Solar and Storage

July 13, 2026 · View on GitHub

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.

Installation

pip install solar-and-storage

Example

Import the packages

import numpy as np

from 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")

Example1

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

Starting with stored energy

Use current_soc to set the starting battery state of charge as a fraction of the battery capacity. For example, a half-full battery can discharge during an initial high-price period:

prices = np.zeros(24)
prices[0] = 100
solar = np.zeros(24)

solar_and_storage = SolarAndStorage(
    prices=prices,
    solar_generation=list(solar),
    current_soc=0.5,
    battery_eta_charge=1,
    battery_eta_discharge=1,
)
result_df = solar_and_storage.get_results()

Current SOC example

Thanks

Thanks you to the follow repos for inspiration

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_{Peter Dudfield}
💻

_gilbertgong
💻

_davidhuanggg
💻

This project follows the all-contributors specification. Contributions of any kind welcome!