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Evaluating the Benefits of LNG Procurement through Spot Market Purchase
Anton A. Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan (Eds.) Proceedings of the 29th European Symposium on Computer Aided Process Engineering June 16th to 19th, 2019, Eindhoven, The Netherlands. © 2019 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/B978-0-12-818634-3.50288-5
Evaluating the Benefits of LNG Procurement through Spot Market Purchase Mohd Shahrukh,a Rajagopalan Srinivasan,a I.A.Karimi,b a
Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India b
Department of Chemical and Biomolecular Engineering , National University of Singapore, 119077, Singapore [email protected]
Abstract Natural gas (NG) is liquefied for shipping and storage purposes, this gas in liquid state is known as liquefied natural gas (LNG). The trade of LNG is usually regulated by contracts between suppliers and buyers. Historically long term contracts covering 20-25 years have been used; but more recently, due to emergence of new suppliers and consumers spot market purchases of LNG has also become possible. Nowadays, a consumer can opt to procure LNG via long term contracts or from spot market purchase or a combination of the two. We seek to evaluate the relative benefits of long term versus spot purchases. As a first step, in this paper, we report a mixed-integer linear programming (MILP) model to compare the cost of transportation through long term contracts as against spot market purchase. Several examples are solved; the results show that, in every case, spot market purchase is better compared to long term contracts. Keywords: LNG, mixed integer linear programming, Long term contracts.
1. Introduction Consumption of natural gas (NG) has grown steadily in recent years all over the world. Major factors that have contributed to this growth are environmental impact of conventional fossil fuels, abundant availability, and local market liberalization (Pospíšil et al., 2019). Despite NG being the most favored fossil fuel, its transportation to distant markets constitutes a challenge due to its gaseous state. Its liquefied form, liquefied natural gas (LNG), with its 600 time reduction in volume over NG, can be more easily transported using specially designed LNG carriers (Kumar et al., 2011). Trade of LNG is usually regulated by contracts between suppliers and buyers. Historically, there were only a few buyers and suppliers of LNG. So contracts were generally 20-25 years long and thus provided security of demand and supply to sellers and buyers, respectively. In recent years, new suppliers have emerged, and LNG trade is increasing rapidly. Further, local NG market liberalization in many countries has also triggered many small companies to become LNG consumers. The emergence of these new players, especially small companies in the power sector and fertilizer manufacturers which use the regasified LNG as a feedstock has led to competitive LNG markets, but one characterized by high demand variability. Consequently, there is an increased need felt by regasification terminals to increasingly purchase LNG through short term (i.e., spot) contracts. In this paper, we seek to evaluate the relative benefits of
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long term versus spot purchases. Specifically, we develop a mathematical programming to quantify the transportation cost of LNG purchase for a given demand via spot market and long-term contracts. 1.1. Literature Review In literature, procurement of LNG is considered as a contract (vendor) selection problem, mainly from a buyer’s perspective. The problem is formulated from the point of a focal company that needs to procure a certain amount of LNG over a planning horizon. The company can buy LNG from suppliers distributed all over the world. A supplier may offer one or more contract options. Typically, a mathematical model can be used to choose a set of contracts (vendors), which allows the buyer to minimize the procurement cost. Till the 1970’s, vendor selection was done using linear weighting model where each vendor was scored on multiple decision criteria and the vendor with maximum score selected. Later on, researchers started using linear programming (LP) for vendor selection (Khalilpour and Karimi, 2011). Contract selection problems reported in literature are usually from single buyer’s perspective. See for examples (Khalilpour and Karimi, 2011) and (Jang et al., 2017). No model has been proposed to make a comparative study between spot market purchase and long term contracts for a set of buyers. In this paper, we therefore develop separate models for the long term and spot purchase of LNG and compare the optimal transportation costs.
