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Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

Ameena Sorour, Meghdad Fazeli Orcid Logo, Mohammad Monfared Orcid Logo, Ashraf Fahmy Abdo Orcid Logo, Justin Searle Orcid Logo, Richard Lewis

IEEE Access, Volume: 9, Pages: 58953 - 58965

Swansea University Authors: Meghdad Fazeli Orcid Logo, Mohammad Monfared Orcid Logo, Ashraf Fahmy Abdo Orcid Logo, Justin Searle Orcid Logo, Richard Lewis

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DOI (Published version): 10.1109/access.2021.3072961

Abstract

This paper presents a predictive Energy Management System (EMS), aimed to improve the per-formance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electric...

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Published in: IEEE Access
ISSN: 2169-3536 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56658
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Abstract: This paper presents a predictive Energy Management System (EMS), aimed to improve the per-formance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a com-bination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while con-sidering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study.
College: Faculty of Science and Engineering
Funders: The authors would like to thank SPECIFIC-IKC for providing the data from ‘‘Active Buildings’’ demonstrators, which made this project possible. The authors would like to acknowledge QRLP10-G-19022034 from Qatar National Fund (a member of Qatar Foundation) for their financial support.
Start Page: 58953
End Page: 58965

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