Multi-time period operation analysis of coupled transportation and power distribution networks considering self-driving and human-driving behaviors

Zhe Hu, Han Wang, Xiaoyuan Xu, Guanyu Song, Zheng Yan and Yue Chen

Applied Energy, 2025, vol. 382, issue C, No S0306261924026102

Abstract: The rapid development of electric vehicles (EVs) prompts the interaction between transportation networks (TNs) and power distribution networks (PDNs). Meanwhile, the self-driving technology make the interaction process more intelligent. Thus, the human-driving and self-driving behaviors would coexist in a long time and simultaneously influence the operation of the coupled transportation-power distribution networks (TPNs). To depict the TPN operation states, this paper proposes a multi-time period traffic-power flow analysis method, in which a centralized mode and a decentralized mode are established to represent the impacts of self-driving and human-driving behaviors, respectively. For the centralized mode, an upper-level model is built to realize the social optimum of TPNs considering the travel cost of self-driving EVs and the operation cost of PDNs. For the decentralized mode, a lower-level model under stochastic user equilibrium (SUE) principle is proposed to derive the TPN operation state considering the cognitive biases of drivers. The traffic flows and electricity prices are transferred between the two models and then a bi-level traffic-power flow analysis model is formed. A semi-dynamic traffic assignment method is adopted in the bi-level model to depict the dynamic characteristics of TPNs in multiple time periods. Then, an adaptive iterative algorithm is proposed to solve the bi-level model and derive the multi-time period traffic-power flows. Finally, a practical TPN in Shanghai, China is used to verify the effectiveness of the proposed model and the impacts of various parameters on the TPN operation are discussed.

Keywords: Electric vehicle; Power distribution networks; Self-driving behaviors; Human-driving behaviors (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924026102
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:382:y:2025:i:c:s0306261924026102

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.125226

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().