Volume 42 Issue 5
Oct.  2024
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Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(5): 173-184
SHI Zhipeng, JIN Yuzhe, KE Ji, WANG Biao, ZHANG Yipu. A Method for Data and Model Driven Estimation of Traffic Self-Consistent Energy System States in Highway Service Areas[J]. Journal of Transport Information and Safety, 2024, 42(5): 173-184. doi: 10.3963/j.jssn.1674-4861.2024.05.016
Citation: SHI Zhipeng, JIN Yuzhe, KE Ji, WANG Biao, ZHANG Yipu. A Method for Data and Model Driven Estimation of Traffic Self-Consistent Energy System States in Highway Service Areas[J]. Journal of Transport Information and Safety, 2024, 42(5): 173-184. doi: 10.3963/j.jssn.1674-4861.2024.05.016
shu

A Method for Data and Model Driven Estimation of Traffic Self-Consistent Energy System States in Highway Service Areas

doi: 10.3963/j.jssn.1674-4861.2024.05.016
  • 1.

    School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, China

  • 2.

    School of Energy and Electronic Engineering, Chang'an University, Xi'an 710064, China

  • Received Date: 2024-01-31
    Abstract
  • The development construction of a self-consistent energy system for highway service areas is essential technology for integrating transportation and energy. A key focus for recent research is system state estimation. Given the complexity and diversity of these energy systems, relying soley on either data-driven or model-driven approaches often leads to challenges in accurately and comprehensively estimating real-time system states. Therefore, this study explores a hybrid method combining data-driven and model-driven approaches for more efficient system state estimation. In terms of data-driven methods, although deep learning-based photovoltaic power forecasting models demonstrate superior performance, they fail to account for the interdependencies among input features. To address this issue, we developed a Time Convolution-Bidirectional Long Short-Term Memory network (TCN-BiLSTM-SA) based on a Self-Attention mechanism (SA) to predict the photovoltaic output of the system. The SA module adjusts redistributes the weights of the TCN-BiLSTM input features, improving the extraction of spatiotemporal information. On the model-driven side, considering the traffic flow distribution of highway networks, we established a probability model for electric vehicle travel trajectories. By utilizing Monte Carlo simulations, the initial and charging battery capacities are extracted while accounting for various uncertainties such as driver behavior and environmental temperature, thereby approximating accurate results to predict the spatiotemporal distribution of electric vehicle charging loads. Simulation validation using actual data from a self-consistent energy system at a highway service area in Xinjiang indicated that, in terms of photovoltaic forecasting, the proposed model improved the mean absolute error, root mean square error, and coefficient of determination by 25.3%, 16.7%, and 0.7%, respectively, compared to the best model. Furthermore, the proposed model effectively predicted the spatiotemporal distribution of electric vehicle charging loads on highways, achieving a system state estimation accuracy of 89.1%.

     

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