Optimal Dispatch of Electrical Vehicle and PV Power to Improve the Power Quality of an Unbalanced Distribution Grid
Author(s)
Islam, Md Rabiul
Lu, Haiyan
Fang, Gengfa
Li, Li
Hossain, Md Jahangir
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
In the smart grid, the distributed generations play an important role to manage the distribution grid. The renewable energy sources such as PV solar, wind, etc. and the Electric Vehicle's Energy Storage are the prominent distributed generation sources. The distributed generation (DG) reduces power loss and improves the voltage profile and reliability of a low voltage (LV) distribution grid. However, optimal placement and sizing of DGs need to be planned properly. Several researchers planned to place single or multiple DGs at the optimum node with an optimal amount of power dispatch assuming balanced distribution grid. But ...
View more >In the smart grid, the distributed generations play an important role to manage the distribution grid. The renewable energy sources such as PV solar, wind, etc. and the Electric Vehicle's Energy Storage are the prominent distributed generation sources. The distributed generation (DG) reduces power loss and improves the voltage profile and reliability of a low voltage (LV) distribution grid. However, optimal placement and sizing of DGs need to be planned properly. Several researchers planned to place single or multiple DGs at the optimum node with an optimal amount of power dispatch assuming balanced distribution grid. But the DGs are connected at all node/buses which require an optimum amount of power dispatch and distribution grids are seldom balance. Moreover, a few research have been conducted for optimizing DG dispatch in an unbalanced distribution grid. This paper proposes a method to improve voltage profile and reduce the total power loss by optimizing the PV and EVs power dispatch in an unbalanced distribution grid. This study will solve the optimization problem using the Differential evolution (DE) optimization algorithm and compares the performance with the Genetic algorithm (GA). Finally, the efficacy of the proposed method is evaluated by applying to an Australian distribution grid. The proposed method reduces 55.72% real power loss of the network. It is also found that the proposed method improves the bus voltage up to 7.65% and increase the bus voltage above 0.95 p.u at all the nodes.
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View more >In the smart grid, the distributed generations play an important role to manage the distribution grid. The renewable energy sources such as PV solar, wind, etc. and the Electric Vehicle's Energy Storage are the prominent distributed generation sources. The distributed generation (DG) reduces power loss and improves the voltage profile and reliability of a low voltage (LV) distribution grid. However, optimal placement and sizing of DGs need to be planned properly. Several researchers planned to place single or multiple DGs at the optimum node with an optimal amount of power dispatch assuming balanced distribution grid. But the DGs are connected at all node/buses which require an optimum amount of power dispatch and distribution grids are seldom balance. Moreover, a few research have been conducted for optimizing DG dispatch in an unbalanced distribution grid. This paper proposes a method to improve voltage profile and reduce the total power loss by optimizing the PV and EVs power dispatch in an unbalanced distribution grid. This study will solve the optimization problem using the Differential evolution (DE) optimization algorithm and compares the performance with the Genetic algorithm (GA). Finally, the efficacy of the proposed method is evaluated by applying to an Australian distribution grid. The proposed method reduces 55.72% real power loss of the network. It is also found that the proposed method improves the bus voltage up to 7.65% and increase the bus voltage above 0.95 p.u at all the nodes.
View less >
Conference Title
2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS 2019)
Subject
Electrical and Electronic Engineering
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods