arXiv Machine Learning By Chunyang Zhao, Stoyan Trenchev, Shi You, Chresten Tr{\ae}holt

Optimizing Multi-Market Participation of Battery and Electrolyser Systems Based on Field Performance

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arXiv:2608. 16238v1 Announce Type: new Abstract: The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability.

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arXiv Machine Learning
Aug 20

Optimizing Energy Efficiency and Grid Stability via Public EV Charging Flexibility

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.

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
arXiv AI
Sep 21

CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities

CityLearn v3 is a configurable simulation and evaluation framework designed for realistic control studies of renewable energy communities (RECs). It models dynamic participation, equipment availability, service deadlines, and data quality, allowing for flexible-load deadlines, demand-response requests, local energy sharing, and failure scenarios within a single environment. The framework records controller inputs, distinguishes requested actions from applied ones, and provides reference controllers, performance indicators, and trajectory exports for comprehensive comparisons across communities.

By Tiago Fonseca, Luis Lino Ferreira, Armando Sousa, Ava Mohammadi, Zoltan Nagy