MODELS OF EFFICIENT CONSUMER PRICING SCHEMES IN ELECTRICITY MARKETS
Bibliographic record
Abstract
Suppliers in competitive electricity markets regularly respond to prices that change hour by hour or even more frequently, but most consumers respond to price changes on a very different time scale, i. e. they observe and respond to changes in price as reflected on their monthly bills. This thesis examines mixed complementarity programming models of equilibrium that can bridge the speed of response gap between suppliers and consumers, yet adhere to the principle of marginal cost pricing of electricity. It develops a computable equilibrium model to estimate the time-of-use (TOU) prices that can be used in retail electricity markets. An optimization model for the supply side of the electricity market, combined with a price-responsive geometric distributed lagged demand function, computes the TOU prices that satisfy the equilibrium conditions. Monthly load duration curves are approximated and discretized in the context of the supplier's optimization model. The models are formulated and solved by the mixed complementarity problem approach. It is intended that the models will be useful (a) in the regular exercise of setting consumer prices (i. e. , TOU prices that reflect the marginal cost of electricity) by a regulatory body (e. g. , Ontario Energy Board) for jurisdictions (e. g. , Ontario) where consumers' prices are regulated, but suppliers offer into a competitive market, (b) for forecasting in markets without price regulation, but where consumers pay a weighted average of wholesale price, (c) in evaluation of the policies regarding time-of-use pricing compared to the single pricing, and (d) in assessment of the welfare changes due to the implementation of TOU prices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".