New Real-Time Demand Response Market Co-Optimized With Conventional Energy Market
Bibliographic record
Abstract
In addition to procuring energy, consumers in electricity markets procure demand response (DR) services. Demand and supply of energy in the electricity market drives the demand for DR services. Through the net benefits test (NBT), economic procurement of DR is limited to an amount that ensures that consumers benefit with the procurement of DR services. However, the NBT neither a) recognizes the coexistence of the DR market with the energy market; nor b) optimizes social welfare in the DR market in concert with that of the energy market. This lack of accounting for DR market surplus results in economic inefficiency. To address this shortcoming, we advance past works by: a) proposing a real-time DR market where the DR demand curve is a function of opportunity in the energy market; and b) co-optimizing energy and DR markets such that the total social welfare derived from both markets is maximized simultaneously. We also present an optimal power flow formulation and process to implement our ideas in real-time electricity markets. The formulation is tested on a simple test case and a system based on actual Pennsylvania-New Jersey-Maryland (PJM) data. For the PJM case, total social welfare is increased by 1.41% to 3.05% over existing DR procurement strategies, resulting in $14.5M to $30.9M additional benefits per hour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".