Evaluating Electric Vehicles’ Response Time to Regulation Signals in Smart Grids
Bibliographic record
Abstract
Electric vehicles (EVs) are considered as a flexible load in smart grids. This flexibility promotes the EVs to be good candidates for providing the grid with ancillary services, such as regulation services. A group of EVs controlled by an aggregator can collectively work as a regulation reserve in the electric grid. However, a fast response to the regulation commands is crucial to providing reliable regulation service. Typically, the response time to the regulation command requires to be less than four seconds. In this paper, we precisely evaluate the expected time delay from the instant when an aggregator server sends a regulation command to n EVs to the instant when all the EVs' responses are received successfully by the server. To achieve this goal, first, a realistic communication structure between the aggregator server and the EVs is considered. Second, the wireless link between the access point (AP) and the EVs is accurately modeled in order to estimate the average delay. The model is based on Markov chain representation for the wireless IEEE 802.11 MAC protocol. Two important factors are considered in this model. First, the packet loss probability due to the lossy wireless environment has been incorporated into the model. Second, the transition stages for the contention window size of 802.11 MAC protocol to reach the saturation stage is taken into account. The model has been validated by means of extensive simulation using the well-known Network Simulator 2 (NS2) tool. Our analysis shows that one AP is capable of handling up to 1000 EVs without violating the 4-s latency limit when the probability of packet loss is 0.01. However, this number decreases significantly, less than 500 EVs, when the wireless link is experiencing a significant packet loss probability of 0.2. Further, we show that by commanding a subgroup of the charging EVs, it is possible to achieve the same regulation service but with a lesser response time, compared to commanding all charging EVs every time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".