Real-time peak-demand minimization with energy storage using competitive ratio

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

doi:10.1016/j.automatica.2025.112687

Real-time peak-demand minimization with energy storage using competitive ratio

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

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

Abstract

This paper studies minimizing the peak demand for large-load users with energy storage by making irrevocable storage-consuming decisions with unknown future demands in a daily on-peak horizon. We use competitive analysis to optimize online decision-making for robustness and fairness over all possible input sequences. The best competitive ratio (CR) for the online problem is computed by solving a linear number of linear-fractional programs and used to obtain an optimal online strategy, which is a static feedback policy affine over a synthetic state. Further, we generalize the concept of CR to shrinking-horizon CRs in response to real-time inputs and decisions. Accordingly, a real-time optimal online algorithm is developed to achieve the rolling-optimized shrinking-horizon CRs. This algorithm is a dynamic feedback policy, retains the global optimal worst-case performance, and improves the general-case performance. Trace-driven simulations show that the real-time optimal algorithm can significantly decrease the peak demand compared to baseline alternatives under typical settings.

Original language	English
Article number	112687
Journal	Automatica
Volume	184
Early online date	14 Nov 2025
DOIs	https://doi.org/10.1016/j.automatica.2025.112687
Publication status	E-pub ahead of print - 14 Nov 2025

Bibliographical note

Publisher Copyright:
© 2025 Elsevier Ltd

Funding

The work was supported in part by the Research Grants Council (RGC) of Hong Kong under the General Research Fund (Project Nos. 16206324 , 11206821 , 11303421 , 13300525 ), the Research Impact Fund (Project No. 130272 ), and the Collaborative Research Fund (Project No. C1049-24G ); by the Laboratory for AI-Powered Financial Technologies under an InnoHK initiative of the HKSAR; by a Shenzhen-Hong Kong-Macau Science & Technology Project (Category C, Project No. SGDX20220530111203026 ); by a Start-up Research Grant (Project No. UDF01004086 ) from The Chinese University of Hong Kong, Shenzhen ; and by the grant DR25E7 from Lingnan University.

Keywords

Optimization under uncertainties
Resource allocation
Receding horizon control
Demand response

Access to Document

10.1016/j.automatica.2025.112687

Cite this

@article{6146946474d544cd8d19d55669b816d9,

title = "Real-time peak-demand minimization with energy storage using competitive ratio",

abstract = "This paper studies minimizing the peak demand for large-load users with energy storage by making irrevocable storage-consuming decisions with unknown future demands in a daily on-peak horizon. We use competitive analysis to optimize online decision-making for robustness and fairness over all possible input sequences. The best competitive ratio (CR) for the online problem is computed by solving a linear number of linear-fractional programs and used to obtain an optimal online strategy, which is a static feedback policy affine over a synthetic state. Further, we generalize the concept of CR to shrinking-horizon CRs in response to real-time inputs and decisions. Accordingly, a real-time optimal online algorithm is developed to achieve the rolling-optimized shrinking-horizon CRs. This algorithm is a dynamic feedback policy, retains the global optimal worst-case performance, and improves the general-case performance. Trace-driven simulations show that the real-time optimal algorithm can significantly decrease the peak demand compared to baseline alternatives under typical settings.",

keywords = "Optimization under uncertainties, Resource allocation, Receding horizon control, Demand response",

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

note = "Publisher Copyright: {\textcopyright} 2025 Elsevier Ltd",

year = "2025",

month = nov,

day = "14",

doi = "10.1016/j.automatica.2025.112687",

language = "English",

volume = "184",

journal = "Automatica",

issn = "0005-1098",

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T1 - Real-time peak-demand minimization with energy storage using competitive ratio

AU - MO, Yanfang

AU - LIN, Qiulin

AU - CHEN, Minghua

AU - QIN, S. Joe

PY - 2025/11/14

Y1 - 2025/11/14

N2 - This paper studies minimizing the peak demand for large-load users with energy storage by making irrevocable storage-consuming decisions with unknown future demands in a daily on-peak horizon. We use competitive analysis to optimize online decision-making for robustness and fairness over all possible input sequences. The best competitive ratio (CR) for the online problem is computed by solving a linear number of linear-fractional programs and used to obtain an optimal online strategy, which is a static feedback policy affine over a synthetic state. Further, we generalize the concept of CR to shrinking-horizon CRs in response to real-time inputs and decisions. Accordingly, a real-time optimal online algorithm is developed to achieve the rolling-optimized shrinking-horizon CRs. This algorithm is a dynamic feedback policy, retains the global optimal worst-case performance, and improves the general-case performance. Trace-driven simulations show that the real-time optimal algorithm can significantly decrease the peak demand compared to baseline alternatives under typical settings.

AB - This paper studies minimizing the peak demand for large-load users with energy storage by making irrevocable storage-consuming decisions with unknown future demands in a daily on-peak horizon. We use competitive analysis to optimize online decision-making for robustness and fairness over all possible input sequences. The best competitive ratio (CR) for the online problem is computed by solving a linear number of linear-fractional programs and used to obtain an optimal online strategy, which is a static feedback policy affine over a synthetic state. Further, we generalize the concept of CR to shrinking-horizon CRs in response to real-time inputs and decisions. Accordingly, a real-time optimal online algorithm is developed to achieve the rolling-optimized shrinking-horizon CRs. This algorithm is a dynamic feedback policy, retains the global optimal worst-case performance, and improves the general-case performance. Trace-driven simulations show that the real-time optimal algorithm can significantly decrease the peak demand compared to baseline alternatives under typical settings.

KW - Optimization under uncertainties

KW - Resource allocation

KW - Receding horizon control

KW - Demand response

UR - https://www.scopus.com/pages/publications/105021476525

U2 - 10.1016/j.automatica.2025.112687

DO - 10.1016/j.automatica.2025.112687

M3 - Journal Article (refereed)

AN - SCOPUS:105021476525

SN - 0005-1098

VL - 184

JO - Automatica

JF - Automatica

M1 - 112687

ER -

Real-time peak-demand minimization with energy storage using competitive ratio

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Reduced-dimensional predictor learning of co-dynamic data from dynamic systems (源于動態系統的共同動態數據的降維預測學習)

Elementary study of majorization-related integer partial order programming

When phase meets gain (当相位遇见增益)

Cite this

Real-time peak-demand minimization with energy storage using competitive ratio

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Reduced-dimensional predictor learning of co-dynamic data from dynamic systems (源于動態系統的共同動態數據的降維預測學習)

Elementary study of majorization-related integer partial order programming

When phase meets gain (当相位遇见增益)

Cite this