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Flexibility based Coordination Framework For Three-Level Energy Management System

2022· article· en· W4313315997 on OpenAlexafffund
Sadam Hussain, Claude Ziad El‐Bayeh, Ramanunni Parakkal Menon, Chunyan Lai, Ursula Eicker

Bibliographic record

Venue2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST) · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsConcordia University
FundersConcordia University
KeywordsFlexibility (engineering)Computer scienceEnergy managementSystems engineeringEmbedded systemProcess managementEnergy (signal processing)BusinessEngineering

Abstract

fetched live from OpenAlex

With the increasing penetration level of distributed energy resources (DERs), prosumers play a vital role in demand-side management. The prosumers with DER provide flexible services to the system operFator. This work proposes a novel three-level energy management system coordination framework in which prosumers provide upward and downward flexibility to the system operation. The system operates then runs the global optimization of the whole system with the flexibility request to the prosumer. The suggested methodology considers the conflicting techno-economic objectives of the system operator and prosumers. To evaluate the proposed method, we compare two scenarios that are, without flexibility, and with flexibility. Results show that our proposed strategy improves the voltage profiles and reduces power losses, power generation costs, and peak demands from the system operator’s perspective.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.247
Teacher spread0.210 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2022
Admission routes2
Has abstractyes

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