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Record W3092255537

The Water-Energy Nexus â A Modern Case Study to Reassess Hydropower in the Niagara River

2017· dissertation· en· W3092255537 on OpenAlexaboutno aff
Samiha Tahseen

Bibliographic record

VenueTSpace · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)HydropowerWater energyWater-energy nexusWater resource managementHydrology (agriculture)GeographyEngineeringEnvironmental scienceGeotechnical engineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The advent of variable renewable energy has created an urgent need for demand-based generation and storage. At present, with batteries still awaiting a major technological breakthrough, hydropower combined with pumped storage is suggested as a key response to demand variability. Even overlooking the technical demands, developing this potential resource in a sustainable way presents formidable challenges. While sustainability is a concern, the vulnerability of the resource to changing climatic conditions poses a major threat. The present study proposes five modelling approaches (and/or frameworks) as a foundation to a systems approach to hydropower and shows how these tools address the key challenges. Overall, the research addresses the current demand for dispatchable generation particularly in Ontario and proposes several alternatives including their sustainability assessment. Of the five models, the first two explore a variety of remuneration structures for pumped storage in the context of Ontario. The work begins with an optimization approach that evaluates the wholesale market for optimal profit. The tradeoff between hydropower and ecological targets is explored using a Constraint Method. The results are compared with models based on contracted price and an integrated valuation approach that accounts for the socioeconomic attributes of storage using representative applications. While the first two approaches concentrate on the project economies, the third model evaluates the potential for increased hydroelectric generation and assesses its vulnerability scenarios of climate change. A 1D simulation model of the existing power system at Niagara is used to evaluate a variety of innovative operating plans. One such scenario includes a revised approach to daily operation with use of additional storage during the night and timed release during peak demand hours. The final section seeks to improve the existing frameworks for sustainability assessment and then to use these improved metrics to evaluate various proposed generation options. The developed decision support framework, applied to Niagara, allows quantitative evaluation based on survey responses from key stakeholders. In contrast, the fifth and final approach uses the concept of resilience within probabilistic graphical model to account for the inherent uncertainty associated with climate projections. Overall, the five approaches (optimization, integrated valuation, simulation, sustainability and resilience assessment) facilitate rethinking the hydropower system with changing circumstances and subsequent shift in priorities by the development, analysis, and interpretation of models. This thesis contributes towards evaluating the merits of transitions between these approaches for future modelling applications.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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
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
Published2017
Admission routes1
Has abstractyes

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