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Procurement of LNG Under Supply and Demand Uncertainties: Portfolio Optimisation Studies
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Liquefied Natural Gas (LNG) is natural gas that has been cooled to a liquid state for shipping and storage. Liquefaction makes it possible to transport natural gas from producing regions to markets where pipelines are not economically feasible. The LNG supply chain broadly consist of five entities corresponding to extraction, liquefaction, transportation, regasification and distribution. The supply chain originates with natural gas production and then it is transported via pipeline to a liquefaction plant, typically located on a coastline. After liquefaction, it is shipped in liquid form in a LNG carrier to a receiving terminal (frequently located on another continent) where it is regasified to natural gas. This paper focusses on the procurement of LNG by the regasification terminal.

Traditionally, LNG was purchased via Long Term Contracts (LTCs) between buyers and sellers. These contracts are generally 20-25 years long and provide security to importers; however, their rigid take-or-pay clause transfers the risk of surplus volume to the buyer. In recent years, new LNG sellers and buyers have emerged. Further, gas market liberalisation 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 fertiliser manufacturers who use NG as a feedstock has led to a robust demand for LNG, but one characterized by high demand variability. In tandem, a spot market for sale of LNG is also currently emerging. Contrary to LTC’s, the spot market does not require the buyer to commit a specified purchase quantity for the long term. Therefore, spot market gives more flexibility to the buyers. In this paper, we focus the decision of the buyer to procure LNG through a optimal mix of LTCs and spot purchase.

Various uncertainties can affect the LNG supply chain. The supply of LNG may be disrupted due to political instability, occurrence of any extreme event at liquefaction terminals. Similarly, maritime risks such as spill off, adverse weather conditions or pirate attack may delay the delivery of LNG cargo. Demand uncertainty in the regasification terminals will also affect the dynamics of supply chain. Therefore, it is essential for a buyer to select its suppliers by taking these disruptions into account. Since, suppliers also have capacity constraints, a buyer must take these constraints too into account while making strategic decisions (i.e. portfolio development) for procurement of LNG. But disruptions makes this a challenging problem. In this paper, we seek to develop an optimal import portfolio for a LNG buyer.

Currently, LNG portfolio optimisation has been studied through the perspective of ensuring energy security. Most of the literature focuses on selection of optimal set of suppliers rather than mode of procurement i.e. LTC or spot market. Biresselioglu et al. (2012) developed an MILP model which determines the optimal set of sellers for Turkey’s LNG demand while minimizing total import and inventory holding cost as well as levels of risks1. Similarly, Geng et al. (2017) developed a multi objective optimisation problem to generate optimal import portfolio by considering multiple risk factors such as economic risk of exporting countries , maritime risk etc2. Shaikh et al. (2017) modelled a natural gas import scheme for China3. They developed a mathematical model which minimizes the import cost, transport distance, domestic and political instability associated with each supplier. Their model incorporates guiding principles of energy security, maintains diversification and lower dependency on single suppliers. Their model also considers suppliers capacities but model did not considers the demand uncertainty as well as mode of procurement was also not considered. Kim and Kim (2018) developed an optimal portfolio for Korea4. In this study, they developed a two-step portfolio model mean variance optimisation model followed by linear programming model. Their model considered mode of procurement. However, like most of the current studies this model also did not considered demand uncertainty. These kinds of models are beneficial for the customers with very low or no demand uncertainty, but these models fail to account for situations where the buyer faces substantial demand uncertainty or has the option to fulfil the surplus demand from local natural gas production. We seek to address this gap.

In this paper, we consider procurement of LNG by a customer whose demand is uncertain. Since the demand is uncertain, customer cannot commit to large quantity in LTC’s due to take-or-pay clause as well as inventory holding cost. However, if it commits low quantity to LTC’s it may have to satisfy surplus demand from spot market which is generally expensive compared to contracted price; Further, the as availability of cargo in spot market is also a concern. Our goal is to identify the optimal amount that a customer should commit to LTC’s in these circumstances. Toward this, we develop an optimisation model, taking into account the dynamics of the customer and producer. Each customer is modelled with uncertain demand, storage and berth capacities. Transportation is modelled through a set of LNG carriers, each with its capacity. A set of producers is modelled with their respective liquefaction capacity. LNG available in spot market as well as NG available from local production is also modelled. The solution of the optimization model for a specific realization of the uncertainties provides the minimal procurement cost and hence the portfolio that is optimal for that set of realized uncertainties. Next, Monte Carlo simulation of the model is performed to obtain the generalized result i.e. the optimal quantity to be procured by various modes given the expected distribution of customer demand variabilities and supplier unreliability. The results from the paper can be used to develop critical insights about the procurement strategy for customers with high demand variability.

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Journal2019 AIChE Annual Meeting