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Record W2760172647 · doi:10.1109/sege.2017.8052783

Economic analysis of residential solar microgrids

2017· article· en· W2760172647 on OpenAlexaffabout
James Situ, David J. Wright

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMicrogridPhotovoltaic systemNet meteringBattery (electricity)Renewable energyConsumption (sociology)ElectricityComputer scienceAutomotive engineeringEnvironmental economicsEconomicsPower (physics)Distributed generationEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

From homeowner and investor perspective, integration of renewable energy with battery energy storage system (BESS) can have economic benefits. This paper presents the results of battery control in residential microgrid system using photovoltaic (PV) distributed energy resource (DER) in residential applications in Ottawa, Ontario. The battery flow is controlled on an hourly basis to optimize the cost saving from PV microgrid system under net metering and no-feedback utility policies. PV and battery sizes are designed with fixed budgets in a particular price year to match average annual residential electricity power consumption (kWh) and dollar amount of consumption (ĆD) in Ontario. A linear programing (LP) model is developed for PV microgrid system to solve for different scenarios, varying in different price years, net metering and no-feedback utility policies, combinations of different PV-battery size. The LP solution under net metering policy shows the battery flow is only dependent on time-of-use (TOU) rate, while solution under no-feedback policy depends on both TOU rates and utility supply and demand in the microgrid system. In addition, higher financial benefit is realized with larger battery size in the latter price year.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.595

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)0.0010.000

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 head0.007
GPT teacher head0.216
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 teacher head, 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

Citations4
Published2017
Admission routes2
Has abstractyes

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