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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