ev2gym 0.0.12

A realistic V2X environment using gym

ev2gym

A realistic V2X Simulation Environment for large scale EV charging optimization!

Features

• The simulator can be used to evaluate any type of algorithm to gain insights into its efficiency.
• The “gym environment” can readily support the development of RL algorithms.
• Uses only open-source data.
• Replays of simulations are saved and can be solved optimally using the Gurobi Solver.
• Easy to incorporate additional functionality for any use-case.
• Does not simulate the grid yet, but groups EV chargers at the level of the transformer/ parking lot, etc, so extra functionality can be easily added.

Focused on realistic parameters and fully customizable:

• Transformer models
  • Max Current
• Charging Stations models
  • Min and Max charge/discharge power/ Current
  • Voltage and phases, AC or DC
  • Charge and discharge efficiency
  • List of connected transformers
• Electric Vehicles models
  • Connected charging station and port
  • Min and Max battery energy level
  • Time of arrival and departure
  • Energy at arrival/ desired energy at departure
  • Min and Max current /power levels
  • Constant-Current/ Constant-Voltage load-curve option

Data sources

• The number and the topology of Transformers, Charging stations, and Electric Vehicles are parameterizable.
• Charging/ Discharging prices are based on historical day-ahead prices.
• EV spawn rate, time of stay, and energy required are based on realistic distributions ElaadNL,time, day, month and year.
• EV and Charger characteristics are based on real EVs and chargers existing in NL.

File Structure

├── ev2gym
│   ├── __init__.py
│   ├── baselines
│   │   ├── __init__.py
│   │   ├── DDPG
│   │   ├── DT
│   │   ├── gurobi_models

Citation

If you use this code in your research, please cite it using the following BibTeX entry:

@misc{ev2gym,

}

License

This project is licensed under the MIT License - see the LICENSE.md file for details