According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
大规模风电并网导致电力系统惯量和一次调频响应资源减少,大扰动下系统频率安全问题突出。为应对风电不确定性和系统惯量降低的挑战,提出计及风电频率支撑能力和运行风险的鲁棒机组组合(unitcommitment,UC)模型。首先,通过系统发生有功...大规模风电并网导致电力系统惯量和一次调频响应资源减少,大扰动下系统频率安全问题突出。为应对风电不确定性和系统惯量降低的挑战,提出计及风电频率支撑能力和运行风险的鲁棒机组组合(unitcommitment,UC)模型。首先,通过系统发生有功扰动后频率偏差动力学摆动方程建立频率安全的运行约束模型,并嵌入到UC问题中。其次,考虑到风电出力不确定性,提出风电出力鲁棒可行域定义以表征系统接纳风电的安全运行范围,并基于此提出系统运行风险模型。最后,基于两阶段鲁棒优化理论提出计及风电频率支撑能力和运行风险的UC鲁棒优化模型,并采用列和约束生成(column and constraint generation,C&CG)算法求解该模型。在IEEE9和IEEE118节点测试系统进行仿真分析,结果验证了所提模型的有效性。展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
文摘大规模风电并网导致电力系统惯量和一次调频响应资源减少,大扰动下系统频率安全问题突出。为应对风电不确定性和系统惯量降低的挑战,提出计及风电频率支撑能力和运行风险的鲁棒机组组合(unitcommitment,UC)模型。首先,通过系统发生有功扰动后频率偏差动力学摆动方程建立频率安全的运行约束模型,并嵌入到UC问题中。其次,考虑到风电出力不确定性,提出风电出力鲁棒可行域定义以表征系统接纳风电的安全运行范围,并基于此提出系统运行风险模型。最后,基于两阶段鲁棒优化理论提出计及风电频率支撑能力和运行风险的UC鲁棒优化模型,并采用列和约束生成(column and constraint generation,C&CG)算法求解该模型。在IEEE9和IEEE118节点测试系统进行仿真分析,结果验证了所提模型的有效性。
