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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.

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avkhimen/Electric_Vehicle_Charging_Simulation

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Electric Vehicle Charging Simulation

To run the simulation make sure the AESO_2020_demand_price.csv file is in the same directory as the simulation.py file.

See the report here.

To run use:

python simulation.py --n --id_run --pen --scale
  • n: number of iterations, default 10 (int)\
  • id_run: file name to save results, default 'test' (str)\
  • pen: the market penetration of EVs, in number of EVs, default 0.1 (float)\
  • scale: the scaling factor for the model, default 1000 (int)

stats_output_with_v2g.py and stats_output_no_v2g.py are used to generate statistics after the model has been trained.

Requrements:

  • numpy
  • pandas
  • tqdm
  • argprase

About

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.

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