Edited (version 29)
Applying graph theory to clean energy districts
Flow optimisation across heat networks
District heating networks provide heat to buildings through a network of underground pipes. Currently 80% of buildings in Switzerland still use oil and gas for heating, so we need new district networks to replace this with clean energy coming from renewable sources like heat pumps or waste heat from data centers.
Presentation in Google Drive öffnen
Challenge
Optimising the layout and routing through this network is an interesting graph theory challenge. While Minimum Spanning Tree algorithms allow us to get the overall shortest network, in reality district heat networks carry hot water flows and we want to minimse the overall flow to reduce energy loss, while also being able to add new network nodes. In this challenge we will provide data for thermal networks represented as graphs, that participants can try different algorithms and solutions, and visualise the resulting heat flows.
Contact
Planeto, Jonathan Chambers and Stefano Cozza
https://kdrive.infomaniak.com/app/share/745832/19ad001e-e849-4652-8132-f81f12ce5a3f
Data folder
https://kdrive.infomaniak.com/app/share/745832/90ca4574-d096-4672-bf44-db07a58fb955
Data and file drop box https://kdrive.infomaniak.com/app/collaborate/745832/aa3f0bad-3645-485a-8c27-1a5376fc09c6
Google Sheets slideshow to collect ideas and results
Event finish
Sketching
Research
Cost function so we can compare algorithms:
def calculate_total_kwh_m(mst_table):
return (mst_table['routes']*abs(mst_table['heat_total'])).sum()
Iterative optimization with random perturbation of one distance.Reduction in total cost sum(abs(heat_total)*routes):
Project
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requirements.txt content extracted by github copilot:
numpy pandas geopandas pickle-mixin sparse xarray scipy matplotlib contextily
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Start
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Challenge shared
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