Hybrid transportation model forecasting and optimisation with practical fuel supply chain

This research proposes an integrated application model for hybrid transportation system (HTS) that include environmentally sustainable vehicles, such as EVs and hydrogen vehicles as main ingredients. A medium- to long-term market forecasting was performed using autoregressive integrated moving average (ARIMA) model to identify the transportation mix by the year 2030. A fuel supply chain model developed as mixed-integer linear programming model, and solved using GA methods. A case study on three future scenarios for HTS in year 2030 was presented to check, vehicles use CO2 emissions, costs, NG demands, the numbers of fuel distribution centres (FDCs) and NG storage facilities. In the investigated scenarios, the vehicles use was reduced from 97% in 2009 to 81.5%, 72% and 63% respectively. The CO2 emissions were reduced by about 12%. The cost of smearing the second and third scenarios was found to be 19.25 M.USD and 36.79 M.USD more than the reference scenario.

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