2019 [adapted from (BP, 2019)]
The world’s current coal deposits are estimated to be around 1,069,636 million tons (BP,
Major coal deposits are predominantly located in the United States, Russia, China, India, and Australia Currently, coal accounts for 41% of global electricity generation, and its demand is expected to increase in the coming decades, highlighting its continued significance in the global energy landscape.
Figure 1.6: Energy consumption trend from 1991 to 2019 [adapted from (BP, 2019)]
In 2019, the leading coal exporters collectively accounted for nearly 95% of all global coal exports, highlighting their dominant role in the market (Cunningham, Van Uffelen, & Chambers, 2019) Indonesia emerged as the largest coal exporter, with nearly 455 million tonnes (Mt) exported in 2019, representing approximately 38.3% of total worldwide coal exports Australia also played a significant role in global coal trade, further emphasizing the concentration of coal exports among the top producing countries, as illustrated in Figure 1.7.
Kuwait UAE US Libya World
T h o u san d m il li o n b ar re ls
US Russia Australia China India Indonesia Germany Ukraine Poland Kasakhstan
8 nearly 393 Mt followed by Russia United States, South Africa, Colombia, Canada, and Mongolia
Figure 1.7: Coal export market share by country in 2019 [adapted from (BP, 2019)]
According to (IEA, 2019), global coal use is projected to remain flat The total coal demand in Organisation for Economic Co-operation and Development (OECD) 1 regions and China is predicted to decline offsetting the growth in India and other non-OECD Asian nations
The increasing adoption of natural gas for power generation aims to reduce dependency on coal and mitigate environmental pollution Currently, natural gas accounts for approximately 23% of global power production (International Energy Agency and EIA, 2018) In developed countries like Australia, the USA, and the UK, natural gas primarily serves as peaking power generation, providing flexibility and reliability Some nations such as Qatar, the United Arab Emirates, Bangladesh, and Nigeria rely heavily on natural gas, with over 80% of their electricity generated from gas due to limited access to other fuels (International Energy Agency, 2018) Rapid economic growth in Asia, along with certain developed nations, drives increased natural gas use for power generation, leading many to import liquefied natural gas (LNG) for easier transportation because domestic supplies are insufficient.
1 Current OECD member States: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, UK, and US
Indonesia Australia Russia US Colombia South
Future sources of primary energy
By 2060, global power generation is projected to reach 44,914 TWh, marking an 89% increase compared to 2019 (BP, 2019) Non-fossil energy sources are expected to supply approximately 54% of this total, with nuclear and hydropower leading at 15% each, followed by wind at 13%, solar at 7%, and biomass at 4% Meanwhile, fossil fuels like natural gas and coal are predicted to contribute 27% and 18%, respectively, to the global energy mix (EIA, 2019).
Figure 1.8: Future global energy source [adapted from (BP, 2019)]
The demand for oil is expected to decline in the coming years as part of efforts to meet the goals of the Paris Agreement, which aims to stabilize global temperatures and reduce greenhouse gas emissions Without new oil reserves being discovered, fossil fuels are projected to be exhausted by 2070 at the current global energy consumption rate Meanwhile, coal is forecasted to contribute 18% of the world's power production by 2060, with coal consumption for electricity generation increasing by 64% since 2000, highlighting a continued reliance on coal despite the shift toward cleaner energy sources.
Advantages of LNG
Countries lacking access to domestic natural gas primarily import it via pipelines where geographically feasible and as liquefied natural gas (LNG) Natural gas contributes approximately 24.23% to global power generation, highlighting its vital role in the world's energy mix.
LNG is the preferred method for importing natural gas in regions lacking extensive pipeline infrastructure, with total global consumption reaching 39,292 TWh (BP, 2019) Japan is the leading importer of LNG, accounting for approximately 22% of the world's total, followed by China at 17%, South Korea with 11%, India at 7%, and Taiwan at 5%, highlighting the significant role of LNG in meeting energy demands in Asia.
LNG accounted for nearly three-quarters of capacity growth between 2000 and 2019 in developed economies The shift towards gas power generations is due to lower capital cost and shorter project lead times compared to coal and nuclear power plants Furthermore, flexibility in providing either peak or baseload power or supplement to the intermittent renewable source makes it an attractive energy source
The global shift towards using Liquefied Natural Gas (LNG) for power generation is gaining momentum as a cleaner alternative to coal In 2019, the top ten LNG exporters collectively accounted for nearly 59% of the world's total exports, highlighting the concentrated power of key suppliers Qatar leads the market with approximately 29.9% of global LNG exports, followed by Australia at 17.2%, with Malaysia (9.7%), Nigeria (7.2%), and Indonesia (6.4%) also ranking among the top exporters Additionally, Algeria (4.5%), Russia (4.2%), and Trinidad and Tobago are significant contributors, underscoring their important roles in the global LNG supply chain This increasing reliance on LNG is shaping the future of international energy markets and emphasizes the strategic importance of these leading exporters.
