Smart Grids and Power Systems MSc module details
Engineering Business Environment and Research Methods
Equips you with the skills you need to successfully complete a research project of a high standard. The module introduces you to a range of research methodologies and practices relevant to the successful completion of the MSc. The engineering business part of this module is to enable students to understand and reflect upon the role of business in a rapidly changing, globalised world. It identifies opportunities and threats for industry arising from environmental policy, legislation, and societal change, and explores how businesses respond to future environmental challenges; for example, through supply chain management, logistics, life-cycle analysis, green accounting and carbon trading.
Data Analytics for Sustainable Energy Systems
Teaches students the key concepts of data analytics and its application to energy system design and operation. Starting with the fundamentals of scientific programming in Python, students are further introduced to Statistics, Machine Learning, Data Management and Optimisation concepts as applicable to energy systems; some of the tools introduced are uncertainty analysis; supervised and unsupervised machine learning; reinforcement learning; mixed-integer linear programming; model-predictive control; operation management; and decision making under uncertainty. Case studies within the Smart Grid Paradigm are introduced in the second part of the module.
Fuzzy Logic & Evolutionary Computing
Will provide an overview of several aspects of fuzzy logic, including a comprehensive description of various fuzzy paradigms which have become established as useful computational tools. Applications will be discussed, and students will be introduced to problem domains where problem instances may be amenable to solution by fuzzy logic techniques. The second half of the module will cover Evolutionary Computing, a heuristic approach for solving optimisation problems that could not be solved by exact mathematical methods. This class of algorithms are extremely versatile and can tackle optimisation problems in engineering, economics, and all applied sciences. This subject contains algorithmic structure based on metaphors such as evolution and collective intelligence. This module will provide students with an appreciation of both theoretical and implementation issues of such algorithms.
Smart Grids: Modelling, Analysis & Operation
Explores the modelling and analysis of various components and processes in electrical power systems; covering all aspects of generation, transmission & distribution. Starting with the fundamental concepts of per unit, load flow and short circuit analysis, advanced operational aspects such as security constrained optimal power flow, current & voltage protection and operation of power flow controllers will be introduced. Concepts of voltage control, power factor control and further issues related to distribution systems such as harmonic mitigation will be studied with the aid of software and hardware-based experiments. The second part of the module delves into the smart grid concept, touching upon latest developments and technological advances in the sector, such as Integration of Renewable Energy Sources, Active Network Management, Intelligent Data analysis, Decentralized and Intelligent operation, New Market structures and Active Participation from consumers and producers of energy. Concepts of Microgrids, Demand Response Schemes, Forecasting and Scheduling are some of the additional topics covered under the Smart Grid paradigm.
Individual Project
In your final term, you will undertake a research project on a topic of your choice, supervised by an experienced member of research staff. The module aims to introduce the student to the discipline of independent research carried out in a restricted timeframe. It will involve self-organisation, application, analysis, and presentation of the project work. The topic will be chosen from a list provided by staff, grouped by discipline, or chosen by the student and agreed with the dissertation supervisor.