In this paper,a new identification and control scheme for the flexible joint robotic manipulator is proposed.Firstly,by defining some new state variables,the commonly used dynamic equations of the flexible joint robot...In this paper,a new identification and control scheme for the flexible joint robotic manipulator is proposed.Firstly,by defining some new state variables,the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model.Subsequently,an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations.Lastly,by using the singular perturbation theory,an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals.The closed-loop stability of the whole system is proved,and the effectiveness of the proposed schemes is verified by simulations.展开更多
According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak s...According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.展开更多
The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, a...The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.展开更多
柔性负荷参与新型电力系统的优化调度对于提高新能源的消纳能力具有显著作用,但目前柔性负荷潜力尚未充分挖掘。针对这一问题,提出一种基于源荷预测的日前-日内优化调度方法。首先,采用麻雀搜索算法优化卷积长短时记忆神经网络(sparrow ...柔性负荷参与新型电力系统的优化调度对于提高新能源的消纳能力具有显著作用,但目前柔性负荷潜力尚未充分挖掘。针对这一问题,提出一种基于源荷预测的日前-日内优化调度方法。首先,采用麻雀搜索算法优化卷积长短时记忆神经网络(sparrow search algorithm is used to optimize the convolutional long-term and short-term memory neural network,SSA-CNN-LSTM)对新能源和负荷进行日前和日内功率预测;其次,根据柔性负荷的特性和需求响应灵活性,将负荷分为可平移、可转移和可削减负荷等不同类型,以考虑阶梯式碳交易成本的系统运行成本和污染气体排放最优为目标构建源荷互动的日前-日内两阶段低碳环境经济调度模型;最后,利用改进多目标灰狼算法(multi-objective grey wolf algorithm,MOGWO)对模型进行求解。算例分析表明,通过对柔性负荷分类参与调度较传统方式总成本降低8.6%、污染物排放减少4.1%、新能源消纳能力提高4.2%,在多时间尺度内显著降低新能源和负荷响应的不确定性并提高新型电力系统的低碳环境经济综合效益。展开更多
海上具有极为丰富的风资源,研究海上风电制氢技术和荷侧需求响应对风电消纳及平抑海上风电波动性具有重要意义。为此,提出了一种含海上风电制氢(offshore wind power hydrogen production,OWHP)和多重需求响应的含氢综合能源系统(integr...海上具有极为丰富的风资源,研究海上风电制氢技术和荷侧需求响应对风电消纳及平抑海上风电波动性具有重要意义。为此,提出了一种含海上风电制氢(offshore wind power hydrogen production,OWHP)和多重需求响应的含氢综合能源系统(integrated energy system,IES)源-荷多时间尺度优化调度策略。首先,探究了海上风电制氢系统运行机理,构建了含风电制氢、氢气压缩、海水淡化、输氢管道以及气体储氢的海上风电制氢模型,并构建了含燃气掺氢、氢气甲烷化和氢燃料电池的氢能多重利用模型。其次,分析了荷侧资源在各时间尺度的调节特性,提出了多重需求响应模型。最后,为降低海上风电的预测误差对IES运行影响,提出了日前-日内-实时三阶段的多时间尺度优化模型,平抑系统功率波动。算例仿真结果表明,所提模型可有效消纳海上风电资源,提升IES经济、低碳性,并缓解源、荷不确定性对系统运行的影响。展开更多
高比例可再生能源并网和多样化负荷接入加剧了新型配电系统高运行成本和高电压偏差运行风险。针对目前新型配电系统配置阶段未在多时间尺度下考虑运行风险和多元时空灵活性资源协同配置的问题,提出考虑运行风险的新型配电系统多元时空...高比例可再生能源并网和多样化负荷接入加剧了新型配电系统高运行成本和高电压偏差运行风险。针对目前新型配电系统配置阶段未在多时间尺度下考虑运行风险和多元时空灵活性资源协同配置的问题,提出考虑运行风险的新型配电系统多元时空灵活性资源协同配置模型。首先,采用蒙特卡洛抽样和Kmeans聚类算法生成源-荷场景集,并利用多尺度形态学算法对源-荷曲线波形进行多尺度分解;然后,基于条件风险价值(conditional value at risk,CVaR)理论量化评估配电系统多时间尺度运行风险,并在此基础上建立考虑运行风险的新型配电系统多元时空灵活性资源双层配置模型。其中,上层以配电系统年综合成本最小为目标函数对多元时空灵活性资源进行协同配置,下层以期望损失值和基于CVaR的运行风险值最小为目标对系统进行运行优化。最后,通过改进的IEEE 33节点系统进行算例分析,结果表明:所提方法可以有效降低配电系统高运行成本和高电压偏差的运行风险。展开更多
Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few stu...Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale.Combined with hybrid energy storage,the comprehensive use of various uncertainty optimization methods under different time scales will be promising.This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties.Firstly,the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established.Then,an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage.On this basis,a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min,respectively.The results show that①the proposed method improves the economic benefits,and the intra-day and real-time scheduling costs are reduced,respectively;②by adjusting the uncertainty budget in the model,a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved;③reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions,thus reducing the cost of system scheduling.展开更多
基金the Natural Sciences and Engineering Research Council of Canada(No.N00892)。
文摘In this paper,a new identification and control scheme for the flexible joint robotic manipulator is proposed.Firstly,by defining some new state variables,the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model.Subsequently,an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations.Lastly,by using the singular perturbation theory,an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals.The closed-loop stability of the whole system is proved,and the effectiveness of the proposed schemes is verified by simulations.