Oman accounts for 3.2% of global exports, while Papua New Guinea contributes 2.9%, with total exports illustrated in Million Ton Per Annum (MTPA) in Figure 1.9 Australia’s export volume is currently nearly half that of Qatar’s but is projected to surpass Qatar by 2021, indicating a significant shift in export dynamics.
Figure 1.9: Renewable Energy Share of Global Power Production in 2019 [adapted from
Global LNG market and future demand
Over the past decade, the global LNG trade has experienced steady growth, driven by increasing export and import activities among countries worldwide The volumes of LNG traded globally, measured in million tons per annum (MTPA), have significantly risen from 1994 to 2014, as illustrated in Figure 1.10 This upward trend highlights the expanding importance of LNG in the global energy market and underscores its role as a key energy source for many nations.
Figure 1.10: LNG Trade volumes from 1994 to 2014 [adapted from (IGU, 2015)]
The Asia-Pacific region records the largest trade flow route, totaling approximately 77.3 million tonnes (MT), driven by major exporters Australia and Malaysia Key importers include Japan, China, Singapore, Thailand, and South Korea Additionally, trade from the Middle East to Asia accounts for around 36.3 MT, with Qatar leading as the third-highest trade flow in this region.
Africa plays a significant role in global LNG trade, primarily exporting to Europe and Asia, with notable trade flows also extending to North America, Russia, Latin America, and Europe According to Table 1.1, the global LNG export capacity is detailed, highlighting current figures and projecting future growth up to 2025 This data underscores the expanding reach and increasing capacity of LNG exports worldwide, reflecting evolving trade dynamics and rising demand across multiple regions.
Table 1.1: Global export capacity of LNG - current and future data [adapted from : (IGU,
Current capacity until 2019 Future Capacity by 2025
(MTPA) Country Sum of Capacity
As of 2019, 37 countries are importing LNG, with a combined LNG receiving capacity of approximately 819 MTPA operating at over 50% utilization, highlighting the growing global demand for liquefied natural gas Additionally, eight countries—Jordan, Greece, the Philippines, Ghana, Croatia, Vietnam, Cyprus, and El Salvador—are currently developing new LNG import terminals, which is expected to further boost global LNG consumption and infrastructure development.
13 by 2025 with a 12% increase in a total global capacity Table 1.2 listed the global import capacity of LNG including the current and future data until 2023
Table 1.2: Global import capacity of LNG (current and future data) [adapted from : (IGU,
A Current capacity until 2019 B Future Capacity by 2023
(MTPA) Country Sum of Capacity
Figure 1.11 shows the trend of global export and import capacity Both export and import are in rising with a second-degree curve which indicates a rapid rise in this energy sector As many countries are switching from coal to gas to meet the global carbon emission target by
By 2050, the trend of increasing LNG trade will continue until global energy targets are achieved This ongoing growth will drive a rising demand for liquefied natural gas, making it essential for importing countries to seek reliable and strategic trading partners Securing strong partnerships in LNG trade will be crucial to meet future energy needs and ensure stable and sustainable energy supply worldwide.
Figure 1.11: Trend of global export and import capacity
LNG, or liquefied natural gas, is natural gas cooled to approximately -161°C to enable efficient storage and transportation Primarily composed of methane, LNG undergoes a purification process to remove impurities and heavy hydrocarbons before liquefaction Its density ranges from 410 to 500 kg/m³, and it occupies about 1/600th of its original gaseous volume at atmospheric pressure, making it highly cost-effective for long-distance transport LNG is odourless, colourless, non-toxic, and non-corrosive, but safety considerations include its flammability when vaporized, as well as risks of freezing and asphyxiation in liquid form, given its inflammable nature.
Importers Importers future demand Exporters
15 pipelines do not exist A Typical LNG Process is illustrated in Figure 1.12 (Mustary, Chowdhury, Loganathan, & Alam, 2017)
Ocean standard ships designed with various capacities, propulsion systems, and types are essential for transporting LNG across sea routes between loading and unloading terminals A typical LNG transportation network, as illustrated in Figure 1.13, highlights the necessary infrastructure at both loading and unloading terminals to ensure efficient and safe LNG shipping operations.
Figure 1.13: A typical LNG transportation network by ship, adapted from (Mustary et al.,
A typical LNG export facility system requires the following infrastructures:
A typical LNG import facility system requires the following infrastructures:
Storage units for liquefied natural gas (LNG) can be land-based (onshore) or floating, providing flexible options for LNG storage The combination of a storage unit and regasification facility into a single floating unit is known as a Floating Storage and Regasification Unit (FSRU), which is typically installed at the receiving terminal and connected to the main power grid for on-demand gas supply Additionally, FSRUs can be refilled by LNG carriers, ensuring continuous and efficient LNG management, as illustrated in Figures 1.14 and 1.15.
