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

Yanfang Mo; Qiulin Lin; Minghua Chen; S. Joe Qin

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

Yanfang Mo, Qiulin Lin, Minghua Chen, S. Joe Qin

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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 Inc.
ISBN (Electronic)	9781665404433
Publication status	Published - 10 May 2021
Externally published	Yes
Event	2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 - Virtual, Online Duration: 9 May 2021 → 12 May 2021

Publication series

Name	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

Conference

Conference	2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
City	Virtual, Online
Period	9/05/21 → 12/05/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

https://hdl.handle.net/1783.1/135773

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 INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021). Institute of Electrical and Electronics Engineers Inc.. https://hdl.handle.net/1783.1/135773

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

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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 = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 ; Conference date: 09-05-2021 Through 12-05-2021",

year = "2021",

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day = "10",

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series = "IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 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 INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021, Virtual, Online, 9/05/21. <https://hdl.handle.net/1783.1/135773>

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 Inc., 2021. 9484511 (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › 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 - 2021/5/10

Y1 - 2021/5/10

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

M3 - Conference Paper published in a book

AN - SCOPUS:85109339228

T3 - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

BT - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

Y2 - 9 May 2021 through 12 May 2021

ER -

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 Inc. 2021. 9484511. (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021).

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

Abstract

Publication series

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Bibliographical note

UN SDGs

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