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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.
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.
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.