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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1444727
This article is part of the Research Topic Enhancing Resilience in Smart Grids: Cyber-Physical Systems Security, Simulations, and Adaptive Defense Strategies View all 8 articles
Optimization of power-transportation coupled power distribution network based on stochastic user equilibrium
Provisionally accepted- 1 School of Electrical Engineering, Xi’an University of Technology, Xi’an, China
- 2 Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou, Gansu Province, China
- 3 School of Electrical Engineering, Xi’an University of Technology, Xi'an, China
- 4 Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, China
With the increasing penetration of electric vehicles in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load has generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power-transportation coupling distribution network optimization strategy based on stochastic user traffic equilibrium theory. Firstly, a stochastic user equilibrium mixed traffic flow allocation model that expresses users' non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained by solving using an improved Method of Successive Algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power-transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines.
Keywords: electric vehicle, Stochastic user equilibrium, coupled power-transportation network, demand response, Distribution network
Received: 06 Jun 2024; Accepted: 28 Aug 2024.
Copyright: © 2024 Ma, Jin, Li, Zhen, Xu and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
YanPeng Jin, School of Electrical Engineering, Xi’an University of Technology, Xi'an, China
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