Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System

Yigeng Huangfu; Chongyang Tian; Peng Li; Sheng Quan; Yonghui Zhang; Rui Ma

doi:10.1109/IECON48115.2021.9589935

Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System

Yigeng Huangfu, Chongyang Tian, Peng Li, Sheng Quan, Yonghui Zhang, Rui Ma

School of Automation

Northwestern Polytechnical University Xian

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

2 Scopus citations

Abstract

This paper proposed an energy management strategy (EMS) based on genetic algorithm (GA) for a hybrid energy system consisting of photovoltaic array (PV), fuel cell, electrolyzer, hydrogen storage system and battery. Considering the economy of the system, the equivalent hydrogen consumption of the system is the main part of the objective function, and the fluctuation of fuel cell output power is employed as an auxiliary part. The simulation is implemented in the MATLAB environment. After verification, the light abandon rates of the system under two different conditions are 0.000557% and 1.38%, respectively, which proves the effectiveness and adaptability of the proposed strategy.

Original language	English
Title of host publication	IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665435543
DOIs	https://doi.org/10.1109/IECON48115.2021.9589935
State	Published - 13 Oct 2021
Event	47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 - Toronto, Canada Duration: 13 Oct 2021 → 16 Oct 2021

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2021-October

Conference

Conference	47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Country/Territory	Canada
City	Toronto
Period	13/10/21 → 16/10/21

Keywords

electrolyzer
energy management strategy
fuel cell
genetic algorithm
hybrid energy system

UN SDGs

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

Access to Document

10.1109/IECON48115.2021.9589935

Cite this

Huangfu, Y., Tian, C., Li, P., Quan, S., Zhang, Y., & Ma, R. (2021). Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System. In IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2021-October). IEEE Computer Society. https://doi.org/10.1109/IECON48115.2021.9589935

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title = "Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System",

abstract = "This paper proposed an energy management strategy (EMS) based on genetic algorithm (GA) for a hybrid energy system consisting of photovoltaic array (PV), fuel cell, electrolyzer, hydrogen storage system and battery. Considering the economy of the system, the equivalent hydrogen consumption of the system is the main part of the objective function, and the fluctuation of fuel cell output power is employed as an auxiliary part. The simulation is implemented in the MATLAB environment. After verification, the light abandon rates of the system under two different conditions are 0.000557% and 1.38%, respectively, which proves the effectiveness and adaptability of the proposed strategy.",

keywords = "electrolyzer, energy management strategy, fuel cell, genetic algorithm, hybrid energy system",

author = "Yigeng Huangfu and Chongyang Tian and Peng Li and Sheng Quan and Yonghui Zhang and Rui Ma",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 ; Conference date: 13-10-2021 Through 16-10-2021",

year = "2021",

month = oct,

day = "13",

doi = "10.1109/IECON48115.2021.9589935",

language = "英语",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society",

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Huangfu, Y, Tian, C, Li, P, Quan, S, Zhang, Y & Ma, R 2021, Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System. in IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2021-October, IEEE Computer Society, 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021, Toronto, Canada, 13/10/21. https://doi.org/10.1109/IECON48115.2021.9589935

Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System. / Huangfu, Yigeng; Tian, Chongyang; Li, Peng et al.
IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2021. (IECON Proceedings (Industrial Electronics Conference); Vol. 2021-October).

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

TY - GEN

T1 - Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System

AU - Huangfu, Yigeng

AU - Tian, Chongyang

AU - Li, Peng

AU - Quan, Sheng

AU - Zhang, Yonghui

AU - Ma, Rui

PY - 2021/10/13

Y1 - 2021/10/13

N2 - This paper proposed an energy management strategy (EMS) based on genetic algorithm (GA) for a hybrid energy system consisting of photovoltaic array (PV), fuel cell, electrolyzer, hydrogen storage system and battery. Considering the economy of the system, the equivalent hydrogen consumption of the system is the main part of the objective function, and the fluctuation of fuel cell output power is employed as an auxiliary part. The simulation is implemented in the MATLAB environment. After verification, the light abandon rates of the system under two different conditions are 0.000557% and 1.38%, respectively, which proves the effectiveness and adaptability of the proposed strategy.

AB - This paper proposed an energy management strategy (EMS) based on genetic algorithm (GA) for a hybrid energy system consisting of photovoltaic array (PV), fuel cell, electrolyzer, hydrogen storage system and battery. Considering the economy of the system, the equivalent hydrogen consumption of the system is the main part of the objective function, and the fluctuation of fuel cell output power is employed as an auxiliary part. The simulation is implemented in the MATLAB environment. After verification, the light abandon rates of the system under two different conditions are 0.000557% and 1.38%, respectively, which proves the effectiveness and adaptability of the proposed strategy.

KW - electrolyzer

KW - energy management strategy

KW - fuel cell

KW - genetic algorithm

KW - hybrid energy system

UR - http://www.scopus.com/inward/record.url?scp=85119498972&partnerID=8YFLogxK

U2 - 10.1109/IECON48115.2021.9589935

DO - 10.1109/IECON48115.2021.9589935

M3 - 会议稿件

AN - SCOPUS:85119498972

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021

Y2 - 13 October 2021 through 16 October 2021

ER -

Huangfu Y, Tian C, Li P, Quan S, Zhang Y, Ma R. Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System. In IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2021. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON48115.2021.9589935

Design of Multi-objective Optimization Energy Management Strategy Based on Genetic Algorithm for a Hybrid Energy System

Abstract

Publication series

Conference

Keywords

UN SDGs

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