MétaCan
Menu
Back to cohort

A Multi-Parameter Approach to Optimal Power Dispatch in Grid-Connected Photovoltaic-Battery Systems

2022· article· en· W4310521395 on OpenAlexaffabout
Ebrahim Mohammadi, Gerry Moschopoulos

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsWestern UniversityCarleton University
Fundersnot available
KeywordsPhotovoltaic systemTariffComputer scienceProfit (economics)Stand-alone power systemGrid-connected photovoltaic power systemGridBattery (electricity)ElectricityElectric power systemElectricity pricingGrid parityAutomotive engineeringMathematical optimizationPower (physics)Maximum power point trackingRenewable energyDistributed generationEngineeringElectricity marketElectrical engineeringPhotovoltaicsBusinessEconomicsMathematicsMicroeconomicsVoltage

Abstract

fetched live from OpenAlex

In photovoltaic-battery energy storage systems (PV-BESSs), the optimal power dispatch between the power sources (PV, battery, and the grid) and the load demand is significant, from the viewpoint of system efficiency and household profit. In this paper, a method that considers the time-of-use pricing (TOU) of electricity, the PV feed-in-tariff, and battery lifetime is proposed for the optimal power dispatch of grid-connected PV-BESS systems during a 24-hour period. The proposed method is implemented for a residential PV-battery system using the genetic algorithm (GA) and real TOU electricity pricing data and PV feed-in-tariff data of London, ON, Canada. Simulation results show how the proposed method can result in the optimal power dispatch in the system and how user profit can be maximized.

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: Empirical
Teacher disagreement score0.038
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
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venue2022 IEEE Energy Conversion Congress and Exposition (ECCE)Same topicSmart Grid Energy ManagementFrench-language works237,207