2. Problem Statement Consider a set of buyers and producers of LNG distributed all over the world. Each consumer and producer has its own specific production and consumption profile. Let P (p=1, 2, ...P) be the total number of producer and C (c=1,2, ....C) be the total number of consumers such that I= P ڂI. Each site has a storage tank. A heterogeneous fleet of V ships (v=1,2, ...V) is used to transport LNG and manage the inventory level at all sites. A site i has Ji identical jetties. Thus, at most Ji number of ships may load/unload at a site. For spot market purchase, ship moves among all sites to maintain inventory level at each site, while for long term contract, ships move only between sites that have contracts with each other. Initial positions of ships, capacity, loading/unloading rate, material onboard are known. Site to site travel costs, number of sites, production, and consumption profile are also known along with duration of the planning horizon. Sites having long term contracts with each other are known. The aim is to administer the ship movements over the planning horizon so that inventory levels are maintained at all the sites for both spot market purchase as well as for long term contracts. 2.1. Example 1 This example consists of two ships; five ports in which two of them are production sites (Site 1 and Site 2) while three of them are consumption sites (Site 3, 4 and 5). Every port has one jetty at each site. Each port has storage tank with limited capacity. Planning horizon is of 10 days. At time zero both ships are empty. Initially, ship 1 is at Site 1 while ship 2 is at Site 2. Loading/Unloading rate of both the ships at every site is 12t/day. Complete data for this example is shown in Tables 1 and 2. Producer 1 has contract with Consumer 3 while Producer 2 have contract with Consumers 4 and 5.
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Table 1: Supply and demand data for Example 1 Production / Consumption rate (t/day)
Site
Type
Initial material (t)
1
Producer
2
80
2
2
Producer
6
100
1.25
3
Consumer
3
25
0.5
4
Consumer
2
40
0.5
5
Consumer
5
60
1
Capacity (t)
Table 2: Travel time and travel cost for ships in Example 1 iĺj
Travel time
Travel cost
i1ĺi2
1.62
38.90
i1ĺi3
1.75
42.07
i1ĺi4
2.05
49.21
i1ĺi5
1.99
47.72
i2ĺi3
0.16
3.95
i2ĺi4
0.82
19.76
i2ĺi5
0.73
17.50
i3ĺi4
0.95
22.89
i3ĺi5
0.86
20.64
i4ĺi5
0.33
7.80
3. Mathematical Model The proposed model for spot market purchase is based on the model for bulk maritime logistics for the supply and delivery of multiple chemicals reported by (Li et al., 2010). They developed a model for a multinational company (MNC) which has sites distributed globally. The model considers multiple materials such that the product of one site is a raw material for another. The goal of the model was to develop routing and scheduling for a fleet of ships while ensuring the continuity of operation at all sites at minimum transportation costs. (Li et al., 2010)’s model has been simplified for the transportation of LNG by reducing the dimensions of all variables which explicitly considered the multiple materials to one (LNG). These variables denoted storage tank capacity, material unloading, material onboard etc. For long term contracts, this model is modified with additional constraints to model the stipulated contract terms. 3.1. Modeling long term contracts for procuring LNG Long term contracts are between suppliers and buyers and specify the quality, quantity, payment terms, etc of the product to be delivered to the buyer over a specified period of
M. Shahrukh et al.
1726
time. For modelling long term contracts we consider a set of producers P (p=1,2,…P) with each producer having supply contracts with a set of customers Lcp (Lcp كሻ Ǥ ǡ Ǥ P
¦ ¦z
vijk
=0
1 d v d V ,1 d k d K
(1a)
i =1 jLcp
¦ ¦
zvijk = 0
1 d v d V ,1 d k d K
(1b)
iLcp jLcp , j z i
where zvijk is equal to 1 when ship v is at site i in slot k and at site j in slot k+1, otherwise it is zero. 3.2. Objective Function Ǥ Ǥ ǣ V I +1
I +1
K
¦¦ ¦ ¦Cv
vij
* zvijk
(2)
v =1 i = 0 j = 0, j z i k =1
Ǥሺͳሻ Ǥሺͳሻ Ǣ Ǥ
4. Case Studies We have solved three examples to assess our model. These examples vary in planning horizon, distances between the sites and production and consumption rates. We used Cplex 12.8.0 on a Lenovo idea pad 510 workstation (core i5-7200U, 8GBmemory) for solving these examples running on windows 10. Results of these examples are summarized in Table 3.
