EMSRL is a reinforcement learning PPO algorithm designed to maximize profits by utilizing battery energy storage systems (BESS) and alkaline water electrolyzers (AWE) to manage curtailed energy generated from solar and wind power.
This model is described in the paper: Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning
setup(
name="EMSRL",
version="1.0",
url="https://github.com/kangdj6358/EMSRL",
author="Dongju Kang, Doeun Kang",
license="MIT",
install_requires=[
"gym == 0.18.3",
"ray == 1.9.0",
"ray[rllib] == 1.9.0",
"pandas == 1.3.3",
"openpyxl == 3.0.9",
"torch == 1.9.1",
],
zip_safe=False,
)
You can adjust the hyperparameters in the rl_config in the train_EP.py file.
To train the dataset using PPO, please run
python EMSRL_train_EP.py
After train the data, you can evaluate the results by:
python evaluate_EP.py results/{episode}/PPO/PPO_EMSRLEnv_{}/checkpoint_{000000}/checkpoint-{00} --run PPO --env EMSRLEnv --episodes {0000}
Datasets related to this article can be found at California ISO