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Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management

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

2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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

DOI (Published version): 10.1109/isgteurope52324.2021.9640051

Abstract

A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the ne...

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Published in: 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
Published: IEEE 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa58321
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spelling 2022-12-17T11:15:29.5740581 v2 58321 2021-10-13 Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management 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 2021-10-13 EEEG A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the next day. The proposed method optimizes the use of the battery storage and extends battery lifetime by only storing the required energy by considering the forecasted day-ahead energy at peak time. The proposed EMS has been implemented in MATLAB software and using Active Office Building on the Swansea University campus as a case study. Results are compared favorably with published state-of-the-arts algorithms to demonstrate its effectiveness. Results show a saving of 20% and 41% in total energy cost over six months compared to a forecast-based EMS and to a conventional EMS, respectively. Furthermore, a reduction of 54% in the net energy exchanged with the utility by avoiding the unnecessary charge/discharge cycles. Conference Paper/Proceeding/Abstract 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) IEEE Energy Management System, Battery Storage System, Renewable Energy Sources 18 10 2021 2021-10-18 10.1109/isgteurope52324.2021.9640051 COLLEGE NANME Electronic and Electrical Engineering COLLEGE CODE EEEG Swansea University 2022-12-17T11:15:29.5740581 2021-10-13T15:21:26.9741294 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 58321__21168__388d3765aca0489ca7b6dfa4ec7dc29e.pdf ISGT-2021.pdf 2021-10-13T15:26:21.1251862 Output 499850 application/pdf Accepted Manuscript true true eng
title Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
spellingShingle Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
title_short Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
title_full Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
title_fullStr Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
title_full_unstemmed Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
title_sort Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management
author_id_str_mv b7aae4026707ed626d812d07018a2113
adab4560ff08c8e5181ff3f12a4c36fb
b952b837f8a8447055210d209892b427
0e3f2c3812f181eaed11c45554d4cdd0
author_id_fullname_str_mv b7aae4026707ed626d812d07018a2113_***_Meghdad Fazeli
adab4560ff08c8e5181ff3f12a4c36fb_***_Mohammad Monfared
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
0e3f2c3812f181eaed11c45554d4cdd0_***_Justin Searle
author Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
author2 Ameena Sorour
Meghdad Fazeli
Mohammad Monfared
Ashraf Fahmy Abdo
Justin Searle
Richard Lewis
format Conference Paper/Proceeding/Abstract
container_title 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
publishDate 2021
institution Swansea University
doi_str_mv 10.1109/isgteurope52324.2021.9640051
publisher 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
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description A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the next day. The proposed method optimizes the use of the battery storage and extends battery lifetime by only storing the required energy by considering the forecasted day-ahead energy at peak time. The proposed EMS has been implemented in MATLAB software and using Active Office Building on the Swansea University campus as a case study. Results are compared favorably with published state-of-the-arts algorithms to demonstrate its effectiveness. Results show a saving of 20% and 41% in total energy cost over six months compared to a forecast-based EMS and to a conventional EMS, respectively. Furthermore, a reduction of 54% in the net energy exchanged with the utility by avoiding the unnecessary charge/discharge cycles.
published_date 2021-10-18T04:14:45Z
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score 11.017797

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