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Capabilities of Photovoltaic Solar and Battery Energy Storage Systems in Supporting the Power Grid

2024· article· en· W4404411323 on OpenAlexaff
R. J. Sanderson, Marianne Rodgers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsWind Energy Institute of Canada
Fundersnot available
KeywordsPhotovoltaic systemEnergy storageRooftop photovoltaic power stationGrid-connected photovoltaic power systemGridBattery storagePhotovoltaic mounting systemBattery (electricity)Solar cableStand-alone power systemSolar energyPhotovoltaicsComputer scienceGrid energy storageElectrical engineeringPower (physics)Distributed generationEngineering physicsRenewable energyMaximum power point trackingPhotovoltaic thermal hybrid solar collectorEngineeringSolar mirrorPhysicsVoltage

Abstract

fetched live from OpenAlex

Increasing the amount of renewable energy generators on power grids can impact grid stability due to the renewable energy resource's variability and them supplanting conventional synchronous generation. While synchronous generators traditionally provide both energy and ancillary services, non-synchronous renewable energy generators typically provide only energy. The ability of renewable energy generators to overcome these challenges is critical to maintain grid stability. This work demonstrates the capabilities of a photovoltaic power plant and a battery energy storage system to provide a range of reliability services to the grid. Results from real world demonstrations help utilities and system operators realize the capabilities of the inverter-based energy sources to provide ancillary services and will guide them in the creation of markets for such services. While this work used simple controls and algorithms, results could improve if forecasts and more complex algorithms are incorporated. Nevertheless, the results from this demonstration serve as a proof-of-concept that can show transmission-connected equipment owners the capabilities of the technology.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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