Electricity provision in the UK and around the world is undergoing a profound transformation driven by new technologies, increased demand for fossil fuels in the developing world, and climate policies. The smart grid paradigm encompasses the technological components of this transformation whereby new technologies such as solar panels and batteries will integrate with the existing grid. Enabled by new information and communication technologies, this provides utilities with more efficient ways to manage their infrastructure and can provide consumers with the ability to participate fully in the energy market. Together this will lead to a more flexible and economically efficient system that can also better accommodate and even benefit from new technologies such as electric vehicles and renewables.

This module will provide you with a deep understanding of the concepts and challenges of smart grid including:

- Distributed generation and power network management: basic concepts and challenges.

- Concepts of Smart Grid, Microgrids and Smart metering.

- Modern power network components and topologies.

- Real-time monitoring vs conventional monitoring.

- Communication technologies for Smart/Intelligent Power Network.

- Use of a suitable algorithm for forecasting the energy demand and possible energy generation.

- Design of renewable energy generation based on energy demand.

In this module you will initially work independently to provide a proposed solution to the task provided, after which you will work in a group to implement your solution and document it.

A 2500-word individual report that encompasses research and laboratory-based work, weighted at 50% and assessing learning outcomes 1 and 2, 3, and 4. Meeting AHEP 4 Outcomes: M3, M17

A 3000-word lab-based group portfolio based on design exercises and simulation implementation, weighted at 50% and assessing learning outcomes 2, 3, and 4. Meeting AHEP 4 Outcomes: M2, M5, M16

Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40% unless otherwise stated.

Note: Assessment 2 is a CORE assessment and must be passed with a mark of 50% or above in order to pass the module.

This module will enable students to gain understanding, apply knowledge, analyse and evaluate problems and create solutions through a variety of activities, including:

- Directed and independent learning and reading

- Use of software packages

- Lectures

- Groupwork in laboratory tasks

1. Demonstrate a comprehensive understanding and knowledge of smart grid concepts and systems which is at the forefront of the discipline. (AHEP 4: M3)

Knowledge and Understanding

2. Demonstrate a critical awareness and evaluate current research for tackling complex problems by modelling a system for integration of renewable energy within a smart grid infrastructure. (AHEP 4: M5, M17)

Analysis

3. Evaluate complex issues in Smart Grids, by applying sound judgements in the absence of complete data and employ appropriate decision-making algorithms for efficient power management of the grid. Communicate the methodology, results and conclusions of work done. (AHEP 4: M2, M5, M17)

Problem solving

Application

Communication

4. Demonstrate originality in the application of knowledge, together with a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge in Smart Grids Modelling and Analysis. (AHEP 4: M5, M16, M17)

Application

Team Work

Renewable Energy Laboratory

Suitable Engineering Software packages

Video Material/short courses

Library resources and computing facilities

Acha, E. et al., (2016), VSC-FACTS-HVDC Modelling, Analysis and Simulation in Power Grids, John Wiley & Sons.



Budka, K.C., (2014), Communication Networks for Smart Grids: Making Smart Grid Real, Springer.



Sioshansi, F. P., (2012), Smart Grid: Integrating Renewable, Distributed & Efficient Energy, Elsevier Publications.


Goodfellow I. et al., (2017), Deep Learning (Adaptive Computation and Machine Learning Series). MIT Press, Cambridge, Massachusetts.

Arrillaga, J., & Watson, N. R., (2003), Power System Harmonics, 2nd Edn., John Wiley and Sons.

Mohamed, M. A., & Eltamaly, A.M., (2018) Modelling and Simulation of Smart Grid Integrated with Hybrid Renewable Energy Systems, Springer Print.

Shalev-Shwartz, S. et al., (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, Cambridge, England.

Stuart, B., (2018), Smart Grids: Advanced Technologies and Solutions, 2 Edn., CRC Press.

Tom, M. et al., (2017), Machine Learning, McGraw Hill Education; New York.

Zhong, Q., & Hornik, T., (2012), Control of Power Inverters in Renewable Energy and Smart Grid Integration, Wiley Press.

In this module you will learn about using complex algorithms to manage the power flow in the most effective manner thus helping the UK to achieve a Carbon Neutral energy supply. The electricity grid is a highly complex interconnected system, in which the aim is to provide a steady and reliable electricity supply to all users. As the demand for electricity is increasing at an ever-increasing rate, engineers are constantly investigating ways to utilise the earth’s resources in the most effective manner.