The energy problem is getting bigger and people are trying to reduce their impact on global warming. That's why people put solar panels on their roofs. The downside is that not all the energy gets used, so a lot is returned which reduces the profitability of getting solar panels. In this problem three neighborhoods are considered with 150 houses and 5 batteries. All houses with different power outputs and share the batteries which have a specific capacity. An example of a neighborhood is neighborhood 1 which is shown below.
- The first part of the problem is to connect all houses to a battery.
- Then the price of the neighborhood should be considered and optimized.
- After this, the batteries can be moved to optimize the costs again.
- In the last part of the problem three types of batteries with different prices and capacities are introduced. The algorithm should optimize the amount of batteries used, the position of the batteries, and the type of the battery.
This code is completely written in Python 3.7.1. All the required packages are inside requirements.txt. The packages can be installed by running the code below.
pip install -r requirements.txt
All data is placed in the "data" folder. All python scripts are placed in the "code" folder. All results are placed in the "results" folder. For further explanation see the README files in the folders.
Run the code below to get the results of the different algorithms.
python main.py
To find the statespace, upper bound, lower bound, plots and other results see the README in the "results" folder.
The powerpoint for the presentation can be found in the "presentation" folder within the "results" folder.
Team Niko
David Pantophlet (12466638)
Joost Vos (10284885)
Xandra Vos (10731148)