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Record W2330358177 · doi:10.1109/tsg.2016.2542824

Interfacing Power System and ICT Simulators: Challenges, State-of-the-Art, and Case Studies

2016· article· en· W2330358177 on OpenAlexaff
Sven Müller, Hanno Georg, James Nutaro, E. Widl, Yi Deng, Peter Pálenský, Muhammad Usman Awais, Moustafa Chenine, Markus Küch, Matthias Stifter, Hua Lin, Sandeep K. Shukla, Christian Wietfeld, Christian Rehtanz, Christian Dufour, Xiaoyu Wang, Venkata Dinavahi, Omar Faruque, Wenchao Meng, Shichao Liu, Antonello Monti, Ming Ni, Ali Davoudi, Ali Mehrizi‐Sani

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2016
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of AlbertaCarleton UniversityOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsInformation and Communications TechnologyInterfacingComputer scienceInterdependenceSynchronization (alternating current)Smart gridState (computer science)Event (particle physics)Interface (matter)Electric power systemSet (abstract data type)Systems engineeringPower (physics)TelecommunicationsEngineeringElectrical engineeringWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

With the transition toward a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this paper presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.226
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations118
Published2016
Admission routes1
Has abstractyes

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