Battery energy storage cost and capacity optimization for university research center

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Author(s)
Moghimi, Mojtaba
Garmabdari, Rasoul
Stegen, Sascha
Lu, Junwei
Year published
2018
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Microgrids (MGs) are the essential part of the modern power grids defined as the building blocks of smart grids. Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) combined with Distributed Generators (DGs) form a comprehensive MG, which require the control and Energy Management System (EMS) to fulfill the load and grid requirements. As the need for BESS grows due to uncertainties of RESs, scheduling and cost management of BESSs in the MG becomes more of a concern. In this paper, BESSs have been designed for a university research center to simultaneously overcome the outage problem and shave the peak ...
View more >Microgrids (MGs) are the essential part of the modern power grids defined as the building blocks of smart grids. Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) combined with Distributed Generators (DGs) form a comprehensive MG, which require the control and Energy Management System (EMS) to fulfill the load and grid requirements. As the need for BESS grows due to uncertainties of RESs, scheduling and cost management of BESSs in the MG becomes more of a concern. In this paper, BESSs have been designed for a university research center to simultaneously overcome the outage problem and shave the peak demand considering the BESS sizing and degradation; MG cost minimization, as well as MG scheduling. PV and wind are the RESs employed in this study and in combination; Li-Ion BESS has been utilized to investigate the MG performance. A two-layer optimization algorithm has been presented to optimally define the BESS size and minimize the operational cost of the MG achieving the peak shaving and valley filling objectives. The results prove the functionality and applicability of the proposed system to be implemented as a part of the experimental MG at Griffith University in order to enhance the stability and reliability of the research center and at the same time minimize the operational costs of the MG.
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View more >Microgrids (MGs) are the essential part of the modern power grids defined as the building blocks of smart grids. Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) combined with Distributed Generators (DGs) form a comprehensive MG, which require the control and Energy Management System (EMS) to fulfill the load and grid requirements. As the need for BESS grows due to uncertainties of RESs, scheduling and cost management of BESSs in the MG becomes more of a concern. In this paper, BESSs have been designed for a university research center to simultaneously overcome the outage problem and shave the peak demand considering the BESS sizing and degradation; MG cost minimization, as well as MG scheduling. PV and wind are the RESs employed in this study and in combination; Li-Ion BESS has been utilized to investigate the MG performance. A two-layer optimization algorithm has been presented to optimally define the BESS size and minimize the operational cost of the MG achieving the peak shaving and valley filling objectives. The results prove the functionality and applicability of the proposed system to be implemented as a part of the experimental MG at Griffith University in order to enhance the stability and reliability of the research center and at the same time minimize the operational costs of the MG.
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Conference Title
2018 IEEE/IAS 54TH INDUSTRIAL AND COMMERCIAL POWER SYSTEMS TECHNICAL CONFERENCE (I&CPS)
Volume
2018-May
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Note
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Subject
Electrical energy generation (incl. renewables, excl. photovoltaics)