Analysis of Quantized Double Auctions with Application to Competitive Electricity Markets
Bibliographic record
Abstract
In recently proposed electricity markets, price-based competitive behaviours of power suppliers (i.e., generators), energy service providers and large users (i.e., consumers) have been formulated using various auction algorithms (see Post et al., 1995; Wolfram, 1998; Dekrajangpetch and Shebl, 2000; Nicolaisen et al., 2001; Swider and Weber, 2007). In this paper, quantized Progressive Second Price (PSP) auction algorithms are presented for competitive electricity systems, especially for short-run electric power markets. In Jia and Caines (2008, 2010), two quantized PSP auction algorithms were introduced and analyzed for demand markets, which are called, respectively, the Aggressive-Defensive Quantized PSP (ADQ-PSP) algorithm and the Unique-limit Quantized PSP (UQ-PSP) algorithm. Here we first present an algorithm combined with ADQ-PSP and UQ-PSP features, and apply it to a double power auction system where competition on both power generators and energy service providers (and/or large users) is considered. Double auctions are formulated in this work as two single-sided quantized auctions which depend upon joint market quantities and price constraints. The extended algorithm inherits the performance properties of ADQ-PSP and UQ-PSP in terms of both the social welfare maximization and the rapid convergence rate.
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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 | 0.002 | 0.012 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".