Probabilistic power flow analysis based on arbitrary polynomial chaos expansion of bus voltage phasor

• Main Authors: Jirasak Laowanitwattana, Sermsak Uatrongjit
• Format: Journal
• Published: 2020
• Subjects: Energy; Engineering; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078603750&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/68394
• Institution: Chiang Mai University

© 2020 John Wiley & Sons Ltd An increasing penetration of renewable energy sources, such as solar farms and wind farms, necessitates a usage of statistical analysis and modeling for efficient operation and planning of the power systems. The probabilistic power flow (PPF) analysis based on the generalized polynomial chaos expansion (gPCE) technique has been applied to investigate the effects of random parameters in the power system network. Nevertheless, the gPCE-based methods can be applied to the systems whose random parameters belong to specific probability distributions. If there exists a correlation among random parameters, then a decorrelation technique, for example, a copula function, is required. In this paper, a new approach for PPF analysis based on the arbitrary polynomial chaos expansion (aPCE) has been proposed. A set of basic polynomial functions used in this method is constructed using the recorded dataset of random parameters. The dependency of these uncertain parameters is decoupled by the whitening transformation. Instead of applying the aPCE to the required power system responses such as active power flow, current magnitude, etc, the proposed method uses the aPCE to construct a surrogate model of each bus voltage phasor. The bus voltage phasor model can then be employed to compute other power system responses without performing power flow analysis of the original power system. The proposed technique has been implemented and demonstrated in a MATLAB environment. On the basis of the numerical experiments with the modified IEEE 14-bus and 118-bus systems, the results indicate that the proposed method can achieve better accuracy and use less computation time than the techniques based on the gPCE with Gaussian copula function.