Cloud Digital Twin Concept forDistribution Systems

Taku Noda, Tomo Tadokoro and Yoshimitsu Umahashi
Grid Innovation Research Laboratory
Jan. 9, 2024

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Digital Twin
is a digital simulation model that behaves in the same
way as its real system. To realize a digital twin …
1. We have to prepare
the same simulation
model as the realsystem.
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2. We need acomputing facility
which is fast enoughfor our purpose.
2What is the most time-consuming part of
distribution simulations?
u In the past, the answer was the simulation itself.
u Now, the preparation of data cases for simulations.
u We have thousands of different distribution feeders. For eachfeeder, the topology changes.
u CRIEPI and seven T&D companies have developed the systemcalled GriSim, which is usually used inside an intranet.
u GriSim automatically generates simulation data cases from utility
DBs for power-flow and EMT simulation programs.
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IEEE Electrification Magazine, Dec. 2023
u The development of GriSim and anothersystem for transmission systems has
been documented in the article (right).
u The development of GriSim has been
carried out from Apr. 2016 to Mar. 2021.
u GriSim is used by seven T&D companies
mainly for the assessment of renewableenergy integration.
u The system for transmission systems isunder development with all T&D co.’s.
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4How GriSim Worksu As a batch process, GriSim

gathers necessary data into
GriSim’s DB in common format.
u Step 1: An engineer specifies asimulation type and a feeder.
u Step 2: The client extracts thedata of the specified feeder.
u Step 3: Distribution substations,poles, overhead wires etc. areshown on a map or on a skeleton.
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How GriSim Worksu Step 4: The user supplements

missing data at this point.
u Step 5: As an option, Google
Map integration can be used.
u Step 6: The data case of thespecified simulation type is
automatically generated. In thiscase, the power-flow program
CALDG, the EMT program XTAPand the lightning outage rate
calculation program DLOP.
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Computing Facility for Distribution Digital Twinu We have thousands of different distribution feeders. For eachfeeder, the topology changes. No fixed topology can be assumed.
u Rather than a real-time simulator, a “flexible” computing facility
such as Amazon’s EC2 (elastic computing cloud) is preferred.
u Do we need hard real time for distribution simulations? We needa “fast” computing facility but not a hard real-time simulator.
u So, what we need is a “flexible” and “fast” computing facility sothat any feeder can be simulated with a sufficiently fast speed.
u Now, Amazon’s EC2 can achieve a simulation speed which is fasterthan real time. It means that we can create a Cloud Digital Twin.
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Supporting Facts for Cloud Digital Twinu In the past decade, there was a significant progress in the model-based design (MBD) technologies.
u Before the progress, the implementation of the control system ofa facility, obtained as a result of simulations, involved manpowerand errors. This justified the use of real-time simulators.
u Thanks to the MBD technology progress, the control system usedin the simulations can be burnt into field programmable gatearrays (FPGAs). This now justifies no need of real-time simulators.
u In addition, vendors of distribution facilities are relatively-small
and cannot afford real-time simulators.
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8Conclusion
u We would like to propose the following Cloud Digital Twin

concept for distribution systems.
u By specifying a feeder, a simulation data case is automaticallygenerated by extracting data from existing DBs. For this, we havedeveloped GriSim with the seven T&D companies.
u The simulation data case generated is reproduced on a cloudcomputing facility such as Amazon’s EC2 as a digital twin.
u A control system supplied by a distribution facility vendor istested in the “Cloud Digital Twin,” before deploying in an actual
distribution system thus leading to a faster deployment.
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9Reference
u T. Noda, T. Tadokoro and T. Dozaki, “Automatic Generation ofPower System Simulation Data Cases From Utility Databases:
Introducing a new technology,” IEEE Electrification Magazine, vol.
11, no. 4, pp. 79-85, Dec. 2023, doi: 10.1109/MELE.2023.332052.
URL: https://doi.org/10.1109/MELE.2023.3320521
u T. Tadokoro, T. Noda, K. Ishimoto, N. Okada, S. Uemura and Y.
Shumuta, “Automatic Generation of Input Data for Distribution
System Simulation Programs,” 2018 IEEE Innovative Smart Grid
Technologies - Asia (ISGT Asia), Singapore, 2018, pp. 25-29, doi:
10.1109/ISGT-Asia.2018.8467719.
URL: https://doi.org/10.1109/ISGT-Asia.2018.8467719© CRIEPI 202410

If you have a questions or comments on this topic, let
us know by leaving a message to this video.
Thank you very much forwatching this video.

Taku
Noda© CRIEPI 2024

TomoTadokoro

Yoshimitsu
Umahashi