The distributed generators in the radial distribution network are to improve the Grid performance and its efficiency.These Distributed Generators control the PV bus;it is converted as a remote controlled PVQ bus.This ...The distributed generators in the radial distribution network are to improve the Grid performance and its efficiency.These Distributed Generators control the PV bus;it is converted as a remote controlled PVQ bus.This PVQ bus reduces the power loss and reactive power.Initially,the distributed generators were placed in the system using mathematical modelling or the optimization.This approach improves the efficiency but it has no effect in loss minimization.To minimize the loss the reconfigured network with Genetic algorithm based Distributed generator placement proposed as existing work.This approach minimizes the loss effectively;but the genetic algorithm takes more time for DG placement.Hence,in this,the network reconfiguration is performed using a modified Satin bower bird algorithm after DG placement and DG sizing.Initially,the sensitive analysis applied the loadflow analysis to identify the optimal placement for the distributed generator.Then,the modified Satin Bowerbird(SBO)used for the network reconfiguration.This approach minimizes the loss of effectively by combining the network reconfiguration process.The proposed modified SBO-based network reconfiguration implemented on standard bus systems 33 and 69 using MATLAB R2021b version under Windows 10 environment.The proposed approach compared with the existing work in terms of real power loss and loss reduction.展开更多
Normally, the power system observation is carried out for the optimal PMUs placement with minimum use of unit in the region of the Smart power grid system. By advanced tool, the process of protection and management of...Normally, the power system observation is carried out for the optimal PMUs placement with minimum use of unit in the region of the Smart power grid system. By advanced tool, the process of protection and management of the power system is considered with the measurement of time-synchronized of the voltage and current. In order to have an efficient placement solution for the issue, a novel method is needed with the optimal approach. For complete power network observability of PMU optimal placement a new method is implemented. However, the process of placement and connection of the buses is considered at various places with the same cost of installation. GA based Enhanced Harmony and Binary Search Algorithm (GA-EHBSA) is proposed and utilized with the improvement to have least PMU placement and better optimization approach for finding the optimal location. To evaluate the optimal placement of PMUs the proposed approach is implemented in the standard test systems of IEEE 14-bus, IEEE 24-bus, IEEE 30-bus, IEEE 39-bus and IEEE 57-bus. The simulation results are evaluated and compared with existing algorithm to show the efficient process of optimal PMUs placement with better optimization, minimum cost and redundancy than the existing.展开更多
This research proposes a more advanced way to address Combined Economic Emission Dispatch(CEED)concerns.Economic Load Dispatch(ELD)and Economic Emission Dispatch(EED)have been implemented to reduce generating unit fue...This research proposes a more advanced way to address Combined Economic Emission Dispatch(CEED)concerns.Economic Load Dispatch(ELD)and Economic Emission Dispatch(EED)have been implemented to reduce generating unit fuel costs and emissions.When both economics and emission tar-gets are taken into account,the dispatch of an aggregate cost-effective emission challenge emerges.This research affords a mathematical modeling-based analyti-cal technique for solving economic,emission,and collaborative economic and emission dispatch problems with only one goal.This study takes into account both the fuel cost target and the environmental impact of emissions.This bi-inten-tion CEED problem is converted to a solitary goal function using a price penalty factor technique.In this case,a metaheuristic and an environment-inspired,intel-ligent Spider Monkey Optimization technique(SMO)are used to address the CEED dilemma.By following the generator’s scheduling process,the SMO meth-od is used to regulate the output from the power generation system in terms of pollution and fuel cost.The Fission-Fusion social(FFS)structure of spider mon-keys promotes them to utilize a global optimization method known as SMO dur-ing foraging behaviour.The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and han-dle dispatch problems with constraints.The economic dispatch has been reme-died,and the improved result demonstrates that the system’s performance is stable andflexible in real time.Finally,the system’s output demonstrates that the system has improved in resolving CEED difficulties.When compared to ear-lier investigations,the proposed model’sfindings have improved.As the gener-ating units,wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.展开更多
Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the po...Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.展开更多
Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and s...Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.展开更多
As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which ...As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change.As a result,introducing a policy that promotes renewable energy(RE)generation and integration is inevitable for sustainable development.In this endeavor,electrification of the transport sector rises as key point in reducing the accelerating environment degradation,by the deployment of new type of vehicles referred to as PHEV(plug-in hybrid electric vehicle).Besides being able to use two kinds of drives(the conventional internal combustion engine and the electric one)to increase the total efficiency,they come with a grid connection and interaction capability known as the vehicle-to-grid(V2G)that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer.Unfortunately,all these advantages do not come alone.The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid.In this work the large scale integration impact of EVs will be investigated in details.The results of power flow analysis and the dynamic response of the grid parameters variation are presented,taking the IEEE 14 bus system as a test grid system.展开更多
文摘The distributed generators in the radial distribution network are to improve the Grid performance and its efficiency.These Distributed Generators control the PV bus;it is converted as a remote controlled PVQ bus.This PVQ bus reduces the power loss and reactive power.Initially,the distributed generators were placed in the system using mathematical modelling or the optimization.This approach improves the efficiency but it has no effect in loss minimization.To minimize the loss the reconfigured network with Genetic algorithm based Distributed generator placement proposed as existing work.This approach minimizes the loss effectively;but the genetic algorithm takes more time for DG placement.Hence,in this,the network reconfiguration is performed using a modified Satin bower bird algorithm after DG placement and DG sizing.Initially,the sensitive analysis applied the loadflow analysis to identify the optimal placement for the distributed generator.Then,the modified Satin Bowerbird(SBO)used for the network reconfiguration.This approach minimizes the loss of effectively by combining the network reconfiguration process.The proposed modified SBO-based network reconfiguration implemented on standard bus systems 33 and 69 using MATLAB R2021b version under Windows 10 environment.The proposed approach compared with the existing work in terms of real power loss and loss reduction.
