Analysis of Quantized Double Auctions with Application to Competitive Electricity Markets
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it