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.

Free Wireshark plugin to analyze ISO15118 V2G communication between electric vehicles (EV) and charging stations (EVSE).

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.

The goal of this demo is to illustrate Vehicle-to-grid (V2G) technology in simple multiplayer browser game

Fork from the Martin-P/OpenV2G with the goal to provide an universal command line interface. This is used in the python part of the CCS project, see https://github.com/uhi22/pyPLC.

Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) transient stability simulations conducted on a modified IEEE-3 bus case

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.