An MILP model for optimization of LNG supply chains

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    Sammanfattning

    The demand of energy in the world increases steadily and natural gas has been touted as one of the solutions for the escalating energy consumption. However, natural gas is still unavailable in many energy intensive areas due to geographical restrictions that limit pipeline distribution. The best way to introduce natural gas to such new, scattered, areas is by transporting it as liquefied natural gas (LNG). LNG can be distributed from an LNG import terminal to consumers through a network of smaller satellite terminals with a combination of sea- and land-based transports. Creating such a supply chain network for distribution of LNG to end-users is expensive and capital intensive. A mathematical model to aid in the supply chain design is presented in this paper. The problem is formulated as a mixed integer linear programming (MILP) model where the objective is to minimize the total costs associated with fuel procurement. The use of the model is illustrated by a case study in which the optimal supply chain of LNG for covering certain parts of the energy requirements of a country is designed under different consumer configurations. The solution gives the optimal supply chain structure taking also ship routing into account.
    OriginalspråkOdefinierat/okänt
    Titel på gästpublikation27th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2014
    RedaktörerRon Zevenhoven
    FörlagAbo Akademi University, Thermal and Flow Engineering Laboratory
    Sidor
    ISBN (tryckt)9781634391344
    StatusPublicerad - 2014
    MoE-publikationstypA4 Artikel i en konferenspublikation
    Evenemangconference; 2014-06-15; 2014-06-19 - Åbo Akademi University
    Varaktighet: 15 jun 201419 jun 2014

    Konferens

    Konferensconference; 2014-06-15; 2014-06-19
    Period15/06/1419/06/14

    Nyckelord

    • LNG
    • MILP
    • Optimization
    • supply chain

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