research-article

Crowd-Sourced Storage-Assisted Demand Response in Microgrids

Authors:

Mohammad H. Hajiesmaili,

Enrique Mallada,

Chi-Kin ChauAuthors Info & Claims

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

Pages 91 - 100

https://doi.org/10.1145/3077839.3077841

Published: 16 May 2017 Publication History

Abstract

This paper studies the problem of utilizing heterogeneous energy storage systems, including electric vehicles and residential batteries, to perform demand-response in microgrids. The objective is to minimize the operational cost while fulfilling the demand-response requirement. The design space is to select and schedule a subset of available storage devices that are heterogeneous in operating cost, capacity, and availability in time. Designing a performance-optimized solution, however, is challenging due to the combinatorial nature of the problem with mixed packing and covering constraints, and the essential need for online solution design in practical scenarios where both demand-response requirement and the profile of user-owned storage systems arrive online. We tackle these challenges and design several online algorithms by leveraging a recent theoretical computer science technique which uses a problem-specific exponential potential function to solve online mixed packing and covering problems. We show that the fractional version of the algorithm achieves a logarithmic bi-criteria competitive ratio. Empirical trace-driven experiments demonstrate that our algorithms perform much better than the theoretical bounds and achieve close-to-optimal performance.

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Cited By

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Alinia BHajiesmaili MLee ZCrespi NMallada E(2022)Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak ConstraintsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.29798547:3(537-548)Online publication date: 1-Jul-2022
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cover image ACM Conferences

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

May 2017

388 pages

ISBN:9781450350365

DOI:10.1145/3077839

Copyright © 2017 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: 16 May 2017

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Refereed limited

Funding Sources

NSF CPS grant CNS
University Grants Committee of the Hong Kong Special Administrative Region, China
ARO grant
National Basic Research Program of China

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e-Energy '17

Sponsor:

SIGCOMM

e-Energy '17: The Eighth International Conference on Future Energy Systems

May 16 - 19, 2017

Shatin, Hong Kong

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Overall Acceptance Rate 160 of 446 submissions, 36%

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E-Energy '25

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The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

Rotterdam , Netherlands

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Cited By

Mohanapriya DIndumathi RSubha KNandhini K(2025)Quantum AI-Inspired Blockchain-Assisted Crowdsourced Energy SystemsQuantum AI and its Applications in Blockchain Technology10.4018/979-8-3373-1657-4.ch009(175-202)Online publication date: 17-Jan-2025
https://doi.org/10.4018/979-8-3373-1657-4.ch009
Alinia BHajiesmaili MLee ZCrespi NMallada E(2022)Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak ConstraintsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.29798547:3(537-548)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSUSC.2020.2979854
Yang LHajiesmaili MSitaraman RWierman AMallada EWong W(2020)Online Linear Optimization with Inventory Management ConstraintsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33794824:1(1-29)Online publication date: 5-Jun-2020
https://dl.acm.org/doi/10.1145/3379482
Wang NZhou XLu XGuan ZWu LDu XGuizani M(2019)When Energy Trading Meets Blockchain in Electrical Power System: The State of the ArtApplied Sciences10.3390/app90815619:8(1561)Online publication date: 15-Apr-2019
https://doi.org/10.3390/app9081561
Hviid JJohansen ASangogboye FKjærgaard M(2019)Enabling Auto-Configuring Building ServicesProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328288(68-77)Online publication date: 15-Jun-2019
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Wang STaha AWang JKvaternik KHahn A(2019)Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart GridsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.291656549:8(1612-1623)Online publication date: Aug-2019
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Yang LHajiesmaili MWong W(2019)Online Linear Programming with Uncertain Constraints : (Invited Paper)2019 53rd Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS.2019.8693056(1-6)Online publication date: Mar-2019
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Guan ZLu XWang NWu JDu XGuizani M(2019)Towards secure and efficient energy trading in IIoT-enabled energy internet: A blockchain approachFuture Generation Computer Systems10.1016/j.future.2019.09.027Online publication date: Oct-2019
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Wang STaha AWang J(2018)Blockchain-Assisted Crowdsourced Energy Systems2018 IEEE Power & Energy Society General Meeting (PESGM)10.1109/PESGM.2018.8585864(1-5)Online publication date: Aug-2018
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Hajiesmaili MChen MMallada EChau C(2017)Summary of Recent ResultsACM SIGMETRICS Performance Evaluation Review10.1145/3152042.315206245:2(55-57)Online publication date: 11-Oct-2017
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