Planning with MIP for Supply Restoration in Power Distribution Systems
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2900-2906.
Abstract
The next generation of power systems faces significant challenges, both in coping with increased loading of an aging infrastructure and incorporating renewable energy sources. Meeting these challenges requires a fundamental change in the operation of power systems by replacing human-in-the-loop operations with autonomous systems. This is especially acute in distribution systems, where renewable integration often occurs. This paper investigates the automation of power supply restoration (PSR), that is, the process of optimally reconfiguring a faulty distribution grid to resupply customers. The key contributions of the paper are (1) a flexible mixed-integer programming framework for solving PSR, (2) a model decomposition to obtain high-quality solutions within the required time constraints, and (3) an experimental validation of the potential benefits of the proposed PSR operations.
@inproceedings{ijcai2013p426,
title = {Planning with MIP for Supply Restoration in Power Distribution Systems},
author = {Thiébaux, Sylvie and Coffrin, Carleton and Hijazi, Hassan L. and Slaney, John K.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2900--2906},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/426},
}
TY - CPAPER TI - Planning with MIP for Supply Restoration in Power Distribution Systems AU - Thiébaux, Sylvie AU - Coffrin, Carleton AU - Hijazi, Hassan L. AU - Slaney, John K. T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2900 EP - 2906 UR - https://ijcai.org/proceedings/2013/426 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: