Enhancing Flexibility in V2B Applications with Renewable Energy Resources

  • Maximiliano Trimboli
  • Nicolás Antonelli
  • Luis Avila
Palabras clave: : Electric vehicles · Smart Charging · Renewable Energy · Reinforcement Learning., Electric vehicles, Smart Charging, Reinforcement Learning, Renewable Energy

Resumen

The incorporation of EV parking within vehicle-to-building (V2B) frameworks signifies not only a technological evolution but also a pivotal step towards constructing smarter and environmentally friendly urban environments. This initiative actively contributes to the optimization of system resources while also enabling the incorporation of renewable energy resources. In this study, we propose the development of reinforcement learning (RL) algorithms for the management of smart parking lots, aiming to minimize building energy purchases from the grid while ensuring efficient charging of EVs. The proposed methods obtained a 15% to 17% improvement in the evaluation reward in comparison with rule based method as a benchmark. In the realm of grid energy, they saved 9 to 11% in average purchase cost. In essence, these algorithms, after training, make more efficient decisions than more traditional control methods while ensuring electric vehicle (EV) charging.

Publicado
2024-09-19
Cómo citar
Trimboli, M., Antonelli, N., & Avila, L. (2024). Enhancing Flexibility in V2B Applications with Renewable Energy Resources. Memorias De Las JAIIO, 10(1), 223-236. Recuperado a partir de https://ojs.sadio.org.ar/index.php/JAIIO/article/view/1027
Sección
ASAID - Simposio Argentino de Inteligencia Artificial y Ciencias de Datos