Figure 1.14: LNG exporting facility at Gladstone in Queensland, Australia, [adapted from
LNG Train - where liquifidication process has been undertaken
LNG Storage - after Liquifidication process
Figure 1.15: LNG import facility system using FSRU located at Moheshkhali, Bangladesh
Factors for optimal sourcing strategy for LNG importers
Minimizing transportation costs while maximizing energy security are essential for efficient trade Certain locations are suitable for some regions but not universally ideal, highlighting the importance of strategic site selection Various factors influence trade logistics between locations, including infrastructure, accessibility, and geopolitical considerations, which must be carefully evaluated to optimize trade routes and ensure cost-effectiveness.
• Production capacity of the source terminal
• Distance between source and destination terminals
• Risk on the transportation route
• Trade dispute between source country with other nations
• Source country’s involvement in internal/external conflicts or warfare as geopolitical conflicts in source countries can disrupt LNG supply
• Bilateral relationship between source and destination country
• Trade relationship/volume of export-import between source and destination country
• Economic strength of source country as economic strength provides internal security which provides LNG supply security
• Any economic or UN sanctions on source country which can create supply disruptions
• Medium/long-term political risk index of the source country as a politically stable country can provide energy security is for a sourcing country is safer
• Political stability index of the source country as the politically stable country provides safer and reliable energy security
Production capacity of the source terminal
Each source terminal's annual LNG production capacity, measured in MTPA, directly influences a country's energy security, with higher capacities indicating greater reliability Countries with higher LNG production capacities can reduce supply risks, ensuring a more stable energy supply It is crucial to verify that source terminals can consistently meet the minimum base load requirements at destination terminals to maintain uninterrupted energy delivery.
Price is a crucial factor in selecting the most suitable LNG source, as it varies based on market location and contract type Short-term or spot purchase contracts tend to exhibit higher price volatility, making them less predictable In contrast, long-term contracts offer more stable pricing and greater energy security, providing a reliable supply over an extended period.
Distance between source and destination terminals
Delivery time is directly influenced by route length, with longer routes resulting in increased delivery times As distance increases, both transportation costs and delivery delays tend to rise, making it more efficient to source from locations within the same region or market Additionally, longer distances heighten the risks associated with the delivery process, emphasizing the importance of selecting nearby sources to ensure timely and cost-effective deliveries.
Risk on the transportation route
There are several risks involved in LNG trade:
LNG transportation on the surface faces several key challenges, including excessive corrosion of pipeline infrastructures and increased risk of pipeline integrity loss, which can lead to costly repairs and outages Additionally, blocked furnace nozzles and operational issues at end-users can disrupt the smooth delivery of LNG Contamination of instrumentation and control valves poses safety risks and compromises process efficiency, while the accumulation of impurities within the pipe network can hinder system performance and require rigorous maintenance Addressing these issues is essential for ensuring the safe, efficient, and reliable transport of LNG.
• Safety and Accidents: o Leaking and evaporation o Risk of fire and thermal radiation hazards o Risk of Subversive Attacks to LNG Facilities, Infrastructures and Transports
Risk assessment encompasses both qualitative and quantitative approaches, aiming to identify potential accident scenarios and evaluate their associated risks Qualitative risk assessments focus on pinpointing possible events and estimating their undesirable consequences, helping prioritize the most likely and severe incidents for further analysis Additionally, frequency analysis leverages historical accident data to quantify risk levels, enhancing the accuracy of safety evaluations and supporting informed decision-making.
This study evaluates the risks of attacks or blockades on LNG transportation routes by analyzing historical data of the surrounding areas Key considerations include potential disruptions when ships pass through important canals like the Suez or Panama, which may be blocked due to maintenance or construction activities Additionally, geopolitical conflicts in regions such as the Strait of Hormuz pose significant risks to the safety and security of LNG shipments through these sensitive waterways.
Trade dispute between source country with other nations
Trade sanctions and destination restrictions can disrupt energy security by impacting supply chains Increased tariffs on source countries raise export prices, potentially affecting market stability To ensure reliable energy access, it is economically advantageous to source from politically stable countries with no significant trade disputes.
Source country’s involvement in internal/external conflicts or warfare
Sourcing countries involved in wars or conflicts can significantly disrupt trade and compromise energy supply stability Internal conflicts pose serious risks to infrastructure and overall trade flow, further increasing unpredictability The threat of war and conflict creates a highly volatile environment, making such regions unreliable sources of energy.
Bilateral relationship between source and destination country
The bilateral relationship allows for more flexibility in supply volume and cost flexibility between source and importing country This allows for lower importing cost
Trade relationship/volume of export-import between source and destination country
Trade relationship allows for more flexibility in supply volume and costs flexibility Allows for a potential LNG supply and sustainability/security of supply over the medium to long term
Economic strength of the source country
This offer guarantees specific exported volumes and utilizes a fixed price formula, providing stability for international trade It supports the development of regional gas and LNG supplies through both long-term and spot/short-term contracts in regional and global markets This approach ensures increased supply capacity, accommodating future demand growth for LNG and strengthening market resilience.
