Simulation Model of a Node for Smart Grid Applications, Equipped with Photovoltaic Panel, Energy Storage and Electric Vehicle
Bibliographic record
Abstract
The improvement of renewable energy sources exploitation is a central topic of the present studies in the energetic field. One of the main obstacles to the renewable energy increase in the global energetic mix is represented by the fluctuating nature of renewable power sources. The power fluctuations are cause of stress for the electric grid to which the individual nodes are connected. A wider exploitation of renewable power is possible through the implementation of a “smart grid”, constituted by an interconnection of several smart nodes consisting in a user and a renewable source. To reduce the stochastic effects of the renewable energy source it is possible to introduce in the nodes composing the smart grid an energy storage, finalized to smooth the power peaks, rending the node itself less disturbing for the electric grid. This paper presents a simulation model of a smart node, consisting in a user power profile, a photovoltaic panel, an electric vehicle and a battery. Through the simulation model it is possible to evaluate the node effect on the electric grid, with a quantitative approach. Tests were worked out in different node configurations: basic (the only user power profile), basic with photovoltaic panel, basic with storage, basic with panel and storage and finally the complete configuration including also the electric vehicle. The tests were repeated for a domestic and an industrial user to evaluate the size influence over the parameters of interest
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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 | 0.002 | 0.002 |
| 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".