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<p>With the large-scale integration of renewable energy sources (RES) into modern power systems, their intermittence and volatility have posed new challenges to system reliability. To improve the accuracy of the reliability evaluation of generation and transmission systems containing large-scale wind and Photovoltaic (PV) power, this paper proposes a reliability evaluation method of generation and transmission systems based on the hybrid Copula wind-PV combined output model optimized by Random Forest (RF), considering the correlation between the wind and PV power output. A hybrid Copula model optimized by Random Forest is established to characterize wind-PV correlation and generate combined output scenarios. An improved K-means++ algorithm is adopted for efficient scenario reduction. Using Latin Hypercube Sampling (LHS) and Importance Sampling (IS), the method is validated on IEEE-RTS79 with real wind-PV data. Furthermore, the potential influence of the proportion of wind and PV power installed capacity, the RES penetration and the capacity of energy storage on the reliability of generation and transmission systems is also analyzed.</p>

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Reliability Evaluation of Generation and Transmission Systems Based on the Hybrid Copula Wind-Solar Combined Output Model with Random Forest Optimization

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Haijun Xing,
  • Xiao Yang,
  • Yiwen Sun,
  • Ye Tang,
  • Haojie He

摘要

With the large-scale integration of renewable energy sources (RES) into modern power systems, their intermittence and volatility have posed new challenges to system reliability. To improve the accuracy of the reliability evaluation of generation and transmission systems containing large-scale wind and Photovoltaic (PV) power, this paper proposes a reliability evaluation method of generation and transmission systems based on the hybrid Copula wind-PV combined output model optimized by Random Forest (RF), considering the correlation between the wind and PV power output. A hybrid Copula model optimized by Random Forest is established to characterize wind-PV correlation and generate combined output scenarios. An improved K-means++ algorithm is adopted for efficient scenario reduction. Using Latin Hypercube Sampling (LHS) and Importance Sampling (IS), the method is validated on IEEE-RTS79 with real wind-PV data. Furthermore, the potential influence of the proportion of wind and PV power installed capacity, the RES penetration and the capacity of energy storage on the reliability of generation and transmission systems is also analyzed.