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PowerModelsADA.jl (Power Models Alternating Distributed Algorithms) provides a framework to solve Optimal Power Flow (OPF) problems using alternating distributed algorithms. The package allows to use different distributed algorithms. PowerModelsADA is built on top of PowerModels.jl and JuMP.jl to model and solve the subproblems.
The PowerModelsADA framework is designed to easily incorporate new alternating distributed algorithms. The framework provides means to decompose a test case into multiple areas, model the subproblems associated with each area using PowerModels, solve the supropblems in parallel using multi-threading or multi-processing via Distributed Computing, communicate the shared data between the areas, and calculate the mismatches to decide if the termination criteria are satisfied.
The current version of PowerModelsADA implements four distributed algorithms:
- Alternating Direction Method of Multipliers (ADMM)
- Analytical Target Cascading (ATC)
- Auxiliary Problem Principle (APP)
- Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)
PowerModelsADA can be extended to include variations of the existing algorithms or new user-defined algorithms. More details about the formulations and algorithm implementations are shown in Technical Specifications
PowerModelsADA can be installed using the Julia package manager with
using Pkg
Pkg.add("PowerModelsADA")An example demonstrating how to code up and solve the OPF problem with distributed algorithms is found in Quick Start Guide section of the documentation.
Contributions and enhancements of PowerModelADA are welcomed and encouraged. Please feel free to fork this repository and share your contributions to the main branch with a pull request.
If you find PowerModelsADA useful for your work, please cite our paper:
@ARTICLE{alkhraijah2023powermodelsada,
author={Alkhraijah, Mohannad and Harris, Rachel and Coffrin, Carleton and Molzahn, Daniel K.},
journal={IEEE Transactions on Power Systems},
title={PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms},
year={2023},
volume={},
number={},
pages={1-4},
doi={10.1109/TPWRS.2023.3318858}
}This work is partially supported by the NSF AI Institute for Advances in Optimization (Award #2112533).