Probabilistic scenario analysis of integrated road-power infrastructures with hybrid fleets of EVs and ICVs
提出概率情景分析框架，量化事故场景下电动与燃油车混合车队在道路-电力一体化基础设施中的延误、经济损失和运输可靠性，帮助比较不同基础设施设计方案。
Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, …) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service.