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Record W2006900625 · doi:10.1109/pesmg.2013.6672570

A novel market simulation methodology on hydro storage

2013· article· en· W2006900625 on OpenAlexaboutno aff
Yang Gu, Jordan Bakke, Zheng Zhou, Dale Osborn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBiddingEconomic dispatchElectricity marketComputer sciencePower system simulationProduction (economics)Energy storageHydroelectricityEnergy marketEnvironmental economicsOperations researchElectricityReliability engineeringElectric power systemEconomicsMicroeconomicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a simulation methodology that evaluates the economic performance of hydro storage system in the day-ahead (DA) and real-time (RT) markets. A detailed hydro storage system model is proposed to capture the unique characteristics of hydro storage units. A long-term production cost simulation model is employed which performs chronological hourly Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) and cooptimizes both the energy market and the ancillary services market. Three MH RT bidding strategies are proposed to represent how MH bids to the RT market. A unique RT market bidding method named Tiered Bidding is proposed to represent hydro storage units' RT market offer curves. The proposed methodology is applied to the Eastern Interconnection system to evaluate the economic benefits of Manitoba Hydro's active market participation in MISO's RT energy and ancillary services market.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.032
GPT teacher head0.254
Teacher spread0.222 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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