EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging

Develop and evaluate any type of smart charging algorithm: from simple heuristics, Model Predictive Control, Mathematical Programming, to Reinforcement Learning!

EV2Gym is fully customizable and easily configurable!

The EV2Gym Paper can be found at: link.

The developed MPC algorithms Paper can be found at: link.

Installation

Install the package using pip:

pip install ev2gym

Run the example code below to get started ...

from ev2gym.models.ev2gym_env import EV2Gym
from ev2gym.baselines.mpc.V2GProfitMax import V2GProfitMaxOracle
from ev2gym.baselines.heuristics import ChargeAsFastAsPossible

config_file = "ev2gym/example_config_files/V2GProfitPlusLoads.yaml"

# Initialize the environment
env = EV2Gym(config_file=config_file,
              save_replay=True,
              save_plots=True)
state, _ = env.reset()
agent = V2GProfitMaxOracle(env,verbose=True) # optimal solution
#        or 
agent = ChargeAsFastAsPossible() # heuristic
for t in range(env.simulation_length):
    actions = agent.get_action(env) # get action from the agent/ algorithm
    new_state, reward, done, truncated, stats = env.step(actions)  # takes action

• For Reinforcement Learning:

To train an RL agent, using the StableBaselines3 library, you can use the following code:

import gymnasium as gym
from stable_baselines3 import PPO, A2C, DDPG, SAC, TD3
from sb3_contrib import TQC, TRPO, ARS, RecurrentPPO

from ev2gym.models.ev2gym_env import EV2Gym
# Choose a default reward function and state function or create your own!!!
from ev2gym.rl_agent.reward import profit_maximization, SquaredTrackingErrorReward, ProfitMax_TrPenalty_UserIncentives
from ev2gym.rl_agent.state import V2G_profit_max, PublicPST, V2G_profit_max_loads

config_file = "ev2gym/example_config_files/V2GProfitPlusLoads.yaml"
env = gym.make('EV2Gym-v1',
                config_file=config_file,
                reward_function=reward_function,
                state_function=state_function)
# Initialize the RL agent
model = DDPG("MlpPolicy", env)
# Train the agent
model.learn(total_timesteps=1_000_000,
            progress_bar=True)
# Evaluate the agent
env = model.get_env()
obs = env.reset()
stats = []
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)

    if done:
        stats.append(info)

!!! You can develop your own reward and state functions and use them in the environment.

Table of Contents

• Installation
• Overview
• Configuration File
• File Structure
• Citing
• License
• Contributing

Overview

• 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.
• 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.
• The number and the topology of Transformers, Charging stations, and Electric Vehicles are parameterizable.
• The user can import custom data.
• Uses only open-source data:
  • EV spawn rate, time of stay, and energy required are based on realistic probability distributions ElaadNL conditioned on time, day, month and year.
  • Pecan Street data is used for the load profiles.
  • Renewables Ninja data is used for the PV generation profiles.
  • EV and Charger characteristics are based on real EVs and chargers existing in NL (RVO Survey).
  • Charging/ Discharging prices are based on historical day-ahead prices from ENTSO-e.

Focused on realistic parameters and fully customizable:

• Power Transformer model:
  • Max Power Limit
  • Inflexible Loads, PV, Capacity Reduction events
• Charging Stations model:
  • Min and Max charge/discharge power/ Current
  • Voltage and phases, AC or DC
  • List of connected transformers
• Electric Vehicle model:
  • 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 power levels
  • Charge and discharge efficiency
  • Constant-Current/ Constant-Voltage load-curve option
• Battery Degradation model:
  • Cyclic aging
  • Calendar aging

An EV2Gym simulation comprises three phases: the configuration phase, which initializes the models; the simulation phase, which spans $T$ steps, during which the state of models like EVs and charging stations is updated according to the decision-making algorithm; and finally, in the last phase, the simulator generates evaluation metrics for comparisons, produces replay files for reproducibility, and generates real-time renders for evaluation.

Configuration File

The configuration file is used to set the parameters of the simulation. The configuration file is a YAML file that contains the following parameters:

##############################################################################
# Simulation Parameters
##############################################################################
timescale: 15 # in minutes per step
simulation_length: 96 #90 # in steps per simulation

##############################################################################
# Date and Time
##############################################################################
# Year, month, 
year: 2022 # 2015-2023
month: 1 # 1-12
day: 17 # 1-31
# Whether to get a random date every time the environment is reset
random_day: False # True or False
# Simulation Starting Hour and minute do not change after the environment has been reset
hour: 12 # Simulation starting hour (24 hour format)
minute: 0 # Simulation starting minute (0-59)
# Simulate weekdays, weekends, or both
simulation_days: both # weekdays, weekends, or both
# EV Spawn Behavior
scenario: public # public, private, or workplace
spawn_multiplier: 10 # 1 is default, the higher the number the more EVs spawn

##############################################################################
# Prices
##############################################################################
discharge_price_factor: 1.2 # how many times more abs(expensive/cheaper) it is to discharge than to charge

