Solar Roof Top Generation, Marginal Cost, Financial Impacts on the Utility of Sri Lanka
Bibliographic record
Abstract
The intensity of solar radiation in Sri Lanka is 1,247-2,106 kWh/m2 per annum (SEA, 2014). There are existing solar generation capacities of 177 MW by using solar roof top systems and 51 MW of the utility scale solar plants in the country as at 28th March 2019. The Government of Sri Lanka(GOSL) introduce Building Integrated Photo Voltaic program since 2009 basically to bank the surplus of electricity units with the Utility. In 2016 GOSL introduced cash payback method for surplus energy generated by Roof Top Solar installations. Some of the stakeholders of the electricity sector argue that the Roof Top Solar generation program has negative financial impact on the financial position of the utility. The impact of the Solar Roof Top program on revenue of the Utility and the customer tariff system has been studied. Results show that Feed in Tariff of the Solar Roof Top is comparatively low with most of the thermal power generation. According to the findings of the study it can be concluded that the financial impact of the program is beneficial to the economy as a whole, but marginally negative to the short terms cash flow of the utility. Anyhow it is seen that such utility centric negativity can be ameliorated though due tariff structure. The government has to consider about the electricity policy of customer tariff in order to provide the concessions only for the needy people.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".