Optimal peak-minimizing online algorithms for large-load users with energy storage

Yanfang MO; Qiulin LIN; Minghua CHEN; S. Joe QIN

doi:10.1109/INFOCOMWKSHPS51825.2021.9484511

Optimal peak-minimizing online algorithms for large-load users with energy storage

Yanfang MO
, Qiulin LIN
, Minghua CHEN
, S. Joe QIN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

9 Citations (Scopus)

Abstract

The peak-demand charge motivates large-load customers to flatten their demand curves, while their self-owned renewable generations aggravate demand fluctuations. Thus, it is attractive to utilize energy storage for shaping real-time loads and reducing electricity bills. In this paper, we propose the first peak-aware competitive online algorithm for leveraging stored energy (e.g., in fuel cells) to minimize peak-demand charges. Our algorithm decides the discharging quantity slot by slot to maintain the optimal worst-case performance guarantee (namely, competitive ratio) among all deterministic online algorithms. Interestingly, we show that the best competitive ratio can be computed by solving a linear number of linear-fractional problems. We can also extend our competitive algorithm and analysis to improve the average-case performance and consider short-term prediction.

Original language	English
Title of host publication	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781665404433
ISBN (Print)	9781665447140
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484511
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS 2021) - Virtual, Vancouver, Canada Duration: 10 May 2021 → 13 May 2021

Publication series

Name	IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

Conference

Conference	2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS 2021)
Country/Territory	Canada
City	Vancouver
Period	10/05/21 → 13/05/21

Bibliographical note

The work presented in this paper was supported in part by a Start-up Grant (Project No. 9380118) from City University of Hong Kong.

Funding

The work presented in this paper was supported in part by a Start-up Grant (Project No. 9380118) from City University of Hong Kong. Q. Lin was with The Chinese University of Hong Kong during this work.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/INFOCOMWKSHPS51825.2021.9484511

Cite this

MO, Y., LIN, Q., CHEN, M., & QIN, S. J. (2021). Optimal peak-minimizing online algorithms for large-load users with energy storage. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 Article 9484511 (IEEE Conference on Computer Communications Workshops, INFOCOM Wksps). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484511

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title = "Optimal peak-minimizing online algorithms for large-load users with energy storage",

abstract = "The peak-demand charge motivates large-load customers to flatten their demand curves, while their self-owned renewable generations aggravate demand fluctuations. Thus, it is attractive to utilize energy storage for shaping real-time loads and reducing electricity bills. In this paper, we propose the first peak-aware competitive online algorithm for leveraging stored energy (e.g., in fuel cells) to minimize peak-demand charges. Our algorithm decides the discharging quantity slot by slot to maintain the optimal worst-case performance guarantee (namely, competitive ratio) among all deterministic online algorithms. Interestingly, we show that the best competitive ratio can be computed by solving a linear number of linear-fractional problems. We can also extend our competitive algorithm and analysis to improve the average-case performance and consider short-term prediction.",

author = "Yanfang MO and Qiulin LIN and Minghua CHEN and QIN, \{S. Joe\}",

note = "The work presented in this paper was supported in part by a Start-up Grant (Project No. 9380118) from City University of Hong Kong.; 2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS 2021) ; Conference date: 10-05-2021 Through 13-05-2021",

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MO, Y, LIN, Q, CHEN, M & QIN, SJ 2021, Optimal peak-minimizing online algorithms for large-load users with energy storage. in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021., 9484511, IEEE Conference on Computer Communications Workshops, INFOCOM Wksps, Institute of Electrical and Electronics Engineers, 2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS 2021), Vancouver, British Columbia, Canada, 10/05/21. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484511

Optimal peak-minimizing online algorithms for large-load users with energy storage. / MO, Yanfang; LIN, Qiulin; CHEN, Minghua et al.
IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. Institute of Electrical and Electronics Engineers, 2021. 9484511 (IEEE Conference on Computer Communications Workshops, INFOCOM Wksps).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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AU - MO, Yanfang

AU - LIN, Qiulin

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AB - The peak-demand charge motivates large-load customers to flatten their demand curves, while their self-owned renewable generations aggravate demand fluctuations. Thus, it is attractive to utilize energy storage for shaping real-time loads and reducing electricity bills. In this paper, we propose the first peak-aware competitive online algorithm for leveraging stored energy (e.g., in fuel cells) to minimize peak-demand charges. Our algorithm decides the discharging quantity slot by slot to maintain the optimal worst-case performance guarantee (namely, competitive ratio) among all deterministic online algorithms. Interestingly, we show that the best competitive ratio can be computed by solving a linear number of linear-fractional problems. We can also extend our competitive algorithm and analysis to improve the average-case performance and consider short-term prediction.

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MO Y, LIN Q, CHEN M, QIN SJ. Optimal peak-minimizing online algorithms for large-load users with energy storage. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. Institute of Electrical and Electronics Engineers. 2021. 9484511. (IEEE Conference on Computer Communications Workshops, INFOCOM Wksps). Epub 2021 Jul 19. doi: 10.1109/INFOCOMWKSHPS51825.2021.9484511

Optimal peak-minimizing online algorithms for large-load users with energy storage

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Access to Document

Other files and links

Fingerprint

IEEE INFOCOM 2021 Best Poster Award (Non-LU)

Cite this

Optimal peak-minimizing online algorithms for large-load users with energy storage

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Access to Document

Other files and links

Fingerprint

Prizes

IEEE INFOCOM 2021 Best Poster Award (Non-LU)

Cite this