UPS Deploy - Costs Optimization with Gurobi

Deploying a UPS could be an expensive task considering how heavy and big its components are. Battery cabinets could weigh tons and PDUs (Power Distribution Units) are also critical and costly. Space constraints are also pitfalls we can find often when there are other pieces of equipment in the same room. Then, sometimes PDUs o battery cabinets must be installed far away from the UPS, involving crazy long cables, which causes to deal with considerable costs and voltage drops. In this example, we have a typical case where the UPS has been installed, but there are just eight available spots for battery cabinets and PDUs.

The deploying costs for each spot as well as the cost of cables are known, but there are further constraints to be taken into account:

Spots 7 and 8 are not suitable for battery cabinets due to constraints in the floor construction.

At least, four PDUs must be deployed.

Just two battery banks will be installed

The must be enough PDUs to fully load the UPS (240kVA)

In this example, an Integer-Programming problem has been proposed and solved subject to the aforementioned constraints. Python and Gurobipy have been used and the results are displayed in the Jupyter Notebook published on GitHub: https://github.com/ArnaldoMatute/Optimized-UPS-Deployment/blob/main/DeployUPS.ipynb