Any economic or UN sanctions on the source country
UN sanctions on sourcing country will severely affect the energy security of an importing country Countries must look for alternate supply country which will affect the cost
Medium/long-term political risk index of the source country
To identify a potential sourcing country, it is essential to assess the risk posed by political uncertainty, especially those extending beyond one year A quantitative matrix model is used to categorize countries into seven risk levels, ranging from 1 (low risk) to 7 (high risk), helping businesses make informed sourcing decisions based on political stability.
21 that are in volatile regions usually have a higher risk of political instability and is not a reliable source of energy security
Political stability index of the source country
Another sourcing strategy is to assess the political stability index of the source country The index of Political stability is the measure of the likelihood that a government will be overthrown by unconstitutional or violent means Political stability and economic growths are deeply interconnected A change in government can sometimes have a significant impact on business The new government may be less business-friendly and this can affect the taxation and regulation which will have an impact on energy security.
Objective and scope
Currently, no existing model integrates all identified risk factors to determine the optimal LNG sources for ensuring energy security The primary goal of this study is to develop a comprehensive model that optimizes LNG sourcing costs while prioritizing energy security for the destination This innovative approach aims to balance cost-efficiency with reliable energy supply, addressing current gaps in LNG procurement strategies.
This study analyzes global LNG shipments via sea routes, focusing on key risk factors such as geographical locations, foreign affairs, and the economic and political conditions of source and destination countries It intentionally excludes variables like weather conditions, ship efficiency, and tanker risks to concentrate on the impact of geopolitical and economic factors The goal is to develop a comprehensive model that assesses risks influencing LNG transportation routes worldwide, providing valuable insights for industry stakeholders.
A detailed literature review has been performed in Chapter 2.
The rationale of the research
Recent data indicate that the liquification capacity is increasing exponentially over the year and the degasification capacity is increasing globally as more and more countries started LNG importing into this market to meet their future energy demand Also, the LNG price is decreasing in recent years and the price is expected to be decreased shortly On the other hand, there is a global target to limit carbon emission by 2050 To meet this target more and more countries are switching from coal to natural gas As a result, there will be an increasing demand for LNG soon and a lot more country will be coming to the market So, there is a need for a generalised mathematical model to find out the optimum sourcing strategy As
Currently, there is no existing model to optimize source and destination selection for energy transportation To minimize costs and enhance energy security, a new computational model has been developed to optimize the destination country's logistics This model enables emerging market entrants to efficiently plan their energy imports and allows LNG exporting countries to identify the most advantageous destination markets for future trade Additionally, this versatile model can be adapted for other commodities like coal and oil, making it a valuable tool for optimizing global energy and commodity distribution.
This chapter reviews the existing literature related to the main objectives of this study, providing a comprehensive overview of current knowledge in the field A summarized table highlights key research parameters and identifies gaps in the current research landscape Based on these insights, relevant research questions have been formulated to guide further investigation.
Current body knowledge on optimal sourcing strategy of LNG
The increases in demand for global LNG have attracted increasing attention in the LNG value chain (Cho, Lim, Kim, & Biobaku, 2018) & (Christiansen, Fagerholt, Nygreen, & Ronen,
The LNG value chain consists of five key phases, starting with gas production and the liquefaction and LNG storage phases, both occurring in the producing country The stored LNG is then transported via ocean vessels to the sourcing country in the third phase The final two phases involve unloading the LNG upon arrival in the sourcing country, completing the supply chain A visual representation of this process is shown in Figure 2.1.
Figure 2.1: LNG value chain [adapted from (Cho et al., 2018)]
LNG Inventory Routing is designed to optimize production, inventory scheduling, and transportation plans to effectively meet customer demand (Cho et al., 2018) It also aims to maximize profits for LNG suppliers by efficiently managing resources During the initial phases, LNG suppliers focus on determining maximum production and storage capacities, the appropriate number of LNG carriers by type, and developing an efficient shipping schedule.
The third phase, known as the shipping phase, involves ships operated by the producer as a mixed fleet, consisting of vessels owned by the producer or by individual customers All ships within this fleet are included in the producer's scheduled operations Efficient design and management of the global fleet are essential to enhancing productivity and boosting revenue Ship routing and operational strategies play a critical role in optimizing fleet performance, and this aspect will be examined further in this thesis.
24 scheduling and optimal sourcing strategy are the focus of this review, is the major determinant of the fleet productivity
LNG shipping faces unique challenges beyond just coordinating inventories and routes, making strategic planning crucial As the demand for Liquefied Natural Gas (LNG) transportation rises, developing an effective LNG procurement strategy is essential to minimize overall costs and ensure reliable supply commitments Additionally, optimizing ship fuel consumption plays a vital role in reducing operational expenses and improving efficiency in LNG logistics (Palti-Guzman, 2018).
