solar-and-storage 0.0.2

Modelling for Solar and Storage

Solar and Storage

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

TODO

Example

Import the packages

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots

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 = make_subplots(rows=3, cols=1, subplot_titles=["Solar profile", "Price", "SOC"])
fig.add_trace(go.Scatter(y=e_soc[:24], name="SOC"), row=3, col=1)
fig.add_trace(go.Scatter(y=solar, name="solar", line_shape="hv"), row=1, col=1)
fig.add_trace(
    go.Scatter(y=solar_power_to_grid, name="solar to gird", line_shape="hv"), row=1, col=1
)
fig.add_trace(go.Scatter(y=prices, name="price", line_shape="hv"), row=2, col=1)


fig.show(rendered="browser")

The first plot shows the solar profile, the second shows the prices that day. The third shows the battery profile. You can see that the battery charged from the solar site at the end of the solar maximum

Thanks

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