A distributed continuous time consensus algorithm for maximize social welfare in micro grid

Zao Fu, Xing He, Tingwen Huang, Haitham Abu-Rub

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

This paper considers a social maximize welfare problem in a micro grid. Firstly, to enhance capacity ability and the output stability of generators in a micro grid, a novel social welfare optimization problem is modeled using wavelet neural network and flywheel energy storage system. Based on augmented Lagrangian function, a continuous time distributed gradient algorithm is proposed for the novel model. In the framework of nonsmooth analysis and algebraic graph theory, we prove that with the algorithm, the optimal solution can always be found asymptotically. Simulation results on 14-bus and 100-bus systems are presented to substantiate the performance and characteristics of the proposed algorithm.

Original languageEnglish
Pages (from-to)3966-3984
Number of pages19
JournalJournal of the Franklin Institute
Volume353
Issue number15
DOIs
Publication statusPublished - 1 Oct 2016

Fingerprint

Microgrid
Welfare
Continuous Time
Maximise
Augmented Lagrangian Function
Nonsmooth Analysis
Wavelet Neural Network
Energy Storage
Gradient Algorithm
Storage System
Distributed Algorithms
Graph theory
Flywheels
Optimal Solution
Generator
Optimization Problem
Energy storage
Output
Neural networks
Simulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

Cite this

A distributed continuous time consensus algorithm for maximize social welfare in micro grid. / Fu, Zao; He, Xing; Huang, Tingwen; Abu-Rub, Haitham.

In: Journal of the Franklin Institute, Vol. 353, No. 15, 01.10.2016, p. 3966-3984.

Research output: Contribution to journalArticle

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