Bibliographic record
Abstract
The electric utilities approach for restructuring the power market determines the failure or success of electric utility deregulation. It will cost billions of dollars, if restructuring is not done properly. Federal Energy Regulatory Commission (FERC) Order 2000 endorses competitive power markets, and price signals for the purpose of managing electricity grid congestion and achieving reliability. In a deregulated competitive electricity market, companies have to pay for the reactive power losses out of the revenues they earn. If the investors are reimbursed for reliability, there might be more investments. California deregulated in 1998 but the deregulated market was not structured efficiently and allowed some companies to manipulate the market by sending the power out of California and then reselling it back into the state. The utilities were not allowed long-term contracts and were required to sell many of their existing plants. California's experience is unique; in fact, when done well, the success stories in Pennsylvania, Ohio, Texas, England, and Japan show the benefit to both consumers and sellers from electric utility deregulation. Deregulation has been successful in New York, Virginia, and Ontario by protecting the customers from price volatility by price caps. By definition, price caps are not effective in a deregulated market, however, a price cap (i.e., a little regulation) to protect consumers in the transition period to deregulation is good. The price caps can be removed at a later date when the deregulated industry has matured like the power market in New Jersey. Circumstances like the August 14th, 2003 blackout in the northeast of the United States (not caused by deregulation) brought industry uncertainty to investors and consumers. Under deregulation, dispersed power generation (such as co-generation, biomass, microturbines, solar photovoltaic cells, wind turbines, fuel cells, geothermal, and diesel generators) is being promoted vigorously and more prominence is being put in the ancillary services and FACTS devices because of shortage of transmission lines in a deregulated power market. One of the results of economic deregulation of the electric power industry has been the development of a market for advanced nuclear power plants that will be cheaper to build and cheaper to run. In conclusion, by implementing a limited price control, the electric utility deregulation can be successful.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".