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Lead the way for your career with Advanced Digital Energy Systems MSc

Energy supply is fundamentally important to our homes and workplaces. Future energy supply has to be stable, secure, not only affordable but also sustainable, which makes energy systems a complex problem. Digital Energy Systems is an emerging discipline using powerful digital tools and various digital models to solve and manage the increasingly complex modern energy systems. Within this discipline, digital tools and models (such as Artificial Intelligence, Blockchain technology) are used to analyse data from different energy systems and sources and drive new control and operational strategies and business models.


Overview

  • Start dateOctober 2020
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course is suitable for Electrical Engineering, Computer Science, Mathematics, Engineering and Information Technology and Energy graduates and practicing IT or Energy engineers wishing to pursue a technical management career in the strongly growing digital energy sector. It develops professional engineers and scientists with the multidisciplinary skills and ability to analyse current and future energy engineering problems.


Data and data skills are really important to the energy sector is as its developing and transitioning. There's the 3Ds I like to think of: de-centralisation, de-carbonisation and digitalisation and all of those are clearly going to need a lot of data and automation in order to be successful. So AI and blockchain technologies are really important to that transition but it's really important that we do have the right skills and the technologies to process that data and use it accordingly and innovation and skill development in that area is really important.

AI blockchain in the energy sector. It's about that user experience. It's about taking that user centric design, place for place and understanding how we can shift that mass market and then how we can guarantee the experience that is a commercially viable for that organisation and doesn't degrade the experience that they currently have with a centralised generation system.

I think energy is one of our key challenges we face as a society. If we don't address the energy challenge, if we don't decarbonise our energy systems which make up a big chunk of our CO2 production, at the moment all else probably won't matter. Why AI and blockchain? Because I think they are two of the key technologies which are going to help us address those now there are lots of other technologies as well there are things like the Internet of Things and I think there's still a lot of work to be done on other social sciences and psychology of how to change people's behaviour because ultimately all of these technologies only make a difference if people decide to use them if people act on the advice for example given by an AI system if people trust to hand over the controls to an automated system.

Your career

The international nature of this growing field allows Cranfield graduates to develop diverse and rewarding careers all over the world in industry, government or research.
Example careers:

  • Energy Analyst – data science,
  • Offshore Energy Analyst,
  • Energy and Sustainability Analyst,
  • Research Analyst - Energy.

Cranfield Careers Service
Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. We will also work with you to identify suitable opportunities and support you in the job application process for up to three years after graduation.

Why this course?

Developed economies now face a number of challenges in procuring energy security and responding to energy pricing and affordability issues, as well as dealing with contributions to carbon emissions such as the UK Government’s ambitious targets of reducing greenhouse gas emissions to close to zero in the power sector by 2050.

You will benefit from dedicated state-of-the-art facilities including unique engineering-scale facilities for the development of efficient technologies with low CO2 emissions. In addition to management, communication, team work and research skills, each student will attain at least the following learning outcomes from this degree course:

  • Design an appropriate data acquisition system for energy related processes,
  • Critically analyse industrial data collected from different energy systems,
  • Develop systematic strategies using a range of software for energy systems modelling, optimisation and control to resolve the technical issues involved in the design and operation of industrial energy systems.
This MSc is supported by our team of professorial thought leaders, including Professor Phil Hart, who is influential in the field of digital energy, and an integral part of this MSc

Informed by Industry

We have a world class reputation for our industrial-scale research and pilot-scale demonstration programmes in the energy sector. Close engagement with the energy and transport sectors over the last 20 years has produced long-standing strategic partnerships with the sectors’ most prominent players. The strategic links with industry ensures that all of the material taught on the course is relevant, timely and meets the needs of organisations operating within the energy sector. This industry-led education makes our graduates some of the most desirable in the world for energy companies to recruit.


Course details

The taught programme for the Advanced Digital Energy Systems masters is generally delivered from October to February and is comprised of eight modules. Each of the first five modules are delivered over one week of intensive delivery with a second week being free from structured teaching to allow time for more independent learning and reflection. The final three modules are delivered over two weeks.

Water course structure diagram

Students on the part-time programme will complete all of the modules based on a flexible schedule that will be agreed with the course director.


Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.

Group project

The group project is an applied, multidisciplinary, team-based activity. Often solving real-world, industry-based problems, you are provided with the opportunity to take responsibility for a consultancy-type project while working under academic supervision. Success is dependent on the integration of various activities and working within agreed objectives, deadlines and budgets. Transferable skills such as team work, self-reflection and clear communication are also developed.

Individual project

The individual project is the chance for you to focus on an area of particular interest to you and your future career. You will select the individual project in consultation with the Thesis Co-ordinator, your allocated supervisor and your Course Director. These projects provide you with the opportunity to demonstrate your ability to carry out independent research, think and work in an original way, contribute to knowledge, and overcome genuine problems in the energy industry. Many of the projects are supported by external organisations.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Sustainable and Conventional Energy Technologies

Aim
    This module aims to introduce you to the fundamentals of basic sustainable and conventional energy technologies with a view to distinguish key factors that should be considered in planning and operation.
Syllabus
    • Basic definitions: energy source, product and resource,
    • From source to resource: the energy project,
    • Geo-resources fundamentals: petroleum engineering, geothermal engineering and CO2 capture and sequestration,
    • From raw energy source to marketable product: conversion processes,
    • Finite vs. renewable energy: impact on resources quantification and classification (the UNFC-2009 example).
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Analyse potential energy projects developing technological solutions for energy production,
  2. Appraise basic principles of sustainable energy technologies, including wind, wave, tidal, solar and geothermal energy,
  3. Distinguish the key technological aspects of oil and gas exploration and production,
  4. Describe the basic structure of thermal energy conversion, including gas and coal fired power plants.

