4.6 Article

Assessing benefits in the flexibility of refined oil logistics from pipeline network integration reform: A case from South China

Journal

CHEMICAL ENGINEERING SCIENCE
Volume 253, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2022.117605

Keywords

Refined oil logistics optimization; Flexibility indicator; Policy quantification; 3E analysis; Uncertainty

Funding

  1. National Natural Science Foundation of China [51874325]
  2. Science Foundation of China University of Petroleum, Beijing [2462021BJRC009]

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This paper proposes a framework based on the MILP optimization model to quantify the flexibility of refined oil logistics and evaluates the impact of the pipeline network integration reform. The results show that the reform has improved the logistics flexibility, leading to lower freights, reduced GHG emissions, higher pipeline utilization, and more efficient oil product turnover. The study also provides insights into the reasons behind the results through 3E analysis.
The pipeline network integration reform enables unified management of pipelines from different entities. For refined oil logistics, this paper proposes a framework based on the MILP optimization model to quan-tify its flexibility. Considering the uncertainty, three disturbances occur in the logistics concurrently, and 10,000 simulations are performed to obtain the turnover cost. The ratio of pipeline transportation cost to the calculated average turnover cost is defined as the flexibility indicator. Taking China's largest refined oil pipeline network as an example, the results show that the flexibility rises 8.9% after the reform. The paper also quantifies the impact of the reform on logistics flexibility in South China, which is embodied in achieving lower freights and GHG emissions, lower impact by fluctuations, higher pipeline utilization, more efficient oil product turnover, and the avoiding of depot shortages when facing logistical distur-bances. The underlying reasons for the results and 3E analysis are analyzed. (c) 2022 Elsevier Ltd. All rights reserved.

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