Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty

Jiaming Hu; Xiaojun Zhou; Chaojie Li

doi:10.1109/ICISCI53188.2022.9941449

Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty

Jiaming Hu, Xiaojun Zhou, Chaojie Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Integrated Energy System (IES) is critical to improving energy efficiency and promoting the large-scale development of renewable energy sources. However, uncertain renewable wind energy brings challenges to the IES's optimal operation. In this article, a chance constrained weighted goal programming (CCWGP) model is proposed to address this issue. First, the CCWGP is introduced to establish multiple chance constraints on gas turbine reserve capacity to achieve system risk quantification. Then, the objective function is established that aims to minimize the IES's operating cost and risk. Finally, the state transition algorithm (STA), an intelligent optimization algorithm, is used to solve this high-dimensional complex problem in the multi-energy coupling system. Experimental results verify that the proposed method can effectively balance the safety and economy of the system. © 2022 IEEE.

Original language	English
Title of host publication	2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI)
Publisher	IEEE
Pages	56-61
Number of pages	6
ISBN (Electronic)	978-1-6654-5619-7, 978-1-6654-5618-0
ISBN (Print)	978-1-6654-5620-3
DOIs	https://doi.org/10.1109/ICISCI53188.2022.9941449
Publication status	Published - 2022
Externally published	Yes
Event	2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI 2022) - Changsha, China Duration: 15 Oct 2022 → 17 Oct 2022

Publication series

Name	International Conference on Intelligent Systems and Computational Intelligence, ICISCI

Conference

Conference	2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI 2022)
Abbreviated title	ICISCI2022
Place	China
City	Changsha
Period	15/10/22 → 17/10/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

chance constrained goal programming
Integrated energy system
state transition algorithm

Access Output

10.1109/ICISCI53188.2022.9941449

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Hu, J., Zhou, X., & Li, C. (2022). Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty. In 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI) (pp. 56-61). (International Conference on Intelligent Systems and Computational Intelligence, ICISCI). IEEE. https://doi.org/10.1109/ICISCI53188.2022.9941449

Hu, Jiaming ; Zhou, Xiaojun ; Li, Chaojie. / Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty. 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI). IEEE, 2022. pp. 56-61 (International Conference on Intelligent Systems and Computational Intelligence, ICISCI).

@inproceedings{89fabab60fb9480cb17d47ccb79a34a1,

title = "Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty",

abstract = "Integrated Energy System (IES) is critical to improving energy efficiency and promoting the large-scale development of renewable energy sources. However, uncertain renewable wind energy brings challenges to the IES's optimal operation. In this article, a chance constrained weighted goal programming (CCWGP) model is proposed to address this issue. First, the CCWGP is introduced to establish multiple chance constraints on gas turbine reserve capacity to achieve system risk quantification. Then, the objective function is established that aims to minimize the IES's operating cost and risk. Finally, the state transition algorithm (STA), an intelligent optimization algorithm, is used to solve this high-dimensional complex problem in the multi-energy coupling system. Experimental results verify that the proposed method can effectively balance the safety and economy of the system. {\textcopyright} 2022 IEEE.",

keywords = "chance constrained goal programming, Integrated energy system, state transition algorithm",

author = "Jiaming Hu and Xiaojun Zhou and Chaojie Li",

year = "2022",

doi = "10.1109/ICISCI53188.2022.9941449",

language = "English",

isbn = "978-1-6654-5620-3",

series = "International Conference on Intelligent Systems and Computational Intelligence, ICISCI",

publisher = "IEEE",

pages = "56--61",

booktitle = "2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI)",

address = "United States",

note = "2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI 2022), ICISCI2022 ; Conference date: 15-10-2022 Through 17-10-2022",

}

Hu, J, Zhou, X & Li, C 2022, Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty. in 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI). International Conference on Intelligent Systems and Computational Intelligence, ICISCI, IEEE, pp. 56-61, 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI 2022), Changsha, China, 15/10/22. https://doi.org/10.1109/ICISCI53188.2022.9941449

Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty. / Hu, Jiaming; Zhou, Xiaojun; Li, Chaojie.
2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI). IEEE, 2022. p. 56-61 (International Conference on Intelligent Systems and Computational Intelligence, ICISCI).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty

AU - Hu, Jiaming

AU - Zhou, Xiaojun

AU - Li, Chaojie

PY - 2022

Y1 - 2022

N2 - Integrated Energy System (IES) is critical to improving energy efficiency and promoting the large-scale development of renewable energy sources. However, uncertain renewable wind energy brings challenges to the IES's optimal operation. In this article, a chance constrained weighted goal programming (CCWGP) model is proposed to address this issue. First, the CCWGP is introduced to establish multiple chance constraints on gas turbine reserve capacity to achieve system risk quantification. Then, the objective function is established that aims to minimize the IES's operating cost and risk. Finally, the state transition algorithm (STA), an intelligent optimization algorithm, is used to solve this high-dimensional complex problem in the multi-energy coupling system. Experimental results verify that the proposed method can effectively balance the safety and economy of the system. © 2022 IEEE.

AB - Integrated Energy System (IES) is critical to improving energy efficiency and promoting the large-scale development of renewable energy sources. However, uncertain renewable wind energy brings challenges to the IES's optimal operation. In this article, a chance constrained weighted goal programming (CCWGP) model is proposed to address this issue. First, the CCWGP is introduced to establish multiple chance constraints on gas turbine reserve capacity to achieve system risk quantification. Then, the objective function is established that aims to minimize the IES's operating cost and risk. Finally, the state transition algorithm (STA), an intelligent optimization algorithm, is used to solve this high-dimensional complex problem in the multi-energy coupling system. Experimental results verify that the proposed method can effectively balance the safety and economy of the system. © 2022 IEEE.

KW - chance constrained goal programming

KW - Integrated energy system

KW - state transition algorithm

UR - http://www.scopus.com/inward/record.url?scp=85142905276&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85142905276&origin=recordpage

U2 - 10.1109/ICISCI53188.2022.9941449

DO - 10.1109/ICISCI53188.2022.9941449

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-5620-3

T3 - International Conference on Intelligent Systems and Computational Intelligence, ICISCI

SP - 56

EP - 61

BT - 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI)

PB - IEEE

T2 - 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI 2022)

Y2 - 15 October 2022 through 17 October 2022

ER -

Hu J, Zhou X, Li C. Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty. In 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI). IEEE. 2022. p. 56-61. (International Conference on Intelligent Systems and Computational Intelligence, ICISCI). Epub 2022 Nov 11. doi: 10.1109/ICISCI53188.2022.9941449

Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty

Abstract

Publication series

Conference

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this