风电出力随机波动及难以准确预测和调控的特性使得大规模风电接入给系统的备用决策和发电调度带来难题。在定义期望失负荷比例(expected load not supplied ratio,ELNSR)的基础上,综合考虑机组强迫停运率、负荷及风电出力预测误差等不...风电出力随机波动及难以准确预测和调控的特性使得大规模风电接入给系统的备用决策和发电调度带来难题。在定义期望失负荷比例(expected load not supplied ratio,ELNSR)的基础上,综合考虑机组强迫停运率、负荷及风电出力预测误差等不确定性因素,推导出系统运行备用与ELNSR之间的量化关系,并将该量化关系作为发电调度的约束,建立含大规模风电的电力系统发电和备用协调调度模型。算例结果表明所建模型能够兼顾经济性和可靠性,协调风电和火电的出力分配,并能给出对应可靠性要求的运行备用在火电机组间的优化分配方案。展开更多
Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Th...Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.展开更多
文摘风电出力随机波动及难以准确预测和调控的特性使得大规模风电接入给系统的备用决策和发电调度带来难题。在定义期望失负荷比例(expected load not supplied ratio,ELNSR)的基础上,综合考虑机组强迫停运率、负荷及风电出力预测误差等不确定性因素,推导出系统运行备用与ELNSR之间的量化关系,并将该量化关系作为发电调度的约束,建立含大规模风电的电力系统发电和备用协调调度模型。算例结果表明所建模型能够兼顾经济性和可靠性,协调风电和火电的出力分配,并能给出对应可靠性要求的运行备用在火电机组间的优化分配方案。
文摘随着波动性的风光等新能源并网比例不断提高,电力系统需要配备更多的调节能力。如何量化应对新能源出力等不确定性所需的调节能力是大规模新能源接入系统面临的一个新问题。该文采用通用生成函数(universal generating function,UGF)建立包括风电出力、负荷出力及机组随机故障的不确定性模型,进而将UGF与随机生产模拟(probabilistic production simulation,PPS)相结合,旨在建立反映发电侧调节能力的运行备用容量与可靠性之间的关系;并通过建立日前发电-备用双层模型实现确保系统可靠运行的发电计划。上层规划模型根据预测的负荷、结合风电预测出力制定基于指定可靠性的备用容量约束的日前机组组合方案;下层模型考虑各种不确定性因素,利用基于UGF的PPS建立系统运行备用与可靠性的量化关系,进而校验上层规划的机组组合方案是否能提供足够的备用,不足时则反馈给上层进行修正。通过改进的IEEE-118节点系统的仿真计算验证了所提模型的合理性和方法的有效性。
基金supported in part by the Natural Sciences and Engineering Research Council(NSERC)of Canada and the Saskatchewan Power Corporation(SaskPower).
文摘Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.
