Distributed AC Power Flow Optimization

Abstract

This repository hosts the codes for an advanced toolbox designed for solving distributed power flow problems. It is based on the research presented in three pivotal papers [1][2][3]. The toolbox is an evolution of the platform introduced in [3], which provided a framework for rapid prototyping using the Gauss-Newton Augmented Lagrangian Alternating Direction Inexact Newton (GN-ALADIN) method. Enhancements in this toolbox include improved scalability of the Gauss-Newton ALADIN method [2] and advanced problem reformulation capabilities using a hypergraph-based communication structure (introduced in [1]).

Key Features

• Integration with MATPOWER: Users can combine multiple MATPOWER casefiles into a single, unified casefile for analysis.
• Distributed Reformulation: Formulation of AC power flow problems as distributed optimization problems, enabling efficient handling of large-scale systems.
• Gauss-Newon ALADIN: Implementation of the Hypergraph Distributed Sequential Quadratic Programming (HDSQP) method for solving complex power flow problems.
• HDSQP Approach: Ability to reformulate problems with a communication structure corresponding to a hypergraph and use Hypergraph Distributed Sequential Quadratic Programming (HDSQP) method for solving complex power flow problems.

Getting Started

Prerequisites

• Ensure that you have MATLAB with MATPOWER toolbox.
• Install IPOPT and CasADi following their respective installation guides (option).

Installation

Provide step-by-step instructions on how to set up the project environment:

# Clone the repository
git clone https://github.com/xinliang-dai/rapidPF.git

# Navigate to the project directory
cd 00_use-case/

Usage

• Merging MATPOWER Casefiles: Use the provided utility scripts to merge multiple casefiles.
• Formulating Problems: Follow the examples in the documentation to formulate your distributed power flow problems.
• Solving Problems: Utilize the Gauss-Newton ALADIN and HDSQP provided methods to solve the formulated problems.
• Documentation For detailed usage instructions, theoretical background, and examples, please refer to the online documentation available at Toolbox Documentation

Citation

[1] Hypergraph-Based Fast Distributed AC Power Flow Optimization, 62nd IEEE Conference on Decision and Control CDC, 2023

[2] Rapid Scalable Distributed Power Flow with Open-Source Implementation, 9th IFAC Conference on Networked Systems NECSYS, 2022

[3] Distributed power flow and distributed optimization—Formulation, solution, and open source implementation, Sustainable Energy, Grids and Networks, 2021

@inproceedings{dai2023hybrid,
    title={Hypergraph-Based Fast Distributed {AC} Power Flow Optimization},
    author={Dai, Xinliang and Lian, Yingzhao and Jiang, Yuning and Jones, Colin N and Hagenmeyer, Veit},
    booktitle={62rd Proc. IEEE Conf. Decis. Control (CDC)},
    year={2023},

@inproceedings{dai2022rapid,
    title = {Rapid Scalable Distributed Power Flow with Open-Source Implementation},
    volume = {55},
    number = {13},
    pages = {145-150},
    year = {2022},
    note = {9th IFAC Conference on Networked Systems NECSYS 2022},
    issn = {2405-8963},
    author={Dai, Xinliang and Cai, Yichen and Jiang, Yuning and Hagenmeyer, Veit},
}

@article{muhlpfordt2021distributed,
    title={Distributed power flow and distributed optimization—Formulation, solution, and open source implementation},
    author={M{\"u}hlpfordt, Tillmann and Dai, Xinliang and Engelmann, Alexander and Hagenmeyer, Veit},
    journal={Sustain. Energy, Grids Netw.},
    volume={26},
    pages={100471},
    year={2021},
    publisher={Elsevier}