An LNG ship is capable of transporting different types of LNG; however, it typically carries only one type per voyage to ensure safety and efficiency It is essential to fill the vessel to capacity at the loading port to prevent sloshing and reduce transportation costs (Kuo et al., 2009) Additionally, the sourcing country's LNG production capacity and the ship's loading capacity are critical factors influencing the overall logistics and operational efficiency of LNG transportation.
Boil-off significantly impacts LNG delivery capacity, with longer transportation distances leading to increased evaporation On average, 0.11% to 0.13% of the total tank capacity evaporates daily during transit, reducing the effective volume delivered to customers.
Keeping a certain amount of LNG in the onboard tank is standard practice to maintain the tank’s temperature and keep the LNG cooled during the return journey An empty LNG tank requires approximately 24 hours to cool down at the loading port, which is an uneconomical process for shipping operations.
Cruising speed, sailing conditions, and port policies significantly impact the duration of a maritime voyage (Aydin, Lee, & Mansouri, 2017) Additionally, some ports restrict access to their regasification terminals, accepting only ships owned by specific entities, which can influence overall voyage planning and timing.
Transportation costs comprise both fixed and variable components Fixed costs primarily include time charter rates, providing predictable expenses regardless of shipping activity Variable costs fluctuate based on factors such as port and canal fees, which depend on the ship type and contractual terms Additionally, bunker costs represent a significant variable component, influenced by ship size, cargo load, and voyage duration, impacting overall transportation expenses.
Since the fixed costs cannot be changed during the time horizon, the cost of sailing a scheduled voyage is assumed to only be dependent on the capacity class of the ship, the duration of the voyage and the regasification terminal visited
Hennig et al (2017) demonstrated how operations research can optimize large-scale maritime crude oil transportation by addressing complex variables such as port loading requirements, shipment pickups and deliveries, and ship routing Their model considers factors including ship speed, cargo-dependent pickup and delivery times, port access restrictions, ship capacity limits, and multi-day time windows They incorporated continuous variables to effectively distribute cargo across various routes and proposed advanced algorithms like cutting planes and a branch-and-cut-and-price approach to minimize transportation costs However, their model did not include an optimal sourcing strategy, highlighting an area for further development in maritime logistics optimization.
Kontovas et al (2011) investigated how reducing vessel speed can lower fuel consumption and emissions while aiming to decrease port service time Their research focused on the relationship between engine load and specific fuel consumption, using regression analysis to identify optimal vessel speeds for container ships They concluded that speed reduction, under certain conditions, effectively reduces emissions and overall fuel use To offset potential delays caused by slower speeds, they proposed a “booking by rendezvous” system to minimize port time However, their study did not account for the impact of sea conditions on vessel performance and fuel efficiency.
Ronen (2017) developed a model relating to the annual operating cost of containerships on a route to their sailing speed and developed a procedure to identify the optimal sailing speed
In this model, several key assumptions were made to simplify analysis and ensure accuracy Firstly, the route, or sequence of calling ports, is predefined, establishing a fixed pathway for operations Secondly, a weekly service frequency is required to meet demand efficiently Thirdly, vessels operating on the route are considered similar in physical and economic characteristics, often referring to sister ships, to facilitate practical implementation However, it is important to note that Ronen’s model did not account for the impact of sea conditions on route performance, which could influence operational considerations (Ronen, 2017).
Type and size of the ship
Rakke et al (2011) developed a maritime routing problem model using mixed-integer programming (MIP) to optimize an annual delivery program (ADP) for fulfilling long-term LNG contracts at minimum cost while maximizing revenue from spot market sales The model considers variables such as the number of ships, customer contracts, and the planning horizon, generating all possible scheduled voyages in advance To address the problem's complexity and size, a rolling horizon heuristic (RHH) was introduced, which iteratively solves smaller sub-problems over shorter planning periods This approach enables efficient solutions within reasonable timeframes and yields high-quality ADPs that meet the objectives of the problem owner.
Meng et al (2011) addressed an optimal operating strategy problem in the liner shipping industry, focusing on service frequency, containership fleet deployment, and sailing speed for long-haul routes They modeled this challenge as a complex mixed-integer nonlinear programming problem that is difficult to solve using traditional algorithms To overcome this, they proposed a highly efficient and exact branch-and-bound based e-optimal algorithm Their approach determines the optimal containership type and number, service frequency, and sailing speed for each route segment, aiming to minimize average daily operating costs (Meng & Wang, 2011).
Summary of current studies on LNG sourcing and research gap
The number of published research articles in this area is significantly limited The limited publications may be categorised according to study parameters work summarises these distributions
Table 2.1: Summary of related published research work
Current literature review summaries, as presented in Table 2.1, reveal that there is no existing generalized model capable of determining optimal sourcing locations for international LNG trading These models need to incorporate critical factors such as risks associated with international LNG procurement The absence of a comprehensive framework highlights the need for further research to develop reliable tools for decision-making in this complex industry.
References Price Speed Distance Capacity/Size of
Research questions
Following research questions have been formulated to address in this study:
• What are the important parameters that influence the cost or security of LNG transportation?
