Adaptive frequency optimization control strategy of electric vehicles participation in energy storage considering user active response margin
Bibliographic record
Abstract
Electric vehicles have steadily emerged as important resources for flexible frequency management in the power grid due to their energy storage capabilities and quick ability to respond. To address a series of operational issues arising from the large percentage of distributed power supply connected to the distribution network, an adaptive frequency optimization control strategy is proposed for EVs participating in energy storage, taking into account the user’s active response margin. Firstly, based on the vehicle’s dynamic features, the user active response margin is suggested to fully utilize the EVs’ participating energy storage capacity. Secondly, the framework of frequency optimization control proposes margin adaptive droop control. It refers to the exploration of the potential storage capacity margin of EVs, enabling the power grid dispatching center to automatically adjust EVs’ charging and discharging power according to the active response of users, thereby achieving the purpose of stabilizing the power grid frequency. On this basis, combined with the improved automatic generation control link, the power distribution between the EV and each generator set is optimized to form an adaptive frequency optimization control strategy. Finally, as shown in the simulation results, the proposed strategy can lessen the frequency shift from various dimensions, resulting in a 73.2% decrease in overshoot and an increase of 5.86% and 0.40% in average rise time and average settling time, respectively. Meanwhile, EVs’ energy storage capacity is enhanced, and user frequency regulation’s incentive revenue has risen by 62.97%. This strategy can be applied in the frequency regulation pilot of virtual power plants and has practical application value for building a new type of power system integrating power sources, grids, loads and storage. • The user active response margin is presented to the energy storage structure of EV participation. • Propose the margin adaptive droop control in the frequency control framework. • Use the adaptive particle swarm optimization algorithm to optimize the parameters of automatic generation control link.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".