Data-Driven Carbon Footprint Management of Electric Vehicles and Emission Abatement in Electricity Networks

Electric vehicles (EVs) are believed to play an important role in mitigating carbon emissions in the transportation sector. However, EVs may still cause carbon emissions in the power sector if they are charged by electricity generated from burning fossil fuels like coal. Researchers have focused on managing emissions on the power generation sector. However, the underlying driver of carbon emissions is the demand of consumers. Given this background, a probabilistic carbon footprint management strategy is proposed for EVs in this paper. First, the conventional deterministic carbon emission flow model is extended to a probabilistic one (PCEF) to track the carbon footprint of EVs considering various kinds of uncertainties based on non-intrusive load monitoring (NILM) and the two-point estimation method (2PEM). Second, a stochastic chance-constrained carbon footprint management model for EV charging is presented to address the carbon obligation allocation of EVs from the perspective of consumption and provide a technical basis for demand-driven stimulation to reduce carbon emissions. Third, an efficient method is proposed to solve the formulated chance-constrained problem based on nonparametric Bayesian modeling and inference. The proposed model and method are demonstrated by the IEEE 39-bus power system. The feasibility of the proposed PCEF model is validated. According to simulation results, the computation speed of the proposed PCEF model is enhanced from 3456.8 seconds to 1341.3 seconds compared with the Monte Carlo simulation by sacrificing the accuracy within 2%. Besides, the proposed emission control strategy can attain a better emission control performance compared with the other state-of-art methods.