Smart Grid Technologies and Implementations

Smart grid has been advocated in both developing and developed countries in many years to deal with large amount of energy deficit and air pollutions. However, many literatures talked about some specific technologies and implementations, few of them could give a clear picture on the smart grid implementations in a macro scale like what is the main consideration for the smart grid implementations, how to examine the power system operation with communication network deployment, how to determine the optimal technology scheme with consideration of economic and political constraints, and so on. Governments and related institutions are keen to evaluate the cost and benefit of new technologies or mechanisms in a scientific way rather than making decision blindly. Decision Support System, which is an information system based on interactive computers to support decision making in planning, management, operations for evaluating technologies, is an essential tool to provide decision makers with powerful scientific evidence. The objective of the thesis is to identify the data and information processing technologies and mechanisms which will enable the further development of decision support systems that can be used to evaluate the indices for smart grid technology investment in the future. First of all, the thesis introduces the smart grid and its features and technologies in order to clarify the benefits can be obtained from smart grid deployment in many aspects such as economics, environment, reliability, efficiency, security and safety. Besides, it is necessary to understand power system business and operation scenarios which may affect the communication network model. This thesis, for the first time, will give detailed requirements for smart grid simulation according to the power system business and operation. In addition, state of art monitoring system and communication system involved in smart grid for better demand side management will be reviewed in order to find out their impacts reflecting to the power systems. The methods and algorithms applied to the smart grid monitoring, communication technologies for smart grid are summarized and the monitoring systems are compared with each other to see the merits and drawbacks in each type of the monitoring system. In smart grid environment, large number of data are need to be processed and useful information are required to be abstracted for further operation in power systems. Machine learning is a useful tool for data mining and prediction. One of the typical machine learning artificial algorithms, artificial neural network (ANN) for load forecasting in large power system is proposed in this thesis and different learning methods of back-propagation, Quasi-Newton and Levenberg-Marquardt, are compared with each other to seek the best result in load forecasting. Bad load forecasting may leads to demand and generation mismatch, which could cause blackout in power systems. Load shedding schemes are powerful defender for power system from collapsing and keep the grid in integral to a maximum extent. A lesson learned from India blackout in July 2012 is analyzed and recommendations on preventing grid from blackout are given in this work. Also, a new load shedding schemes for an isolated system is proposed in this thesis to take full advantage from information sharing and communication network deployment in smart grid. Lastly, the new trend of decision support system (DSS) for smart grid implementation is summarized and reliability index and stability scenarios for cost benefit analysis are under DSS consideration. Many countries and organizations are setting renewable penetration goals when planning the contribution to reduce the greenhouse gas emission in the future 10 or 20 years. For instance, UK government is expecting to produce 27% of renewable energies EU-wide before 2030. Some simulations have been carried out to demonstrate the physical insight of a power system operation with renewable energy integration and to study the non-dispatchable energy source penetration level. Meanwhile, issues from power system reliability which may affect consumers are required to take into account. Reliability index of Centralized wind generations and that of distributed wind generations are compared with each other under an investment perspective

A risk-informed approach to setting economically-justifiable maintenance strategies for railway tracks

The global railway infrastructure is carrying ever increasing amounts of railway and freight traffic which in turn is causing accelerated rates of infrastructure deterioration. Given the pressure to increase track utilization, the ageing infrastructure on which much of the railway transport systems are founded, and the constrained budgets under which the infrastructure is managed, appropriate maintenance needs to be predicted, prioritized, planned and carried out efficiently and economically. This doctoral research aims to develop a means of appraising railway track maintenance strategies economically while taking into account the associated risks and uncertainties. To this end, this research proposes, a Whole Life Cycle Cost Analysis (WLCCA) under uncertainty, while considering the direct and indirect costs of track maintenance, and the benefits to train operation, users, safety and the environment. The developed risk-informed approach is demonstrated via case studies on three different route types within the UK mainline railway network