Energy Engineering MSc modules

Engineering Business Environment and Research Methods

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.

Data Analytics for Sustainable Energy Systems

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.

Sustainable Energy and Transport

This module comprises two parts. The first part introduces students to the principles of sustainability in energy systems and the role of sustainable energy systems in the realisation of the United Nations Sustainable Development Goals. It further looks at the various energy technologies and examines their global prospects, as well as environmental and cost implications. Furthermore, it covers renewable energy systems in detail and introduces students to their techno-economic analysis using industry-recognised software. Other topics covered in this part of the module include energy economics; heating and cooling; energy storage; flexibility; energy policy and regulation; and energy access and reliability improvement in low-income economies.

The second part of this module introduces students to low-carbon transport technologies, their characteristics, as well as applications. Building on the knowledge gained from the first part, it assesses the energy supply chains that are essential to the sustainability of these technologies. It further explores the operational factors of sustainable transport technologies together with their integration with smart cities and grids. Other issues addressed include behavioural, social, financial, environmental, and political issues within the context of a sustainable low-carbon economy. This part also looks at mobility service delivery and new business models, as well as the socio-economic and policy aspects of sustainable transport and market development.

Power Generation and Transmission

The first part of this module aims to present advanced topics in applied thermodynamics, combustion and heat transfer. Students will acquire skills to solve engineering problems in these fields using both analytical and computational methods

The second part of this module explores the technical aspects of electrical power generation and transmission including: 1. Renewable electricity generation technologies 2. Fossil fuelled generation technologies 3. Nuclear generation technologies 4. Transmission network topology and power flow analysis. The characteristics of each generation technology will be covered, in terms of ramp up/down rate, carbon intensity, capacity. The strengths and weaknesses of generation technologies will be explored. Issues for transmission of power will be covered, including conventional network topologies, fault conditions and associated problems, voltage levels and network scales, power flow constraints and the challenges of accommodating increasing proportions of renewable generation in the network. Focus will be on techniques to deal with the challenges of matching supply and demand in the presence of increased renewable electricity generation.

Individual Project

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.