In order to quantify the uncertainty of multi-energy flow distribution in the integrated energy system, a probabilistic multi-energy flow calculation method of integrated energy system based on data-driven is proposed. Firstly, a unified multi-energy flow calculation model suitable for different working modes of compressors in integrated energy system is established, and the impact of different operating modes of compressors on the multi-energy flow distribution is also discussed. Secondly, a probabilistic multi-energy flow calculation method based on support vector regression is developed. The method first constructs a data set by calculating deterministic multi-energy flow repeatedly, and then the support vector regression is used to mine the nonlinear mapping relationship between known loads, network node information and unknown node parameters in the integrated energy system. Finally, through case analysis, it is verified that the proposed unified multi-energy flow model can be applied to different compressor working conditions. By comparing with traditional probabilistic multi-energy flow calculation methods, it is shown that the proposed data-driven probabilistic multi-energy flow calculation method has higher computational accuracy and efficiency.

In order to reduce fuel consumption and carbon emissions, hybrid diesel-electric locomotives are the main upgrade direction for traditional diesel locomotives. This research first addresses the issue that existing diesel generator models cannot effectively describe the physical processes of hybrid diesel-electric locomotives under extreme conditions where the diesel engine load exceeds its rated power, and proposes a modeling method for diesel generators with adaptive excitation regulation. Next, to overcome the problem that typical energy management strategies cannot achieve optimal fuel efficiency under all operating conditions for hybrid locomotives, a road condition-based energy management strategy is proposed. Based on the fuel-saving performance of two typical energy control strategies under different operating conditions, the optimal control strategy for each condition is obtained, the operating condition sections are defined according to the track map, and the optimal energy control strategy for each section is determined, ultimately minimizing fuel consumption throughout the entire process. Finally, based on the Rtlab/dSPACE hardware-in-the-loop simulation platform, the proposed adaptive excitation regulation model for locomotives is first validated. Then, the rationality of the proposed energy management strategy is verified in terms of fuel consumption and carbon emissions. Experimental results show that the proposed road condition-based energy management strategy improves fuel efficiency by 15.9% and 9.6% compared with traditional logic threshold-based and fuzzy control-based energy management strategies, respectively.