ͶǤͳǤ ͳ In this example, Site 2 has contracts with Sites 4 and 5 while Site 1 has a contract with Site 3. Results shows that Ship 1 loads 5.58 t of LNG during [0,0.465] from Site 2 then moves to Site 4 and Site 5 to deliver 4.0 and 1.58 t of LNG at time (in days) 1.28 and 3.53 respectively. Then, it moves to Site 2 again to load 5.48 t of LNG and delivers it to Site 5 from time [5.581, 6.038]. Ship 2 loads 0.78 t of LNG from Site 1 during [0, 0.065] and delivers it to Site 3. Then it moves back to Site 1 to load 2.21 t of LNG which is delivered to Site 3. Fig 1 shows the resulting inventory profile of the sites over the planning horizon.
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When the same example is solved as a spot market purchase problem, there is a significant reduction in the transportation cost. Here Ship 1 loads 5 t of LNG from Site 2 and delivers it to Sites 4 and 5. Ship 2 loads 1.2 t of LNG from Site 1 during [0,0.1] and delivers it to Site 3 and then moves to Site 2 to load 5 t of LNG which is delivered at Site 5 in between [4.56,4.97]. It then returns to Site 2 to load 2.8 t which is delivered at Site 3 during [5.99, 6.22]. The total transportation cost is therefore reduced by 46.64%. Fig 2 shows the inventory profile of the sites over the planning horizon with spot purchases. Table 3: Results for examples
Example
Planning Horizon
No. of Voyages
No of Iterations
Spot / Spot / Long Long Term Term
Time (min: sec: ms) Spot / Long Term
Optimal Cost Spot / Long Term
1
10
7/9
5435k/211k
00:50:40/02:43:29
88.82/146.7
2
15
10/11
8264k/1632k
03:56:82/00:43:26
134.11/136.77
3
25
3/6
10k/13k
00:00:90/00:01:48
127.03/220.76
5. Discussion We solved two other examples with different parameters (see Table 3). In these examples also, the transportation cost with spot market purchase is less than with the long-term contract. The difference in the transportation costs between the long term and spot purchases increases significantly when the producer and consumer sites are at large distances. These clearly indicate that spot purchases lead to a more optimal usage of the ships – i.e., the same demands can be more effectively met in the absence of long-term contracts. The current model relies on a number of simplifying assumptions. Here, the price of the LNG cargo has not been taken into account. In practice, purchases through long term contracts may have price advantage. The net effect of lower cargo price and a higher transportation cost therefore needs to be evaluated. Also, the current model assumes deterministic values for all parameters; in reality, these would vary based on demand. In future, we aim to develop a model that can incorporate effect of demand variability.
References Jang, W., Hong, H.-U., Han, S.H., Baek, S.W., 2017. Optimal Supply Vendor Selection Model for LNG Plant Projects Using Fuzzy-TOPSIS Theory. J. Manag. Eng. 33, 04016035. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000474 Khalilpour, R., Karimi, I.A., 2011. Selection of Liquefied Natural Gas (LNG) Contracts for Minimizing Procurement Cost. Ind. Eng. Chem. Res. 50, 10298–10312. https://doi.org/10.1021/ie200275m Kumar, S., Kwon, H.-T., Choi, K.-H., Lim, W., Cho, J.H., Tak, K., Moon, I., 2011. LNG: An ecofriendly cryogenic fuel for sustainable development. Appl. Energy 88, 4264–4273. https://doi.org/10.1016/j.apenergy.2011.06.035
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Li, J., Karimi, I.A., Srinivasan, R., 2010. Efficient bulk maritime logistics for the supply and delivery of multiple chemicals. Comput. Chem. Eng. 34, 2118–2128. https://doi.org/10.1016/j.compchemeng.2010.07.031 Pospíšil, J., Charvát, P., Arsenyeva, O., Klimeš, L., Špiláþek, M., Klemeš, J.J., 2019. Energy demand of liquefaction and regasification of natural gas and the potential of LNG for operative thermal energy storage. Renew. Sustain. Energy Rev. 99, 1–15. https://doi.org/10.1016/j.rser.2018.09.027
Fig 1: Inventory profile at sites while procuring LNG via Long term contracts
Fig 2: Inventory profile at sites while procuring via spot market purchase.
