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Pumped Hydro Energy Storage: A Multi-Reservoir Continuous Supply Hydroelectric Generation and Storage System

2024· article· en· W4392981264 on OpenAlexaff
Mark R.J. Legacy, Emma Van Wyk, Joshua Brinkerhoff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsHydroelectricityEnergy storagePumped-storage hydroelectricityWater storageEnvironmental sciencePetroleum engineeringDistributed generationRenewable energyGeologyElectrical engineeringEngineeringPower (physics)PhysicsGeomorphology

Abstract

fetched live from OpenAlex

This paper presents a novel application of Pumped Storage Hydro (PSH) in which seawater and constructed reservoirs are used to generate renewable, gravitational potential energy. With the goal of net-zero carbon emissions by 2050, tapping hydropower as an alternative energy source is increasingly appealing to governments. The long duration storage system detailed in this paper boasts low grid reliance due to external tidal forces and solar/wind providing the necessary pumping power. Two constructed reservoirs stacked vertically differentiate this technology from traditional pumped storage hydro technologies. Penstock fed turbines generate electricity, while a pump situated at the lower reservoir increases the gravitational potential energy where it can be stored until needed. When released, the stored water creates electricity by driving a turbine, and returns to the ocean. In an open or closed loop system, the presented technology can continue creating electricity for a wide range of applications. The Long Duration Energy Storage (LDES) system presented is therefore applicable in many areas of the world and allows meaningful steps towards climate conscious energy production.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.733

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)0.0000.000

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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