基于随机潮流和风险价值的含大规模风电系统高风险连锁故障评估

随着系统中风电渗透率的增加,风电出力不确定性对电力系统连锁故障演化路径的影响不可忽视。基于此,引入随机潮流和风险价值理论,构建一种高风险连锁故障事故链模型,分析含大规模风电系统连锁故障事件的风险程度。首先基于多点风电随机注入相关性的随机潮流方法,提出了前瞻故障实时概率和严重度的后续故障搜索指标,据其风险价值选取高风险故障作为下级故障。其次,为反映风电随机性对风险指标的影响,采用模糊聚类建立风电场景,计算系统失负荷损失风险指标。基于该指标可分析当前运行点处于安全运行区域还是连锁故障高风险区域,指导系统日内调度。最后以改进的IEEE 39节点系统为例,说明了所提模型和算法的合理性,并分析了风电渗透率和风电预测误差对连锁故障停电风险的影响。

High Risk Cascading Outage Assessment in Power Systems With Large-scale Wind Power Based on Stochastic Power Flow and Value at Risk

With increase of wind power penetration rate in power system, the influence of wind power output uncertainty on evolution path of power system cascading outage cannot be ignored. Based on this, this paper proposes a high risk fault chain model, and uses stochastic power flow and value at risk theory to analyze the risk level of cascading outages. Firstly, during fault chain forecasting, the follow-up fault search indicators combining real-time failure probability and fault consequence are constructed based on stochastic power flow considering correlation of wind power. Subsequent faults are selected according to the values at risk of these indicators. Secondly, in order to reflect the impact of wind power randomness on risk indicators, fuzzy clustering is used to establish wind power scenarios, then system loss of a load risk indicator is calculated. Based on the indicator, it can be analyzed whether current operating point is in a safe or a high-risk area, thereby guiding the system intraday scheduling. Finally, the improved IEEE 39-node system is taken as an example to illustrate rationality of the proposed model and algorithm. And the influence of wind power penetration rate and wind power forecasting error on the risk of cascading outages are analyzed.