Cost-aware Data Aggregation and Energy Decentralization with Electrical Vehicles in Microgrids through LTE Links
Bibliographic record
Abstract
Microgrids are the building blocks of resilient and sustainable smart cities. In remote areas, well-designed and managed microgrids can operate as standalone mode. Since renewable energy causes more intermittency to the electricity grid, interruptions to the electricity supply due to uncertain weather condition or supply shortage to charge Electrical Vehicles (EVs) in microgrids reveals extra cost especially during the peak hours. EVs can be utilized to stand this temporary circumstances with the help of decentralized services by supplying the excess electricity which is stored in the EV batteries to other microgrids. Dynamic electricity prices which are determined by smart grids are necessary to manage and control this process. The reliability of the communication link between eNodeB’s and EVs plays an important role to ensure successful delivery of the transmitted data during data aggregation process in microgrids. In this paper, the interaction between smart microgrids (i.e. buyer) and electrical vehicles (i.e. seller) for short-term power supply, and formulate a Mixed Integer Linear Programming (MILP) to obtain the best set of EVs in order to obtain cost-aware solution. In addition, considering the EV-microgrid integration over LTE links, we demonstrate the impact of the physical downlink shared channel (PDSCH) throughput performance on the following outputs of the model: total cost, the outstanding energy and the electrical vehicle’s revenue. We simulate the throughput performance in a multiuser multiple-input multiple-output (MU-MIMO) scenario.
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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 | 0.000 | 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".