Evaluating the Benefits of LNG Procurement through Spot Market Purchase Mohd Shahrukh,a Rajagopalan Srinivasan,a I.A.Karimi,b a
Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India b
Department of Chemical and Biomolecular Engineering , National University of Singapore, 119077, Singapore [email protected]
Abstract Natural gas (NG) is liquefied for shipping and storage purposes, this gas in liquid state is known as liquefied natural gas (LNG). The trade of LNG is usually regulated by contracts between suppliers and buyers. Historically long term contracts covering 20-25 years have been used; but more recently, due to emergence of new suppliers and consumers spot market purchases of LNG has also become possible. Nowadays, a consumer can opt to procure LNG via long term contracts or from spot market purchase or a combination of the two. We seek to evaluate the relative benefits of long term versus spot purchases. As a first step, in this paper, we report a mixed-integer linear programming (MILP) model to compare the cost of transportation through long term contracts as against spot market purchase. Several examples are solved; the results show that, in every case, spot market purchase is better compared to long term contracts. Keywords: LNG, mixed integer linear programming, Long term contracts.
1. Introduction Consumption of natural gas (NG) has grown steadily in recent years all over the world. Major factors that have contributed to this growth are environmental impact of conventional fossil fuels, abundant availability, and local market liberalization (Pospíšil et al., 2019). Despite NG being the most favored fossil fuel, its transportation to distant markets constitutes a challenge due to its gaseous state. Its liquefied form, liquefied natural gas (LNG), with its 600 time reduction in volume over NG, can be more easily transported using specially designed LNG carriers (Kumar et al., 2011). Trade of LNG is usually regulated by contracts between suppliers and buyers. Historically, there were only a few buyers and suppliers of LNG. So contracts were generally 20-25 years long and thus provided security of demand and supply to sellers and buyers, respectively. In recent years, new suppliers have emerged, and LNG trade is increasing rapidly. Further, local NG market liberalization in many countries has also triggered many small companies to become LNG consumers. The emergence of these new players, especially small companies in the power sector and fertilizer manufacturers which use the regasified LNG as a feedstock has led to competitive LNG markets, but one characterized by high demand variability. Consequently, there is an increased need felt by regasification terminals to increasingly purchase LNG through short term (i.e., spot) contracts. In this paper, we seek to evaluate the relative benefits of
1724
M. Shahrukh et al.
long term versus spot purchases. Specifically, we develop a mathematical programming to quantify the transportation cost of LNG purchase for a given demand via spot market and long-term contracts. 1.1. Literature Review In literature, procurement of LNG is considered as a contract (vendor) selection problem, mainly from a buyer’s perspective. The problem is formulated from the point of a focal company that needs to procure a certain amount of LNG over a planning horizon. The company can buy LNG from suppliers distributed all over the world. A supplier may offer one or more contract options. Typically, a mathematical model can be used to choose a set of contracts (vendors), which allows the buyer to minimize the procurement cost. Till the 1970’s, vendor selection was done using linear weighting model where each vendor was scored on multiple decision criteria and the vendor with maximum score selected. Later on, researchers started using linear programming (LP) for vendor selection (Khalilpour and Karimi, 2011). Contract selection problems reported in literature are usually from single buyer’s perspective. See for examples (Khalilpour and Karimi, 2011) and (Jang et al., 2017). No model has been proposed to make a comparative study between spot market purchase and long term contracts for a set of buyers. In this paper, we therefore develop separate models for the long term and spot purchase of LNG and compare the optimal transportation costs.
