A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calcu...A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes.The power rating of the ES is adjusted on the basis of the available active power at the bus.And in the optimization problem,it is expressed as the power ratio of the noncritical load(NCL)and critical load(CL).The implemented ES model is flexible,which can be used on any bus and any phase.The model determines the output voltage from the parameters and operating conditions at the point of common coupling(PCC).These conditions are integrated using the backward/forward sweep method(BFSM)and are updated during power flow calculations.The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm,which computes power flow and ES placement simultaneously.The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.展开更多
With the development of distribution automation system, the centralized meter reading system has been adopted more and more extensively, which provides real-time electricity consumption data of end-users, and conseque...With the development of distribution automation system, the centralized meter reading system has been adopted more and more extensively, which provides real-time electricity consumption data of end-users, and consequently lays foundation for operating condition on-line analysis of distribution network. In this paper, a modified back/forward sweep method, which directly uses real-time electricity consumption data acquired from the centralized meter reading system, is proposedto realize voltage analysis based on 24-hour electricity consumption data of a typical transformer district. Furthermore, the calculated line losses are verified through data collected from the energy metering of the distribution transformer, illustrating that the proposed method can be applied in analyzing voltage level and discovering unknown energy losses, which will lay foundation for on-line analysis, calculation and monitoring of power distribution network.展开更多
This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified...This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified values to model uncertainty parameters in radial distribution network power flow analysis,whereas the Grey Wolf Optimizer(GWO)was employed to optimize photovoltaic distributed generation(PVDG)placement and sizing,aiming to minimize power losses and improve voltage deviations.Load uncertainties in the residential,commercial,and industrial sectors were modeled using triangular fuzzy membership functions derived from real-world data representing Malaysian urban loads.Simulations on the 33-bus distribution network validated the approach and demonstrated its effectiveness in handling fuzzy uncertainties across three load sectors.The findings showed that the proposed F-BFS-GWO method significantly reduced the total power losses and improved the voltage profiles.Under high load conditions,active power losses were reduced by approximately 28.04%in residential,46.06%in commercial,and 46.24%in industrial sectors at the highest membership degree in the fuzzy set,compared to the scenario without photovoltaic generation.The critical voltage magnitudes at the weakest bus under high-load conditions in the fuzzy set also improve significantly,reaching nearly 1.0 p.u.The main contributions of this work are the integration of fuzzy-logic within a BFS framework to manage multi-sector load uncertainties,coupled with a hybrid F-BFS-GWO algorithm that enhances system planning and optimization under the risk of uncertainty of photovoltaic generation and load demand.展开更多
基金supported by Consejo Nacional de Humanidades,Ciencia y Tecnología(CONAHCYT)—México(No.863547)the fellowship 2021-000001-01NACF-00604 given to the G.H.Valencia-Riverathe scholarships 175599,64698,253652,and 296574,given to G.Tapia-Tinoco,A.Garcia-Perez,D.Granados-Lieberman,and M.Valtierra-Rodriguez,respectively,through the Sistema Nacional de Investigadoras e Investigadores(SNII)-CONAHCYT-México.
文摘A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes.The power rating of the ES is adjusted on the basis of the available active power at the bus.And in the optimization problem,it is expressed as the power ratio of the noncritical load(NCL)and critical load(CL).The implemented ES model is flexible,which can be used on any bus and any phase.The model determines the output voltage from the parameters and operating conditions at the point of common coupling(PCC).These conditions are integrated using the backward/forward sweep method(BFSM)and are updated during power flow calculations.The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm,which computes power flow and ES placement simultaneously.The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.
文摘With the development of distribution automation system, the centralized meter reading system has been adopted more and more extensively, which provides real-time electricity consumption data of end-users, and consequently lays foundation for operating condition on-line analysis of distribution network. In this paper, a modified back/forward sweep method, which directly uses real-time electricity consumption data acquired from the centralized meter reading system, is proposedto realize voltage analysis based on 24-hour electricity consumption data of a typical transformer district. Furthermore, the calculated line losses are verified through data collected from the energy metering of the distribution transformer, illustrating that the proposed method can be applied in analyzing voltage level and discovering unknown energy losses, which will lay foundation for on-line analysis, calculation and monitoring of power distribution network.
基金Universiti Malaysia Pahang Al-Sultan Abdullah and International Islamic University Malaysia for providing the research grant RDU223219.
文摘This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified values to model uncertainty parameters in radial distribution network power flow analysis,whereas the Grey Wolf Optimizer(GWO)was employed to optimize photovoltaic distributed generation(PVDG)placement and sizing,aiming to minimize power losses and improve voltage deviations.Load uncertainties in the residential,commercial,and industrial sectors were modeled using triangular fuzzy membership functions derived from real-world data representing Malaysian urban loads.Simulations on the 33-bus distribution network validated the approach and demonstrated its effectiveness in handling fuzzy uncertainties across three load sectors.The findings showed that the proposed F-BFS-GWO method significantly reduced the total power losses and improved the voltage profiles.Under high load conditions,active power losses were reduced by approximately 28.04%in residential,46.06%in commercial,and 46.24%in industrial sectors at the highest membership degree in the fuzzy set,compared to the scenario without photovoltaic generation.The critical voltage magnitudes at the weakest bus under high-load conditions in the fuzzy set also improve significantly,reaching nearly 1.0 p.u.The main contributions of this work are the integration of fuzzy-logic within a BFS framework to manage multi-sector load uncertainties,coupled with a hybrid F-BFS-GWO algorithm that enhances system planning and optimization under the risk of uncertainty of photovoltaic generation and load demand.
