A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production proc...A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.展开更多
This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,s...This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.展开更多
This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power sys...This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.展开更多
Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a ...Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.展开更多
The sudden generation-consumption imbalance is becoming more frequent in modern power systems, causing voltage and frequency stability issues. One potential solution is load participation in primary control. We formul...The sudden generation-consumption imbalance is becoming more frequent in modern power systems, causing voltage and frequency stability issues. One potential solution is load participation in primary control. We formulate a novel optimal load control(NOLC) problem that aims to minimize the disutility of controllable loads in providing primary regulation. In this paper, we show that the network dynamics, coupled with welldefined load control(obtained via optimality condition), can be seen as an optimization algorithm to solve the dual problem of NOLC. Unlike most existing load control frameworks that only consider frequency response, our load-side primary control focuses on frequency, voltage, and aggregate cost. Simulation results imply that the NOLC approach can ensure better frequency and voltage regulations. Moreover, the coordination between NOLC and other devices enabled in the system, the NOLC performance against the total size of controllable loads, and the NOLC effectiveness in a multi-machine power system are also verified in MATLAB/Simulink.展开更多
Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solva...Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U1911205,62073300,and 62076225)the National Key Research and Development Program of China(No.2021YFB3301602).
文摘A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.
文摘This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.
基金supported by the National Natural Science Foundation of China(Grant 62103101)the Natural Science Foundation of Jiangsu Province of China(Grant BK20210217)+5 种基金the China Postdoctoral Science Foundation(Grant 2022M710680)the National Natural Science Foundation of China(Grant 62273094)the"Zhishan"Scholars Programs of Southeast Universitythe Fundamental Science(Natural Science)General Program of Jiangsu Higher Education Institutions(No.21KJB470020)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202102)the Introduced Talents Scientific Research Start-up Fund Project,Nanjing Institute of Technology(No.YKJ202133).
文摘This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.
文摘Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.
基金supported in part by the National Natural Science Foundation of China (No.U1909201)。
文摘The sudden generation-consumption imbalance is becoming more frequent in modern power systems, causing voltage and frequency stability issues. One potential solution is load participation in primary control. We formulate a novel optimal load control(NOLC) problem that aims to minimize the disutility of controllable loads in providing primary regulation. In this paper, we show that the network dynamics, coupled with welldefined load control(obtained via optimality condition), can be seen as an optimization algorithm to solve the dual problem of NOLC. Unlike most existing load control frameworks that only consider frequency response, our load-side primary control focuses on frequency, voltage, and aggregate cost. Simulation results imply that the NOLC approach can ensure better frequency and voltage regulations. Moreover, the coordination between NOLC and other devices enabled in the system, the NOLC performance against the total size of controllable loads, and the NOLC effectiveness in a multi-machine power system are also verified in MATLAB/Simulink.
基金This work was supported by the Dalian Key Field Innovation Team Project(2020RT04)Airport Terminal Wisdom Environment Security and Energy Saving Laboratory of Guangdong Airport Baiyun Information Technology Co.,Ltd.in China.
文摘Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.
