Skip to content

MeloCoding80/Battery-optimization-with-genetic-algorithms

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Battery-optimization-with-genetic-algorithms

Optimization of a customer's owned battery to take advantage of volatile electricity market prices

The objective is to model and optimize a system composed of:

  • an electricity consumer
  • a battery
  • solar panels
  • a grid

The only componenent over which we will have control is the battery. By optimizing its daily charging and discharging cycles, we will be able to take advantage or avoid high electricity prices, with this simple principle:

  • when electricy is cheap on the market, we will buy more electricity from the grid that what is needed by the consumer, and store the excess in the battery
  • when electricity is expensive, we will consume the electricity previously stored in the battery instead of buying from the grid. In certain cases, we will even be able to sell electricity on the market.

This optimization will result in a lower electricty bill at the end of the day. However an arbitrage will have to be made at every step, comparing the battery's degradation cost from a charge/discharge cycle with the profit it generates.

For further details and explanations, please refer to the Python Notebooks and to the "Layout & equations _ Public.docx" Word document.

About

Optimization of a customer's owned battery to take advantage of volatile electricity market prices

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published