Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demand...Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance optimization.Scheduling strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread attention.This paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency.Subsequently,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling modes.The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments.Finally,the limitations of the existing schemes are analyzed,and prospects are envisioned.展开更多
To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track ...To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track was derived based on the principle of stationary potential energy.Considering interlayer evolution and structural crack propagation,an optimized joint repair model for the track was established and validated.Subsequently,the impact of joint repair on track damage and arch stability under extreme temperatures was studied,and a comprehensive evaluation of the feasibility of joint repair and the evolution of damage after repair was conducted.The results show that after the joint repair,the temperature rise of the initial damage of the track structure can be increased by 11℃.Under the most unfavorable heating load with a superimposed temperature gradient,the maximum stiffness degradation index SDEG in the track structure is reduced by about 81.16%following joint repair.The joint repair process could effectively reduce the deformation of the slab arching under high temperatures,resulting in a reduction of 93.96%in upward arching deformation.After repair,with the damage to interfacing shear strength,the track arch increases by 2.616 mm.展开更多
The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distribute...The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.展开更多
This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to re...This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to reflect the structural response and the joint load distribution under large deformation.To avoid a failure of fastener joints,topology optimization is then carried out to minimize the structural end compliance in the equilibrium state while controlling joint loads intensities over fasteners.During nonlinear analysis and optimization,a novel implementation of adjoint vector sensitivity analysis along with super element condensation is introduced to address numerical instability issues.The degrees of freedom of weak regions are condensed so that their influences are excluded from the iterative Newton-Raphson(NR)solution.Numerical examples are presented to validate the efficiency and robustness of the proposed method.The effects of joint load constraints and geometrical nonlinearity are highlighted by comparing numerical optimization results.展开更多
Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to e...Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.展开更多
With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those tec...With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future.展开更多
This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a no...This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.展开更多
The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the ope...The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.展开更多
Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
The purpose of this paper is to present an extended topology optimization method for the stiffeners layout design of aircraft assembled structures. Multi-fastener joint loads and manufacturing constraints are consider...The purpose of this paper is to present an extended topology optimization method for the stiffeners layout design of aircraft assembled structures. Multi-fastener joint loads and manufacturing constraints are considered simultaneously. On one hand, the joint loads are calculated and constrained within a limited value to avoid the failure of fasteners. On the other hand, the manufacturing constraints of the material distribution in the machining directions of stiffeners are implemented by an improved piecewise interpolation based on a beveled cut-surface. It is proven that the objective function is strictly continuous and differentiable with respect to the piecewise interpolation. The effects of the extended method with two different constraints are highlighted by typical numerical examples. Compared with the standard topology optimization, the final designs have clearly shown the layout of stiffeners and the joint loads have been perfectly constrained to a satisfying level.展开更多
A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video tra...A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.展开更多
This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is use...This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is used to determine the loading conditions of the system and maximum system loading capacity. The index value has to be minimum in the optimal network reconfiguration of load balancing. The tabu search algorithm is employed to search for the optimal network reconfiguration. The basic idea behind the search is a move from a current solution to its neighborhood by effectively utilizing a memory to provide an efficient search for optimality. It presents low computational effort and is able to find good quality configurations. Simulation results for a radial 69-bus system. The study results show that the optimal on/off patterns of the switches can be identified to give the best network reconfiguration involving balancing of feeder loads while respecting all the constraints.展开更多
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金supported in part by the Key Research and Development Program of Shaanxi under Grant 2023-ZDLGY-34.
文摘Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance optimization.Scheduling strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread attention.This paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency.Subsequently,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling modes.The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments.Finally,the limitations of the existing schemes are analyzed,and prospects are envisioned.
基金Project(K2022G038)supported by the Science Technology Research and Development Program of China State Railway Group Co.,LtdProject(52178405)supported by the National Natural Science Foundation of China。
文摘To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track was derived based on the principle of stationary potential energy.Considering interlayer evolution and structural crack propagation,an optimized joint repair model for the track was established and validated.Subsequently,the impact of joint repair on track damage and arch stability under extreme temperatures was studied,and a comprehensive evaluation of the feasibility of joint repair and the evolution of damage after repair was conducted.The results show that after the joint repair,the temperature rise of the initial damage of the track structure can be increased by 11℃.Under the most unfavorable heating load with a superimposed temperature gradient,the maximum stiffness degradation index SDEG in the track structure is reduced by about 81.16%following joint repair.The joint repair process could effectively reduce the deformation of the slab arching under high temperatures,resulting in a reduction of 93.96%in upward arching deformation.After repair,with the damage to interfacing shear strength,the track arch increases by 2.616 mm.
文摘The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.
基金co-supported by National Key Research and Development Program(No.2017YFB1102800)NSFC for Excellent Young Scholars(No.11722219)Key Project of NSFC(Nos.51790171,5171101743,51735005,11620101002,and 11432011).
文摘This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to reflect the structural response and the joint load distribution under large deformation.To avoid a failure of fastener joints,topology optimization is then carried out to minimize the structural end compliance in the equilibrium state while controlling joint loads intensities over fasteners.During nonlinear analysis and optimization,a novel implementation of adjoint vector sensitivity analysis along with super element condensation is introduced to address numerical instability issues.The degrees of freedom of weak regions are condensed so that their influences are excluded from the iterative Newton-Raphson(NR)solution.Numerical examples are presented to validate the efficiency and robustness of the proposed method.The effects of joint load constraints and geometrical nonlinearity are highlighted by comparing numerical optimization results.
基金Supported by National Key Research and Development Program of China (Grant Nos.2022YFB4703000,2019YFB1309900)。
文摘Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.
文摘With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future.
文摘This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.
基金supported by the National Natural Science Foundation of China (No. 51178141)National Major Science and Technology Program for Water Pollution Control and Treatment (2012ZX07408-002-004-002)
文摘The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
基金supported by National Natural Science Foundation of China (Nos. 11432011, 11620101002)National key research and development program of China (No. 2017YFB1102800)Key Research and Development Program of Shaanxi, China (No. S2017-ZDYF-ZDXM-GY-0035)
文摘The purpose of this paper is to present an extended topology optimization method for the stiffeners layout design of aircraft assembled structures. Multi-fastener joint loads and manufacturing constraints are considered simultaneously. On one hand, the joint loads are calculated and constrained within a limited value to avoid the failure of fasteners. On the other hand, the manufacturing constraints of the material distribution in the machining directions of stiffeners are implemented by an improved piecewise interpolation based on a beveled cut-surface. It is proven that the objective function is strictly continuous and differentiable with respect to the piecewise interpolation. The effects of the extended method with two different constraints are highlighted by typical numerical examples. Compared with the standard topology optimization, the final designs have clearly shown the layout of stiffeners and the joint loads have been perfectly constrained to a satisfying level.
基金National Natural Science Foundation of China(No.61301101)
文摘A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.
文摘This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is used to determine the loading conditions of the system and maximum system loading capacity. The index value has to be minimum in the optimal network reconfiguration of load balancing. The tabu search algorithm is employed to search for the optimal network reconfiguration. The basic idea behind the search is a move from a current solution to its neighborhood by effectively utilizing a memory to provide an efficient search for optimality. It presents low computational effort and is able to find good quality configurations. Simulation results for a radial 69-bus system. The study results show that the optimal on/off patterns of the switches can be identified to give the best network reconfiguration involving balancing of feeder loads while respecting all the constraints.
