Multi-Objective Power Management for EV Fleet With MMC-Based Integration Into Smart Grid
Bibliographic record
Abstract
Modular multilevel converter (MMC) is a possible solution for integrating an electric vehicle (EV) fleet into the smart grid. Considering the differences of initial state of charge (SOC) and the inherent randomness in charge/discharge demands from the various EVs connected to the MMC, it becomes challenging to control the MMC in order to satisfy the network and EV fleet requirements. This paper proposes a multi-objective power management strategy for a MMC-Based EV fleet to be integrated into the smart grid using a virtual SOC model and coordinated control of the power flow among the arms of the same and different phase-units by taking advantage of circulation current. The model of the MMC-based EV fleet system is developed in MATLAB/Simulink and simulation results show that the multi-objectives of differential charging/discharging power control and interactive power management between EVs and utility can be effectively realized, while the three-phase output power of MMC is kept in balance and circulation current is controlled, thus, mitigating the negative impact of large-scale application of EVs on the utility grid.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".