为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,...为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。展开更多
Introduction:Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind.The fluctuation of the wind generation has a great i...Introduction:Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind.The fluctuation of the wind generation has a great impact on the unit commitment.Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way.Methods:In this paper,a combined approach based on Extreme Learning Machine(ELM)and an error correction model is proposed to predict wind power in the short-term time scale.Firstly an ELM is utilized to forecast the short-term wind power.Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method.Results:For short-term forecasting,the Extreme Learning Machine(ELM)doesn’t perform well.The overall NRMSE(Normalized Root Mean Square Error)of forecasting results for 66 days is 21.09%.For the ultra-short term forecasting after error correction,most of forecasting errors lie in the interval of[-10 MW,10 MW].The error distribution is concentrated and almost unbiased.The overall NRMSE is 5.76%.Conclusion:The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error(NRMSE).展开更多
This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of w...This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.展开更多
文摘为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。
基金supported by National Natural Science Foundation of China under grants 51477174 and 51077126the Key Project of Chinese Ministry of Education under Grant 109017The authors also acknowledge Specialized Research Fund for the Doctoral Program of Higher Education of China under contract 20110008110042 and the support from China Electric Power Research Institute under contract DZB51201503568.
文摘Introduction:Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind.The fluctuation of the wind generation has a great impact on the unit commitment.Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way.Methods:In this paper,a combined approach based on Extreme Learning Machine(ELM)and an error correction model is proposed to predict wind power in the short-term time scale.Firstly an ELM is utilized to forecast the short-term wind power.Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method.Results:For short-term forecasting,the Extreme Learning Machine(ELM)doesn’t perform well.The overall NRMSE(Normalized Root Mean Square Error)of forecasting results for 66 days is 21.09%.For the ultra-short term forecasting after error correction,most of forecasting errors lie in the interval of[-10 MW,10 MW].The error distribution is concentrated and almost unbiased.The overall NRMSE is 5.76%.Conclusion:The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error(NRMSE).
基金the Key Research&Development Program of Xinjiang(Grant Number 2022B01003).
文摘This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.
