A Python Library to run solar and storage optimization. This uses mixed integer linear programming and maximises revenue made by charging and discharging the battery. The model uses variable prices and a solar generation profile.
pip install solar-and-storage
Import the packages
import numpy as np
from solar_and_storage.solar_and_storage import SolarAndStorage
Make the fake price and solar data
# make prices
prices = np.zeros(24) + 30
prices[6:19] = 40
prices[9] = 50
prices[12:14] = 30
prices[16:18] = 50
prices[17] = 60
# make solar profile
solar = np.zeros(24)
solar[8:16] = 2.0
solar[10:14] = 4.0
Then run optimization
solar_and_storage = SolarAndStorage(prices=prices, solar_generation=list(solar))
solar_and_storage.run_optimization()
result_df = solar_and_storage.get_results()
Now plot the data
fig = solar_and_storage.get_figure()
fig.show(rendered="browser")
The first plot shows the solar profile, the second shows the prices that day. The third shows the battery profile. Finally the fourth shows profit. You can see that the battery charged from the solar site at the end of the solar maximum
Thanks you to the follow repos for inspiration
- https://github.com/ADGEfficiency/energy-py-linear
- https://github.com/wzyfrank/battery/
- https://github.com/greysonchung/Battery-Optimisation/
- https://github.com/edu230991/battery-optimization/ sdk-python-ci.yml
Thanks goes to these wonderful people (emoji key):
Peter Dudfield 💻 |
gilbertgong 💻 |
This project follows the all-contributors specification. Contributions of any kind welcome!