research-article

Industrial Demand-Side Flexibility: A Benchmark Data Set

Authors:

Dorothea Wagner,

Veit HagenmeyerAuthors Info & Claims

e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

Pages 460 - 473

https://doi.org/10.1145/3307772.3331021

Published: 15 June 2019 Publication History

Abstract

To cope with the new demands of an electrical grid based on mostly renewable energy, more flexibility on the demand-side is needed. To test new demand-side management strategies, energy consumption data sets which come with some information about the inherent flexibility of the processes, are needed. However, such data sets are often commercially sensitive and thus not published or replaced with entirely artificial data. In the present paper, we introduce a new benchmark data set containing scheduling scenarios of industrial processes with flexibility information. The instances are based on a real-world data set of a small scale industrial facility, from which we extract process characteristics using a novel motif discovery technique. We provide an in-depth analysis of the benchmark data set and show that it is suitable to evaluate smart-grid scheduling techniques.

References

[1]

Sankar Ashok. 2006. Peak-load management in steel plants. Applied Energy 83, 5 (2006), 413--424.

[2]

Lukas Barth, Veit Hagenmeyer, Nicole Ludwig, and Dorothea Wagner. 2018. How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes. In Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy '18, Unknown (Ed.). ACM Press, New York, New York, USA, 43--62.

Digital Library

[3]

Lukas Barth, Nicole Ludwig, Esther Mengelkamp, and Philipp Staudt. 2018. A comprehensive modelling framework for demand side flexibility in smart grids. Computer Science - Research and Development 33, 13 (2018), 1865--2042.

[4]

Simon Bischof, Holger Trittenbach, Michael Vollmer, Dominik Werle, Thomas Blank, and Klemens Böhm. 2018. HIPE -- an Energy-Status-Data Set from Industrial Production. In Proceedings of ACM e-Energy (e-Energy 2018). ACM, New York, NY, USA.

Digital Library

[5]

Nina Boogen, Souvik Datta, and Massimo Filippini. 2017. Demand-side management by electric utilities in Switzerland: Analyzing its impact on residential electricity demand. Energy Economics 64 (2017), 402--414.

[6]

Jeremy Buhler and Martin Tompa. 2002. Finding motifs using random projections. Journal of computational biology: a journal of computational molecular cell biology 9, 2 (2002), 225--242.

[7]

Bill Chiu, Eamonn Keogh, and Stefano Lonardi. 2003. Probabilistic discovery of time series motifs. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, Ted Senator (Ed.). ACM, New York, NY, 493--498.

Digital Library

[8]

Martin Ester, Hans-Peter Kriegel, JÃűrg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press, 226--231.

Digital Library

[9]

Cheng Fan. 2015. TSMining: Mining Univariate and Multivariate Motifs in Time-Series Data. R package version 1.0.

[10]

Paddy Finn and Colin Fitzpatrick. 2014. Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing. Applied Energy 113 (2014), 11--21.

[11]

Sebastian Gottwalt, Wolfgang Ketter, Carsten Block, John Collins, and Christof Weinhardt. 2011. Demand side management---A simulation of household behavior under variable prices. Energy Policy 39, 12 (2011), 8163--8174.

[12]

Willy Herroelen, Erik Demeulemeester, and Bert De Reyck. 1999. A Classification Scheme for Project Scheduling. Springer US, Boston, MA, 1--26.

[13]

Rainer Kolisch, Christoph Schwindt, and Arno Sprecher. 1999. Benchmark Instances for Project Scheduling Problems. In Project Scheduling, Jan Węglarz (Ed.). Springer, Boston, MA, 197--212.

[14]

Rainer Kolisch and Arno Sprecher. 1997. PSPLIB - A project scheduling problem library. European Journal of Operational Research 96, 1 (1997), 205--216.

[15]

Ying Li, Boon Loong Ng, Mark Trayer, and Lingjia Liu. 2012. Automated Residential Demand Response: Algorithmic Implications of Pricing Models. IEEE Transactions on Smart Grid 3, 4 (2012), 1712--1721.

[16]

Thillainathan Logenthiran, Dipti Srinivasan, and Tan Zong Shun. 2012. Demand Side Management in Smart Grid Using Heuristic Optimization. IEEE Transactions on Smart Grid 3, 3 (2012), 1244--1252.

