| 引用本文: | 廖家齐,于若英,刘瑜俊,等.基于自适应高斯混合模型的含高渗透率分布式光伏电力系统风险评估[J].电力系统保护与控制,2024,52(19):144-156.[点击复制] |
| LIAO Jiaqi,YU Ruoying,LIU Yujun,et al.Risk assessment of a power system with a high penetration of distributed photovoltaic based on self-adaptive Gaussian mixture model[J].Power System Protection and Control,2024,52(19):144-156[点击复制] |
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| 摘要: |
| 高渗透率分布式光伏(distributed photovoltaic, DPV)的接入增加了电力系统的运行风险。针对出力分布呈现形态复杂的特征，首先，提出一种基于改进近邻传播聚类的自适应高斯混合模型，优化了分布式光伏联合出力概率拟合迭代过程。然后，提出基于改进三阶多项式正态估计过程的Nataf变换方法，结合半不变量和Cornish-Fisher级数展开，实现分布式光伏出力相关性条件下的概率潮流计算。最后，采用电压越限和线路重过载指标计算电力系统运行风险。基于修改的IEEE 14节点电力系统，对不同分布式光伏渗透率的接入场景进行仿真。以蒙特卡洛模拟作为对比，结果表明所提方法在电网状态变量的概率分布计算上具有更高的精度，并验证了评估结果能够有效反映不同分布式光伏渗透率对电力系统风险水平的影响。 |
| 关键词: 分布式光伏 高斯混合模型 近邻传播聚类 Nataf变换 概率潮流 风险评估 |
| DOI：10.19783/j.cnki.pspc.240386 |
| 投稿时间：2024-04-07修订日期：2024-06-05 |
| 基金项目:国家重点研发计划项目资助(2022YFB2402900) |
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| Risk assessment of a power system with a high penetration of distributed photovoltaic based on self-adaptive Gaussian mixture model |
| LIAO Jiaqi1,YU Ruoying1,LIU Yujun1,YU Peng2,ZHOU Chang1,XU Xiaohui1 |
| (1. China Electric Power Research Institute, Nanjing 210003, China; 2. State Grid Shandong Electric
Power Company, Jinan 250001, China) |
| Abstract: |
| The connection of a high penetration of distributed photovoltaic (DPV) increases power system risk. Given the characteristics of complicated forms of power distribution, first a self-adaptive Gaussian mixture model based on the improved affinity propagation clustering is proposed to optimize the probability fitting iteration process of DPV joint output. Then a Nataf transformation method is given based on an improved third-order polynomial normal estimation process, and combined with a cumulant method and a Cornish-Fisher series, the probabilistic power flow calculation in conditions of correlation variables of DPV is realized. Finally, indicators of voltage limit and line overload are adopted to calculate the operational risk of the power system. Scenarios of different levels of DPV penetration are simulated using a modified IEEE 14-node power system. Compared with the Monte Carlo simulation, the results show that the method proposed has a higher accuracy on the probability distribution calculation of the status variables of the power grid. It is shown that the evaluation results can effectively reflect the effects of different penetration levels of DPV on the level of power system risk. |
| Key words: distributed photovoltaic Gaussian mixture model affinity propagation clustering Nataf transformation probabilistic power flow risk assessment |
