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

Abstract

The urbanization and expansion of megalopolises have led to concerns on traffic, energy crisis and deteriorated green-house gas emissions, and thus the electric vehicles (EVs) are expected to be an essential role in alleviating these problems. In this study, a flexible-possibilist chanced constraints programming (FCCP) model is developed to plan low-carbon energy-transportation systems at the metropolitan scale (METS), which can incorporate multiple uncertainties in both the 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, hence is superior to conventional approaches. The FCCP model is then applied for the planning METS in Beijing, and solutions are obtained under different satisfactory degrees and confidence levels. The results reveal that: 1) the power demand will be increasingly dependent on the imported power and renewable energy in Beijing; 2) the mass roll-out of EVs 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 approximately 4 × 109 dollars; 3) the carbon emissions will decrease with the growing number of EVs, the upgraded power supply pattern and the stringent policies. These findings could support decision-makers to plan the METS system when faced with multiple uncertainties.