Optimization of Aggregate Capacity of PEVs for Frequency Regulation Service in Day-Ahead Market
Bibliographic record
Abstract
An aggregator can coordinate plug-in electric vehicles (PEVs) to provide frequency regulation service to an independent system operator (ISO). The aggregator can participate in the electricity markets of ISOs which provide economic incentives for PEV frequency regulation service. While the ISOs typically use forward market [e.g., day-ahead market (DAM)] to trade frequency regulation service, the available regulation capacity of an aggregator is subject to the random arrival and departure of the PEVs. In the DAM, the aggregator submits a bid to indicate its available capacity on the next day. This motivates us to study the problem of how an aggregator determines its bid in the DAM, given the uncertainty of the available regulation capacity of the PEVs. The DAM is used to trade the frequency regulation capacity in California ISO (CAISO) and New York ISO (NYISO). We consider two types of DAMs based on the market rules of CAISO and NYISO. For the first type, the exact amount of regulation capacity submitted in the DAM needs to be fulfilled on the next day. For the second type, a market participant can settle a shortage of capacity by paying a penalty to the ISO. In both cases, the aggregator can participate in the real-time market to sell extra capacity on the next day. We formulate the problem for determining the bid using stochastic programming. As PEVs have uncertain arrival and departure times, our problem formulation incorporates risk management using the conditional value at risk. Efficient algorithms are proposed for solving the formulated problem. PEV charging data collected in Vancouver, BC, Canada, is used in our simulations. We compare the profit of the aggregator when it participates in the markets of CAISO and NYISO. Our simulation results show that the uncertainty of the PEVs' available capacity has less effect on the profit and financial risk as the number of PEVs increases.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".