Evaluation of hour rate effects for rural consumers using generation photovoltaic and/or biomass
Bibliographic record
Abstract
Low Voltage (LV) consumers are considered the most responsible for loading the electrical systems during peak hours by, increasing the expansion costs of the distribution system. To stimulate the efficient use of electricity, the National Electric Energy Agency (ANEEL) established a new tariff modality for low voltage consumers, the White Tariff, effective since January 2018. The White Tariff is an hourly rate with three different time schedules throughout a day. In addition to this new tariff modality, ANEEL established the conditions for access to distributed micro-generation and mini-generation since 2012 through the Normative Resolution (RN) No. 482, to encourage the inclusion of Distributed Generation (DG) in Low Voltage consumers. In this sense, the proposal of this dissertation is to present a study of the adhesion effects to the White Tariff for rural consumers using simultaneously photovoltaic and/or biomass energy sources, considering for this analysis the current energy tariffs and the typical load curves for each consumption range. The program Hybrid Optimization Model for Electric Renewables (HOMER) was used to perform the economic feasibility simulations and analysis and to make decisions in adhering or not to the White Tariff in conjunction with DG. Through this analysis, it was hoped to obtain an overview of the advantages and disadvantages that this new charging system compared to the conventional charging for rural consumers and the concessionaire, as well as the consumer's viability to join or not the renewable energy sources for insertion in distributed generation. Several configurations were evaluated under economic aspects and for different regions (Region South, Region Northeast and Northern Hemisphere). The results made it possible to identify which consumption ranges and regions would benefit the most through joining the White Tariff in conjunction with GD. It was concluded that out of the fifteen scenarios analyzed in only nine of these, the use of the Hourly Rate in conjunction with the DG became economically viable, with two scenarios for the South Region, two others for the Northeast Region and five scenarios for the Province of Ontario.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".