research-article

How much demand side flexibility do we need?: Analyzing where to exploit flexibility in industrial processes

Authors:

Veit Hagenmeyer,

Dorothea WagnerAuthors Info & Claims

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

Pages 43 - 62

https://doi.org/10.1145/3208903.3208909

Published: 12 June 2018 Publication History

Abstract

We introduce a novel approach to demand side management: Instead of using flexibility that needs to be defined by a domain expert, we identify a small subset of processes of e. g. an industrial plant that would yield the largest benefit if they were time-shiftable.

To find these processes we propose, implement and evaluate a framework that takes power usage time series of industrial processes as input and recommends which processes should be made flexible to optimize for several objectives as output. The technique combines and modifies a motif discovery algorithm with a scheduling algorithm based on mixed-integer programming.

We show that even with small amounts of newly introduced flexibility, significant improvements can be achieved, and that the proposed algorithms are feasible for realistically sized instances. We thoroughly evaluate our approach based on real-world power demand data from a small electronics factory.

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Cited By

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https://doi.org/10.3390/app14146035
Siddiquee SAgyeman KBruton KHoward BO'Sullivan D(2024)A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial ConsumersIEEE Transactions on Industry Applications10.1109/TIA.2023.333421860:2(2577-2587)Online publication date: Mar-2024
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How much demand side flexibility do we need?: Analyzing where to exploit flexibility in industrial processes

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cover image ACM Conferences

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

June 2018

657 pages

ISBN:9781450357678

DOI:10.1145/3208903

Copyright © 2018 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 the author(s) 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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Published: 12 June 2018

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Deutsche Forschungsgemeinschaft

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e-Energy '18

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e-Energy '18: The Ninth International Conference on Future Energy Systems

June 12 - 15, 2018

Karlsruhe, Germany

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

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https://doi.org/10.3390/app14146035
Siddiquee SAgyeman KBruton KHoward BO'Sullivan D(2024)A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial ConsumersIEEE Transactions on Industry Applications10.1109/TIA.2023.333421860:2(2577-2587)Online publication date: Mar-2024
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Galenzowski JWaczowicz SHagenmeyer V(2023)A new Data-Driven Approach for Comparative Assessment of Baseline Load Profiles Supporting the Planning of Future Charging InfrastructureCompanion Proceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3599733.3600245(8-20)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3599733.3600245
Schmeck HMonti AHagenmeyer V(2022)Energy informaticsCommunications of the ACM10.1145/351166665:4(58-63)Online publication date: 19-Mar-2022
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