Railway Power Supply Models and Methods for Long-term Investment Analysis

The aim of the project is to suggest an investment planning program where the welfare of the society is to be maximized. In order to be able to decide on a wise investment plan, one needs to know the consequences of different choices of power system configurations. Therefore the impacts of different future traffic demands are of interest for a railway power system owner. Since investments are supposed to last a long time, their future usage has to be considered. Moreover, the lead times of investments can be of considerable duration lengths. Because of the uncertainty of the future, deterministic case studies might not be suitable and then a large number of outcomes are to be studied, probable outcomes as well as outcomes with a high level of impact. In order to be able to make a valid long-term investment analysis of the railway power supply system, one needs to use proper railway power supply models and methods. The aim of this thesis is to present a stable modeling and methodological basis for the coming investment planning phase of this PhD research project. The focus is set on studying the consequences of a railway power supply system which is too weak. The thesis contains an overview of models of some electrical and mechanical relations important for electric traction systems. Some of these models are further developed, and some are modified for improved computational properties. A flexible electric traction system simulator based on the above mentioned models has been developed and the applied methods and resulting abilities are presented. The main scientific contribution of this thesis is that a fast and approximative neural network model, which calculates some important aggregated results of the interaction between the railway power system and the train traffic, has been developed. This approximative model was developed in order to reduce computation times. Reduction of computation times is very