Market Participation of Energy Storage Systems for Frequency Regulation Service: A Bi-level Model
Bibliographic record
Abstract
This paper examines the prospect of using the energy storage systems (ESSs) in the distribution network for frequency regulation service under the two-settlement market mechanism. A bi-level problem is formulated to determine the bidding strategy for the ESS which provides regulation service for the system operator in the day-ahead and real-time markets, where the upper-level problem maximizes the ESS' revenue from frequency regulation and the lower-level problem models the system operator's market clearing. The problem is rendered applicable for the ESSs in the distribution network by addressing the power flow constraints. The uncertainty associated with other competitive ESSs and the system frequency deviations are incorporated by using scenarios for possible realizations. The formulated problem is transformed to a mixed-integer linear program by replacing the lower-level problem with the Karush-Kuhn-Tucker (KKT) optimality conditions and tackling the nonconvexity in the objective function based on strong duality. Case studies are carried out on an IEEE 37-bus test feeder by using market data from California Independent System Operator (CAISO). The results demonstrate that the ESS can increase its revenue from frequency regulation by using our proposed method to determine the bidding strategy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".