##############################################################################
# Charging Network
##############################################################################
v2g_enabled: True # True or False
number_of_charging_stations: 15
number_of_transformers: 3
number_of_ports_per_cs: 2
# Provide path if you want to load a specific charging topology,
# else write None for a randomized one with the above parameters
charging_network_topology: None #./config_files/charging_topology_10.json

##############################################################################
# Power Setpoints Settings
##############################################################################
# How much the power setpoints can vary in percentage compared to the nominal power
# The higher the number the easier it is to meet the power setpoints, the opposite for negative numbers
power_setpoint_flexiblity: 20 # (in percentage +/- %)

##############################################################################
# Inflexible Loads, Solar Generation, and Demand Response
##############################################################################
# Whether to include inflexible loads in the transformer power limit, such as residential loads
tr_seed: -1 # Seed for the transformer loads, -1 for random

inflexible_loads:
  include: False # True or False
  inflexible_loads_capacity_multiplier_mean: 0.8 # 1 is default, the higher the number the more inflexible loads
  forecast_mean: 100 # in percentage of load at time t%
  forecast_std: 0 # in percentage of load at time t%

# PV solar Power
solar_power:
  include: False # True or False
  solar_power_capacity_multiplier_mean: 2 # 1 is default, the higher the number the more solar power
  forecast_mean: 100 # in percentage of load at time t%
  forecast_std: 0 # in percentage of load at time t%

# Whether to include demand response in the transformer power limit
demand_response:
  include: False # True or False
  events_per_day: 1
  #How much of the transformer power limit can be used for demand response
  event_capacity_percentage_mean: 25 # (in percentage +/- %)
  event_capacity_percentage_std: 5 # (in percentage +/- %)
  event_length_minutes_min: 60
  event_length_minutes_max: 60
  event_start_hour_mean: 18
  event_start_hour_std: 2
  # How many minutes ahead we know the event is going to happen
  notification_of_event_minutes: 15

##############################################################################
# EV Specifications
##############################################################################
heterogeneous_ev_specs: False #if False, each EV has the same specifications
# such as battery capacity, charging rate, etc.

##############################################################################
# Default Model values
##############################################################################
# These values are used if not using a charging network topology file or 
# if the EV specifications are not provided

# Default Transformer model
transformer:
  max_power: 100 # in kW
# Default Charging Station model
charging_station:  
  min_charge_current: 0 # Amperes
  max_charge_current: 56 # Amperes
  min_discharge_current: 0 # Amperes
  max_discharge_current: -56 # Amperes
  voltage: 230 # Volts
  phases: 3 # 1,2, or 3
# Default EV model
ev:
  battery_capacity: 50 # in kWh
  min_battery_capacity: 10 # in kWh
  desired_capacity: 40 # in kWh
  max_ac_charge_power: 22 # in kW
  min_ac_charge_power: 0 # in kW
  max_dc_charge_power: 50 # in kW
  max_discharge_power: -22 # in kW
  min_discharge_power: 0 # in kW
  ev_phases: 3
  transition_soc: 1 # 0-1 (0% - 100%)
  charge_efficiency: 1 # 0-1 (0% - 100%)
  discharge_efficiency: 1 # 0-1 (0% - 100%)
  min_time_of_stay: 120 # in minutes

File Structure

The file structure of the EV2Gym package is as follows:

├── ev2gym
│   ├── baselines
│   │   ├── gurobi_models/
│   │   ├── mpc/
│   │   ├── heuristics.py
│   ├── data/
│   ├── models
│   │   ├── ev2gym_env.py
│   │   ├── ev.py
│   │   ├── transformer.py
│   │   ├── ev_charger.py
│   │   ├── replay.py
│   │   ├── grid.py
│   ├── rl_agent
│   │   ├── reward.py
│   │   ├── state.py
│   ├── utilities
│   │   ├── loaders.py
│   │   ├── utils.py
│   │   ├── arg_parser.py
│   ├── example_config_files
│   │   ├── BusinessPST.yaml
│   │   ├── PublicPST.yaml
│   │   ├── V2GProfitPlusLoads.yaml
│   ├── visuals
│   │   ├── plots.py
│   │   ├── renderer.py
│   ├── scripts/

Class Diagram of the EV2Gym Environment:

Citing EV2Gym

If you use this code in your research, please cite as:

@ARTICLE{10803908,
  author={Orfanoudakis, Stavros and Diaz-Londono, Cesar and Emre Yılmaz, Yunus and Palensky, Peter and Vergara, Pedro P.},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking}, 
  year={2025},
  volume={26},
  number={2},
  pages={2410-2421},
  keywords={Vehicle-to-grid;Smart charging;Optimization;Benchmark testing;Batteries;Data models;Schedules;Reinforcement learning;Prediction algorithms;Power transformers;Electric vehicle optimization;gym environment;reinforcement learning;mathematical programming;model predictive control (MPC)},
  doi={10.1109/TITS.2024.3510945}}

License

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

Contributing

EV2Gym is an open-source project and welcomes contributions! Please get in contact with us if you would like to discuss about the simulator.