Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework

Yitong Shang; Zekai Li; Ziyun Shao; Linni Jian

doi:10.1109/ICPSAsia52756.2021.9621448

Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework

Yitong Shang
, Zekai Li
, Ziyun Shao
, Linni Jian^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The bidirectional energy flow between plug-in electric vehicles (PEVs) and power grids enables load flatting and self-consumption of the photovoltaic (PV) output. However, two critical issues should be addressed. One is how to conduct the gap between the decision makings optimized with predictive data and the reality, and the other is how to ensure efficiency of V2G dispatching. In order to tackle these problems, this work proposes a distributed V2G dispatching via long short term memory (LSTM) network within cloud-edge collaboration framework. In the cloud side, the LSTM network is applied merely utilizing the present data to obtain the prediction models of V2G dispatching. Then, these models are sent to the edge side and updated in a regular time. In edge side, the distributed dispatching is conducted to decrease the computational complexity. The proposed framework is verified by numerical analysis, which illustrates that the effectiveness, efficiency and applicability of the V2G operation.

Original language	English
Title of host publication	2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1531-1538
Number of pages	8
ISBN (Electronic)	9781665434980
DOIs	https://doi.org/10.1109/ICPSAsia52756.2021.9621448
State	Published - 2021
Externally published	Yes
Event	2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021 - Chengdu, China Duration: 18 Jul 2021 → 21 Jul 2021

Publication series

Name	2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021

Conference

Conference	2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021
Country/Territory	China
City	Chengdu
Period	18/07/21 → 21/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

LSTM network
cloud-edge collaboration
distributed V2G dispatching
load flatting
self-consumption of PV

Access to Document

10.1109/ICPSAsia52756.2021.9621448

Cite this

Shang, Y., Li, Z., Shao, Z., & Jian, L. (2021). Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework. In 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021 (pp. 1531-1538). (2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPSAsia52756.2021.9621448

Shang, Yitong ; Li, Zekai ; Shao, Ziyun et al. / Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework. 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1531-1538 (2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021).

@inproceedings{d7b597b9efd2442d9ef749667f858abf,

title = "Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework",

abstract = "The bidirectional energy flow between plug-in electric vehicles (PEVs) and power grids enables load flatting and self-consumption of the photovoltaic (PV) output. However, two critical issues should be addressed. One is how to conduct the gap between the decision makings optimized with predictive data and the reality, and the other is how to ensure efficiency of V2G dispatching. In order to tackle these problems, this work proposes a distributed V2G dispatching via long short term memory (LSTM) network within cloud-edge collaboration framework. In the cloud side, the LSTM network is applied merely utilizing the present data to obtain the prediction models of V2G dispatching. Then, these models are sent to the edge side and updated in a regular time. In edge side, the distributed dispatching is conducted to decrease the computational complexity. The proposed framework is verified by numerical analysis, which illustrates that the effectiveness, efficiency and applicability of the V2G operation.",

keywords = "LSTM network, cloud-edge collaboration, distributed V2G dispatching, load flatting, self-consumption of PV",

author = "Yitong Shang and Zekai Li and Ziyun Shao and Linni Jian",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021 ; Conference date: 18-07-2021 Through 21-07-2021",

year = "2021",

doi = "10.1109/ICPSAsia52756.2021.9621448",

language = "英语",

series = "2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1531--1538",

booktitle = "2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021",

address = "美国",

}

Shang, Y, Li, Z, Shao, Z & Jian, L 2021, Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework. in 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021. 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1531-1538, 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021, Chengdu, China, 18/07/21. https://doi.org/10.1109/ICPSAsia52756.2021.9621448

Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework. / Shang, Yitong; Li, Zekai; Shao, Ziyun et al.
2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1531-1538 (2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework

AU - Shang, Yitong

AU - Li, Zekai

AU - Shao, Ziyun

AU - Jian, Linni

PY - 2021

Y1 - 2021

N2 - The bidirectional energy flow between plug-in electric vehicles (PEVs) and power grids enables load flatting and self-consumption of the photovoltaic (PV) output. However, two critical issues should be addressed. One is how to conduct the gap between the decision makings optimized with predictive data and the reality, and the other is how to ensure efficiency of V2G dispatching. In order to tackle these problems, this work proposes a distributed V2G dispatching via long short term memory (LSTM) network within cloud-edge collaboration framework. In the cloud side, the LSTM network is applied merely utilizing the present data to obtain the prediction models of V2G dispatching. Then, these models are sent to the edge side and updated in a regular time. In edge side, the distributed dispatching is conducted to decrease the computational complexity. The proposed framework is verified by numerical analysis, which illustrates that the effectiveness, efficiency and applicability of the V2G operation.

AB - The bidirectional energy flow between plug-in electric vehicles (PEVs) and power grids enables load flatting and self-consumption of the photovoltaic (PV) output. However, two critical issues should be addressed. One is how to conduct the gap between the decision makings optimized with predictive data and the reality, and the other is how to ensure efficiency of V2G dispatching. In order to tackle these problems, this work proposes a distributed V2G dispatching via long short term memory (LSTM) network within cloud-edge collaboration framework. In the cloud side, the LSTM network is applied merely utilizing the present data to obtain the prediction models of V2G dispatching. Then, these models are sent to the edge side and updated in a regular time. In edge side, the distributed dispatching is conducted to decrease the computational complexity. The proposed framework is verified by numerical analysis, which illustrates that the effectiveness, efficiency and applicability of the V2G operation.

KW - LSTM network

KW - cloud-edge collaboration

KW - distributed V2G dispatching

KW - load flatting

KW - self-consumption of PV

UR - https://www.scopus.com/pages/publications/85123359831

U2 - 10.1109/ICPSAsia52756.2021.9621448

DO - 10.1109/ICPSAsia52756.2021.9621448

M3 - 会议稿件

AN - SCOPUS:85123359831

T3 - 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021

SP - 1531

EP - 1538

BT - 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021

Y2 - 18 July 2021 through 21 July 2021

ER -

Shang Y, Li Z, Shao Z, Jian L. Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework. In 2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1531-1538. (2021 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2021). doi: 10.1109/ICPSAsia52756.2021.9621448

Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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