PowerModelsADA: A Framework for Solving Optimal Power Flow Using Distributed Algorithms

被引：1

作者：

Alkhraijah, Mohannad ^{[1
]}

Harris, Rachel ^{[1
]}

Coffrin, Carleton ^{[2
]}

Molzahn, Daniel K. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

[2] Los Alamos Natl Lab, Adv Network Sci Initiat, Los Alamos, NM 87545 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2024年 / 39卷 / 01期

关键词：

Load flow; Generators; Optimization; Data structures; Benchmark testing; Loading; Distributed algorithms; Distributed optimization; optimal power flow; CONSTRAINED UNIT COMMITMENT;

D O I：

10.1109/TPWRS.2023.3318858

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This letter demonstrates use cases for PowerModelsADA and validates its implementation with multiple OPF formulations.

引用

页码：2357 / 2360

页数：4

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