Optimal Power Flow Scheduling of Distributed Microgrid Systems Considering Backup Generators
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Moghimi, Mojtaba
Yang, Fuwen
Gray, Evan
Lu, Junwei
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Perth, Australia
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Abstract
—Due to the great renewable integration capability of Microgrids (MG), reliability, resiliency and stability improvement, MGs play a crucial role in the next generation of electrical power networks. Besides, MGs provide significant potential economic and environmental benefits. However, the intermittent nature of renewable sources necessitates utilising energy storage devices, which would impose new challenges to the energy management of MGs. This paper proposes a centralised optimal power flow controller to minimise the operation cost of distributed microgrid systems while satisfying the technical constraints, considering the battery energy storage depreciation, transmission losses, and dispatchable units’ characteristics. Due to the extensive computational time required to solve the nonlinear optimisation problem, the distributed microgrid system is formulated as a Mixed Integer Linear Problem. To investigate the performance and functionality of the proposed approach, it is applied to an actual case study at Griffith University, consisting of four MGs. The obtained results confirm the cost-effectiveness and applicability of the proposed model and control approach.
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2019 9th International Conference on Power and Energy Systems, ICPES 2019
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© 2019 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.
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Electrical engineering
distributed microgrids
power flow optimisation
energy management system
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Garmabdari, R; Moghimi, M; Yang, F; Gray, E; Lu, J, Optimal Power Flow Scheduling of Distributed Microgrid Systems Considering Backup Generators, 2020