Assessment of Factors Affecting Onshore Wind Power Deployment in India
Bibliographic record
Abstract
Abstract The Present study focuses on the impact of various factors on the growth of wind power generation in seven most wind energy prone states of India, that contain 97 % of India’s total wind power potential. The impact of state-wise policy parameters Feed-in Tariff (FIT) rate, Renewable Purchase Obligations (RPO) and Power Purchase Agreement (PPA) are evaluated in terms of aggregate policy indices that indicate the likelihood of wind power deployment in that state, through multivariate statistical analysis. The wind energy technology with reference to wind turbine specific policies, scaling of the project, the impact of hybrid policy, grid-related technological advancements and the improvement of capacity utilization factor (CUF) are discussed. Further, the impact of per capita net domestic product (PCNDP) and power demand-supply scenario are assessed. It was found that these two factors are non-influential on wind power growth. The outcome of the present study is that aggregate policy indices, captive/third party use of feasibility, presence of repowering policy, actual CUF obtained at the location, delay in cash flow and total available power potential are the factors that significantly influence the growth of cumulative installed capacity. This study provides an insight for policymakers for a quantitative assessment of the existing policies along with other factors and assists the project developers to compare and identify suitable locations for wind power projects in the near future. The exchange rate of 1 USD ($) = 69.38 INR (₹) has been taken throughout the manuscript.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".