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
针对可再生能源高比例渗透下多区域综合能源互联系统(Multi-region integrated energy interconnection system,MRIEIS)所面临的消纳与经济性挑战,构建了一个包含电、氢、热、冷多能流的多时间尺度优化调度模型。该模型以系统日总运行...针对可再生能源高比例渗透下多区域综合能源互联系统(Multi-region integrated energy interconnection system,MRIEIS)所面临的消纳与经济性挑战,构建了一个包含电、氢、热、冷多能流的多时间尺度优化调度模型。该模型以系统日总运行成本最低为目标,建立动态多能流枢纽深度集成了电转氢(Power to hydrogen,P2H)、储氢罐、氢气管网以及燃气轮机(GT)掺氢等动态调度关键技术。通过日前、日内、实时三阶段滚动优化对系统进行精细化调度。算例分析基于一个包含居民、工业和混合型区域的典型场景,结果表明,该模型能够有效实现系统经济性与环保性的统一,总运行成本控制在56.48万元,同时系统总可再生能源利用率高达98.53%。氢能作为灵活的能量载体,其时空价值得到了充分发挥。掺氢策略有效刺激了氢能消耗,形成了“制-储-输-用”的闭环,为构建以新能源为主体的新型电力系统提供了可行的技术路径和调度策略参考。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.
文摘针对可再生能源高比例渗透下多区域综合能源互联系统(Multi-region integrated energy interconnection system,MRIEIS)所面临的消纳与经济性挑战,构建了一个包含电、氢、热、冷多能流的多时间尺度优化调度模型。该模型以系统日总运行成本最低为目标,建立动态多能流枢纽深度集成了电转氢(Power to hydrogen,P2H)、储氢罐、氢气管网以及燃气轮机(GT)掺氢等动态调度关键技术。通过日前、日内、实时三阶段滚动优化对系统进行精细化调度。算例分析基于一个包含居民、工业和混合型区域的典型场景,结果表明,该模型能够有效实现系统经济性与环保性的统一,总运行成本控制在56.48万元,同时系统总可再生能源利用率高达98.53%。氢能作为灵活的能量载体,其时空价值得到了充分发挥。掺氢策略有效刺激了氢能消耗,形成了“制-储-输-用”的闭环,为构建以新能源为主体的新型电力系统提供了可行的技术路径和调度策略参考。
