An MILP model for optimization of LNG supply chains

    Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

    Abstrakti

    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.
    AlkuperäiskieliEi tiedossa
    Otsikko27th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2014
    ToimittajatRon Zevenhoven
    KustantajaAbo Akademi University, Thermal and Flow Engineering Laboratory
    Sivut
    ISBN (painettu)9781634391344
    TilaJulkaistu - 2014
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
    Tapahtumaconference; 2014-06-15; 2014-06-19 - Åbo Akademi University
    Kesto: 15 kesäkuuta 201419 kesäkuuta 2014

    Konferenssi

    Konferenssiconference; 2014-06-15; 2014-06-19
    Ajanjakso15/06/1419/06/14

    Keywords

    • LNG
    • MILP
    • Optimization
    • supply chain

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