Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

Elkazaz, Mahmoud; Sumner, Mark; Thomas, David

Abstract

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.

Citation

Elkazaz, M., Sumner, M., & Thomas, D. (2019). Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification. Energies, 12(14), 1-16. https://doi.org/10.3390/en12142712

• Journal Article Type: Article
• Acceptance Date: Jul 9, 2019
• Online Publication Date: Jul 16, 2019
• Publication Date: Jul 16, 2019
• Deposit Date: Jul 31, 2019
• Publicly Available Date: Jul 31, 2019
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 14
• Article Number: 2712
• Pages: 1-16
• DOI: https://doi.org/10.3390/en12142712
• Keywords: microgrid energy management system; mixed integer linear programming; adaptive neuro-fuzzy system; short term energy forecasting; real-time predictive controller; adaptive autoregression forecasting algorithm
• Public URL: https://nottingham-repository.worktribe.com/output/2362147
• Publisher URL: https://www.mdpi.com/1996-1073/12/14/2712