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vehicle-to-grid

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This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is modelled as a multi-objective multi-agent cooperative game. Project is part of fulfillment criteria for ECE 730 course at the University of Alberta.

  • Updated Apr 11, 2022
  • Python
Reliable-frequency-regulation-through-vehicle-to-grid

Code and data for the article "Reliable frequency regulation through vehicle-to-grid: Encoding legislation with robust constraints" by Dirk Lauinger, François Vuille, and Daniel Kuhn available at https://pubsonline.informs.org/doi/10.1287/msom.2022.0154 and https://arxiv.org/pdf/2005.06042v4.pdf. This project was funded by the Institut VEDECOM.

  • Updated May 1, 2024

Smart Nanogrid Gym is an OpenAI Gym environment for simulation of a smart nanogrid incorporating renewable energy systems, battery energy storage systems, electric vehicle charging station, grid connection, a connected building and using vehicle-to-everything methodology.

  • Updated May 22, 2023
  • Jupyter Notebook

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