Planning for low-carbon energy-transportation system at metropolitan scale: A case study of Beijing, China

The urbanization and megalopolis have deteriorated traffic concerns, energy crisis and carbon pollution, such that the electric vehicles are expected to be an essential role. In this study, a flexible-possibilist chanced constraints programming (FCCP) model is developed to plan the low-carbon energy-transportation systems at a metropolitan scale (METS), which have multiple uncertainties in soft constraints and objective function. By integrating the possibilist programming with fuzzy sets and chanced constraint, the FCCP could tackle multiple complexities such as the combination of vague possibilities, flexibilities and probabilities, which is superior to the conventional approaches. The FCCP model is then applied for dealing with METS of Beijing, and solutions are obtained under different satisfactory degrees and confidence levels. The results reveal that: 1) the power demand will increasingly depend on the imported power and renewable energy power in Beijing; 2) the implementation of electric vehicles will reduce 6.7 million tonnes of CH, 44.7 million tonnes of CO and 1.08 × 105 million tonnes of CO2 respectively, while the need of battery supply facilities will cost approximate 4 × 109 dollars; 3) the carbon mitigation will decrease with the growing number of EVs, the upgraded power supply pattern and the cruel policies. These findings could provide support for decision-makers to plan the METS system with multiple uncertainties.