Optimal Incentive Design for Targeted Penetration of Renewable Energy Sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental concerns arising from fossil-fuel-based generation have propelled the integration of less-polluting energy sources in the generation portfolio and simultaneously have motivated increased energy conservation programs. In today's deregulated electricity market, most participants [e.g., GENCOs and local distribution companies (LDCs)] focus on maximizing their profits, and thus they need to be incentivized to invest in renewable generation and energy conservation, which are otherwise not profitable ventures. Therefore, this paper proposes a novel holistic generation expansion plan (GEP) model that enables the central planning authority (CPA) to design optimal incentive rates for renewable integration and energy conservation targets, considering the investor interests and constraints. The model also determines the siting, sizing, timing, and technology required to adequately supply the projected demand over the planning horizon. The model is applied to the generation planning of Ontario, Canada, based on realistic data, to determine appropriate incentives for investors in renewable generation and energy conservation by LDCs. The obtained optimal incentives are shown to be similar to the ones currently in place in Ontario, with a slightly shorter payback period for investors. The effect of uncertainties associated with solar and wind energy availability on the GEP model is also examined using Monte Carlo simulations.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it