[en] The energy transition into a modern power system requires energy flexibility. Demand Response (DR) is one promising option for providing this flexibility. With the highest share of final energy consumption, the industry has the potential to offer DR and contribute to the energy transition by adjusting its energy demand. This paper proposes a mathematical optimization model that uses a generic data model for flexibility description. The optimization model supports industrial companies to select when (i.e., at which time), where (i.e., in which market), and how (i.e., the schedule) they should market their flexibility potential to optimize profit. We evaluate the optimization model under several synthetic use cases developed upon the learnings over several workshops and bilateral discussions with industrial partners from the paper and aluminum industry. The results of the optimization model evaluation suggest the model can fulfill its purpose under different use cases even with complex use cases such as various loads and storages. However, the optimization model computation time grows as the complexity of use cases grows.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Energy Computer science Management information systems
Author, co-author :
BAHMANI, Ramin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
VAN STIPHOUDT, Christine ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
POTENCIANO MENCI, Sergio ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
SCHOEPF, Michael ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
FRIDGEN, Gilbert ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
External co-authors :
no
Language :
English
Title :
Optimal industrial flexibility scheduling based on generic data format
Publication date :
07 September 2022
Event name :
11th DACH+ Conference on Energy Informatics
Event place :
Freiburg im Breisgau, Germany
Event date :
from 15-09-2022 to 16-09-2022
Audience :
International
Journal title :
Energy Informatics
eISSN :
2520-8942
Publisher :
SpringerOpen, United Kingdom
Special issue title :
Proceedings of the 11th DACH+ Conference on Energy Informatics
AG Energiebilanzen eV (2021) Auswertungstabellen zur Energiebilanz Deutschland—Daten für die Jahre von 1990 bis 2020”. Retrieved March 29, 2022 from (2021)
Angizeh F, Parvania M, Fotuhi-Firuzabad M, Rajabi-Ghahnavieh A (2019) Flexibility scheduling for large customers. IEEE Trans Smart Grid 10(1):371–379
Ashok S, Banerjee R (2001) An optimization mode for industrial load management. IEEE Trans Power Syst 16(4):879–884
Bank L, Wenninger S, Köberlein J, Lindner M, Kaymakci C, Weigold M, Sauer A, Schilp J (2021) Integrating energy flexibility in production planning and control—an energy flexibility data model-based approach. Institutionelles Repositorium der Leibniz Universität Hannover, Hannover
Barth L, Ludwig N, Mengelkamp E, Staudt P (2018) A comprehensive modelling framework for demand side flexibility in smart grids. Comput Sci Res Dev 33(1–2):13–23
Bauernhansl T, Bauer D, Abele E, Ahrens R, Bank L, Brugger M, Colangelo E, Eigenbrod H, Fridgen G, Vazquez FG, Grigorjan A, Jarke M, Keller R, Lodwig R, Pullmann J, Reinhart G, Rösch M, Sauer A, Schel D, Schlereth A, Schott P, Schulz F, Sedlmeir J, Seitz P, Simon P, Weber T (2019) Industrie 4.0 als befähiger für energieflexibilität. In: Sauer A, Abele E, Buhl HU (eds) Energieflexibilität in der Deutschen Industrie: Ergebnisse aus dem Kopernikus-Projekt—Synchronisierte und Energieadaptive Produktionstechnik zur Flexiblen Ausrichtung Von Industrieprozessen Auf Eine Fluktuierende Energieversorgung—SynErgie. Fraunhofer Verlag, Stuttgart
Bundesnetzagentur: (2022) SMARD—Market data. Retrieved March 29, 2022 from (2022). https://www.smard.de/en/
Castro PM, Harjunkoski I, Grossmann IE (2009) New continuous-time scheduling formulation for continuous plants under variable electricity cost. Ind Eng Chem Res 48(14):6701–6714. 10.1021/ie900073k
Commission E, for Energy D-G, Antretter M, Klobasa M, Kühnbach M, Singh M, Knorr K, Schütt J, Boer J, Rolser O, Hernandez Diaz D, Fitzschen F, Garcerán A, Reina R, Stemmer S, Steinbach J, Popovski E (2022) Digitalisation of Energy Flexibility
Fridgen G, Keller R, Thimmel M, Wederhake L (2017) Shifting load through space-the economics of spatial demand side management using distributed data centers. Energy Policy 109:400–413
Gong X, De Pessemier T, Martens L, Joseph W (2019) Energy- and labor-aware flexible job shop scheduling under dynamic electricity pricing: a many-objective optimization investigation. J Clean Prod 209:1078–1094