[17]

Nicole Ludwig, Lukas Barth, Dorothea Wagner, and Veit Hagenmeyer. 2019. Benchmark Dataset for "Industrial Demand-Side Flexibility: A Benchmark Data Set". KITOpen Repository. (2019).

[18]

Nicole Ludwig, Simon Waczowicz, Ralf Mikut, and Veit Hagenmeyer. 2017. Mining Flexibility Patterns in Energy Time Series from Industrial Processes. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, Frank Hoffmann, E. Hüllermeier, and Ralf Mikut (Eds.). KIT Scientific Publishing, 13--32.

[19]

Nicole Ludwig, Simon Waczowicz, Ralf Mikut, and Veit Hagenmeyer. 2018. Assessment of Unsupervised Standard Pattern Recognition Methods for Industrial Energy Time Series. In Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy '18, Unknown (Ed.). ACM Press, New York, New York, USA, 434--435.

Digital Library

[20]

Mette K. Petersen, Lars H. Hansen, Jan Bendtsen, Kristian Edlund, and Jakob Stoustrup. 2014. Heuristic Optimization for the Discrete Virtual Power Plant Dispatch Problem. IEEE Transactions on Smart Grid 5, 6 (2014), 2910--2918.

[21]

Kostantinos N. Plataniotis and Dimitris Hatzinakos. 2000. Gaussian mixtures and their applications to signal processing. In Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems, Stergios Stergiopoulos (Ed.). CRC Press, Boca Raton, Chapter 3.

[22]

Pilar Tormos and Antonio Lova. 2001. A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling. Annals of Operations Research 102, 1/4 (2001), 65--81.

[23]

Sean Yaw, Brendan Mumey, Erin McDonald, and Jennifer Lemke. 2014. Peak demand scheduling in the Smart Grid. In 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 770--775.

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Kinast ABraune R(2025)Towards Benchmarking of Collaborative Robots in ProductionProcedia Computer Science10.1016/j.procs.2025.01.212253(1505-1514)Online publication date: 2025
https://doi.org/10.1016/j.procs.2025.01.212
Lara-Abelenda FChushig-Muzo DWägner ATayefi MSoguero-Ruiz C(2025)Interpretable and multimodal fusion methodology to predict severe hypoglycemia in adults with type 1 diabetesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2025.110142144(110142)Online publication date: Mar-2025
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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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Index Terms

Industrial Demand-Side Flexibility: A Benchmark Data Set

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cover image ACM Other conferences

e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

June 2019

589 pages

ISBN:9781450366717

DOI:10.1145/3307772

Copyright © 2019 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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SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2019

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Deutsche Forschungsgemeinschaft

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e-Energy '19

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SIGEnergy

e-Energy '19: The Tenth ACM International Conference on Future Energy Systems

June 25 - 28, 2019

AZ, Phoenix, USA

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Lara-Abelenda FChushig-Muzo DWägner ATayefi MSoguero-Ruiz C(2025)Interpretable and multimodal fusion methodology to predict severe hypoglycemia in adults with type 1 diabetesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2025.110142144(110142)Online publication date: Mar-2025
https://doi.org/10.1016/j.engappai.2025.110142
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
https://doi.org/10.1109/TIA.2023.3334218
Wan YHe ZGao YXue Y(2024)Long-haul truck charging planning problem considering time flexibility and energy flexibilityEnergy10.1016/j.energy.2024.132361(132361)Online publication date: Jul-2024
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Siddiquee SAgyeman KBruton KHoward BO'Sullivan D(2022)A Data-driven Assessment Model for Demand Response Participation Benefit of Industries2022 IEEE Texas Power and Energy Conference (TPEC)10.1109/TPEC54980.2022.9750797(1-6)Online publication date: 28-Feb-2022
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Shimim FWhitaker B(2022)A Reinforcement Learning Approach to the Dynamic Job Scheduling Problem2022 IEEE Green Energy and Smart System Systems(IGESSC)10.1109/IGESSC55810.2022.9955328(1-6)Online publication date: 7-Nov-2022
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Kristy CZagloel T(2020)An Integrated Analytical Hierarchy Process and Monte Carlo Method Approach for Supplier Selection in Construction's Supply ChainProceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering10.1145/3400934.3400989(300-304)Online publication date: 16-Jun-2020
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