Electric_Vehicle_Charging_Simulation
Electric_Vehicle_Charging_Simulation copied to clipboard
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 model...
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