research-article

Online microgrid energy generation scheduling revisited: the benefits of randomization and interval prediction

Authors:

Mohammad H. Hajiesmaili,

Longbu HuangAuthors Info & Claims

e-Energy '16: Proceedings of the Seventh International Conference on Future Energy Systems

Article No.: 1, Pages 1 - 11

https://doi.org/10.1145/2934328.2934329

Published: 21 June 2016 Publication History

Abstract

Energy generation scheduling is a fundamental problem in microgrid design that determines the on/off status and the output level of energy sources with the goal of minimizing the cost and satisfying both electricity and heat demand. The uncertainty in both renewable generation and microgrid demand makes the problem drastically different from its counterparts and in traditional power systems and brings out the essential need of online algorithm design. In the literature, an online deterministic algorithm called CHASE has achieved a competitive ratio of 3, which is the best possible among deterministic algorithms. In addition, it has been shown the accurate prediction can improve the performance. This paper revisits the problem by investigating the benefits of randomization and interval prediction, i.e., relaxing accurate prediction assumption by considering an interval of valid ranges for future demand. We propose rCHASE, a randomized algorithm that achieves competitive ratio of around 2.128, improving beyond the best deterministic algorithm. Then, we propose iCHASE, an interval prediction-aware algorithm that is built upon rCHASE and a new extension we developed for the classic ski-rental problem. Our trace-driven experiments demonstrate that iCHASE outperforms CHASE; the average cost reduction of iCHASE is 15.85%, while CHASE reduces the cost by 9.1%.

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Menati AChau SChen MLehnhoff SIrwin DWang D(2022)Competitive prediction-aware online algorithms for energy generation scheduling in microgridsProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538867(383-394)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3538637.3538867
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Index Terms

Online microgrid energy generation scheduling revisited: the benefits of randomization and interval prediction
1. Hardware
  1. Power and energy
    1. Energy distribution
      1. Smart grid
2. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms

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cover image ACM Other conferences

e-Energy '16: Proceedings of the Seventh International Conference on Future Energy Systems

June 2016

266 pages

ISBN:9781450343930

DOI:10.1145/2934328

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2016

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University Grants Committee of the Hong Kong Special Administrative Region, China
National Natural Science Foundation of China Grant
National Basic Research Program of China Grant
National Basic Research Program of China
University Grants Committee of the Hong Kong Special Administrative Region, China, General Research Fund

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e-Energy'16

e-Energy'16: The Seventh International Conference on Future Energy Systems

June 21 - 24, 2016

Ontario, Waterloo, Canada

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Menati AChau SChen MLehnhoff SIrwin DWang D(2022)Competitive prediction-aware online algorithms for energy generation scheduling in microgridsProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538867(383-394)Online publication date: 28-Jun-2022
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Lee RMaghakian JHajiesmaili MLi JSitaraman RLiu Z(2021)Online Peak-Aware Energy Scheduling with Untrusted AdviceProceedings of the Twelfth ACM International Conference on Future Energy Systems10.1145/3447555.3464860(107-123)Online publication date: 22-Jun-2021
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