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Combined Physics-Data Driven Modeling for Design and Operation Optimization of an Energy Concept

Kansara, Rushit Amishbhai and Lockan, Michael (2023) Combined Physics-Data Driven Modeling for Design and Operation Optimization of an Energy Concept. In: 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2023. ECOS 2023, 2023-06-26 - 2023-06-30, LAS PALMAS DE GRAN CANARIA, SPAIN. doi: 10.52202/069564-0120. ISBN 978-171387492-8.

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Official URL: https://www.proceedings.com/069564-0120.html

Abstract

The industrial sector accounts for a huge amount of energy- and process-related CO2 emissions. One decarbonization strategy is to build an energy concept which provides electricity and heat for industrial processes using combination of different renewable energy sources such as photovoltaic, wind turbine, and solar thermal collector system combined with energy conversion power-to-heat components such as heat pump, electric boiler etc. The challenge for the industries is the economic aspect of the decarbonization, as industries require a cost-efficient solution. The total cost for an industrial energy concept includes investment and operating costs. This complex problem of minimizing cost and emission requires two major tasks: (I) modeling of components and (II) multi-objective coupled design and operation optimization of the energy concept. The optimal design and capacity of the components and optimal system operation depend majorly on the modeling of the components. The modeling of the components is either physics-driven or data-driven. The corresponding multi-objective coupled optimization is a complex problem with a large number of variables and constrains involved. This paper shows different types of physics- and data-driven modeling of energy components for the multi-objective coupled optimization for minimizing cost and emission of an industrial process as a case study. The optimization problem is solved as single-level problem and bi-level problem with different combinations of physics- and data-driven models. Different modeling techniques and their influence on the optimization are compared in terms of computational effort, solution accuracy and optimal capacity of components. The results show that the combination of physics and data-driven models have computational time reduction up to 37% with high accuracy compared to complete physics-driven models for the considered case study. Specific combination of physics-driven and polynomial regression models show the best trade-off between computational speed and accuracy.

Item URL in elib:https://elib.dlr.de/200398/
Document Type:Conference or Workshop Item (Speech)
Title:Combined Physics-Data Driven Modeling for Design and Operation Optimization of an Energy Concept
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kansara, Rushit AmishbhaiUNSPECIFIEDhttps://orcid.org/0000-0001-9819-0321148637501
Lockan, MichaelUNSPECIFIEDhttps://orcid.org/0009-0002-8765-8667155228132
Date:30 June 2023
Journal or Publication Title:36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2023
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.52202/069564-0120
ISBN:978-171387492-8
Status:Published
Keywords:energy concept, renewable energy sources, coupled optimization, data-driven modeling
Event Title:ECOS 2023
Event Location:LAS PALMAS DE GRAN CANARIA, SPAIN
Event Type:international Conference
Event Start Date:26 June 2023
Event End Date:30 June 2023
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:High-Temperature Thermal Technologies
DLR - Research area:Energy
DLR - Program:E SP - Energy Storage
DLR - Research theme (Project):E - Low-Carbon Industrial Processes
Location: Cottbus
Institutes and Institutions:Institute of Low-Carbon Industrial Processes > Simulation and Virtual Design
Deposited By: Kansara, Rushit Amishbhai
Deposited On:13 Dec 2023 12:54
Last Modified:24 Apr 2024 21:00

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