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Record W1993901885 · doi:10.1109/pscc.2014.7038312

Effect of price responsive demand on the operation of microgrids

2014· article· en· W1993901885 on OpenAlexaff
Felipe O. Ramos, Claudio A. Cañizares, Kankar Bhattacharya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrogridPrice elasticity of demandDemand responseRenewable energyPeak demandElasticity (physics)ScheduleLoad managementElectricityComputer scienceEnergy storageScheduling (production processes)Mathematical optimizationEconomicsMicroeconomicsEngineeringPower (physics)Operations managementElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, a demand elasticity model is developed and tested for the dispatch of microgrids. The price obtained from dispatching the network in a base-case scenario is used as input to a demand elasticity model; this demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a microgrid can be obtained, to study the impact of demand response on unit commitment. This way, for a microgrid, the effect on the scheduling of diesel generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a benchmark microgrid show that the proposed 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios. Moreover, it is confirmed that elastic demand in a microgrid reduces electricity price variability and mitigates the need for storage in the presence of high penetration of renewable energy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.138

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2014
Admission routes1
Has abstractyes

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