Planning for Low-Carbon Energy-Transportation System at Metropolitan Scale: A Case Study of Beijing, China

Abstract

The urbanization and megalopolis have deteriorated traffic concerns, energy crisis and carbon pollution. In this study, a flexible-possibilist chanced constraints programming (FCCP) model is developed to plan the energy-transportation systems at a metropolitan scale (METS), which have multiple uncertainties in soft constraints and objective function. The FCCP could tackle multiple complexities such as the combination of vague possibilities, flexibilities and probabilities. Superior to the conventional optimization approaches, the FCCP model would avoid losing uncertain information and enhance the robustness. It can also quantitatively analyze the effects of the uncertain parameters on system cost. 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; 2) the implementation of electric vehicles will improve the environment by reducing the pollutant emission, 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 air quality requirement. These findings could provide support for decision-makers to plan the METS system with multiple uncertainties.