DeepAI AI Chat
Log In Sign Up

A Linear-Programming Approximation of AC Power Flows

06/16/2012
by   Carleton Coffrin, et al.
0

Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems. However, these approximations fail to capture reactive power and voltage magnitudes, both of which are necessary in many applications to ensure voltage stability and AC power flow feasibility. This paper proposes linear-programming models (the LPAC models) that incorporate reactive power and voltage magnitudes in a linear power flow approximation. The LPAC models are built on a convex approximation of the cosine terms in the AC equations, as well as Taylor approximations of the remaining nonlinear terms. Experimental comparisons with AC solutions on a variety of standard IEEE and MatPower benchmarks show that the LPAC models produce accurate values for active and reactive power, phase angles, and voltage magnitudes. The potential benefits of the LPAC models are illustrated on two "proof-of-concept" studies in power restoration and capacitor placement.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/15/2021

Neural Network-based Power Flow Model

Power flow analysis is used to evaluate the flow of electricity in the p...
10/18/2019

Ensemble learning based linear power flow

This paper develops an ensemble learning-based linearization approach fo...
11/06/2017

PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations

In recent years, the power system research community has seen an explosi...
01/31/2017

A Hybrid Approach for Secured Optimal Power Flow and Voltage Stability with TCSC Placement

This paper proposes a hybrid technique for secured optimal power flow co...
03/07/2019

Optimisation and Comprehensive Evaluation of Alternative Energising Paths for Power System Restoration

Power system restoration after a major blackout is a complex process, in...
04/29/2020

A Flexible Storage Model for Power Network Optimization

This paper proposes a simple and flexible storage model for use in a var...
03/26/2021

Embedding Power Flow into Machine Learning for Parameter and State Estimation

Modern state and parameter estimations in power systems consist of two s...