Energy Community Planning

This repository presents the implementation code of the open-access journal article Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities.

Citation

If you find this code useful and use it in your work, please reference our article:

Leprince, J., Schledorn, A., Guericke, D., Dominkovic, D., Madsen, H., and Zeiler, W., 2023. Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities

BibTex:
@article{LEPRINCE2023121589,
title = {Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities},
journal = {Applied Energy},
volume = {348},
pages = {121589},
year = {2023},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2023.121589},
url = {https://www.sciencedirect.com/science/article/pii/S0306261923009534},
author = {Julien Leprince and Amos Schledorn and Daniela Guericke and Dominik Franjo Dominkovic and Henrik Madsen and Wim Zeiler},
keywords = {Energy communities, District energy management, Optimal energy planning, Stochastic optimization, Occupant behavior, Demand side management},
}

Repository contributors

Dr. Julien Leprince, Amos Schledorn

Repository structure

energycommunityplanning
└─ data
|   ├─ in                               <- input - scenario data
|   └─ out                              <- output - energy planning design strategies
├─ src
|   ├─ 1_scenario_generation            <- scenario seasonal bootstrapping and clustering
|   ├─ 2_main_distributed_problem       <- distributed stochastic optimization problem
|   ├─ 2_poc_centralized_problem        <- centralized stochastic optimization problem serving convergence proof of concept
|   ├─ 3_sensitivityanalysis            <- local sensitivity analysis
|   ├─ 4_results_visualization          <- result visualization code source
|   ├─ RC_models                        <- lumped resistance capacity model implementation
|   └─ parameters                       <- optimization problem parameter file
└─ README.md                            <- README for developers using this code

License

This project is licensed under the MIT License - see the LICENSE.md file for details