A MODELING METHOD FOR WIND AND SOLAR POWER SERIES BASED ON A TWO-STAGE CLUSTERING AND MCMC ALGORITHM

Guo Hongxia; Zou Guilin; Wang Ziqiang; Chen Lingxuan; Ma Qian; Chen Yiping

doi:10.19912/j.0254-0096.tynxb.2023-1473

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 491-502. DOI: 10.19912/j.0254-0096.tynxb.2023-1473

A MODELING METHOD FOR WIND AND SOLAR POWER SERIES BASED ON A TWO-STAGE CLUSTERING AND MCMC ALGORITHM

Guo Hongxia¹, Zou Guilin¹, Wang Ziqiang², Chen Lingxuan¹, Ma Qian², Chen Yiping²

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Abstract

In time series production simulation of wind-solar power systems, generating wind-solar output sequences with temporal and spatial correlation characteristics is crucial for improving the reliability of production simulation and guiding the planning and operation of power systems. A method is proposed to address the stochastic modeling problem of wind-solar output. It involves modeling wind-solar-related output sequences using a two-stage clustering and a two-layer Markov chain model. Different wind and solar typical daily output patterns are initially obtained through a two-stage clustering process. In the first stage, a self-organized mapping clustering method is used to identify solar output patterns under various meteorological conditions. The second stage utilizes the affinity propagation clustering method to group the wind power output samples that correspond to different solar output patterns. Secondly, a two-layer Markov chain model is constructed to depict the interdependent variations in wind-solar power output. A univariate Markov chain model is developed in the upper layer to illustrate the day-to-day transition of the wind-solar power pattern, while a bivariate Markov chain model is established in the lower layer to depict the state transition of the neighboring moments of the wind-solar power within the intraday. Finally, the MCMC simulation method is used to generate the wind-solar power sequence for a specified duration. The simulation example demonstrates that the proposed method outperforms the traditional MCMC method and Copula model in all evaluation indicators. It can generate a power output sequence that more accurately reflects the actual correlation between wind and solar.

Key words

time series / wind farm / solar power stations / cluster analysis / MCMC method / spatio-temporal correlation

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Guo Hongxia, Zou Guilin, Wang Ziqiang, Chen Lingxuan, Ma Qian, Chen Yiping. A MODELING METHOD FOR WIND AND SOLAR POWER SERIES BASED ON A TWO-STAGE CLUSTERING AND MCMC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 491-502 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1473

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