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The Economics of Wind Power

2016· article· en· W2517073011 on OpenAlexafffund
G. Cornelis van Kooten

Bibliographic record

VenueAnnual Review of Resource Economics · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsWind powerElectricityStand-alone power systemEnvironmental economicsElectricity retailingDispatchable generationElectricity marketElectricity generationNatural resource economicsEconomicsCost of electricity by sourceInvestment (military)Electricity pricingIncentiveBusinessRenewable energyEnvironmental scienceDistributed generationMicroeconomicsPower (physics)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Many countries have incentivized wind power projects to reduce their reliance on fossil fuels for generating electricity. As shown in this review, the benefits and costs of integrating electricity from an intermittent wind source into a preexisting electricity grid depend on the operating protocols of the electricity system, the preexisting generation mix, wind profiles, and the nature of economic incentives. Electricity systems are discussed from generation through transmission and distribution to retail demand, including how wind energy impacts investment in marginal (peak time) generating assets. The discussion also examines issues that could limit the usefulness of wind power at the high penetration rates now envisioned: the inability to store electricity, the need for fast-responding backup-generating capacity, network instability, low-capacity factors, and inappropriate incentives. Overall, this review finds that the costs of wind power likely exceed the benefits and that there may be limits to the proportion of electricity that can be generated by wind.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.003
GPT teacher head0.185
Teacher spread0.182 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations27
Published2016
Admission routes2
Has abstractyes

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