Optimal allocation of demand response considering transmission system congestion
Bibliographic record
Abstract
Abstract The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed nature throughout the grid. Aggregators can be created to manage a set of such demand response resources, but deciding how to allocate an aggregator’s resources is an important problem. One of the aspects that needs more attention is the impact of the transmission system on these decisions. In this paper, we propose a short-term optimization model for allocating demand response(DR) resources as well as generation resources to supply external demand that is offered after the scheduling decision is made. The DR resources will only be available for use after the scheduling decision is made. Finally, our work also considers the impact of congestion in the transmission system when allocating DR. We propose the use of a semidefinite relaxation to provide a good initial point to solve our model with the aim of guaranteeing that we will find an optimal solution. Results from numerical tests with the IEEE 96-RTS and the ACTIVSG500 test grids are reported. We found that DR resources mitigates congestion management, allowing the generators to supply more of the external demand that is offered. Besides that, we observe that using our proposed solution methodology, we were able to obtain optimal solution for both cases studies, which is not the case when solving the original formulation for the ACTIVSG500 grid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".