Course modules
Compulsory modules
All the modules in the following list need to be taken as part of this course.
Renewable Energy Technologies 1
Module Leader
Dr Sagar Jain
Aim
An understanding of the principles of renewable energy technologies is key to assimilate the technological basis of the systems and applications. The module provides the fundamentals of the renewable energy technologies and their impact on global and national energy system. The purpose of this module is to introduce the basis for assessment of the performances of solar (both PV and CSP), wind, wave and tidal, geothermal as well as hydro-electricity technologies. By the end of the module, you will have a better understanding of the various renewable technologies and will have the opportunity to visit a PV solar plant to see the real dimension of an operational plant.
Syllabus
Photovoltaic technology,
Concentrated solar power technology,
Onshore and offshore wind energy: fundamentals of wind turbines and placement,
Geothermal Systems (including ground-source heat pumps),
Wave and tidal energy technologies,
Hydro-electricity technology and systems.
Intended learning outcomes
On successful completion of this module you should be able to:

Identify the different components and main configuration of the different renewable technologies covered in the module,
Articulate the fundamental principles, terminology and key issues related to the most used renewable energy technologies,
Critically compare the challenges for the development and operation of the major technologies, including government regulation and policy,
Identify gaps in the knowledge and discuss potential opportunities for further development, including technology and economic potential.
Renewable Energy Technologies 2
Module Leader
Dr Jerry Luo
Aim
This module provides detailed knowledge in energy storage, bioenergy, energy harvesting and energy distribution. This module also provides you with knowledge and experience in designing and analysing renewable energy infrastructures in energy storage, distribution and corresponding renewable energy applications.
Syllabus
Energy storage materials and technologies

Electrochemical and battery energy storage,
Thermal energy storage,
Medium to large scale energy storage,
Hydrogen storage.
Bioenergies

Biorefinery,
Biofuels.
Energy distribution

Smart grid and micro-grid,
Smart grid for case study
Machine learning in energy.
Energy harvesting

Energy harvesting technologies,
Case Study.
Intended learning outcomes
On successful completion of this module you should be able to:

Critically evaluate the key benefits and challenges of energy storage, bioenergy, energy harvesting and distribution in renewable energy,
Identify the appropriate energy storage and distribution methods for different types of renewable energy systems,
Analyse the main configurations and components in energy storage and distribution for renewable energy systems,
Justify the importance of materials, control, integration and information management issues in renewable energy,
Appraise future technology and socio-economic trends in sustainability and assess associated opportunities and challenges.
Cybersecurity for Energy Systems
Module Leader
Dr Adam Zagorecki
Aim
This module introduces the cyberspace aspects of digital energy systems. It will focus on threats, actors and exploitation of infrastructure. The module also covers security technologies available to support and protect digital energy systems, as well as security requirements and corresponding vulnerabilities.
Syllabus
Introduction to Cyber Security
Understanding cyberspace, cyber-crime, cyber-attack and cyber-war,
The different categories of threat actors and their motivations .
Attacks and Vulnerabilities
An overview of common cyber-attacks, for example, SQL injection, XSS, and enumeration,
Explanation of how these attacks can be mitigated, including the use of penetration testing,
Understanding the human aspects of vulnerabilities, for example, insider threat and social engineering.
Critical Infrastructure
Critical Infrastructure, defining criticality, global and national level view, organisational level view, supply chain perspective,
Critical Information Infrastructure SCADA, defining SCADA, role in critical infrastructure and processes,
Network monitoring, operations management, indicators and warnings, intrusion detection, penetration testing, the strategic context.
Intended learning outcomes
On successful completion of this module you should be able to:

Knowledge
Assess cyber operations from a variety of threat actors,
Evaluate the different cyber vulnerabilities and how they might impact an organisation,
Appraise the strengths and weaknesses of various security technologies and their suitability for protecting an organisation.
Skills
Develop a security strategy using appropriate technologies and techniques,
Prioritise cyber threats and vulnerabilities based on their potential business impact.
Data Analytics for Energy Systems
Module Leader
Dr Chao Long
Aim
This module will introduce you to data analytics, overview challenges and solutions in using data analytical tools in energy systems, present approaches to predictive and descriptive data mining, classification, statistical methods, regression models and explain unsupervised learning techniques suitable for new information discovery. Students may benefit from knowledge of basic concepts of statistics methods for performance assessment and evaluation, regression models (linear, non-linear, Gaussian, Bayesian Logistic), and classification methods.
Syllabus
Introduction to Data Analytics,
Statistics refresher and data pre-processing,
Predictive analytics: regression refresher and classification methods,
Clustering and dimensionality reduction,
Graph analysis and visualisation,
Software and tools for data analytic,
Case study: application of data pre-processing and data analytical tools for a specific dataset.
Intended learning outcomes
On successful completion of this module you should be able to:

