Development of Probalistic Techniques for Network Assessment with Significant Wind Generation

Abstract

Due to increasing awareness of global warming and high energy costs, more electrical power is being generated by using renewable sources. However some of these sources are not as predictable as conventional generation and they also lack the ability to be dispatched in the same way. The increase in the amount of wind power connected to transmission networks has been significant in some countries. But due to the stochastic nature of wind power, it is difficult to predict exactly how much power can be generated at any given time. This variable nature of wind power can cause line overloading and high voltage problems. To overcome these problems transmission networks can be upgraded but the cost of upgrade can make it uneconomical to accommodate wind power. Although wind turbines have very high availability rates, their ability to generate wind power depends on the wind speed. Most wind farms have capacity factors in the range of 30%-40%. The probability of wind farms operating at their rated output is quite low. As most techniques used to analyse new connections to transmission grids are based on conventional generation, these techniques can not be used for wind generation as they do not consider the variable nature of wind power. Probabilistic techniques have been used particularly in deregulated power systems where more than one company is involved in transmission system operation. Ireland has very high potential for wind generation due to its geographical location. But its transmission network is weak in some of the areas suitable for wind generation and the network has a low level of interconnection with other networks. Having a high level of wind generation can create significant reliability problems. To accommodate more wind generation, different analysis techniques have to be used to consider the variable nature of wind speed. The purpose of this research is to study and develop these probabilistic techniques and to investigate how these techniques can be used in Ireland to identify possible line overloading problems due to wind generation. Different cases with wind generation where probabilistic methods can be used or have been used are studied. A small part of the Irish transmission network with a significant level of wind generation connected is chosen for probabilistic analysis. Deterministic approaches are generally used to investigate the performance of the network. In this study, it is shown how probabilistic techniques can be used to give a clear picture of wind generation effects on transmission line overloading. The Line Flow Sensitivity Factor (LFSF) method is used to speed up the probabilistic analysis. By using probabilistic techniques for different periods of the year, analysis based on line overloading and reverse power flow are carried out. The amount of Expected Energy Not Produced (EENP) is calculated for different periods of the year. Based on the EENP, it can be decided whether it is economical to upgrade the transmission network or to curtail wind power during high wind production periods. DECLARATION I certify that this thesis which I now submit for examination for the award of Master of Philosophy is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work. This thesis was prepared according to the regulations for postgraduate study by research of the Dublin Institute of Technology and has not been submitted in whole or in part for an award in any other Institute or University. The work reported on in this thesis conforms to the principles and requirements of the Institute's guidelines for ethics in research ___________________________ Asim Mumtaz ACKNOWLEDGEMENT I would like to thank the following people who helped me to complete this thesis. Firstly Dr. Michael Conlon, research supervisor, for generously donating his time, giving instruction and feedback for this thesis. Paddy O’Kane and Carrie Byrne from Airtricity for providing valuable information and assistance to the project. Finally I would like to thank my fellow research students in the Power Research Group for always helping me and being happy to discuss any problem.