The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To th...The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.展开更多
Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy...Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.展开更多
The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints are to be considered jointly is inrestigated. Firstly, an average cost function of...The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints are to be considered jointly is inrestigated. Firstly, an average cost function of the tracking error and the plant input energy over a class of stochastic model errors are defined. Then, we obtain an internal model controller design method that minimizes the average performance and further studies optimal tracking performance for integrator and dead time plant in the simultaneous presence of plant uncertainty and control energy constraint. The results can be used to evaluate optimal tracking performance and control energy in practical designs.展开更多
为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integ...为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integrated demand response,IDR)模型。然后,为减少源、荷预测误差对IES运行的影响,分别构建日前低碳经济调度模型和日内双时间尺度滚动优化平抑模型。最后,算例仿真设置不同场景进行对比分析。结果表明,相比传统IDR,多重IDR能有效挖掘用户响应潜力,提升系统经济性。此外,计及多重IDR的多时间尺度调度策略能有效缓解源、荷误差带来的功率波动并降低系统碳排放量,实现IES低碳、经济和稳定运行。展开更多
随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,...随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。展开更多
Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the...Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.展开更多
为解决综合能源系统(integrated energy systems,IES)难以量质协同运行,且求解时间较长的问题,该文提出了一种计及[火用]效率的电-气-热综合能源系统多目标优化调度方法。首先,该文构建了IES模型,结合热力学第一、二定律,推导了该IES不...为解决综合能源系统(integrated energy systems,IES)难以量质协同运行,且求解时间较长的问题,该文提出了一种计及[火用]效率的电-气-热综合能源系统多目标优化调度方法。首先,该文构建了IES模型,结合热力学第一、二定律,推导了该IES不同能源网络的源侧和负荷侧的[火用]方程以及[火用]效率等表达式;其次,为降低求解时间,提出了一个基于线性权重法的多目标函数求解与决策方法;最后,通过算例分析,验证了所提模型与方法的有效性。分析结果表明,相比于传统IES的优化问题,模型中考虑[火用]效率的多目标函数保证了能源品质的传输,促进了IES的量质协同运行,且该文所提求解方法的求解时间相比于遍历权重法降低了90.22%。展开更多
区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然...区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然后,建立碳排放量最少和运行成本最小的多目标优化模型,引入碳排放惩罚因子,将多目标转换为单目标进行求解;最后,通过实际RIES进行仿真验证,仿真结果表明所提方法的准确性与有效性。所建模型可以很好地兼顾系统的环保性和经济性,能够更好地处理不确定性,实现系统的经济优化运行。展开更多
基金supported by the National Natural Science Foundation of China(No.12171145)。
文摘The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.
基金National Natural Science Foundation of China under Grant Nos.51639006 and 51725901
文摘Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.
基金the High Technology Research and Development (863) Program (2003AA517020).
文摘The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints are to be considered jointly is inrestigated. Firstly, an average cost function of the tracking error and the plant input energy over a class of stochastic model errors are defined. Then, we obtain an internal model controller design method that minimizes the average performance and further studies optimal tracking performance for integrator and dead time plant in the simultaneous presence of plant uncertainty and control energy constraint. The results can be used to evaluate optimal tracking performance and control energy in practical designs.
文摘为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integrated demand response,IDR)模型。然后,为减少源、荷预测误差对IES运行的影响,分别构建日前低碳经济调度模型和日内双时间尺度滚动优化平抑模型。最后,算例仿真设置不同场景进行对比分析。结果表明,相比传统IDR,多重IDR能有效挖掘用户响应潜力,提升系统经济性。此外,计及多重IDR的多时间尺度调度策略能有效缓解源、荷误差带来的功率波动并降低系统碳排放量,实现IES低碳、经济和稳定运行。
文摘随着电动汽车(electric vehicle,EV)普及度的不断提高,工业园区内的EV用户日益增多,其充放电行为给园区综合能源系统(park integrated energy system,PIES)的规划运行带来极大挑战。文中提出考虑EV充放电意愿的PIES双层优化调度。首先,基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的EV充放电模型;然后,以PIES总成本最小和EV充电费用最小为目标建立双层优化调度模型,通过Karush-Kuhn-Tucker(KKT)条件将内层模型转化为外层模型的约束条件,从而快速稳定地实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的PIES双层优化调度的有效性和可行性。
文摘Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.
文摘为解决综合能源系统(integrated energy systems,IES)难以量质协同运行,且求解时间较长的问题,该文提出了一种计及[火用]效率的电-气-热综合能源系统多目标优化调度方法。首先,该文构建了IES模型,结合热力学第一、二定律,推导了该IES不同能源网络的源侧和负荷侧的[火用]方程以及[火用]效率等表达式;其次,为降低求解时间,提出了一个基于线性权重法的多目标函数求解与决策方法;最后,通过算例分析,验证了所提模型与方法的有效性。分析结果表明,相比于传统IES的优化问题,模型中考虑[火用]效率的多目标函数保证了能源品质的传输,促进了IES的量质协同运行,且该文所提求解方法的求解时间相比于遍历权重法降低了90.22%。
文摘区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然后,建立碳排放量最少和运行成本最小的多目标优化模型,引入碳排放惩罚因子,将多目标转换为单目标进行求解;最后,通过实际RIES进行仿真验证,仿真结果表明所提方法的准确性与有效性。所建模型可以很好地兼顾系统的环保性和经济性,能够更好地处理不确定性,实现系统的经济优化运行。
