Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Sui, Bingyan; Hou, Kai; Jia, Hongjie; Mu, Yunfei; Yu, Xiaodan

doi:10.1007/s40565-018-0384-6

Cited by 17 publications

(4 citation statements)

References 30 publications

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“…The skewness Sk and kurtosis Ku of the distribution reflect the extent of departure from the symmetry of the normal distribution [34]. Through mathematical derivation, the Sk and Ku can be expressed by the variable's third cumulant κ3 and the fourth cumulant κ4 in standard measure, usually as shown in (25).…”

Section: A Pre-identification Indices For CM Selectionmentioning

confidence: 99%

“…All the case studies are performed with the MATLAB platform. The bus active and reactive basic loads in power system are generally considered to satisfy the normal distribution and their standard deviations are assumed to be 10% [19], [27] or 30% [25] of their expectations. In the below case studies, assume that the standard deviations of all bus active and reactive basic loads are 30% of their expectations.…”

Section: Case Studymentioning

confidence: 99%

“…ME is used in PLF to make a reasonable inference on the unknown probability distribution of random power flow. In [25], one bus is integrated with 30 MW wind turbines based on the IEEE 118-bus system. As the change of scenarios, ME always gets accurate PDF.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

2021

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The increase of wind generation has challenged the conventional way of probabilistic load flow (PLF) calculation. A reliable and efficient PLF method is required to face the stochastic nature of various power systems with wind generation (WG). Firstly, the paper analyzes several typical cumulant methods (CMs) for PLF, such as Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), and maximum entropy (ME). Then, an improved integrated CM by probability distribution pre-identification is proposed for power systems with WG based on doubly fed induction generations (DFIGs). The skewness and kurtosis are used as probability distribution pre-identification indices in the CM framework. Meanwhile, the influence of the DFIG control strategy on reactive power is considered in the load flow model and the moment calculation. Finally, the accuracy and efficiency of the proposed method are validated with the IEEE test system. In various scenarios, suitable CM is selected and applied to the PLF based on pre-identifying distribution characteristics. Results reveal that probabilistic density functions (PDFs) of bus voltages and line flows obtained by the proposed method have both accuracy and efficiency.INDEX TERMS Probabilistic load flow (PLF), integrated cumulant method (integrated CM), doubly fed induction generation (DFIG), Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), maximum entropy (ME).

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Section: A Pre-identification Indices For CM Selectionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

2021

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“…Reference [27] applied Gaussian mixture approximation method to handle non-normal correlated random variables. References [28,29] applied the maximum entropy instead of series expansions to calculate probability density functions (PDFs). It could improve the accuracy of PDFs, but required a relatively more complex process.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic load flow method considering large-scale wind power integration

Deng

Zhang

Jin

et al. 2019

J. Mod. Power Syst. Clean Energy

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The increasing penetration of wind power brings great uncertainties into power systems, which poses challenges to system planning and operation. This paper proposes a novel probabilistic load flow (PLF) method based on clustering technique to handle large fluctuations from large-scale wind power integration. The traditional cumulant method (CM) for PLF is based on the linearization of load flow equations around the operating point, therefore resulting in significant errors when input random variables have large fluctuations. In the proposed method, the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster. With such pre-processing, the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy. The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples. The proposed method is validated on modified IEEE 9-bus and 118-bus test systems with additional wind farms. Compared with the traditional CM, 2m?1 point estimate method (PEM), Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS) based MCS, the proposed method can achieve a better performance with consideration of both computational efficiency and accuracy.

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Security risk assessment of wind integrated power system using Parzen window density estimation

2022

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Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Cited by 17 publications

References 30 publications

An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

Probabilistic load flow method considering large-scale wind power integration

Security risk assessment of wind integrated power system using Parzen window density estimation

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