文摘Normally, the power system observation is carried out for the optimal PMUs placement with minimum use of unit in the region of the Smart power grid system. By advanced tool, the process of protection and management of the power system is considered with the measurement of time-synchronized of the voltage and current. In order to have an efficient placement solution for the issue, a novel method is needed with the optimal approach. For complete power network observability of PMU optimal placement a new method is implemented. However, the process of placement and connection of the buses is considered at various places with the same cost of installation. GA based Enhanced Harmony and Binary Search Algorithm (GA-EHBSA) is proposed and utilized with the improvement to have least PMU placement and better optimization approach for finding the optimal location. To evaluate the optimal placement of PMUs the proposed approach is implemented in the standard test systems of IEEE 14-bus, IEEE 24-bus, IEEE 30-bus, IEEE 39-bus and IEEE 57-bus. The simulation results are evaluated and compared with existing algorithm to show the efficient process of optimal PMUs placement with better optimization, minimum cost and redundancy than the existing.
文摘This research proposes a more advanced way to address Combined Economic Emission Dispatch(CEED)concerns.Economic Load Dispatch(ELD)and Economic Emission Dispatch(EED)have been implemented to reduce generating unit fuel costs and emissions.When both economics and emission tar-gets are taken into account,the dispatch of an aggregate cost-effective emission challenge emerges.This research affords a mathematical modeling-based analyti-cal technique for solving economic,emission,and collaborative economic and emission dispatch problems with only one goal.This study takes into account both the fuel cost target and the environmental impact of emissions.This bi-inten-tion CEED problem is converted to a solitary goal function using a price penalty factor technique.In this case,a metaheuristic and an environment-inspired,intel-ligent Spider Monkey Optimization technique(SMO)are used to address the CEED dilemma.By following the generator’s scheduling process,the SMO meth-od is used to regulate the output from the power generation system in terms of pollution and fuel cost.The Fission-Fusion social(FFS)structure of spider mon-keys promotes them to utilize a global optimization method known as SMO dur-ing foraging behaviour.The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and han-dle dispatch problems with constraints.The economic dispatch has been reme-died,and the improved result demonstrates that the system’s performance is stable andflexible in real time.Finally,the system’s output demonstrates that the system has improved in resolving CEED difficulties.When compared to ear-lier investigations,the proposed model’sfindings have improved.As the gener-ating units,wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.
文摘Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.
基金supported by the National Natural Science Foundation of China(Nos.51767022 and 51575469)
文摘Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.
文摘As the world witnesses a continual increase in the global energy demand,the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change.As a result,introducing a policy that promotes renewable energy(RE)generation and integration is inevitable for sustainable development.In this endeavor,electrification of the transport sector rises as key point in reducing the accelerating environment degradation,by the deployment of new type of vehicles referred to as PHEV(plug-in hybrid electric vehicle).Besides being able to use two kinds of drives(the conventional internal combustion engine and the electric one)to increase the total efficiency,they come with a grid connection and interaction capability known as the vehicle-to-grid(V2G)that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer.Unfortunately,all these advantages do not come alone.The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid.In this work the large scale integration impact of EVs will be investigated in details.The results of power flow analysis and the dynamic response of the grid parameters variation are presented,taking the IEEE 14 bus system as a test grid system.
