A method for optimal pricing of electric supply including transmission system considerations
Bibliographic record
Abstract
Determining real-time electricity rate structures is currently receiving a great deal of attention. In this paper, a strategy for pricing electricity supply is formulated and evaluated. Unlike other methods, which use only the variation of fuel cost for generation to estimate the rate structures, the proposed pricing algorithm incorporates the optimal allocation of transmission system operating costs based on time-of-use pricing. The transmission costs are obtained by assigning a price to each unit of power flow in the network. The assignment does not discriminate between participants located at differing parts of the network. The real-time pricing reflects the instantaneous cost of production and functions as a load management tool because this interacts with consumer behavior. The demand for power flows and transmission on an electricity supply system, like the demand for any bundle of economic goods, depends upon the assigned transmission prices, together with the economic benefit to the consumer. It is assumed that there are no privately owned generating plants and that all plants and transmission lines are operated by the utility. The modeling scheme is applied to the IEEE standard 5, 14, 30 and 57 bus power systems and involves solving a modified optimal power flow problem iteratively using the MINOS package. It is concluded that the method has wide potential application in electricity supply pricing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".