This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach" by Artur Grigorev, Tuo Mao, Adam Berry, Joachim Tan, Loki Purushothaman, Adriana-Simona Mihaita. The paper has been published and presented during the IEEE ITSC 2021 conference. The preprint is available: https://arxiv.org/abs/2110.14064 .
You can find a working queue model in "queue_model.py" file.
This EV charging station queue simulation program reads file "Northern_Sydney_EV_charger_list.csv" and outputs queue simulation results into file "q2080_2016_seq.csv". It relies on multiprocessing package to perform parallel simulation.
- Duration of modeling (day, week, month)
- Number of plugs on EV stations
- Distribution of time intervals between arrivals
- Distribution of charging time: normaly distributed between 20% and 80%.
- Max queue size
- Power supply at EV charger: KW/h
- (O1) Mean queue length of an EV station [n]'] = HOURQUEUE[i]
- (O2) Mean waiting time in queue at an EV station [hours]
- (O3) Mean service time to charge at an EV station [hours]
- (O4) Total time spent overall at an EV station [hours]
- (O5) Total energy consumption of an EV station [kWh]
- (O6) Maximum recorded queue length of an EV station [n]
- (O7) Maximum waiting time in queue at an EV station [hours]
- (O8) Maximum time spent overall at an EV station [hours]
- (O9) Maximal energy consumption of an EV station [kW]
- Consumed electricity by hour [kWh]
- Total waiting time (minutes) by hour
- Overall Mean Service time/day'
To perform calculations for specific OD traffic flow (2016, OD15, OD30) change the line: DICT['StationFlow'] = float(dt[dt.Name==N]['2016 volume']) at the "Setup" section (to 2016, 15 or 30).
The structure of the framework:
The code to produce lineplots is in "lineplots.ipynb":
The code to produce supplementary animation is in "anim.ipynb":
