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
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
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
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.
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.
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。
