Indexed by:

Journal paper

First Author:

Wei Gan

Correspondence Author:

Wei Yao

Co-author:

Mingyu Yan,Jianbo Guo,Jiakun Fang,Xiaomeng Ai,Jinyu Wen

Journal:

IEEE Transactions on Industry Applications

Included Journals:

SCI

Place of Publication:

United States

Discipline:

Engineering

First-Level Discipline:

Electrical Engineering

Funded by:

21643786

Document Type:

J

Volume:

58

Issue:

2

Page Number:

2875 - 2886

ISSN No.:

0093-9994

Key Words:

Benders decomposition; Coordinated planning; Hydrogen vehicles; Multiple energy networks; Power to gas; Renewable energy

DOI number:

10.1109/TIA.2021.3109558

Date of Publication:

2022-03-01

Impact Factor:

4.079

Abstract:

The growing penetration of hydrogen vehicles and modern energy conversion technologies strengthen the coupling of transportation and energy networks. This article proposes a hydrogen supply infrastructure planning model for the integration of hydrogen vehicles and renewable energy. To flexibly meet the energy demand of hydrogen vehicles, the proposed model makes investment decisions for various facilities, including hydrogen pipelines, hydrogen refueling stations, power to gas devices, and renewable energy generators. Besides, with the pipeline transportation method applied, the hydrogen network is constructed and coordinated with the electricity and transportation networks. The multinetwork synergistic effect is thus fully utilized, bringing higher operational flexibility and investment economy. Furthermore, a two-stage stochastic planning model is provided to accommodate the variability of renewable energy and traffic loads. To reduce the computational complexity of the proposed stochastic planning model, both linearization techniques and Benders decomposition algorithm are applied. Simulation results of the 8-node system and the 24-node system demonstrate the effectiveness of the proposed model and algorithm. Compared to the uncoordinated model, the proposed model saves by 11% of the total cost for the 24-node test system. Also, the computational performance of the Benders decomposition algorithm surpasses that of the basic algorithm by more than 24.6% in the two test systems.

Links to published journals:

https://ieeexplore.ieee.org/abstract/document/9529024