Challenge
Rising energy prices and complex load profiles made it increasingly difficult for companies to keep procurement costs under control. The goal was to develop a data-driven solution to identify savings potential and optimize electricity purchasing decisions.
Solution
I built a mathematical optimization model using Python, Pyomo, and Gurobi, designed to minimize electricity procurement costs. The model incorporated real-world cost structures and load profiles, enabling efficient decision-making based on quantitative analysis.
Impact
The solution was successfully applied in a real business context, leading to measurable cost savings and more efficient energy procurement strategies.
Next Steps
A link to the GitHub repository will be added soon, providing full details including the problem description, datasets, and source code.
đź”— Download both the notebook and dataset from our GitHub repository: Here
A special thanks to Edmond Tefong for preparing the solution and python code of this Case Study.
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