Review and analysis of wind and solar PV farms power outputs to meet Ontario hourly electricity demand with optimal sizing of PV farms, wind farms, and energy storage systems
Bibliographic record
Abstract
This study explores the feasibility of eliminating natural gas-based power generation from Ontario’s power grid by focusing on integrating renewable energy sources such as solar and wind energy. Due to the weather dependent nature of these energy sources, the integration of energy storage systems (ESS) into the power grid was also examined to ensure grid stability. Two types of ESS examined were the battery energy storage system (BESS) and pumped hydroelectric storage (PH). The analyses founds that the most cost-effective power generation configuration is to expand the current wind energy generation to 4 times of its current size, solar energy to 5.67 times, nuclear energy to 1.1 times and utilize 289 BESS units. Moreover, a 3-year long continuous analysis was performed to assess the configuration’s long-term stability and its adaptability to change in demand, it was found that BESS is better suited for wind energy due to its faster response time while solar energy favors PH as the energy storage solution due to having larger storage capacity.
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 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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".