A study of smart grids for railways

The smart grid is a next-generation electric power supply grid in which IT technologies are applied to optimise energy efficiency through the exchange of power information between suppliers and consumers in real time. To date, smart grid research has been focussed on the home and domestic sectors, while little progress has been made in developing smart grids for railways. This likely stems from the complexity of electric railway traction systems, which must supply multiple electrical trains with variable electrical loads depending on varying combinations of trains and train operation requirements including maintaining constant speed and reducing speed. Therefore, this thesis proposes to assess the hypothesis that renewable energy, which is one of the representative technologies used in smart grids, can in fact be applied in railway traction power systems. This thesis is also an oriented study that provides guidance in the advance planning of grid systems to be implemented in the near future. In addition to contributions to the development of railway traction systems, it is also hoped that the results will help in intensifying the international competitiveness of railways in terms of greenhouse gas reduction and the reduction of energy imports

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Investigating the Ability of Smart Electricity Meters to Provide Accurate Low Voltage Network Information to the UK Distribution Network Operators

This research presents a picture of the current status and the future developments of the LV electricity grid and the capabilities of the smart metering programme in the UK as well as investigating the major research trends and priorities in the field of Smart Grid. This work also extensively examines the literature on the crucial LV network performance indicators such as losses, voltage levels, and cable capacity percentages and the ways in which DNOs have been acquiring this knowledge as well the ways in which various LV network applications are carried out and rely on various sources of data. This work combines 2 new smart meter data sets with 5 established methods to predict a proportion of consumer’s data is not available using historical smart meter data from neighbouring smart meters. Our work shows that half-hourly smart meter data can successfully predict the missing general load shapes, but the prediction of peak demands proves to be a more challenging task. This work then investigates the impact of smart meter time resolution intervals and data aggregation levels in balanced and unbalanced three phase LV network models on the accuracy of critical LV network performance indicators and the way in which these inaccuracies affect major smart LV network application of the DNOs in the UK. This is a novel work that has not been carried out before and shows that using low time resolution and aggregated smart meter data in load flow analysis models can negatively affect the accuracy of critical low voltage network estimates