TU Darmstadt / ULB / TUbiblio

Comparative Study of Algorithms for Optimized Control of Industrial Energy Supply Systems

Kohne, Thomas ; Ranzau, Heiko ; Panten, Niklas ; Weigold, Matthias (2020)
Comparative Study of Algorithms for Optimized Control of Industrial Energy Supply Systems.
(29-30 October 2020)
doi: 10.1186/s42162-020-00115-7
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-efficient and flexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced control algorithms e. g. based on Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS.This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that controllers based on DRL and MILP have a huge potential to reduce energy-related cost of up to 50% for less complex and around 6% for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2020
Autor(en): Kohne, Thomas ; Ranzau, Heiko ; Panten, Niklas ; Weigold, Matthias
Art des Eintrags: Bibliographie
Titel: Comparative Study of Algorithms for Optimized Control of Industrial Energy Supply Systems
Sprache: Englisch
Publikationsjahr: 28 Oktober 2020
Ort: Sierre, Switzerland
Buchtitel: Proceedings of the 9th DACH+ Conference on Energy Informatics, Vol. 3, Suppl. 1
Veranstaltungsdatum: 29-30 October 2020
DOI: 10.1186/s42162-020-00115-7
Kurzbeschreibung (Abstract):

Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-efficient and flexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced control algorithms e. g. based on Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS.This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that controllers based on DRL and MILP have a huge potential to reduce energy-related cost of up to 50% for less complex and around 6% for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.

Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW)
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > ETA Energietechnologien und Anwendungen in der Produktion
Hinterlegungsdatum: 22 Dez 2020 11:04
Letzte Änderung: 09 Jun 2022 05:15
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen