Chance-Constrained Frequency Regulation with Energy Storage Systems in Distribution Networks
Bibliographic record
Abstract
One of the applications of energy storage systems (ESSs) is to support frequency regulation in power systems. In this paper, we consider such an application and address the challenges of uncertain frequency changes, limited energy storage, as well as distribution network constraints. We formulate a bi-level optimization problem that includes the operation objectives of the system operator and the ESSs, using chance constraints to account for uncertain frequency changes. The frequency regulation decision of the system operator depends on the ESSs decision to participate in the regulation service as well as the distribution network constraints. Due to the interdependencies between the ESSs demand fluctuations and the distribution network power flow changes, the system operator requires the ESSs' operation information for frequency regulation decisions, which may not be available from the ESSs. Therefore, we propose a decentralized algorithm such that the system operator and the ESSs can pursue their own operation objectives, while ensuring the distribution network constraints are satisfied. We evaluate the performance of our method on IEEE 37-bus and 123-bus test feeders by considering combinations of ESSs with different sizes. Simulation results demonstrate that our approach can successfully coordinate the ESSs to regulate the frequency deviations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".