This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute ac...This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.展开更多
Cooperative multi-agent reinforcement learning（ MARL） is an important topic in the field of artificial intelligence,in which distributed constraint optimization（ DCOP） algorithms have been widely used to coordinat...Cooperative multi-agent reinforcement learning（ MARL） is an important topic in the field of artificial intelligence,in which distributed constraint optimization（ DCOP） algorithms have been widely used to coordinate the actions of multiple agents. However,dense communication among agents affects the practicability of DCOP algorithms. In this paper,we propose a novel DCOP algorithm dealing with the previous DCOP algorithms＇ communication problem by reducing constraints.The contributions of this paper are primarily threefold：（1） It is proved that removing constraints can effectively reduce the communication burden of DCOP algorithms.（2） An criterion is provided to identify insignificant constraints whose elimination doesn＇t have a great impact on the performance of the whole system.（3） A constraint-reduced DCOP algorithm is proposed by adopting a variant of spectral clustering algorithm to detect and eliminate the insignificant constraints. Our algorithm reduces the communication burdern of the benchmark DCOP algorithm while keeping its overall performance unaffected. The performance of constraint-reduced DCOP algorithm is evaluated on four configurations of cooperative sensor networks. The effectiveness of communication reduction is also verified by comparisons between the constraint-reduced DCOP and the benchmark DCOP.展开更多
Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power c...Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power control(PC) in D2D-aided content delivery scenario for both user fairness(UF)and system throughput(ST) under QoS requirement.Due to the complexity of the problem,we decompose it into two components:CA is formulated from graph perspective to mitigate severe co-channel interference,which turns out to be the Max K-cut problem;LA and PC are jointly optimized to utilize the gain achieved from CA for supreme performance,and specifically,genetic algorithm(GA) is adopted to optimize LA,but when deriving the fitness of each chromosome,PC optimization will be involved.Thanks to numerical results,we elucidate the efficacy of our scheme.展开更多
Based on monotonicity analysis and computer symbolic manipulating technique,a procedure for determining constraints compatibility in design optimization hasbeen proposed in this paper. By using the proposed method rel...Based on monotonicity analysis and computer symbolic manipulating technique,a procedure for determining constraints compatibility in design optimization hasbeen proposed in this paper. By using the proposed method relationshipsbetween constrains can be determined and the optimization is greatly simplifid.The method is code with intelligent production systems.展开更多
基金supported by the China National Natural Science Foundation(52177082)China National Key R&D Program(2020YFC0827001)Science and Technology Project of Jilin Electric Power Co.,Ltd(2020JBGS-03).
文摘This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.
基金Supported by the National Social Science Foundation of China(15ZDA034,14BZZ028)Beijing Social Science Foundation(16JDGLA036)JKF Program of People’s Public Security University of China(2016JKF01318)
文摘Cooperative multi-agent reinforcement learning（ MARL） is an important topic in the field of artificial intelligence,in which distributed constraint optimization（ DCOP） algorithms have been widely used to coordinate the actions of multiple agents. However,dense communication among agents affects the practicability of DCOP algorithms. In this paper,we propose a novel DCOP algorithm dealing with the previous DCOP algorithms＇ communication problem by reducing constraints.The contributions of this paper are primarily threefold：（1） It is proved that removing constraints can effectively reduce the communication burden of DCOP algorithms.（2） An criterion is provided to identify insignificant constraints whose elimination doesn＇t have a great impact on the performance of the whole system.（3） A constraint-reduced DCOP algorithm is proposed by adopting a variant of spectral clustering algorithm to detect and eliminate the insignificant constraints. Our algorithm reduces the communication burdern of the benchmark DCOP algorithm while keeping its overall performance unaffected. The performance of constraint-reduced DCOP algorithm is evaluated on four configurations of cooperative sensor networks. The effectiveness of communication reduction is also verified by comparisons between the constraint-reduced DCOP and the benchmark DCOP.
基金supported by the National 863 projects of China(2014AA01A706)
文摘Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power control(PC) in D2D-aided content delivery scenario for both user fairness(UF)and system throughput(ST) under QoS requirement.Due to the complexity of the problem,we decompose it into two components:CA is formulated from graph perspective to mitigate severe co-channel interference,which turns out to be the Max K-cut problem;LA and PC are jointly optimized to utilize the gain achieved from CA for supreme performance,and specifically,genetic algorithm(GA) is adopted to optimize LA,but when deriving the fitness of each chromosome,PC optimization will be involved.Thanks to numerical results,we elucidate the efficacy of our scheme.
文摘Based on monotonicity analysis and computer symbolic manipulating technique,a procedure for determining constraints compatibility in design optimization hasbeen proposed in this paper. By using the proposed method relationshipsbetween constrains can be determined and the optimization is greatly simplifid.The method is code with intelligent production systems.