2. Problem Statement Consider a set of buyers and producers of LNG distributed all over the world. Each consumer and producer has its own specific production and consumption profile. Let P (p=1, 2, ...P) be the total number of producer and C (c=1,2, ....C) be the total number of consumers such that I= P ڂI. Each site has a storage tank. A heterogeneous fleet of V ships (v=1,2, ...V) is used to transport LNG and manage the inventory level at all sites. A site i has Ji identical jetties. Thus, at most Ji number of ships may load/unload at a site. For spot market purchase, ship moves among all sites to maintain inventory level at each site, while for long term contract, ships move only between sites that have contracts with each other. Initial positions of ships, capacity, loading/unloading rate, material onboard are known. Site to site travel costs, number of sites, production, and consumption profile are also known along with duration of the planning horizon. Sites having long term contracts with each other are known. The aim is to administer the ship movements over the planning horizon so that inventory levels are maintained at all the sites for both spot market purchase as well as for long term contracts. 2.1. Example 1 This example consists of two ships; five ports in which two of them are production sites (Site 1 and Site 2) while three of them are consumption sites (Site 3, 4 and 5). Every port has one jetty at each site. Each port has storage tank with limited capacity. Planning horizon is of 10 days. At time zero both ships are empty. Initially, ship 1 is at Site 1 while ship 2 is at Site 2. Loading/Unloading rate of both the ships at every site is 12t/day. Complete data for this example is shown in Tables 1 and 2. Producer 1 has contract with Consumer 3 while Producer 2 have contract with Consumers 4 and 5.
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Table 1: Supply and demand data for Example 1 Production / Consumption rate (t/day)
Site
Type
Initial material (t)
1
Producer
2
80
2
2
Producer
6
100
1.25
3
Consumer
3
25
0.5
4
Consumer
2
40
0.5
5
Consumer
5
60
1
Capacity (t)
Table 2: Travel time and travel cost for ships in Example 1 iĺj
Travel time
Travel cost
i1ĺi2
1.62
38.90
i1ĺi3
1.75
42.07
i1ĺi4
2.05
49.21
i1ĺi5
1.99
47.72
i2ĺi3
0.16
3.95
i2ĺi4
0.82
19.76
i2ĺi5
0.73
17.50
i3ĺi4
0.95
22.89
i3ĺi5
0.86
20.64
i4ĺi5
0.33
7.80
3. Mathematical Model The proposed model for spot market purchase is based on the model for bulk maritime logistics for the supply and delivery of multiple chemicals reported by (Li et al., 2010). They developed a model for a multinational company (MNC) which has sites distributed globally. The model considers multiple materials such that the product of one site is a raw material for another. The goal of the model was to develop routing and scheduling for a fleet of ships while ensuring the continuity of operation at all sites at minimum transportation costs. (Li et al., 2010)’s model has been simplified for the transportation of LNG by reducing the dimensions of all variables which explicitly considered the multiple materials to one (LNG). These variables denoted storage tank capacity, material unloading, material onboard etc. For long term contracts, this model is modified with additional constraints to model the stipulated contract terms. 3.1. Modeling long term contracts for procuring LNG Long term contracts are between suppliers and buyers and specify the quality, quantity, payment terms, etc of the product to be delivered to the buyer over a specified period of
M. Shahrukh et al.
1726
time. For modelling long term contracts we consider a set of producers P (p=1,2,…P) with each producer having supply contracts with a set of customers Lcp (Lcp كሻ Ǥ ǡ Ǥ P
¦ ¦z
vijk
=0
1 d v d V ,1 d k d K
(1a)
i =1 jLcp
¦ ¦
zvijk = 0
1 d v d V ,1 d k d K
(1b)
iLcp jLcp , j z i
where zvijk is equal to 1 when ship v is at site i in slot k and at site j in slot k+1, otherwise it is zero. 3.2. Objective Function Ǥ Ǥ ǣ V I +1
I +1
K
¦¦ ¦ ¦Cv
vij
* zvijk
(2)
v =1 i = 0 j = 0, j z i k =1
Ǥሺͳሻ Ǥሺͳሻ Ǣ Ǥ
4. Case Studies We have solved three examples to assess our model. These examples vary in planning horizon, distances between the sites and production and consumption rates. We used Cplex 12.8.0 on a Lenovo idea pad 510 workstation (core i5-7200U, 8GBmemory) for solving these examples running on windows 10. Results of these examples are summarized in Table 3.