Power Systems Analysis

Aim
    The module aims to provide you with modelling and simulation skills of design, operation and control a complex power system. These skills are relevant to power industry, such as electricity system operators, power asset owners, distributed network operators, and power system consultancy companies.
Syllabus
    • Smart grid,
    • Condition monitoring techniques,
    • Admittance matrix and load flow problems,
    • Gauss Seidl Power Flow method – theory and practise,
    • Newton Raphson Power Flow method – theory and practise,
    • Information and communication techniques for power system,
    • Power system planning and operation.
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Design power system using advanced digital tools,
  2. Analyse system monitoring data from a utility,
  3. Evaluate cost-benefits of power system digitalisation,
  4. Optimise digital energy system using modelling and simulation approach,
  5. Apply condition monitoring techniques to power system infrastructure.

Cybersecurity for Energy Systems

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
  1. Assess cyber operations from a variety of threat actors,
  2. Evaluate the different cyber vulnerabilities and how they might impact an organisation,
  3. Appraise the strengths and weaknesses of various security technologies and their suitability for protecting an organisation.
  • Skills
  1. Develop a security strategy using appropriate technologies and techniques,
  2. Prioritise cyber threats and vulnerabilities based on their potential business impact.

Data Analytics and Blockchain

Aim
    This module will introduce you to data analytics, overview challenges and solutions in this area, present approaches to predictive and descriptive data mining and explain unsupervised learning techniques suitable for new information discovery. Theory of Distributed Ledger Technology and Blockchain will also be introduced in this module. You may benefit from knowledge of basic concepts of statistics for performance assessment and evaluation, and the disruptive technology of Blockchain. 
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,
    • Theory of Distributed Ledger Technology and Blockchain,
    • Case study: application of data pre-processing and Distributed Ledger Technology Blockchain techniques for data analytics.
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Critically analyse stages of the data analytics workflow; and establish a data analytics workflow based on the available data and formulated requirements,
  2. Critically evaluate data analysis and visualisation techniques with respect to data analytics stages,
  3. Analyse and apply algorithms for discovery of new information from the large data sets,
  4. Evaluate performance of the algorithms and quality of the data analysis outcomes,
  5. Set up underlying mechanisms for Blockchain networks, and evaluate the capability of Blockchain in data analytics and management.

Artificial Intelligence for Energy Systems

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 likely in Matlab or R) for applying machine learning and AI in 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, K-means method), 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:

  1. Critically analyse the state-of-the-art of the applications of machine learning (ML) and AI technology in the energy industry,
  2. Identification and assessment of requirements of different AI/ML techniques and their contributions to improve the planning and operation of energy systems,
  3. Implement AI/ML algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study,
  4. Evaluate the advantages and disadvantages of particular AI techniques within the context of the energy industry.

Applications of Blockchain Technology

Aim
    The 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 of writing a private Blockchain network potentially to be used in 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 and disadvantages will be discussed in group discussions. A 2-day lab session for simulations will be carried out to allow you to have practical experience and skills in 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 in lab simulations and writing a private Blockchain network potentially to be used in energy industry,
    • Technology selection: According to the areas of applications and required functions, 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).
Intended learning outcomes

On successful completion of this module you should be able to:

  1. 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 energy industry,
  2. 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,
  3. Design and implement lab simulations individually to create a Blockchain network.

Energy Entrepreneurship

Module Leader
  • Dr Stephanie Hussels
Aim

    In this world of downsizing, restructuring and technological change, notions of traditional careers and ways of creating value have all been challenged. People are depending more upon their own initiative to realise success. Never, it seems, have more people been starting their own companies than now, particularly to exploit the World Wide Web. There’s no single Government (in either the developed or the developing world), which is not paying at least lip service to enterprise development. The aim of this module is to provide you with knowledge and skills relevant for starting and managing new ventures across the entrepreneurial life cycle. Moreover, it will prepare you on how to prepare a business pitch to an investor.

Syllabus
    • Entrepreneurial risk, performance and environment,
    • Business planning techniques and their application in entrepreneurial ventures
    • Venture strategy in dynamic markets
    • Start-up and resources to exploit a profit opportunity
    • The evolution of the venture and managing growth
    • Protecting and securing intellectual capital: IPR and antitrust law
    • Financial management for new ventures: financing a start-up 
    • The entrepreneurial financing process: buying and selling a venture.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Assess the impact of the business environment on entrepreneurial opportunity identification and exploitation.
  • Critically apply the theoretical underpinning of entrepreneurship to the process of managing risk in new ventures and supporting their development.
  • Compare and contrast how managerial challenges vary across the life cycle of an entrepreneurial venture.
  • Assess the likely financial needs of a new venture and pitch for finance.
  • Develop and write a credible business plan for a new venture.


Energy Systems Case Studies

Module Leader
  • Dr Xin Zhang
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 best available technology [BAT] for the specific case study.
  • Demonstrate the ability to work as part of a group to achieve the stated requirements of the module brief.
  • Demonstrate the ability to 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.