A New Filter Collaborative State Transition Algorithm for Two-Objective Dynamic Reactive Power Optimization

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.

Impact evaluation of large scale integration of electric vehicles on power grid

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.

Optimal location of interline power flow controller for controlling multi transmission line： A new integrated technique

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.