ͶǤͳǤ ͳ In this example, Site 2 has contracts with Sites 4 and 5 while Site 1 has a contract with Site 3. Results shows that Ship 1 loads 5.58 t of LNG during [0,0.465] from Site 2 then moves to Site 4 and Site 5 to deliver 4.0 and 1.58 t of LNG at time (in days) 1.28 and 3.53 respectively. Then, it moves to Site 2 again to load 5.48 t of LNG and delivers it to Site 5 from time [5.581, 6.038]. Ship 2 loads 0.78 t of LNG from Site 1 during [0, 0.065] and delivers it to Site 3. Then it moves back to Site 1 to load 2.21 t of LNG which is delivered to Site 3. Fig 1 shows the resulting inventory profile of the sites over the planning horizon.
Evaluating the benefits of spot market purchase
1727
When the same example is solved as a spot market purchase problem, there is a significant reduction in the transportation cost. Here Ship 1 loads 5 t of LNG from Site 2 and delivers it to Sites 4 and 5. Ship 2 loads 1.2 t of LNG from Site 1 during [0,0.1] and delivers it to Site 3 and then moves to Site 2 to load 5 t of LNG which is delivered at Site 5 in between [4.56,4.97]. It then returns to Site 2 to load 2.8 t which is delivered at Site 3 during [5.99, 6.22]. The total transportation cost is therefore reduced by 46.64%. Fig 2 shows the inventory profile of the sites over the planning horizon with spot purchases. Table 3: Results for examples
Example
Planning Horizon
No. of Voyages
No of Iterations
Spot / Spot / Long Long Term Term
Time (min: sec: ms) Spot / Long Term
Optimal Cost Spot / Long Term
1
10
7/9
5435k/211k
00:50:40/02:43:29
88.82/146.7
2
15
10/11
8264k/1632k
03:56:82/00:43:26
134.11/136.77
3
25
3/6
10k/13k
00:00:90/00:01:48
127.03/220.76
5. Discussion We solved two other examples with different parameters (see Table 3). In these examples also, the transportation cost with spot market purchase is less than with the long-term contract. The difference in the transportation costs between the long term and spot purchases increases significantly when the producer and consumer sites are at large distances. These clearly indicate that spot purchases lead to a more optimal usage of the ships – i.e., the same demands can be more effectively met in the absence of long-term contracts. The current model relies on a number of simplifying assumptions. Here, the price of the LNG cargo has not been taken into account. In practice, purchases through long term contracts may have price advantage. The net effect of lower cargo price and a higher transportation cost therefore needs to be evaluated. Also, the current model assumes deterministic values for all parameters; in reality, these would vary based on demand. In future, we aim to develop a model that can incorporate effect of demand variability.
References Jang, W., Hong, H.-U., Han, S.H., Baek, S.W., 2017. Optimal Supply Vendor Selection Model for LNG Plant Projects Using Fuzzy-TOPSIS Theory. J. Manag. Eng. 33, 04016035. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000474 Khalilpour, R., Karimi, I.A., 2011. Selection of Liquefied Natural Gas (LNG) Contracts for Minimizing Procurement Cost. Ind. Eng. Chem. Res. 50, 10298–10312. https://doi.org/10.1021/ie200275m Kumar, S., Kwon, H.-T., Choi, K.-H., Lim, W., Cho, J.H., Tak, K., Moon, I., 2011. LNG: An ecofriendly cryogenic fuel for sustainable development. Appl. Energy 88, 4264–4273. https://doi.org/10.1016/j.apenergy.2011.06.035
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M. Shahrukh et al.
Li, J., Karimi, I.A., Srinivasan, R., 2010. Efficient bulk maritime logistics for the supply and delivery of multiple chemicals. Comput. Chem. Eng. 34, 2118–2128. https://doi.org/10.1016/j.compchemeng.2010.07.031 Pospíšil, J., Charvát, P., Arsenyeva, O., Klimeš, L., Špiláþek, M., Klemeš, J.J., 2019. Energy demand of liquefaction and regasification of natural gas and the potential of LNG for operative thermal energy storage. Renew. Sustain. Energy Rev. 99, 1–15. https://doi.org/10.1016/j.rser.2018.09.027
Fig 1: Inventory profile at sites while procuring LNG via Long term contracts
Fig 2: Inventory profile at sites while procuring via spot market purchase.