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

自动化学院

Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
出版商	IEEE Computer Society
ISBN（电子版）	9781665435543
DOI	https://doi.org/10.1109/IECON48115.2021.9589935
出版状态	已出版 - 13 10月 2021
活动	47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 - Toronto, 加拿大期限: 13 10月 2021 → 16 10月 2021

出版系列

姓名	IECON Proceedings (Industrial Electronics Conference)
卷	2021-October

会议

会议	47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
国家/地区	加拿大
市	Toronto
时期	13/10/21 → 16/10/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/IECON48115.2021.9589935

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); 卷 2021-October). IEEE Computer Society. https://doi.org/10.1109/IECON48115.2021.9589935

@inproceedings{778a01de8f9f4d7983de350b386d24f4,

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",

}

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. 在 IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), 卷 2021-October, IEEE Computer Society, 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021, Toronto, 加拿大, 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 等.
IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2021. (IECON Proceedings (Industrial Electronics Conference); 卷 2021-October).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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. 在 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

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此