Conference paper
Optimal allocation of distributed energy storage systems in unbalanced distribution networks
2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (Brisbane, Australia, 08/12/2021–10/12/2021)
2021
Abstract
The increased penetration of renewable distributed generation (DG) such as wind power and photovoltaics (PV) in distribution networks has brought significant economic and environmental benefits to society. However, due to the intermittent nature of wind and solar, massive integration of such kinds of DG might cause severe problems, such as greater power loss, reduced power quality, and system efficiency. The integration of energy storage systems (ESS) provides an effective way to handle the above issues due to its capability of stabilizing voltage and frequency. Hence, it is necessary to optimally allocate the ESS to exert maximum support on the distribution network. This paper presents an approach for optimal allocation of ESS to improve voltage profile and reduce power losses and line loading in the unbalanced distribution system. The proposed methodology is tested on an unbalanced medium voltage IEEE-33 bus system with higher wind and PV generation. DIgSILENT PowerFactory is used for constructing and testing the system model. The simulation results demonstrate the effectiveness of the proposed approach in improving the power quality and system efficiency of unbalanced distribution systems.
Details
- Title
- Optimal allocation of distributed energy storage systems in unbalanced distribution networks
- Authors/Creators
- D. Zhang (Author/Creator) - Murdoch UniversityC.K. Das (Author/Creator) - Edith Cowan UniversityGM. Shafiullah (Author/Creator) - Murdoch UniversityK.W. Wong (Author/Creator) - Murdoch University
- Publication Details
- 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
- Conference
- 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (Brisbane, Australia, 08/12/2021–10/12/2021)
- Identifiers
- 991005544214707891
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Conference paper
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