Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources

Huynh Van Ky, Ngo Van Duong, Dinh Duong Le and Nhi Thi Ai Nguyen
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Huynh Van Ky: The University of Danang, 41 Le Duan st., Danang 59000, Vietnam
Ngo Van Duong: The University of Danang, 41 Le Duan st., Danang 59000, Vietnam
Dinh Duong Le: Faculty of Electrical Engineering, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang st., Danang 59000, Vietnam
Nhi Thi Ai Nguyen: Faculty of Electrical Engineering, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang st., Danang 59000, Vietnam

Energies, 2018, vol. 11, issue 10, 1-12

Abstract: In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.

Keywords: probabilistic power flow; renewable power; high-dimensional data; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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