Optimum energy resource scheduling in a microgrid using a distributed algorithm framework

•A distributed energy management method for a microgrid is proposed.•Optimal power flow problem within distributed algorithm is considered.•Alternating Direction.•Method of the multiplier (ADMM) algorithm is presented. Smart Energy Management System (SEMS) as the mastermind of microgrid is a robust...

Full description

Saved in:
Bibliographic Details
Published in:Sustainable cities and society Vol. 37; pp. 222 - 231
Main Authors: Rokni, Seyed Ghasem Mirbabaee, Radmehr, Masoud, Zakariazadeh, Alireza
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2018
Subjects:
Distributed algorithms
Dual decomposition
Energy management system
Energy scheduling
Micro-grid
Optimization
Dual decomposition
Energy scheduling
Micro-grid
Distributed algorithms
Energy management system
Optimization
ISSN:2210-6707, 2210-6715
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A distributed energy management method for a microgrid is proposed.•Optimal power flow problem within distributed algorithm is considered.•Alternating Direction.•Method of the multiplier (ADMM) algorithm is presented. Smart Energy Management System (SEMS) as the mastermind of microgrid is a robust software to manage both demands and generation units. Moreover, SEMS sets optimal regulations between different energy resources. A centralized solution for MG energy management system requires high computational capabilities due to a non-linear and discrete nature of the problem. In this paper, a distributed energy management system called Alternating Direction Method of the multiplier (ADMM) has been proposed in order to jointly schedule the central controller as well as local controllers. The algorithm considers optimal power flow equations within the distributed energy management problem. The proposed distributed algorithm has been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Our findings show that the proposed method decrease the operational cost of MG.
ISSN:2210-6707
2210-6715
DOI:10.1016/j.scs.2017.11.016