TY - GEN
T1 - Digital Twins for Optimizing the Real-Time Economy of Integrated Energy Systems
AU - Kajihara, Takanori
AU - Garrett, Daniel
AU - Kim, Junyung
AU - Lin, Linyu
AU - Browning, Jeren M.
AU - Talbot, Paul
N1 - Funding Information:
This work was supported by the Integrated Energy Systems Program at Idaho National Laboratory under DOE operations contract no. DE-AC07-05ID14517.
Publisher Copyright:
© 2023 American Nuclear Society, Incorporated.
PY - 2023/7/15
Y1 - 2023/7/15
N2 - Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.
AB - Economic and safe operation of integrated energy systems (IES) requires real-time optimization (RTO) of the control and actions conducted on each system component. In this regard, digital twins (DTs), which consist of a physical system, a virtual system, and the data communication that occurs between the two, are essential for effective RTO. Through the data warehouse, the virtual system is constantly updated with real-time data from the physical system, and functions as the model in the optimization framework. The reduced-order model of the dynamic process model in the virtual system is used in the optimization framework. The optimization results are then returned, via the data warehouse, as control actions to the physical system. This work demonstrates the software capabilities of DT assets for an IES in the context of preparing a DT for an experimental system comprised of Idaho National Laboratory (INL)’s Thermal Energy Delivery System and battery system. For the virtual demonstration, the DTs encompass (1) a physical system, including the Modelica models of the Thermal Energy Delivery System and the battery system; (2) virtual optimization via the Optimization of Real-Time Capacity Allocation (ORCA) platform; and (3) the open-source data warehouse software DeepLynx. This work assesses the performance of ORCA, which utilizes a reduced-order model built using the Risk Analysis Virtual Environment (RAVEN) and trained on the Modelica models and real-time data pipeline through the graph database hosted in DeepLynx. The proposed optimization workflow will be an RTO model based on DTs and the data they generate.
KW - DeepLynx
KW - IES
KW - ORCA
KW - RAVEN
KW - Real-time optimization
UR - https://www.scopus.com/pages/publications/85183329809
UR - https://www.osti.gov/biblio/1989353
UR - https://www.ans.org/pubs/proceedings/article-53885/
U2 - 10.13182/NPICHMIT23-40917
DO - 10.13182/NPICHMIT23-40917
M3 - Conference contribution
AN - SCOPUS:85183329809
T3 - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
SP - 1668
EP - 1677
BT - Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
PB - American Nuclear Society
T2 - 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023
Y2 - 15 July 2023 through 20 July 2023
ER -