基金support of the projects Youth Science Foundation of Gansu Province(Source-Grid-Load Multi-Time Interval Optimization Scheduling Method Considering Wind-PV-CSP Combined DC Transmission,No.22JR11RA148)Youth Science Foundation of Lanzhou Jiaotong University(Research on Coordinated Dispatching Control Strategy of High Proportion New Energy Transmission Power System with CSP Power Generation,No.2020011).
文摘According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.
文摘The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.
文摘柔性负荷参与新型电力系统的优化调度对于提高新能源的消纳能力具有显著作用,但目前柔性负荷潜力尚未充分挖掘。针对这一问题,提出一种基于源荷预测的日前-日内优化调度方法。首先,采用麻雀搜索算法优化卷积长短时记忆神经网络(sparrow search algorithm is used to optimize the convolutional long-term and short-term memory neural network,SSA-CNN-LSTM)对新能源和负荷进行日前和日内功率预测;其次,根据柔性负荷的特性和需求响应灵活性,将负荷分为可平移、可转移和可削减负荷等不同类型,以考虑阶梯式碳交易成本的系统运行成本和污染气体排放最优为目标构建源荷互动的日前-日内两阶段低碳环境经济调度模型;最后,利用改进多目标灰狼算法(multi-objective grey wolf algorithm,MOGWO)对模型进行求解。算例分析表明,通过对柔性负荷分类参与调度较传统方式总成本降低8.6%、污染物排放减少4.1%、新能源消纳能力提高4.2%,在多时间尺度内显著降低新能源和负荷响应的不确定性并提高新型电力系统的低碳环境经济综合效益。
文摘海上具有极为丰富的风资源,研究海上风电制氢技术和荷侧需求响应对风电消纳及平抑海上风电波动性具有重要意义。为此,提出了一种含海上风电制氢(offshore wind power hydrogen production,OWHP)和多重需求响应的含氢综合能源系统(integrated energy system,IES)源-荷多时间尺度优化调度策略。首先,探究了海上风电制氢系统运行机理,构建了含风电制氢、氢气压缩、海水淡化、输氢管道以及气体储氢的海上风电制氢模型,并构建了含燃气掺氢、氢气甲烷化和氢燃料电池的氢能多重利用模型。其次,分析了荷侧资源在各时间尺度的调节特性,提出了多重需求响应模型。最后,为降低海上风电的预测误差对IES运行影响,提出了日前-日内-实时三阶段的多时间尺度优化模型,平抑系统功率波动。算例仿真结果表明,所提模型可有效消纳海上风电资源,提升IES经济、低碳性,并缓解源、荷不确定性对系统运行的影响。
文摘高比例可再生能源并网和多样化负荷接入加剧了新型配电系统高运行成本和高电压偏差运行风险。针对目前新型配电系统配置阶段未在多时间尺度下考虑运行风险和多元时空灵活性资源协同配置的问题,提出考虑运行风险的新型配电系统多元时空灵活性资源协同配置模型。首先,采用蒙特卡洛抽样和Kmeans聚类算法生成源-荷场景集,并利用多尺度形态学算法对源-荷曲线波形进行多尺度分解;然后,基于条件风险价值(conditional value at risk,CVaR)理论量化评估配电系统多时间尺度运行风险,并在此基础上建立考虑运行风险的新型配电系统多元时空灵活性资源双层配置模型。其中,上层以配电系统年综合成本最小为目标函数对多元时空灵活性资源进行协同配置,下层以期望损失值和基于CVaR的运行风险值最小为目标对系统进行运行优化。最后,通过改进的IEEE 33节点系统进行算例分析,结果表明:所提方法可以有效降低配电系统高运行成本和高电压偏差的运行风险。
基金supported by the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.“Research on coordinated optimal configuration and operation control technology for long-term and short-term hybrid energy storage considering multi-time scale matching requirements”(No.5211DS230001).
文摘Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale.Combined with hybrid energy storage,the comprehensive use of various uncertainty optimization methods under different time scales will be promising.This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties.Firstly,the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established.Then,an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage.On this basis,a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min,respectively.The results show that①the proposed method improves the economic benefits,and the intra-day and real-time scheduling costs are reduced,respectively;②by adjusting the uncertainty budget in the model,a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved;③reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions,thus reducing the cost of system scheduling.
