An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to pow...

Full description

Saved in:
Bibliographic Details
Published inWind energy (Chichester, England) Vol. 23; no. 3; pp. 575 - 592
Main Authors Chen, Jialin, Zhou, Zheng, Karunakaran, Venkatesan, Zhao, Shiwen
Format Journal Article
LanguageEnglish
Published Bognor Regis John Wiley & Sons, Inc 01.03.2020
Subjects
Distributed generation
Economic forecasting
Electricity pricing
Energy
Energy management
energy management strategy
grid‐connected MG
Hybrid systems
Power flow
power flow constraints
quasi‐oppositional‐chaotic symbiotic organisms search algorithm
random forest
Renewable energy
Search algorithms
Online AccessGet full text

Cover

More Information
Summary:This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi‐oppositional‐chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid‐connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two‐strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1095-4244
1099-1824
DOI:10.1002/we.2443