This paper proposes a dual alternative iter-ation algorithm-based hierarchical MPC(DAMPC)strategy to realize frequency regulation control and active power allocation of wind-storage coupling system.The proposed DAMPC ...This paper proposes a dual alternative iter-ation algorithm-based hierarchical MPC(DAMPC)strategy to realize frequency regulation control and active power allocation of wind-storage coupling system.The proposed DAMPC strategy involves a top-level grid fre-quency model predictive control(FMPC)strategy and a bottom-level multi-objective model predictive control(MMPC)strategy.In the FMPC strategy,to improve the frequency regulation performance,the active power ref-erence of the wind-storage coupling system is generated by minimizing the frequency deviation,where the fre-quency reference is calculated by considering the active power deviation and its integral.In the MMPC strategy,the active power reference is optimally allocated to the wind turbine generators(WTGs)and battery energy storage system(BESS)by raising the minimum rotor speed,minimizing the pitch angle deviation and state of charge(SOC)deviation.To solve the multi-objective al-location optimization problem with high efficiency,a dual alternative iteration algorithm(DAIA)is proposed to update the global and local control vectors with the dual vector.Extensive simulations validate the effectiveness of the proposed DAMPC strategy in frequency regulation and active power allocation.展开更多
A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-base...A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.展开更多
As an alternative to the conventional steam Rankine Cycle,Kalina Cycle has witnessed a growing interest over the past years for high-temperature applications(A working fluid temperature of 500◦C at the turbine inlet)....As an alternative to the conventional steam Rankine Cycle,Kalina Cycle has witnessed a growing interest over the past years for high-temperature applications(A working fluid temperature of 500◦C at the turbine inlet).However,the possibility of implementing an additional multi-phase expander on the weak ammonia-water solution loop of the Kalina cycle was hardly analyzed in the available literatures.In this research,two novel Kalina cycles(Kalina cycle-12A and Kalina cycle-12B)have been presented by integrating a multi-phase expander in addition to the turbine installed downstream of the Kalina evaporator.For Kalina cycle-12A,this additional multi-phase expander is positioned downstream of the Kalina separator and on the weak ammonia-water solution loop for Kalina Cycle-12B.A detailed mathematical model based on the thermodynamic laws has been developed to solve and optimize the Kalina Cycles.The influence of critical decision parameters,specifically the ammonia concentration on working fluid and evaporation pressure,were investigated.The optimization was performed based on the objective to maximize the net power output from the multi-phase expander under steady-state operating conditions.When the performance of the proposed Kalina cycles was compared with the conventional Kalina Cycle-12,both of them demonstrated superior performance,i.e.,net power output and peak thermal efficiency increased by a maximum value of 3.23%for the proposed Kalina Cycle-12A cycle and 3.94%for the proposed Kalina Cycle-12B cycle.In terms of second law efficiency,Kalina Cycle-12A is 3.68 percent more efficient than Kalina Cycle-12,while Kalina Cycle-12B is 4.04 percent more efficient.Furthermore,2nd law analysis also reveals,maximum destruction of exergy occurs at the condensers of the cycles.展开更多
文摘This paper proposes a dual alternative iter-ation algorithm-based hierarchical MPC(DAMPC)strategy to realize frequency regulation control and active power allocation of wind-storage coupling system.The proposed DAMPC strategy involves a top-level grid fre-quency model predictive control(FMPC)strategy and a bottom-level multi-objective model predictive control(MMPC)strategy.In the FMPC strategy,to improve the frequency regulation performance,the active power ref-erence of the wind-storage coupling system is generated by minimizing the frequency deviation,where the fre-quency reference is calculated by considering the active power deviation and its integral.In the MMPC strategy,the active power reference is optimally allocated to the wind turbine generators(WTGs)and battery energy storage system(BESS)by raising the minimum rotor speed,minimizing the pitch angle deviation and state of charge(SOC)deviation.To solve the multi-objective al-location optimization problem with high efficiency,a dual alternative iteration algorithm(DAIA)is proposed to update the global and local control vectors with the dual vector.Extensive simulations validate the effectiveness of the proposed DAMPC strategy in frequency regulation and active power allocation.
基金The National Natural Science Foundation of China(No.61771126,61372104)the Science and Technology Project of State Grid Corporation of China(o.SGRIXTKJ[2015] 349)
文摘A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.
文摘As an alternative to the conventional steam Rankine Cycle,Kalina Cycle has witnessed a growing interest over the past years for high-temperature applications(A working fluid temperature of 500◦C at the turbine inlet).However,the possibility of implementing an additional multi-phase expander on the weak ammonia-water solution loop of the Kalina cycle was hardly analyzed in the available literatures.In this research,two novel Kalina cycles(Kalina cycle-12A and Kalina cycle-12B)have been presented by integrating a multi-phase expander in addition to the turbine installed downstream of the Kalina evaporator.For Kalina cycle-12A,this additional multi-phase expander is positioned downstream of the Kalina separator and on the weak ammonia-water solution loop for Kalina Cycle-12B.A detailed mathematical model based on the thermodynamic laws has been developed to solve and optimize the Kalina Cycles.The influence of critical decision parameters,specifically the ammonia concentration on working fluid and evaporation pressure,were investigated.The optimization was performed based on the objective to maximize the net power output from the multi-phase expander under steady-state operating conditions.When the performance of the proposed Kalina cycles was compared with the conventional Kalina Cycle-12,both of them demonstrated superior performance,i.e.,net power output and peak thermal efficiency increased by a maximum value of 3.23%for the proposed Kalina Cycle-12A cycle and 3.94%for the proposed Kalina Cycle-12B cycle.In terms of second law efficiency,Kalina Cycle-12A is 3.68 percent more efficient than Kalina Cycle-12,while Kalina Cycle-12B is 4.04 percent more efficient.Furthermore,2nd law analysis also reveals,maximum destruction of exergy occurs at the condensers of the cycles.
