Electricity provision in 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 (Artificial Intelligent) for forecasting the energy demand and possible energy generation.
- Design of renewable energy generation based on energy demand.

Assessment 1 will assess learning outcomes 1 and 2 as well as AHEP3: SM7M, SM8M, EA5m, EL11M, EL12M, P9m

Assessment 2 will assess learning outcomes 3 and 4 as well as AHEP3: EA6M, EA7M, D9M, G1.

Note: assessment 2 is a CORE assessment and must be passed in order to pass the module.

You will be provided with formative assessment and feedback throughout the semester.

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

- Directed learning and reading
- Use of software packages
- Lectures
- Groupwork in laboratory tasks

1. Demonstrate a comprehensive understanding and knowledge at the forefront of smart grid concepts and systems. (AHEP3: SM7M, SM8M, P9m, G1) Knowledge and Understanding

2. Critically evaluate current legislation and future regularity requirement for developing a sustainable smart grid model infrastructure. Communicate the methodology, and evaluation of work done. (AHEP3: EL11M, EL12M, G1) Enquiry, Communication, Reflection

3. 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. (AHEP3: EA6M, EA5m, EA7M, G1) Analysis

4. Evaluate complex issues, by applying sound judgements in the absence of complete data. Employ appropriate decision-making algorithms for efficient power management of the grid. Communicate the methodology, results and conclusions of work done. (AHEP3: EA5m, EA7M, D9M, G1) Problem solving, Application, Communication

Renewable Energy Laboratory
Suitable Engineering Software packages
Video Material/short courses
Lectures, library, computing facilities, Internet, Industrial Collaboration

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

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

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

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

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

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

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

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

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

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

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 high complex interconnected system, in which the aim is to provide a steady and reliable electricity supply to all users, especially 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.
In this module you will initially work independently to provide a proposed solution to the task provided after which you will work as a group of Engineers to implement your solution and document it. This assessment will be 50% individual report and 50% group report.