基于切片采样的风力发电并网系统概率潮流计算

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Abstract Gibbs sampling algorithm that is widely used in Markov Chain Monte Carlo (MCMC) simulation method suffers from complicated sampling iterations when accurate results from probabilistic load flow is required.According to the defect,an improved MCMC method based on Slice sampling is proposed in this paper and is integrated into probabilistic load flow algorithm for wind farms integration system.The probabilistic model of wind farm outputs is firstly constructed by weighted Gaussian mixture distribution (WGMD).Then,the sample space of wind farm outputs is obtained by Slice sampling from the WGMD of wind farm outputs.Finally,the samples from the sample space of wind farm outputs are calculated by load flow and the results of these two sampling methods are compared in IEEE 39-bus system.It is shown that the proposed method can distinctly improve the calculation accuracy of MCMC method.Additionally,the Markov Chain generated by Slice sampling can reach a stationary distribution more quickly and stably than Gibbs sampling with the same iterations.

Key words： Wind farms integration probabilistic load flow slice sampling Gibbs sampling Markov Chain Monte Carlo simulation method

Received: 10 July 2015 Published: 26 December 2016

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TM712

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	Zhang Xiaoying
	Wang Kun
	Zhang Labao

Cite this article:

Zhang Xiaoying,Wang Kun,Zhang Labao. Probabilistic Load Flow Calculation Based on Slice Sampling for Wind Farms Integration System[J]. Transactions of China Electrotechnical Society, 2016, 31(23): 100-106.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I23/100

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