• How to estimate the cost of LNG transportation?
• How the cost of LNG transportation can be minimized?
• How the risk of LNG transportation can be assessed?
• How cost-effectively LNG can be transported from the exporting country to an importing country?
• What will be the cost prediction model for an importing country?
• How can the model be implemented to find out optimal sourcing country?
This chapter presents a comprehensive model to estimate LNG prices, incorporating sea transportation costs for specific destinations, with key parameters identified to enable accurate calculations A combined qualitative and quantitative analytical approach has been developed to assess risks, ensuring energy security and facilitating the ranking of sourcing locations based on their reliability and cost-effectiveness.
Methodology
To develop a generalised optimal LNG sourcing model for a given destination, the following
Following steps were carried out for price estimation model:
• Identification of important parameters associated with LNG transportation via sea routes
• Definition of all the input and output variables for the model
• The formulation for the output variables
• Development of a computational model using computer software
• Testing the model using sample data and verify its correctness
Following steps were carried out for optimal sourcing model:
• Identification of key factors that affect the LNG trade between exporting and importing countries
• Development of the optimal sourcing model by qualitative and quantitative output data analysis to rank the sourcing locations for a given destination
A detailed flow chart is shown Figure 3.1 for the optimal sourcing model for this study
32 Figure 3.1: Flow chart for the optimal sourcing model
• Distance between source and destination
• Duration from Source to Destination
• Total LNG cost per trip
• Total CIF price per ship
• Shipping cost per unit LNG
• CIF price per unit LNG
• Total cost for annual capacity
• Name of the source port
• Total capacity of the source port
• Name of the destination port
• Total capacity of the destination port
• Shipping cost per unit LNG
• CIF price per unit LNG
• Total cost for annual capacity
• Loading time at source port
• Unloading time at destination port
Optimal sources are sorted according to total points qualitative and quantitative output data analysis on the remains destinations on the following factors:
• Trade dispute with other nations
• Trade relationship/volume-export-import
• Medium/long-term political risk
Points allocated for each factors based on ranking by comparative analysis
Filtered destinations based on capacity and delivery time
Price estimation model
A basic costing model for LNG price from source to the destination is illustrated in Figure 3.2 It shows that the total cost at the destination can be calculated if three components of cost (i.e gas production cost, liquefaction cost and shipping cost) are known In this model, gas production cost and liquefaction cost for unit volume have been collected from the external data sources Also, the transportation cost can be calculated depending on the location of the sources and the destinations and route preferences Collection of the necessary data for this model will be discussed in chapter 4
The model also requires input values for the properties of LNG carrier: ship capacity and speed including the chartering rate and associate cost components
Important parameters associated with LNG transportation cost
Transportation cost of LNG mainly depends on the following factors:
• Selection on type of LNG carrier: capacity, speed
• Type of charting: Chartering fee including brokerage charge
• Time of the booking for the journey
• Route length (depends on the location of the source and destination)
• The toll on the route: canal charges (e.g Panama or Suez charge)
• Other charges: insurance of goods, demurrage
• Loading and unloading time at the ports
3.2.1.1 Speed and capacity of LNG carrier
LNG transportation relies on different types of ships, with capacity and speed being key factors influencing costs Increasing the capacity of an LNG carrier reduces the unit transportation cost, making larger ships more cost-effective Additionally, higher ship speeds also contribute to lowering overall transportation expenses Optimizing both capacity and speed is essential for efficient and cost-effective LNG shipping.
Upgrading to LNG carriers with newer, more fuel-efficient propulsion systems reduces delivery times and lowers total chartering fees Modern LNG carriers consume less fuel, making them more cost-effective to operate compared to older ships Consequently, chartering these newer vessels results in cheaper rental rates and overall transportation costs, providing significant savings for operators.
Following are the three main chartering methods used for LNG carrier hiring:
A demise-type charter is typically used for long-term contracts, where the charterer assumes responsibility for all operating expenses, including fuel, crew, port charges, protection and indemnity insurance, and hull insurance, making it ideal for long-term planning In contrast, voyage and time charters are generally short-term arrangements; a voyage charter involves hiring a vessel and crew for a specific voyage between a load and discharge port, with the charterer responsible for fuel, port charges, commissions, and a freight payment to the owner A time charter allows the charterer to lease the vessel for a set period, with the owner managing the vessel's operations but the charterer directing ports and routes, and covering fuel, port, and crew costs Payments for using the vessel, known as freight, are made on per-ton or lump-sum basis, while the owner covers port costs (excluding stevedoring), fuel, and crew expenses The concept of laytime—allocated time for loading and unloading—applies in voyage charters; exceeding laytime results in demurrage charges, which are factored into transportation cost estimations in this study.
