DT Duality Theory FD Finite Difference
Bibliographic record
Abstract
Abstract—Large scale solar photovoltaic (PV) generation is now a viable, economically feasible and clean energy supply option. Incentive schemes, such as the Feed-in-Tariff (FIT) in Ontario, have attracted large-scale investments in solar PV generation. In a previous work, the authors presented an investor-oriented planning model for optimum selection of solar PV investment decisions. In this paper, a method for determining the sensitivity indices, based on the application of duality theory on the Karush-Kuhn-Tucker (KKT) optimality conditions, pertaining to the solar PV investment model is presented. The sensitivity of the investors ’ profit to various parameters, for a case study in Ontario-Canada are presented and discussed and these are found to be very close to those obtained using the Monte Carlo simulation and finite-difference (individual parameter perturbation) based approaches. Furthermore, a novel relationship is proposed between the sensitivity indices and the investor’s profit for a given confidence level to evaluate the risk for an investor in solar PV projects. Index Terms—Solar photovoltaic, investor planning, sensitivity indices, duality theory, risk assessment I. Nomenclature The main notations used throughout the paper are stated below for quick reference. In the paper, matrices are in boldface, vectors are in bold and italics, while scalar quantities are in italics. Subscripts and superscripts base Base case optimization output
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".