Energy Engineering

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. Challenging questions are asked such as: can industry be a positive force for good? How do businesses learn and adapt to new challenges and economic models? This module benefits practitioners in industry, and future academics exploring the sustainability of engineering businesses.

The module also teaches students self-direction, and originality in problem solving. The research methods and associated study skills parts of the module provide students with the skills to successfully complete a research project.

Teaching of Research Methods (RM) will be integrated with the sustainable engineering part, through coursework and assignments. RM Material includes: understanding the research of others, literature reviewing, research methodologies, data interpretation and analysis, research ethics, intellectual property and report writing. A central aim is to prepare students for their dissertation or research project with the assignments related to planning a research project.

As energy systems become smarter, their data footprint increases drastically. It is imperative to be able to manage these large datasets, for the sustainability of the global energy system. Data management, as used here, includes data acquisition, cleaning, manipulation, processing, and storage. This module teaches students the key concepts of data analytics and its application to energy system design and operation. It starts with a revision of the fundamentals of scientific programming in Python, to provide students with the requisite skills for advanced topics later in the module. The Python programming language has been chosen by virtue of its popularity in industry and its plethora of open-source Data Science libraries. Students are further introduced to Statistics, Machine Learning, and Optimisation to equip them with the skills required for solving moderately advanced problems in, but not limited to, uncertainty analysis; supervised and unsupervised machine learning; reinforcement learning; mixed-integer linear programming; model-predictive control; operation management; and decision making under uncertainty.

The second part of the module is activity based, and applies the concepts studied in the first part to carefully selected real-world case studies from all stages of the energy value chain. Case studies could be drawn from: demand forecasting in multi-vector energy systems, renewable energy generation prediction, electric vehicle charge scheduling, model-predictive control of distributed energy systems, outage management in electricity grids, load management, energy theft detection, economic dispatch of power systems, consumer profiling, and energy market analysis.

This module provides an in-depth study of renewable energy systems, with a strong emphasis on sustainable generation methods, how the energy is used and the dynamics of demand. This includes the necessity for energy storage, and the economic mechanisms that drive the shift to cleaner energy systems. Key topics include hydroelectricity, wind, and solar power, along with practical tools and methods for analysing their technical and economic viability. Using industry-recognised software, you will learn to conduct techno-economic analyses, calculate the Levelised Cost of Energy (LCOE) for various technologies, and evaluate cost components to optimise energy solutions. Other topics covered in this module include energy economics; energy storage and demand flexibility; energy policy and regulation; and energy access and reliability improvement in low-income economies.

When looking at energy storage the module acknowledges the growing demand for electricity in powering both heat and transport sectors, with an emphasis on low-carbon transport technologies, their characteristics, as well as applications. It further explores the operational factors of sustainable transport technologies together with their integration with smart cities, rural communities and electricity networks. You will engage in sessions focusing on demand-side flexibility, energy storage and both grid-connected and off-grid renewable systems. Engineering principles such as the design of synchronous and induction generators are covered to enhance understanding of renewable generation technologies and electric vehicles. Technologies for energy conversion and storage, including fuel cells, electrolysers, batteries, hydrogen and other low-carbon fuels, are explored. Concepts like unit commitment and electricity trading introduce you to market operations and the need for dynamically balancing electricity supply and demand. Practical case studies and hands-on experience with state-of-the-art equipment, open-source and lab-installed software provide transferable skills for industry, preparing you to tackle challenges in renewable energy generation, storage and usage, and to contribute to the implementation of sustainable energy systems worldwide.

The module delves into the technical aspects of electrical power generation and transmission. The working concept, modelling, and operation of various power system equipment such as synchronous and induction machines, power transformers, transmission lines, circuit breakers and insulators will be covered to enhance understanding of grid equipment and functionality. You will explore grid modelling and power flow analysis, focusing on the complexities of integrating renewable energy sources into conventional power systems. You will study transmission network topologies, voltage levels, fault conditions, and power flow constraints, with an emphasis on challenges arising from renewable integration. Economic operation of power systems, including balancing supply and demand, will be addressed, preparing students to optimize system operations in renewable-dominant grids. You will gain an understanding of HVDC systems and converters, power electronics-based equipment and system protection strategies among other concepts within the field of power system operation. You will also learn about fundamental concepts in thermodynamics and their application to turbines and compressors.

Through the use of industry-recognized software, you will simulate and model grid and power generation scenarios, building transferable skills for careers in electrical power systems. You will also work on state-of-the-art smart grid trainer equipment that emulates the operation of the grid from generation up to distribution in lab-scale. By the end of the module, you will be prepared to contribute to the design, operation, and optimization of resilient, sustainable electrical grids.

This module merges 2 previously distinct modules, Dissertation (for non-engineering courses) and Individual Project (for engineering courses). As it will cover a great diversity of courses, it will be delivered as a team effort.

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 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. It must be relevant to the course being taken. The project may involve practical work, or be entirely desktop based. An ethics form will be required with approval but is not marked. The Report should be approximately 10,000 – 15,000 words, reflecting the amount of practical work and the nature of the topic.