Multi‐network coordinated charging infrastructure planning for the self‐sufficient renewable power highway

Author:

Zhang Tian‐Yu1,Yao En‐Jian1,Yang Yang1,Yang Hong‐Ming2,Wang David Z. W.3

Affiliation:

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China

2. Hunan Provincial Engineering Research Center of Electric Transportation and Smart Distributed Network, Hunan Provincial Key Laboratory of Smart Grids Operation and Control, School of Electrical Engineering and Information Changsha University of Science and Technology Changsha China

3. School of Civil and Environmental Engineering Nanyang Technological University Singapore Singapore

Abstract

AbstractDeveloping a self‐sufficient renewable power (RP) road transport (SRPRT) system is an important future direction for transport–energy integration. More well‐developed studies must be conducted on the coordinated planning of transport, power supply, and power generation networks. This paper carries out the joint operation and planning of highway charging networks with the wind‐photovoltaic‐energy storage (HCN‐WPE) system. Under multi‐network integration and the interaction among multiple entities, a nested bi‐level optimization model is proposed to optimize the users’ charging and travel behavior, charging network's deployment, and power generation system's (PGS) configuration. An H‐M‐L algorithm structure is developed, combining the heuristic algorithm, multi‐agent‐based simulation technology, and linear programming algorithm. Its convergence and applicability are verified on the Nguyen‐Dupius network. An empirical case in the Hu‐Bao‐Wu city agglomeration in China is employed to explore and discuss the managerial insights for the HCN‐WPE system. The study finds that multi‐network coordinated planning can improve the benefits of multiple entities, where the net present value, RP supply rate, and RP consumption rate increase by 12.0%, 3.2%, and 10.5%, compared to independent planning. Network‐level planning can play a management and induction role in balancing the station's load pressure. In addition, the PGS co‐configuration can leverage the complementary power supply of multiple RP generators and the peak cutting and valley filling of energy storage systems, which is essential for achieving the SRPRT goal.

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Wiley

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1. Modeling of spatially embedded networks via regional spatial graph convolutional networks;Computer-Aided Civil and Infrastructure Engineering;2024-06-20

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