Gurobi Optimization (2022) Retrieved March 29, 2022 from. https://www.gurobi.com
Heffron R, Körner M-F, Wagner J, Weibelzahl M, Fridgen G (2020) Industrial demand-side flexibility: a key element of a just energy transition and industrial development. Appl Energy 269:115026
Helin K, Käki A, Zakeri B, Lahdelma R, Syri S (2017) Economic potential of industrial demand side management in pulp and paper industry. Energy 141:1681–1694
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105
Howard D, Ma Z, Jørgensen B (2021) Evaluation of industrial energy flexibility potential: a scoping review, pp. 1074–1079
Huber J, Klempp N, Weinhardt C, Hufendiek K (2018) An interactive online-platform for demand side management. In: e-Energy ’18: Proceedings of the Ninth International Conference on Future Energy Systems, pp. 431–433
Khatri R, Schmidt M, Gasper R (2021) Active participation of industrial enterprises in electricity markets—a generic modeling approach. Energy Inf 4(3):20
Küster T, Rayling P, Wiersig R (2021) Pozo Pardo. Multi-objective optimization of energy-efficient production schedules using genetic algorithms. Optim Eng, F.D
Lindner M, Wenninger S, Fridgen G, Weigold M (2022) Aggregating energy flexibility for demand-side management in manufacturing companies—a two-step method. In: Behrens B-A, Brosius A, Drossel W-G, Hintze W, Ihlenfeldt S, Nyhuis P (eds) Production at the Leading Edge of Technology. Springer, Cham, pp 631–638
Mitra S, Grossmann IE, Pinto JM, Arora N (2012) Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes. Comput Chem Eng 38:171–184
Moon J-Y, Park J (2014) Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. Int J Prod Res 52(13):3922–3939
Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Indus Inf 7(3):381–388
Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77
Potenciano Menci S, van Stiphoudt C, Fridgen G, Schilp J, Köberlein J, Bauernhansl T, Sauer A, Grigorjan A, Schel D, Schlereth A, Schulz F, Weigold M, Lindner M, Schimmelpfenning J, Winter C (2021) Referenzarchitektur der Energiesynchronisationsplattform: Teil der Reihe ”Diskussionspapiere V4—Konzept der Energiesynchronisationsplattform”, s.l. Retrieved March 05, 2022 from (2021). http://publica.fraunhofer.de/dokumente/N-642369.html
Ramin D, Spinelli S, Brusaferri A (2018) Demand-side management via optimal production scheduling in power-intensive industries: the case of metal casting process. Appl Energy 225:622–636
Ruohonen P, Ahtila P (2011) Qualitative analysis of a thermo mechanical pulp and paper mill using advanced composite curves. Energy 36(6):3871–3877
Schott P, Sedlmeir J, Strobel N, Weber T, Fridgen G, Abele E (2019) A generic data model for describing flexibility in power markets. Energies 12(10):1893
Shoreh MH, Siano P, Shafie-khah M, Loia V, Catalão JPS (2016) A survey of industrial applications of demand response. Electric Power Syst Res 141:31–49
Shrouf F, Ordieres-Meré J, García-Sánchez A, Ortega-Mier M (2014) Optimizing the production scheduling of a single machine to minimize total energy consumption costs. J Clean Prod 67:197–207
Union of the Electricity Industry—EURELECTRIC aisbl: Flexibility and Aggregation—Requirements for their interaction in the market. Retrieved March 29, 2022 from (2014)
van Stiphoudt C, Potenciano Menci S, Schöpf M, Fridgen G, Weigold M, Lindner M, Buhl HU, Duda S, Schott P, Weibelzahl M, Wenninger S (2021) Energieflexibilitätsdatenmodell der Energiesynchronisationsplattform: Teil der Reihe ”Diskussionspapiere V4—Konzept der Energiesynchronisationsplattform”, s.l. Retrieved March 05, 2022 from (2021). https://eref.uni-bayreuth.de/68094/
Varelmann T, Erwes N, Schäfer P, Mitsos A (2022) Simultaneously optimizing bidding strategy in pay-as-bid-markets and production scheduling. Comput Chem Eng 157:107610
Wanapinit N, Thomsen J, Kost C, Weidlich A (2021) An milp model for evaluating the optimal operation and flexibility potential of end-users. Appl Energy 282:116183
Xu X, Abeysekera M, Gutschi C, Qadrdan M, Rittmannsberger K, Markus W, Wu J, Jenkins N (2020) Quantifying flexibility of industrial steam systems for ancillary services: a case study of an integrated pulp and paper mill. IET Energy Syst Integr 2(2):124–132
Zhou D, Zhou K, Zhu L, Zhao J, Xu Z, Shao Z, Chen X (2017) Optimal scheduling of multiple sets of air separation units with frequent load-change operation. Sep Purif Technol 172:178–191
