Integrating Net Benefits Test for Demand Response Into Optimal Power Flow Formulation
Bibliographic record
Abstract
In 2011, the Federal Energy Regulatory Commission (FERC) mandated that demand response (DR) procurement pass a net benefits test (NBT) in Order 745 to ensure that the benefits outweighed the costs. Without NBT, DR procurement could result in losses to consumers considering payments for energy and DR services. Current NBT implementations are monthly. However, this neglects to consider two important issues: (a) generator supply curve and demand changes on an hourly basis thus altering the maximum DR quantity that can be dispatched considering NBT; and (b) system-wide implementations don't consider locational marginal prices, line flow limits, and network congestion. Both of these issues can severely distort proper implementation of the NBT. However, FERC allowed the NBT be applied monthly and system-wide in recognition of computational difficulties and lack of methods, and FERC called for research in real-time implementations of NBT. Our work responds to this call. To address these two shortcomings, we propose a new optimal power flow (OPF) formulation in which NBT is implemented into a real-time or near real-time dispatch OPF model and considers the network model in its entirety. Our proposed methodology is applied to a 4-bus test system, a 118-bus modified IEEE system, and a real Ontario system. We demonstrate that the embedding of NBT into real-time OPF formulation yields maximum benefits to remaining consumers when compared to conventional DR formulations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".