lanl-ansi / PowerModelsRestoration.jl

A PowerModels Extension for Optimization of Power Network Restoration
https://lanl-ansi.github.io/PowerModelsRestoration.jl/stable/
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optimization

PowerModelsRestoration.jl

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A PowerModelsRestoration provides extensions to PowerModels for solving the power system restoration tasks. A core building block in PowerModelsRestoration is the Maximum Load Delivery (MLD) problem, which provides a reliable numerical method for solving challenging N-k damage scenarios, such as those that arise in the analysis of extreme events.

Core Problem Specifications

Core Network Formulations

Citing PowerModelsRestoration

If you find the PowerModelsRestoration package useful in your work, we request that you cite the following publication:

@inproceedings{rhodes2020powermodelsrestoration,
  author = {Rhodes, Noah and Fobes, David M and Coffrin, Carleton and Roald, Line},
  title = {PowerModelsRestoration.jl: An open-source framework for exploring power network restoration algorithms},
  booktitle = {2020 Power Systems Computation Conference (PSCC)},
  year = {2020},
  month = {June},
  doi = {10.1016/j.epsr.2020.106736}
}

In addition, if the MLD problem from PowerModelsRestoration useful in your work, we kindly request that you cite the following publication:

@article{8494809,
  author={Carleton Coffrin and Russell Bent and Byron Tasseff and Kaarthik Sundar and Scott Backhaus},
  title={Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis},
  journal={IEEE Transactions on Power Systems},
  volume={34}, number={2}, pages={1450-1458},
  month={March}, year={2019},
  doi={10.1109/TPWRS.2018.2876507}, ISSN={0885-8950}
}

If you use the RRR problem in your work, we kindly request that you cite the folloring publication

@inproceedings{rhodes2022recursive,
  author = {Rhodes, Noah and Coffrin, Carleton and Roald, Line},
  title = {Recursive restoration refinement: A fast heuristic for near-optimal restoration prioritization in power systems},
  booktitle = {2022 Power Systems Computation Conference (PSCC)},
  year = {2022},
  month = {June},
  doi = {10.1016/j.epsr.2022.108454}
}

Citation of the PowerModels framework is also encouraged when publishing works that use PowerModels extension packages.

License

This code is provided under a BSD license as part of the Multi-Infrastructure Control and Optimization Toolkit (MICOT) project, C15024.