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author:

Jiang, Yuewen (Jiang, Yuewen.) [1] (Scholars:江岳文) | Liu, Jianshu (Liu, Jianshu.) [2] | Zheng, Hongqi (Zheng, Hongqi.) [3]

Indexed by:

EI Scopus SCIE

Abstract:

With the extensive utilization of fossil fuels, environmental concerns have been growing. Hydrogen-powered vehicles (HPVs) as well as hydrogen refueling stations (HRSs) are expected to proliferate to alleviate the pollution. In order to promote the economic valibity of HRSs, this paper proposes an optimal scheduling model for distributed HRSs to maximize HRSs' revenue during a day. HRSs serves both the mobility sector with fuel individually and the power system with reserve demand response collaboratively. The elaborated operating of electrolysers and relaxed control of hydrogen storage are involved to enhance practicability and flexibility of the scheduling. Considering that this model has multiple variables, intractable high-dimension nonlinear constraints, a transfer multi-agent reinforcement learning algorithm is developed. The algorithm consists of two parts: the one is the multi-agent optimization method by transforming multi-decision optimization into a multi-agent optimization; the other is to transfer the empirical knowledge of the source task to reinforcement learning according to the similarity between the source task and the target task, making it cut down the blindness of exploration and accelerate convergence. Numerical studies reveal that participating in the reserve services market collaboratively increases overall revenues of HRSs by up to 32.90%, and the computation time is sharply shortened by approximately 34 times compared to the basic reinforcement learning. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Keyword:

Collaborative scheduling Distributed hydrogen refueling Fuel supply learning Reserve ancillary service stations Transfer multi-agent reinforcement

Community:

  • [ 1 ] [Jiang, Yuewen]Fuzhou Univ, Coll Elect andAutomat, Fuzhou 350108, Peoples R China
  • [ 2 ] [Liu, Jianshu]Fuzhou Univ, Coll Elect andAutomat, Fuzhou 350108, Peoples R China
  • [ 3 ] [Zheng, Hongqi]Fuzhou Univ, Coll Elect andAutomat, Fuzhou 350108, Peoples R China
  • [ 4 ] [Jiang, Yuewen]Fujian Univ, Engn Res Ctr Intelligent Distribut Network Equipme, Fuzhou 350108, Peoples R China
  • [ 5 ] [Jiang, Yuewen]Fuzhou Univ, Coll Elect & Automat, Fuzhou 350108, Fujian, Peoples R China

Reprint 's Address:

  • [Jiang, Yuewen]Fuzhou Univ, Coll Elect & Automat, Fuzhou 350108, Fujian, Peoples R China;;

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Source :

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

ISSN: 0360-3199

Year: 2023

Volume: 54

Page: 239-255

8 . 1

JCR@2023

8 . 1 0 0

JCR@2023

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 11

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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