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A realistic V2X environment using gym

Project description

EVsSimulator

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

├── EVsSimulator
│   ├── __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{EVsSimulator,

}

License

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


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