06

Applying graph theory to clean energy districts

Flow optimisation across heat networks

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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

Edited (version 29)

2 months ago ~ oleg

Sketching

Edited (version 28)

2 months ago ~ jonathan_chambers

2 months ago ~ leiv_andresen

Research

Edited (version 25)

2 months ago ~ jonathan_chambers

Edited (version 24)

2 months ago ~ jonathan_chambers

2 months ago ~ tyler_anderson

Cost function so we can compare algorithms:

def calculate_total_kwh_m(mst_table): return (mst_table['routes']*abs(mst_table['heat_total'])).sum()

2 months ago ~ leiv_andresen

Edited (version 18)

2 months ago ~ jonathan_chambers

  Iterative optimization with random perturbation of one distance.Reduction in total cost sum(abs(heat_total)*routes):

2 months ago ~ leiv_andresen

Project

Edited (version 15)

2 months ago ~ jonathan_chambers

Joined the team

2 months ago ~ sari_issa

requirements.txt content extracted by github copilot:

numpy pandas geopandas pickle-mixin sparse xarray scipy matplotlib contextily

2 months ago ~ leiv_andresen

Joined the team

2 months ago ~ lukas_ringlage

Edited (version 13)

2 months ago ~ jonathan_chambers

Joined the team

2 months ago ~ leiv_andresen

Start

Edited (version 12)

2 months ago ~ oleg

Edited (version 9)

2 months ago ~ jonathan_chambers

Edited (version 8)

2 months ago ~ jonathan_chambers

Joined the team

2 months ago ~ jonathan_chambers

Edited (version 1)

3 months ago ~ gaston_energy

Edited (version 6)

3 months ago ~ gaston_energy

Edited (version 2)

3 months ago ~ gaston_energy

Challenge shared
Tap here to review.

3 months ago ~ gaston_energy
 
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