Exploring the pathways to deep decarbonization of transport sector in the post-COVID's new normal

在2022财年，我的主要成就是交通模型与自下而上、技术丰富的能源系统模型的耦合，该模型已在上一财年完成。将能源系统模型与区域交通模型相结合，根据交通模型的输出确定能源服务需求。在满足给定运输需求的前提下，利用线性优化方法内生计算出运输部门的最优能源技术组合，并在一定的约束条件下使系统总成本最小；首先将运输模型计算出的特定运输方式的运输需求输入到能源系统模型中，确定最优技术组合。然后根据每种特定能源类型和技术组合消耗的能源量估计能源消耗，同时使用每种能源类型的排放系数计算二氧化碳排放量，然后将最优技术组合和成本传递回运输模型，以基于修订后的技术组合更新广义运输成本。采用迭代方法解决了模型耦合的收敛问题。能源系统模型和运输模型之间的反馈回路被继续，直到得到一个收敛的解，其中由两个模型计算的运输需求和技术组合在连续迭代中保持稳定。

In the fiscal year 2022, my main accomplishment was the coupling of the transport model and a bottom-up, technology-rich, energy system model, which had been completed in the previous fiscal year. The energy system model was integrated with the regional transport model to determine the energy service demand based on the transport model output. To meet a given transport demand, the optimal set of energy technologies in the transport sector was calculated endogenously using linear optimization while minimizing the total system costs under certain constraints.Firstly, mode-specific transport demand calculated by the transport model was input into the energy system model to determine the optimal technology mix. Energy consumption was then estimated based on the amount of energy consumed by each specific energy type and technology combination, while CO2 emissions were calculated using the emission factors for each energy type.Subsequently, the optimal technology combinations and costs were passed back to the transport model to update the generalized transport cost based on the revised technology mix. An iterative method was utilized to solve the convergence problem of model coupling. The feedback loop between the energy system model and the transport model was continued until a converged solution was obtained, in which the transport demands and the technology combinations calculated by both models remained stable across successive iterations.