Long term Chartering like bareboat charter is cheaper than the short-term Chartering like time and voyage charter Chartering rate fluctuates during the demand and supply Also, the charter rate depends on the time of hiring If the peak demand time the charter rate is higher than the off-peak season See the data below for spot hiring rate Additionally, if the hiring process is done with the help of a broker, then the broker's fees will be added on top of the hiring fees
3.2.1.3 Length of shipping route (depends on the location of the source and destination)
Transportation costs are directly linked to travel distance, with LNG carriers requiring more fuel as the route length increases, leading to higher overall expenses To accurately estimate these distances, a methodology has been developed utilizing GPS coordinates and commercially available tools such as Netpas software by Seafuture Inc and Google Maps by Google Inc This approach ensures precise distance calculations between source and destination terminals, optimizing transportation planning and cost management.
Google Maps enables the identification of LNG terminal locations and their GPS coordinates by providing detailed satellite images of the Earth's surface Utilizing Google Maps web service, both latitude and longitude coordinates, along with satellite imagery, can be collected to precisely pinpoint these sites For example, Figure 3.3(a) displays the satellite image of the Gorgon LNG plant in Western Australia, illustrating how such imagery aids in locating and analyzing LNG facilities Similarly, the destination port can be accurately identified using the same method, as shown in the corresponding figure.
Figure 3.3: Source and destination terminals on Google Maps
To determine the distance between the source and destination locations, identify one of each on the map Use the Google Maps distance measurement tool, as demonstrated in Figure 3.4, to accurately measure the distance between the two terminals for precise routing and planning.
Figure 3.4: Distance measurement with Google Maps web service
Netpas software was utilized to accurately calculate the distance between the source and destination terminals using GPS data, as illustrated in Figure 3.5 The depicted path represents the actual maritime route taken between these terminals.
Figure 3.5: Distance measurement with Netpas software The measurements by both methods provide similar results with less the 1% difference Hence, any method can be used
3.2.1.4 Toll on the route: canal charges (e.g., Panama or Suez)
Using man-made canals like the Suez and Panama Canals offers a shortcut to reduce shipping distances between seas, significantly decreasing delivery times These canals connect two bodies of water directly, allowing ships to bypass longer routes and save time However, utilizing these shortcuts involves additional fees, which are costs associated with the convenience and efficiency they provide Implementing canal routes can optimize maritime logistics by minimizing travel distance and improving transportation speed.
3.2.1.5 Other charges: insurance of goods and demurrage
Insurance is essential to minimize the risk of loss of goods and property during transportation, and its cost is included in the total shipping expenses Additionally, demurrage charges may apply for any delays in the delivery of goods, ensuring timely delivery and compensating for storage costs Proper insurance coverage and understanding demurrage fees are crucial for efficient and risk-managed logistics.
3.2.1.6 Loading and unloading time at the port
This is also a factor as it may increase the cost
Development of a computational model using computer software
For the developed cost estimation model, the following parameters of the LNG carrier need to calculate the transport cost:
• Chartering rate and associated costs such as brokerage fee and insurance cost
• Loading and unloading time at the ports
The delivery time can be calculated by dividing the sea route length with ship speed
Delivery time = route length / ship speed
Total chartering cost = (chartering cost/day) × total time of delivery including loading and unloading of LNG at the ports
A computer model was developed using MATLAB, with specific output variables defined to calculate the required results To ensure accuracy and validation, a complementary spreadsheet model was also created using MS Excel, allowing for comparison and verification of the results from both models This dual-method approach enhances the reliability of the calculations and supports consistent data analysis.
3.2.2.1 Input and output variables for the model
The MATLAB code was developed to accurately calculate the required parameters for the study Key input and output variables were defined in accordance with the project requirements, with their descriptions, data types, and units clearly outlined in Tables 3.1 and 3.2 Additionally, all the formulations used to derive the output variables are detailed in Table 3.3, ensuring transparent and replicable computational procedures.
This dataset provides comprehensive details on maritime shipping logistics, including the origin and destination countries, ports, and their capacities, with destination ports having a total capacity measured in MTPA It also covers shipping specifics such as ship type, capacity, and speed, along with chartering fees, canal charges, and brokerage fees, ensuring transparency in cost analysis Additionally, the information includes parameters for loading and unloading durations at respective ports, as well as insurance costs for goods, which are crucial for cost management and operational planning in the shipping industry.
This comprehensive cost analysis includes key factors such as the distance between source and destination measured in kilometers and the duration of transport in days The total chartering cost and brokerage fees are calculated in USD, along with insurance costs, canal fees, and overall shipping expenses LNG-specific costs are detailed, with total LNG costs per trip and per unit measured in $/GJ, as well as the CIF price per unit LNG The number of trips required and the total annual capacity costs are also outlined, providing a complete overview for efficient logistics planning and cost management.