Critically analyse stages of the data analytics workflow; and establish a data analytics workflow based on the available data and formulated requirements,
Critically evaluate data analysis and visualisation techniques with respect to data analytics stages, using graph analysis and visualisation techniques,
Analyse and apply algorithms for discovery of new information from the large data sets, using statistics, regression, classification methods,
Evaluate performance of the algorithms and quality of the data analysis outcomes.
Artificial Intelligence for Energy Systems
Module Leader
Dr Da Huo
Aim
With more and more measurement and control devices installed in energy systems, data analytics using AI technology to support planning and operation of energy systems has shown significant advantages. The scientific and technical concepts of machine learning and AI methods/tools and their potential advantages in the energy sector will be taught in this module. One example of this is to use smart metering data to analyse a network’s hosting capacity of solar photovoltaic systems, and to analyse a power system’s technical and non-technical energy losses. The module aims to provide you with data analytical skills from machine learning and AI technology, and evaluate the advantages/disadvantages of their applications in the energy industry. The module also aims to provide you with essential skills (e.g. computer programming and coding in Python) for applying machine learning and AI in the energy industry.
Syllabus
Design of an appropriate analysis toolkit specific to analyse the examples of applications of machine learning and AI technology in energy industry,
Analysing the development and scaling/design of the AI technologies by evaluating the advantages/disadvantages of the available examples in the areas of applications in energy systems,
AI techniques (e.g. Artificial Neural Networks, RNN, Reinforcement Learning), and skills to manage, process and use data to support network operation and planning,
Techniques to evaluate where AI can be used and the potential benefits to the energy industry.
Intended learning outcomes
On successful completion of this module you should be able to:

Critically analyse the state-of-the-art of the applications of machine learning (ML) and AI technology in the energy industry,
Identify and assess the requirements of different AI/ML techniques and their contributions to improve the planning and operation of energy systems,
Implement AI/ML algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study,
Evaluate the advantages and disadvantages of particular AI techniques within the context of the energy industry.
Energy Systems Case Studies
Module Leader
Professor Nazmiye Ozkan
Aim
The module aims to provide you with a deep understanding of the truly multidisciplinary nature of a real industrial project. Using a relevant case study, the scientific and technical concepts learned during the previous modules will be brought together and used to execute the analysis of the case study.
Syllabus
Work flow definition: setting up the single aspects to be considered, the logical order, and the interfaces.
Design of an appropriate analysis toolkit specific to the case study
Development of a management or maintenance framework for the case study
Multi-criteria decision analysis [MDCA] applied to energy technologies to identify the best available technology.
Energy technologies and systems: understanding the development and scaling/design of the technologies by applying an understanding of the available resources in the assigned location;
Public engagement strategies and the planning process involved in developing energy technologies.
Intended learning outcomes
On successful completion of this module a student should be able to:

Critically evaluate available technological options, and select the most appropriate method for determining the most preferred technology for the specific case study.
Demonstrate the ability to work as part of a group to achieve the stated requirements of the module brief.
Organise the single-discipline activities in a logical workflow, and to define the interfaces between them, designing an overall multidisciplinary approach for the specific case study.

Applications of Blockchain Technology
Module Leader
Dr Chao Long
Aim
This module aims to provide you with data analytical skills to evaluate the advantages of application of Blockchain technology and state-of-the-art of the applications of Blockchain technology in the energy sector. In addition, you will learn essential computer coding skills for writing a private Blockchain network to be potentially used in the energy industry. The scientific and technical concepts of Blockchain technologies and examples of their applications in the energy sector will be taught in this module. The existing challenges in digital energy systems and potential areas of applying Blockchain and its advantages / disadvantages will be discussed in group sessions. A 2-day lab session for simulations will be carried out to allow you to have practical experience and skills for creating a private Blockchain network.

Syllabus
Design of an appropriate data analytical toolkit specific to evaluate the examples of applications of Blockchain technology in the energy industry,
Blockchain technologies: Analysing the development and scaling/design of the Blockchain technology by evaluating the advantages/disadvantages of the available examples in the assigned areas of applications in energy systems,
Blockchain technologies: Programme in an Ethereum platform, lab simulations and the writing of a private Blockchain network to be potentially used in the energy industry,
Technology selection: According to the areas of applications and required functions, the most appropriate method for determining the best type of Blockchain (e.g. permissioned or permission-less, proof-of-work, proof-of-stake and proof of authority) will be selected.
Intended learning outcomes
On successful completion of this module you should be able to:

Critically analyse the state-of-the-art of the applications of Blockchain technology in energy industry, and understand the examples of these applications in finance and other sectors as well as in the energy industry,
Critically evaluate potential technological options of Blockchain technology, and select the most appropriate method for determining the best type of Blockchain (e.g. permissioned or permission-less, proof-of-work, proof-of-stake and proof of authority) to meet the required functionalities with improved performance for the planning and operation of the energy systems,
Design and implement lab simulations individually to create a Blockchain network.
Energy Entrepreneurship