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An optimisation framework for the strategic design of synthetic natural gas (BioSNG) supply chains

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  • Calderón, Andrés J.
  • Agnolucci, Paolo
  • Papageorgiou, Lazaros G.

Abstract

A general optimisation framework based on a spatially-explicit multiperiod mixed integer linear programming (MILP) model is proposed to address the strategic design of BioSNG supply chains. The framework considers procurement of feedstocks, plantation of energy crops, and different modes for transportation of feedstocks and final products. The mathematical framework allows researches and policy makers to investigate scenarios that promote the development of BioSNG supply chains in a regional and/or national context. The capabilities of the proposed model are illustrated through the implementation of a set of case studies based on the UK. The results revealed that domestic resources in the UK can supply up to 21.4% of the total gas demand projected by the UK National Grid in the scenario “Slow progression” for a planning horizon of 20years. However, despite the considerable potential for production of BioSNG, the role of the government through subsidisation schemes such as feed-in tariff and Renewable Obligation Certificates (ROCs) is crucial in order to make the development of these resources economically attractive for private sectors.

Suggested Citation

  • Calderón, Andrés J. & Agnolucci, Paolo & Papageorgiou, Lazaros G., 2017. "An optimisation framework for the strategic design of synthetic natural gas (BioSNG) supply chains," Applied Energy, Elsevier, vol. 187(C), pages 929-955.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:929-955
    DOI: 10.1016/j.apenergy.2016.10.074
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    1. Samsatli, Sheila & Samsatli, Nouri J., 2018. "A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies," Applied Energy, Elsevier, vol. 220(C), pages 893-920.
    2. Li, Sheng & Gao, Lin & Jin, Hongguang, 2017. "Realizing low life cycle energy use and GHG emissions in coal based polygeneration with CO2 capture," Applied Energy, Elsevier, vol. 194(C), pages 161-171.
    3. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.
    4. Yılmaz Balaman, Şebnem & Wright, Daniel G. & Scott, James & Matopoulos, Aristides, 2018. "Network design and technology management for waste to energy production: An integrated optimization framework under the principles of circular economy," Energy, Elsevier, vol. 143(C), pages 911-933.
    5. Dehghani, Ehsan & Jabalameli, Mohammad Saeed & Jabbarzadeh, Armin, 2018. "Robust design and optimization of solar photovoltaic supply chain in an uncertain environment," Energy, Elsevier, vol. 142(C), pages 139-156.
    6. Ahlström, Johan M. & Pettersson, Karin & Wetterlund, Elisabeth & Harvey, Simon, 2017. "Value chains for integrated production of liquefied bio-SNG at sawmill sites – Techno-economic and carbon footprint evaluation," Applied Energy, Elsevier, vol. 206(C), pages 1590-1608.

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