Risk-averse forward contract for electric vehicle frequency regulation service
Bibliographic record
Abstract
Electric vehicles (EVs) can be coordinated by an aggregator to participate in the electricity day-ahead market (DAM) and provide frequency regulation service. In the DAM, a short-term forward contract is made between the aggregator and an independent system operator (ISO). The contract specifies the contract size, which is the amount of regulation capacity provided by the aggregator for each hour of the next day. However, since the capacity of the aggregator is provided by many EVs instead of a single source, the challenge is how to efficiently aggregate the small and uncertain individual capacity and determine the optimal size of the forward contract. We consider two cases for the contract between the aggregator and the ISO. In the first case, the aggregator needs to ensure that the capacity provided by the EVs on the next day will meet the amount specified in the contract. In contrast, in the second case, the ISO allows an update of the contract size. A stochastic program is formulated to determine the contract size for both cases. Our problem formulation incorporates risk management using the conditional value at risk (CVaR). Chance constraints are embedded when the contract size is fixed. We tackle the chance constraints using the Markov inequality and propose an efficient algorithm. The EV charging data collected in the province of British Columbia, Canada, is used to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm improves the revenue of the aggregator compared to an existing algorithm from the literature.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".