TY - GEN
T1 - A Copula-Based Uncertainty Modeling of Wind Power Generation for Probabilistic Power Flow Study
AU - Philippe, Wolf Peter Jean
AU - Eftekharnejad, Sara
AU - Ghosh, Prasanta K.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In this paper, a probabilistic power flow (PPF) modeling of the uncertainty of wind power generation is proposed. The developed PPF is based on the theories of point estimate method (PEM) and regular vine (R-vine) copula. For a large number of wind power resources, the computational burden related to construction of an R-vine copula increases significantly. Therefore, an algorithm based on Kullback-Leibler (KL) distance is introduced to tackle the computational burden to form an R-vine. The proposed PPF is tested on two different testbeds: IEEE 57-bus and Illinois 200-bus systems. The obtained results show that the proposed method is effective and accurate when compared to PPF results obtained by either a Monte Carlo simulation (MCS) or a multivariate Gaussian copula.
AB - In this paper, a probabilistic power flow (PPF) modeling of the uncertainty of wind power generation is proposed. The developed PPF is based on the theories of point estimate method (PEM) and regular vine (R-vine) copula. For a large number of wind power resources, the computational burden related to construction of an R-vine copula increases significantly. Therefore, an algorithm based on Kullback-Leibler (KL) distance is introduced to tackle the computational burden to form an R-vine. The proposed PPF is tested on two different testbeds: IEEE 57-bus and Illinois 200-bus systems. The obtained results show that the proposed method is effective and accurate when compared to PPF results obtained by either a Monte Carlo simulation (MCS) or a multivariate Gaussian copula.
KW - Monte Carlo Simulation
KW - point estimate method
KW - probabilistic power flow
KW - vine copula
KW - wind power generation
UR - http://www.scopus.com/inward/record.url?scp=85074096317&partnerID=8YFLogxK
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U2 - 10.1109/SEGE.2019.8859746
DO - 10.1109/SEGE.2019.8859746
M3 - Conference contribution
AN - SCOPUS:85074096317
T3 - Proceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019
SP - 218
EP - 222
BT - Proceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2019
Y2 - 12 August 2019 through 14 August 2019
ER -