The Water-Energy Nexus â A Modern Case Study to Reassess Hydropower in the Niagara River
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".