Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrad...Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrade existing systems or to install new systems. Therefore, planning of new installations and determination of best operating conditions of existing systems need power flow analysis. In this way, cost/benefit ratio for both suppliers and customers is maintained. This research involves the design and power flow analysis of IEEE-14 bus system. Newton Raphson method is applied for better efficiency and reduced computational time. Simulation analysis is conducted in ETAP software because of its excessive used in real life systems.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
In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combinat...In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combination of tabu search （TS） algorithm and artificial neural network （ANN） in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemen- ted in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm （GA） and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.展开更多
文摘Power flow analysis is a numerical way of study of behavior of flow of electric power in an interconnected system. In order to meet the growing demands of electrical energy in an optimum way, there is a need to upgrade existing systems or to install new systems. Therefore, planning of new installations and determination of best operating conditions of existing systems need power flow analysis. In this way, cost/benefit ratio for both suppliers and customers is maintained. This research involves the design and power flow analysis of IEEE-14 bus system. Newton Raphson method is applied for better efficiency and reduced computational time. Simulation analysis is conducted in ETAP software because of its excessive used in real life systems.
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.
文摘In this paper, an interline power flow controller （IPFC） is used for controlling multi transmission lines. However, the optimal placement of IPFC in the transmis-sion line is a major problem. Thus, we use a combination of tabu search （TS） algorithm and artificial neural network （ANN） in the proposed method to find out the best placement locations for IPFC in a given multi transmission line system. TS algorithm is an optimization algorithm and we use it in the proposed method to determine the optimum bus combination using line data. Then, using the optimum bus combination, the neural network is trained to find out the best placement locations for IPFC. Finally, IPFC is connected at the best locations indicated by the neural network. Furthermore, using Newton-Raphson load flow algorithm, the transmission line loss of the IPFC connected bus is analyzed. The proposed methodology is implemen- ted in MATLAB working platform and tested on the IEEE-14 bus system. The output is compared with the genetic algorithm （GA） and general load flow analysis. The results are validated with Levenberg-Marquardt back propagation and gradient descent with momentum network training algorithm.
