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arXiv Machine Learning By Marek Miltner, Artem Bryksa, Ond\v{r}ej \v{S}togl, Daniel Va\v{s}ata, Magda Friedjungov\'a, Ram Rajagopal, Old\v{r}ich Star\'y

Optimizing Energy Efficiency and Grid Stability via Public EV Charging Flexibility

Read the original on arXiv Machine Learning →

This study evaluates how flexible electric vehicle charging can improve energy efficiency and grid stability. Using real‑world data from public charging stations in Prague, the authors analyze individual and aggregated charging sessions to show that optimizing charging times reduces energy waste and grid imbalances. By aligning EV charging with periods of lower demand and higher renewable generation, they demonstrate a significant improvement in energy efficiency and a reduced need for costly system support.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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