With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to eva...With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.展开更多
已有关于系统惯性时空分布(spatial-temporal distribution of system inertia,SDSI)的研究大多聚焦于惯量估计,而对其概念的表征并不统一,也缺乏对其性质的深入剖析与相关概念的系统梳理与辨析。该文试图探索大扰动下系统惯性时空分布...已有关于系统惯性时空分布(spatial-temporal distribution of system inertia,SDSI)的研究大多聚焦于惯量估计,而对其概念的表征并不统一,也缺乏对其性质的深入剖析与相关概念的系统梳理与辨析。该文试图探索大扰动下系统惯性时空分布特性的表征形式。基于频率动态过程分析剖析了惯性时空分布特性,提出系统节点惯量概念并给出该参量的性质描述参量,基于惯性物理机理提出了这些参量的量化分析方法并推导了相关系数的差分计算公式。仿真算例验证了对系统惯性时空分布特性所做的分析与判断,利用所提出的表征形式可全面而规范地表征系统惯性时空分布特征。研究可为电力系统惯性时空分布特性表征体系的构建提供参考。展开更多
基金supported in part by the National Key R&D Program of China (No.2017YFE0109000)the project of China Datang Corporation Ltd
文摘With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.
文摘已有关于系统惯性时空分布(spatial-temporal distribution of system inertia,SDSI)的研究大多聚焦于惯量估计,而对其概念的表征并不统一,也缺乏对其性质的深入剖析与相关概念的系统梳理与辨析。该文试图探索大扰动下系统惯性时空分布特性的表征形式。基于频率动态过程分析剖析了惯性时空分布特性,提出系统节点惯量概念并给出该参量的性质描述参量,基于惯性物理机理提出了这些参量的量化分析方法并推导了相关系数的差分计算公式。仿真算例验证了对系统惯性时空分布特性所做的分析与判断,利用所提出的表征形式可全面而规范地表征系统惯性时空分布特征。研究可为电力系统惯性时空分布特性表征体系的构建提供参考。
