GridPLAN
Heat grid planner
Environment: Python
Input: (i) Heat demand data (peak termal power), (ii) Potential heat sources (e.g., rivers, lakes, heat incineration plants, etc.).
Method & highlights: Pre-clustering of heat centers (Mean-shift), connection of most economic connections (greedy search + local graph optimization with SOCP), two-level approach to reduce complexity
Computational efficiency: 2 minutes for a few hundred nodes
Result: Heat distribution grid
Developer: Dr. Alexander Fuchs
Input: Heat demand and generation locations
Output: Clustering
Output: Heat backbone grid
Output: Heat distribution grid