Probabilistic Power Flow Method Considering Continuous and Discrete Variables

Xuexia Zhang, Zhiqi Guo and Weirong Chen
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Xuexia Zhang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Zhiqi Guo: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Weirong Chen: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Energies, 2017, vol. 10, issue 5, 1-17

Abstract: This paper proposes a probabilistic power flow (PPF) method considering continuous and discrete variables (continuous and discrete power flow, CDPF) for power systems. The proposed method—based on the cumulant method (CM) and multiple deterministic power flow (MDPF) calculations—can deal with continuous variables such as wind power generation (WPG) and loads, and discrete variables such as fuel cell generation (FCG). In this paper, continuous variables follow a normal distribution (loads) or a non-normal distribution (WPG), and discrete variables follow a binomial distribution (FCG). Through testing on IEEE 14-bus and IEEE 118-bus power systems, the proposed method (CDPF) has better accuracy compared with the CM, and higher efficiency compared with the Monte Carlo simulation method (MCSM).

Keywords: probabilistic power flow (PPF); discrete variable; cumulant method (CM); deterministic power flow (DPF) calculation (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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