Bibliographic record
Abstract
Presently, economic Demand Response (DR) is procured based solely on energy market characteristics. There is no recognition of DR’s full and unusual impacts on energy markets, and lack of recognition of DR suppliers’ surplus. This incomplete understanding results in inefficient market outcomes and waste. In 2011 through Order 745, the Federal Energy Regulatory Commission (FERC) in the United States partially addressed some of these issues by mandating the Net Benefits Test (NBT), wherein DR can be purchased only when the benefits exceed the costs for consumers. While this shields consumers from harm, this does not yield the most efficient economic results. The ideas in this thesis completes the picture for economic DR. First, a new metric, Actual Price, is defined and used for a system-wide scheduling implementation with DR. Second, a new method to integrate NBT into a real-time optimal power flow (OPF) formulation that accounts for locational pricing is presented. Third, a new zonal planning method for DR procurement is offered, including the introduction of the DR market, which operates alongside the energy market. Lastly, the energy and DR markets are co-optimized in a real-time OPF formulation. The most significant contribution is the creation of a new concept: the DR market. While the DR market has always existed, the lack of recognition of its existence and absence of its model alongside the traditional energy market leads to the optimization of the energy market’s social welfare while ignoring the DR market’s social welfare. The omission of the DR market until now meant that the full economic picture of DR was not seen. This led to sub-optimal outcomes and reduced total social welfare. These new methods were tested on modified IEEE test systems and on real-world systems, such as PJM and Ontario. Results were compared with existing methods in both literature and practice, demonstrating the superior performance of the methods introduced in this thesis. Through implementation of the DR market concept, the thesis reports maximization of total social welfare considering both energy and DR markets for the first time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".