Research on transmission congestion of power system containing wind farms based on genetic algorithm

With large-scale integration of wind power, load flow distribution is highly uncertain. It is unrealistic to adopt traditional deterministic load flow calculation for system operation. What is more, load flow in some transmission lines may be increased to a level where a random event could cause the network collapse. A method of combined cumulants and Gram-Charlier expansion (CCGCE) to calculate probabilistic load flow distribution and a research on relieving transmission congestion by FACTS are proposed in this paper. On the basis of a thorough analysis of the international and domestic researches, this thesis firstly introduces mathematical models, including wind power, FACTS and system models. There are two typical FACTS instruments. One is the SVC, modeled with two ideal switched elements in parallel: a capacitance and an inductance. The other is the TCSC, modeled with three ideal switched elements in parallel: a capacitance, an inductance and a simple wire, which permits the value zero. Secondly, it discusses the procedures of Genetic Algorithm, which includes forming initial population, defining fitness function, and application of crossover, mutation and so on. Thirdly, it carries out two simulations. The first simulation demonstrates the accuracy and computational efficiency of CCGCE method by comparing to Monte Carlo method. The second simulation is composed of two parts. The first part is to find out the appropriate location and capacity of FACTS, and the second part is to check the congestion relieving after the installment of FACTS by CCGCE method. Finally, it suggests that the CCGCE method can significantly reduce the computational time, while maintaining a high degree of accuracy on probabilistic load flow calculation, and concludes that after the installment of FACTS, the transmission congestion of power system containing wind farms can be relieved well.