Hybrid Planning Tool for Solar and Battery Systems in Ontario
Bibliographic record
Abstract
There is increasing momentum for behind-the-meter renewable generation and storage installations due to an increased focus on limiting the environmental impact of electricity generation. Local energy policy influences the sizing and financial viability of these systems while also serving to promote the smart uptake of these technologies on the electricity grid. This paper proposes a methodology for optimal infrastructure sizing of small-scale solar photovoltaic generation and battery energy storage technologies required to become grid neutral under a net metering energy contract. A robust linear programming model is proposed, and probabilistic robustness guarantees are provided by manipulating the magnitude and frequency of uncertainty realizations using a budget of uncertainty approach. A practical test case is performed in Toronto, Ontario, and the results reveal that a 99% robustness guarantee requires additional infrastructure capital costs of $6,700 (26%) over the purely deterministic scenario investment of $25,700. Furthermore, it is shown that the net metering policy does not provide sufficient financial inventive to Ontario homeowners, and project costs exceed benefits by between $4,400 and $9,200 depending on robustness. Finally, Ontario net metering policy in its current form does not incentivize energy storage, and instead relies on the electricity grid as a free and lossless storage device-a practice which is likely unsustainable. Future work is available to enhance the existing methodology or leverage the proposed methodology for application to new fields of research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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.
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".