Table 3.3: Formulation for output parameters
Development of the optimal sourcing model
If there are multiple possible sources for a given destination, quantitative and qualitative data analysis can be performed to rank the optimal sources according to their suitability for a given destination A point-based ranking system has been developed by analysis of the following factors:
• Production capacity of the source terminal
• Distance between source and destination terminals
• Risk on the transportation route
• Trade dispute between source country with other nations
• Source country’s involvement in internal/external conflicts or warfare
• Bilateral relationship between source and destination country
• Trade relationship/volume of export-import between source and destination country
• Economic strength of the source country
• Any economic or UN sanctions on the source country
• Medium/long-term political risk index of the source country
• Political stability index of the source country
The description of the above factors has been discussed in Chapter 1
The steps are given below:
• Price and delivery time estimation for each source
• Create a database of all sources for unit price, delivery time, distance, capacity
• Shortlisting by data filtering based on capacity and delivery time
• Ranking among shortlisted countries according to level: (High, Medium and Low) for all 12 risk factors
• Setting points for each ranking for each country according to the point criteria
• Sum of all points for all 12 risk factors for all shortlisted countries
• Ranking of the shortlisting countries based on points
Initially, using the costing model (described in the earlier section), prices are calculated for all possible sources and an output database is created with the following data fields:
Then, sources are shortlisted based on the following criteria:
• Delivery time to a maximum threshold
It has been mentioned earlier that higher the capacity, energy security is also higher Also, the risk is minimised if the delivery time is not higher
Country rankings are determined through comprehensive risk factor analysis, incorporating both qualitative and quantitative methods For example, LNG unit prices at destination sources follow a hierarchy: Source 1, Source 2, and Source 3, reflecting varying risk levels These factors collectively influence the overall ranking, providing insights into market stability and investment attractiveness across different countries.
Points are assigned based on the production capacity of each source, using a standardized point criteria: Low (0 points), Medium (1 point), and High (2 points) Accordingly, Source 1 receives 2 points for high production capacity, Source 2 earns 1 point for medium capacity, and Source 3 is allocated 0 points for low capacity This scoring system helps evaluate and compare the production potential of different sources effectively.
The analysis involves evaluating all risk factors through comprehensive data collection and comparative analysis to determine their respective risk levels Each source is assessed individually, and risk levels are categorized as high, medium, or low based on a ranking system Table 3.4: Risk Factors Matrix for Points Calculation provides a detailed tabulation of data, illustrating the risk assessment for each source across various factors and outlining the point criteria used for scoring.
Table 3.4: Risk factors matrix for points calculation
1 Production capacity Low=0, Medium=1, High=2 Point
2 Price Low=2, Medium=1, High=0 Point
3 Distance Low=2, Medium=1, High=0 Point
4 Risk on the route Low=2, Medium=1, High=0 Point
5 Trade dispute with other nations Low=2, Medium=1, High=0 Point
6 Conflicts internal/external Low=2, Medium=1, High=0 Point
7 Bilateral relationship Low=0, Medium=1, High=2 Point
8 Trade relationship/volume- export-import Low=0, Medium=1, High=2 Point
9 Economic strength Low=0, Medium=1, High=2 Point
10 UN sanctions Low=2, Medium=1, High=0 Point
11 Medium/long-term political risk Low=2, Medium=1, High=0 Point
12 Political stability index Low=0, Medium=1, High=2 Point
Finally, the sources are ranked based on the total points for all 12 factors from the height to lowest points
In this chapter, collection and processing of necessary data from external sources for the determination of required output parameters have been presented.
Sources of research data
To evaluate the methodology and effectiveness of the developed optimization model, a comprehensive case study was conducted for a specific country, utilizing both historical and recent data from various sources This approach ensured a thorough assessment of the model’s performance, leveraging diverse datasets to validate its accuracy and reliability The data sources used in the research are detailed below, supporting the robustness of the case study and demonstrating the model's applicability across different scenarios.
• National Aeronautics and Space Administration (NASA)
Data of LNG exporting countries
Currently, there are 22 countries in the world export LNG and 3 more countries will start to export by 2023 To evaluate the optimal sources for a given destination, all the sources have
This study incorporates data from 45 different sources, which were meticulously collected and matched to ensure comprehensive coverage of all major LNG producing and exporting countries worldwide A detailed database has been established, encompassing key information to facilitate in-depth analysis of global LNG production and export trends.
• Address/Location of the terminal
Also, based on the address or location of the terminal of geographic location (GPS coordinates) on the Google Maps of the terminal has been identified and included the source database as shown in Figure 4.1
Figure 4.1: Sources on Google Maps
This case study comprehensively includes all active and under-construction sources across 25 countries, providing a complete overview of current infrastructure Key details such as the start year of operation, annual capacity, terminal name, and GPS coordinates are documented for each source These data are organized in Tabular formats, from Table 4.1 to Table 4.25, facilitating easy reference and analysis of the global distribution and capacity of the sources.
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.9: Data for Equatorial Guinea
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.18: Data for Papua New Guinea (PNG)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.21: Data for Russian Federation
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.22: Data for Trinidad and Tobago
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.23: Data for United Arab Emirates (UAE)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Table 4.24: Data for United States of America (USA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)
Start Year - Terminal name [GPS Coordinates] Sum of Capacity (MTPA)