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Optimal dispatch of electrical vehicle and pv power to improve the power quality of an unbalanced distribution grid

Conference Paper


Abstract


  • 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.

Publication Date


  • 2019

Citation


  • Islam, M. R., Lu, H., Fang, G., Li, L., & Hossain, M. J. (2019). Optimal dispatch of electrical vehicle and pv power to improve the power quality of an unbalanced distribution grid. In 2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019 (pp. 258-263). doi:10.1109/HPBDIS.2019.8735488

Scopus Eid


  • 2-s2.0-85068398217

Web Of Science Accession Number


Start Page


  • 258

End Page


  • 263

Abstract


  • 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.

Publication Date


  • 2019

Citation


  • Islam, M. R., Lu, H., Fang, G., Li, L., & Hossain, M. J. (2019). Optimal dispatch of electrical vehicle and pv power to improve the power quality of an unbalanced distribution grid. In 2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019 (pp. 258-263). doi:10.1109/HPBDIS.2019.8735488

Scopus Eid


  • 2-s2.0-85068398217

Web Of Science Accession Number


Start Page


  • 258

End Page


  • 263