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

Department of Data Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › 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 (INFOCOM WKSHPS)
Publisher	IEEE
ISBN (Electronic)	9781665404433
ISBN (Print)	9781665447140
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484511
Publication status	Published - 2021
Event	40th IEEE Conference on Computer Communications (INFOCOM 2021) - Virtual, Vancouver, BC, Canada Duration: 10 May 2021 → 13 May 2021 https://infocom2021.ieee-infocom.org/index.html https://www.sdsc.cityu.edu.hk/news-event/news/sdsc-faculty-awarded-ieee-infocom-2021

Publication series

Name	IEEE INFOCOM - IEEE Conference on Computer Communications, INFOCOM

Conference

Conference	40th IEEE Conference on Computer Communications (INFOCOM 2021)
Place	Canada
City	Vancouver, BC
Period	10/05/21 → 13/05/21
Internet address	https://infocom2021.ieee-infocom.org/index.html https://www.sdsc.cityu.edu.hk/news-event/news/sdsc-faculty-awarded-ieee-infocom-2021

UN SDGs

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

SDG 7 Affordable and Clean Energy

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@inproceedings{2c543cccd55b4aa885bf6d1c365180ca,

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\}",

year = "2021",

doi = "10.1109/INFOCOMWKSHPS51825.2021.9484511",

language = "English",

isbn = "9781665447140",

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publisher = "IEEE",

booktitle = "IEEE INFOCOM 2021 - IEEE Conference on Computer Communications (INFOCOM WKSHPS)",

address = "United States",

note = "40th IEEE Conference on Computer Communications (INFOCOM 2021) ; Conference date: 10-05-2021 Through 13-05-2021",

url = "https://infocom2021.ieee-infocom.org/index.html, https://www.sdsc.cityu.edu.hk/news-event/news/sdsc-faculty-awarded-ieee-infocom-2021",

}

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 (INFOCOM WKSHPS)., 9484511, IEEE INFOCOM - IEEE Conference on Computer Communications, INFOCOM, IEEE, 40th IEEE Conference on Computer Communications (INFOCOM 2021), Vancouver, BC, 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 (INFOCOM WKSHPS). IEEE, 2021. 9484511 (IEEE INFOCOM - IEEE Conference on Computer Communications, INFOCOM).

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

TY - GEN

T1 - Optimal peak-minimizing online algorithms for large-load users with energy storage

AU - Mo, Yanfang

AU - Lin, Qiulin

AU - Chen, Minghua

AU - Qin, S. Joe

PY - 2021

Y1 - 2021

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/85109339228

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U2 - 10.1109/INFOCOMWKSHPS51825.2021.9484511

DO - 10.1109/INFOCOMWKSHPS51825.2021.9484511

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

SN - 9781665447140

T3 - IEEE INFOCOM - IEEE Conference on Computer Communications, INFOCOM

BT - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications (INFOCOM WKSHPS)

PB - IEEE

T2 - 40th IEEE Conference on Computer Communications (INFOCOM 2021)

Y2 - 10 May 2021 through 13 May 2021

ER -

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

Abstract

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