This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and e...This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss.GA fitness function is introduced including the active power losses,reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable.GA fitness function is subjected to voltage constraints,active and reactive power losses constraints and DG size constraint.展开更多
The increasing penetration of wind power brings great uncertainties into power systems,which poses challenges to system planning and operation.This paper proposes a novel probabilistic load flow(PLF)method based on cl...The increasing penetration of wind power brings great uncertainties into power systems,which poses challenges to system planning and operation.This paper proposes a novel probabilistic load flow(PLF)method based on clustering technique to handle large fluctuations from large-scale wind power integration.The traditional cumulant method(CM)for PLF is based on the linearization of load flow equations around the operation point,therefore resulting in significant errors when input random variables have large fluctuations.In the proposed method,the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster.With such pre-processing,the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy.The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples.The proposed method is validated on modified IEEE 9-bus and 118-bus test achieve a better performance with the consideration of both traditional CM,2 m+1 point estimate method(PEM),Monte Carlo simulation(MCS)and Latin hypercube sampling(LHS)based MCS,the proposed method can achieve a better performance with the consideration of bothcomputational efficiency and accuracy.展开更多
This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading ef...This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.展开更多
文摘This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA(Genetic Optimization algorithm).It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss.GA fitness function is introduced including the active power losses,reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable.GA fitness function is subjected to voltage constraints,active and reactive power losses constraints and DG size constraint.
基金supported by the National Key Research and Development Program of China(No.2017YFB0903400).
文摘The increasing penetration of wind power brings great uncertainties into power systems,which poses challenges to system planning and operation.This paper proposes a novel probabilistic load flow(PLF)method based on clustering technique to handle large fluctuations from large-scale wind power integration.The traditional cumulant method(CM)for PLF is based on the linearization of load flow equations around the operation point,therefore resulting in significant errors when input random variables have large fluctuations.In the proposed method,the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster.With such pre-processing,the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy.The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples.The proposed method is validated on modified IEEE 9-bus and 118-bus test achieve a better performance with the consideration of both traditional CM,2 m+1 point estimate method(PEM),Monte Carlo simulation(MCS)and Latin hypercube sampling(LHS)based MCS,the proposed method can achieve a better performance with the consideration of bothcomputational efficiency and accuracy.
基金supported by National Natural Science Foundation of China(No.51377103)the technology project of State Grid Corporation of China:Research on Multi-Level Decomposition Coordination of the Pareto Set of Multi-Objective Optimization Problem in Bulk Power System(No.SGSXDKYDWKJ2015-001)the support from State Energy Smart Grid R&D Center(SHANGHAI)
文摘This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.
