MétaCan
Menu
← Back to cohort
Record W3024223789 · doi:10.1149/ma2020-013517mtgabs

An Evaluation of Storage for Commercial Scale Demand Charge Management in British Columbia

2020· article· en· W3024223789 on OpenAlexaffabout
Laura Magallanes, Andrew Rowe

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTariffBattery (electricity)Environmental economicsEnergy storagePeak demandRenewable energyService (business)BusinessOperations managementEnvironmental scienceElectrical engineeringEconomicsEngineeringPower (physics)Electricity

Abstract

fetched live from OpenAlex

British Columbia provides electrical service using regulated rates for energy and demand charges. Under this limited market structure, where there is no time-of-use tariff, benefits of behind-the-meter storage are derived from demand charge management or renewable integration. Potential cost savings for a commercial load (a university) using electrical storage for demand charge reductions are investigated. Battery sizes of 500 kW/2 MWh and 1 MW/3 MWh with round trip efficiencies between 80 - 100 % are analyzed. Demand data with 15-minute resolution over one year is tested using simple battery dispatch strategies. Neglecting investment costs, annual operational savings vary from 50 - 70 K$/year depending on battery rating, efficiency and dispatch strategy. Cost savings and value may be increased with optimized storage rating and dispatch strategy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.023
GPT teacher head0.243
Teacher spread0.221 · 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 designObservational
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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicSmart Grid Energy Management→French-language works237,207→