Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation

•The Energy Management System achieves a daily cost reduction of up to 30%.•The use of two layer Energy Management System greatly affects the economic results.•The use of RHPC enables the MG to accurately follow the reference values.•The use of RHPC enables the MG to achieve a better economic result...

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Bibliographic Details
Published inInternational journal of electrical power & energy systems Vol. 115; p. 105483
Main Authors Elkazaz, Mahmoud, Sumner, Mark, Thomas, David
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2020
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Summary:•The Energy Management System achieves a daily cost reduction of up to 30%.•The use of two layer Energy Management System greatly affects the economic results.•The use of RHPC enables the MG to accurately follow the reference values.•The use of RHPC enables the MG to achieve a better economic results.•Experimental verification of the strategy successfully dealt with energy market. The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer, which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2019.105483