SMART ENERGY SYSTEMS DESIGN AND ANALYSIS - 2024/5

Module Overview

Energy systems are becoming increasingly complex in terms of configuration, type of sources and distribution. The common challenges include the integration of fluctuating energy sources, disruption in energy demand and distribution bottlenecks. The inclusion of different, often disparate technologies associated with renewable energy technologies, such as solar, wind, bio-generation and hydrogen sources, and frequently opposing requirements of sustainability, economic viability and legislation impose further complexity and requirement for smart solutions at operational and design levels.

This module covers a multidisciplinary space between energy engineering and information technology to support timely solutions in real-life environments. It consists of two parts: i) lectures covering fundamentals of energy networks (centralized vs distributed), energy supply chains and operational bottlenecks, energy integration and different optimization methods, machine learning and Artificial Intelligence (AI) techniques, and ii) supervised group project work to find example solutions by applying the methods taught in the first part.

The information obtained in this module closely relates to all other modules in the programme (particularly Introduction to Renewable Energy Systems and Sustainable Energy Storage), which together provide a complete picture of the sustainable energy sector, from the fundamental principles to niche applications in designing autonomous smart energy systems based on AI.

Module provider

Chemistry and Chemical Engineering

Module Leader

CECELJA Franjo (Chst Chm Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

• Introduction to energy systems and design and operational requirements: introduction to energy generation and storage technologies and integration requirements, energy distribution (supply chain) principles.


• Implication of operational requirements: appreciations of importance of environmental legislation, barriers, costs and economic requirements of energy infrastructure.


• Foundation of data and information acquisition and processing techniques for smart energy systems benefits.


• Optimization techniques for operation of energy systems.


• Machine learning (data driven modelling) and predictive techniques.


• Knowledge modelling and matching for symbiotic networks.


• Smart network development: practical and hands on considerations of energy supply chain synthesis, operational advances and/or symbiotic formulation.

Assessment pattern

Alternative Assessment

Where an individual fails the group project an equivalent individual project covering the same learning outcomes will be provided.

Assessment Strategy

The assessment strategy is designed to:

Present up-to-date concepts, theories, and technologies of smart energy system operation and design, combined with problem-solving sessions that give group work and discussion opportunities. Typically, these sessions will be guided and informed by research advances in the area of Sustainable Energy. The subject will be addressed both qualitatively and quantitatively, particularly through state-of-the-art smart energy systems and optimisation methods.

Thus, the summative assessment for this module consists of:

• Coursework – group project on real-life problem solving, (covering all LOs).


• Examination, (covering all L0s).

• Formative verbal feedback is given during laboratory experiments and group project.


• Formative feedback on coursework and group project is given verbally and available on SurreyLearn to provide feedback on understanding of smart energy network operation and design and respective problem formulation and solution.


• The students will receive written feedback on their coursework

Module aims

• The module aims to introduce students to the engineering principles of energy source integration and energy distribution and respective challenges. The students will gain and overview and hands on experience with implementation modern artificial intelligence (machine learning and knowledge based systems) and optimization techniques to overcome technological, environmental and economic challenges, including future developments in energy sector.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Present up to date concept, theory and technologies of smart energy systems, combined with problem solving sessions giving opportunity for group work and discussion. The subject will be addressed both qualitatively and quantitatively, the latter through state-of-the-art optimization, machine learning and knowledge modelling methods and techniques.

The learning and teaching methods include:

Combined lectures/tutorial session and supervised group practical problem solving exercise.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENGM314

Other information

The school of Chemistry and Chemical Engineering/Sustainable Energy MSc is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Employability

Most of tutorial sessions and the coursework are designed and positioned here such to provide students with and expose them to a more authentic (real world) problem solving experience towards at the end of their programme. Through this students will gain experience of report writing for specific audiences/stakeholders, and ability to demonstrate capability in problem solving directly applicable to a wide range of sectors, which could be cited in interviews and applications to show students’ experience of applying scholarly knowledge to another sector.

Digital capabilities

Significant level of digital skill is a clear output of this module which students gain through a direct and indirect exposure:

Digital capability gained through a direct exposure includes the use of General Algebraic Modelling System (GAMS), a software tool for solving optimization and decision making problems. During every tutorial students use GAMS in computer laboratory gaining experience in running the software but also in using computers in more general terms.

Digital capability gained through an indirect exposure includes teaching materials and key content available in multimedia forms through the Virtual Learning Environment Surreylearn..

Global and Cultural Capabilities

Engineering in general and Decision Making in particular are global synonyms and the tools and skills used on this module can be used internationally and multiculturally. Students learn about generic engineering and professional code of conduct and the importance of respect in teamwork. Students learn to work together in groups with other students from different backgrounds to solve a problem. This module allows students to develop skills that will allow them to develop applications with global reach and collaborate with their peers around the world.

Sustainability

Students will complete this module with social, ethical and contextually aware knowledge. This module has gender inequality (in the broadest and most inclusive use of the term) at its core, aligned with the UN’s gender equality sustainability goal. It also seeks to ensure community sustainability through the knowledge, skills and awareness students will have upon completion of the module.

Resourcefulness and resilience

This module directly contributes to the educational elements of resourcefulness and resilience as students are honing their autonomous learning to a sophisticated and advanced level. Throughout the module, from concept of formulating the problem to implementation of tools and finding solutions, students will be highly independent, yet supported by their supervisor in the course of tutorials. Students will gain particular skills in informed decision making as this is the core nature of the module They will have to problem solve, navigate ethical considerations and consider their arguments and findings in context. Within a network of support, students will further develop the extent to which they are independent and resourceful learners with a great deal of confidence in conducting and leading independent work towards solution.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.