A Power Flow Method For Smart Grid And Its Convergence Analysis

A smart grid is a modernized electrical grid that uses information and communications technology to gather and act on information, such as information about the behaviors of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.With the increasing interest in smart grid, distribution power flow is more important for applications like VAR planning, switching, state estimation and especially optimization than ever. Typically, a distribution system originates at a substation and continues to a lower voltage for delivery to the customers. There are several tools for transmission system analysis.These techniques however sometimes fail to converge when applied to distribution systems due to their higher resistance/reactance ratio of the lines, making them ill conditioned. Distribution systems typically have a radial topological structure where the loads are not always constant power. With the increase in distributed generation there is a critical need to develop analysis tools to study the effect they will have on the distribution systems. Also, smart grid are different from terrestrial distribution systems, as they are tightly coupled and have multiple generators. Newton-Raphson is sensitive to initial value and another two tools does not apply to analyse power flow of smart grid. So this thesis give the convergence theorem of Newton-like power flow algorithm and the iteration times estimated theorem of Newton-like power flow algorithm.This thesis focuses on developing a method to perform the power flow analysis of terrestrial as well as shipboard power systems. The algorithm which is defined NGA algorithm is based on the Newton-Raphson process and uses a genetic algorithm and two theorems to get initial value which approach to the solution of equation. The algorithm is built and tested on IEEE test cases. Finally, we use Tongliao central station data to simulate, and analyze the simulation results. We draw the useful conclusions that the proposed improved flow calculation method with Genetic Algorithm is feasible and effective.