Demand response procurement framework: a new four‐step probabilistic method
Bibliographic record
Abstract
This study presents a new market‐driven and transparent pricing mechanism for demand response (DR) that ensures social welfare is maximised. The existing methods have DR priced at the electricity market clearing price (EMCP), where the EMCP is determined in a market solely comprised of aggregated generator price bids and demand bids. DR supply bids are not included in the current economic market model, resulting in inefficient markets. The authors also present a new metric, Actual Price, which captures two key elements missed by EMCP: (a) the price paid to DR suppliers (EMCP covers only the price paid to generators); and (b) the reduced pool of paying consumers when DR suppliers leave the buyer pool. An implementable process for DR planning using the authors’ new concepts is presented. Results are shown for systems with and without location pricing. The results demonstrate that the proposed DR procurement method yields lower Actual Prices than existing methods and results in savings for customers. These ideas can guide regulators in determining market‐based pricing policies for DR as well as Independent System Operators and system operators in determining DR procurement levels.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".