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

Elkazaz, Mahmoud; Sumner, Mark; Thomas, David

Abstract

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.

Citation

Elkazaz, M., Sumner, M., & Thomas, D. (2020). Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation. International Journal of Electrical Power and Energy Systems, 115, Article 105483. https://doi.org/10.1016/j.ijepes.2019.105483

• Journal Article Type: Article
• Acceptance Date: Aug 12, 2019
• Online Publication Date: Aug 23, 2019
• Publication Date: 2020-02
• Deposit Date: Sep 24, 2019
• Publicly Available Date: Sep 25, 2019
• Journal: International Journal of Electrical Power & Energy Systems
• Print ISSN: 0142-0615
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 115
• Article Number: 105483
• DOI: https://doi.org/10.1016/j.ijepes.2019.105483
• Keywords: Electrical and Electronic Engineering; Energy Engineering and Power Technology
• Public URL: https://nottingham-repository.worktribe.com/output/2655588
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0142061519302297
• Additional Information: This article is maintained by: Elsevier; Article Title: Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation; Journal Title: International Journal of Electrical Power & Energy Systems; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ijepes.2019.105483; Content Type: article; Copyright: Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved.