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Record W4385627093 · doi:10.1109/tsg.2023.3302520

Novel Incentive-Based Multi-Level Framework for Flexibility Provision in Smart Grids

2023· article· en· W4385627093 on OpenAlexafffund
Sadam Hussain, Omar Alrumayh, Ramanunni Parakkal Menon, Chunyan Lai, Ursula Eicker

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2023
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsConcordia University
FundersConcordia UniversityCanada Excellence Research Chairs, Government of Canada
KeywordsNews aggregatorSmart gridFlexibility (engineering)IncentiveComputer scienceScheduleGridElectricityDemand responseElectric power systemReliability engineeringProsumerRisk analysis (engineering)Environmental economicsEngineeringPower (physics)BusinessRenewable energyEconomicsMicroeconomicsElectrical engineering

Abstract

fetched live from OpenAlex

Within smart grids, end-users often seek to reduce electricity costs using a home energy management system, while the power utility aims to optimize the grid operation. This article proposes a novel three-level framework to tackle the contradictory nature of the objectives of end-user and distribution system operators (DSOs) by upward and downward flexibility provision. In addition, the proposed method designs a novel incentive program based on the flexibility provided by the end-users. Then, the aggregated flexibility is offered to the DSO. The model takes advantage of the local flexibility of home appliances to re-schedule their use based on the flexibility request from the DSO. The aggregator provides incentive programs based on the flexibility provided by the prosumers to reduce/shift energy consumption. The proposed methodology considers the alignment of the conflicting techno-economic objectives of the prosumers and system operators. The results show that our proposed strategy has increased the monetary benefits for prosumers for their flexibility services provided to the DSO compared to other scenarios. Moreover, the proposed method improves the voltage profiles and reduces the peak load and power losses by 20.8% and 24.6%, respectively, of the overall system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.294
Teacher spread0.218 · 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 teacher head, not a consensus.

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

Citations19
Published2023
Admission routes2
Has abstractyes

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