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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
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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.</abstract><type>Journal Article</type><journal>IEEE Access</journal><volume>9</volume><journalNumber/><paginationStart>58953</paginationStart><paginationEnd>58965</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2169-3536</issnPrint><issnElectronic>2169-3536</issnElectronic><keywords/><publishedDay>23</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-04-23</publishedDate><doi>10.1109/access.2021.3072961</doi><url/><notes/><college>COLLEGE NANME</college><department>Electronic and Electrical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EEEG</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>The authors would like to thank SPECIFIC-IKC for providing the data from &#x2018;&#x2018;Active Buildings&#x2019;&#x2019; demonstrators, which made this project possible. 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spelling 2022-07-07T14:30:54.0428223 v2 56658 2021-04-15 Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study b7aae4026707ed626d812d07018a2113 0000-0003-1448-5339 Meghdad Fazeli Meghdad Fazeli true false adab4560ff08c8e5181ff3f12a4c36fb 0000-0002-8987-0883 Mohammad Monfared Mohammad Monfared true false b952b837f8a8447055210d209892b427 0000-0003-1624-1725 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 0e3f2c3812f181eaed11c45554d4cdd0 0000-0003-1101-075X Justin Searle Justin Searle true false 6b3559a0b9ac5d4048d50c09d0a5b42e Richard Lewis Richard Lewis true false 2021-04-15 EEEG 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. Journal Article IEEE Access 9 58953 58965 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 2169-3536 23 4 2021 2021-04-23 10.1109/access.2021.3072961 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University 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. 2022-07-07T14:30:54.0428223 2021-04-15T12:55:44.1964218 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Ameena Sorour 1 Meghdad Fazeli 0000-0003-1448-5339 2 Mohammad Monfared 0000-0002-8987-0883 3 Ashraf Fahmy Abdo 0000-0003-1624-1725 4 Justin Searle 0000-0003-1101-075X 5 Richard Lewis 6 56658__19751__ec713fe9b3334149be24b27212565f40.pdf 56658.pdf 2021-04-26T09:15:35.4710213 Output 1347571 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License true eng http://creativecommons.org/licenses/by/4.0/
title Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
spellingShingle Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
Richard Lewis
title_short Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
title_full Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
title_fullStr Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
title_full_unstemmed Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
title_sort Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study
author_id_str_mv b7aae4026707ed626d812d07018a2113
adab4560ff08c8e5181ff3f12a4c36fb
b952b837f8a8447055210d209892b427
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6b3559a0b9ac5d4048d50c09d0a5b42e
author_id_fullname_str_mv b7aae4026707ed626d812d07018a2113_***_Meghdad Fazeli
adab4560ff08c8e5181ff3f12a4c36fb_***_Mohammad Monfared
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
0e3f2c3812f181eaed11c45554d4cdd0_***_Justin Searle
6b3559a0b9ac5d4048d50c09d0a5b42e_***_Richard Lewis
author Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
Richard Lewis
author2 Ameena Sorour
Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
Richard Lewis
format Journal article
container_title IEEE Access
container_volume 9
container_start_page 58953
publishDate 2021
institution Swansea University
issn 2169-3536
2169-3536
doi_str_mv 10.1109/access.2021.3072961
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description 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.
published_date 2021-04-23T04:11:46Z
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score 11.017797

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