Modelling of optimal sourcing strategy for future LNG demand

<p>Recent data indicates that there is an increasing global demand for natural gas in many countries as these countries use imported gas to meet their shortfalls. On the other hand, several countries have started exporting gas in the form of liquified natural gas called LNG. The trend shows that both export and import are in rising with a second-degree order 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 2050, the trend will be continuing until the target is reached. As a result, there will be increasing demand for LNG trade in future and the need for the quest for good trading partners is crucial for the future energy demand of the importing country. To minimize the overall cost of LNG import ensuring the energy security, the potential country needs to select an optimum strategy for importing LNG from a suitable location/country.</p> <p>Several studies have been carried out by optimising only the cost. They have not considered some other risk factors such as political and economic factors in their modelling. Currently, there is no generalised model available to determine the optimal sourcing locations incorporating important factors including risks in international LNG trading. In this study, 12 risk factors were taken into consideration to evaluate the risk. 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 country.</p> <p>A computational model has been developed to estimate the shipping cost between the source and destination terminals. This model can be used to estimate the CIF (Cost, Insurance and Freight) price of LNG from one destination to another including all the cost components of shipping. Additionally, a strategic tool: optimal sourcing model has been developed to determine the optimal sourcing countries according to their level of stability. A total of 12 risk factors have been taken into consideration for this optimization technique. This model can provide a list of best possible sourcing by analysing the risk and benefit on each of the 12 factors and rank them accordingly. The optimal sourcing model has been developed considering Bangladesh as a prospective LNG importing country. Results from the model have provided a list of suitable sources. The results from this study matched well with the Bangladesh government policy for selecting the souring countries for their future LNG supply for long term basis.</p> <p>The effectiveness of the model is confirmed with the matching of data for Bangladesh. It is therefore expected that the model can be used for other countries who want to select optimal sourcing countries for their future LNG demand by ensuring energy security. Although the model has been developed for LNG importing country for selecting the best possible sourcing country, it can also be used to find the prospective future buyer of LNG.</p> <p>Also, the impact of ship types has been evaluated.  Results confirmed that proper selection of ship for their capacity can minimize the cost significantly. Larger capacity ship with high speed can be more economical and can reduce the shipping cost up to 40% compared to smaller ships with relatively lower speed.</p> <p>Furthermore, the effect of using the canal has been evaluated with a case study. Results confirm that the uses of Suez Canal to transport LNG from Algeria to Bangladesh can reduce the delivery time around 35%. However, there are cost, and risk involved to travel through the channel.</p> <p>The developed model is suitable for any destination country to find out the optimum sourcing strategy. On the other hand, LNG exporting countries can also use the same model to identify optimal destination countries for future trade. The model can also be used for optimising the sourcing strategy for other energy imports such as oil and coal by updating the input database based on specific types of commodity.</p>