EPE8207 : Smart Grids and Applications of Computational Intelligence
EPE8207 : Smart Grids and Applications of Computational Intelligence
- Offered for Year: 2024/25
- Module Leader(s): Dr Anurag Sharma
- Owning School: NUIS
- Teaching Location: Singapore
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 15 |
| ECTS Credits: | 8.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
Electrical/Electronic Engineering first degree or other equivalent qualification
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
The aim of this module is to introduce the smart grid concept and its characteristics to the students through the existing and future requirements of the power distribution and transmission networks in relation to increased renewable and distributed generation. It also provides computational intelligent techniques that are mandatory tools required to develop smart grid concept in real.
Outline Of Syllabus
• Barriers and costs derived from existing electrical infrastructure. Future grid development, including the Smart grid, load manipulation, energy storage.
• Concept of Smart grid and Technologies: ICT, Power System Architectures, Smart Grid, Renewable Energy and Energy Storage, Decentralised Control and management, Micro Grid, Smart Homes, Smart Buildings, and Energy Management
• Computational Intelligent techniques: Optimization Techniques, Genetic Algorithm, Fuzzy Logic, Artificial Neural Networks and Multi-agent system
• Applications of Computational Intelligent Techniques for Smart grid: Optimising Energy Management Functions, Forecasting Load, Forecasting Electricity Price, and Forecasting Renewable Energy Power
Learning Outcomes
Intended Knowledge Outcomes
On successful completion of the module the student will be able to:
• Discuss present and future developments in electrical power industry and its infrastructure
• Know and demonstrate the concept of smart grid technologies and its applications to existing/modern power system.
• Know various types of computational intelligent techniques for creating distributed intelligence and optimize smart grid functionalities.
• Analyse and apply computational intelligence techniques for smart grid.
• Create novel smart grid architecture and functionalities.
Intended Skill Outcomes
On successful completion of the module the student will be able to:
• Know latest trend in electrical power engineering, what smart grid is and where it focuses on.
• Analyse and evaluate smart grid functionalities and develop solutions that can make correct decisions on limited information.
• Identify the importance of cross-discipline knowledge for meaningful contribution to the energy debate.
• Apply computational intelligent techniques for real-wold problems and forming an engineering argument.
• Demonstrate a critical understanding of the relevant theoretical concepts and practical implementation of a smart grid with computational intelligent techniques.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 30 | 1:00 | 30:00 | Lectures |
| Scheduled Learning And Teaching Activities | Lecture | 6 | 1:00 | 6:00 | Drop-In/Surgery |
| Guided Independent Study | Assessment preparation and completion | 1 | 50:00 | 50:00 | Directed reading which includes IEEE papers and web refs. |
| Guided Independent Study | Assessment preparation and completion | 1 | 13:00 | 13:00 | Revision for exam. |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Coursework |
| Guided Independent Study | Assessment preparation and completion | 1 | 1:00 | 1:00 | Examination |
| Guided Independent Study | Independent study | 1 | 30:00 | 30:00 | Personal study throughout teaching period to follow up taught classes. |
| Total | 150:00 |
Teaching Rationale And Relationship
Lectures provide core material and guidance for further reading. Problem solving is introduced through lectures and practiced during private study.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 60 | 1 | A | 30 | 24 hrs Take home exam (to be submitted within 24 hours of being set) |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 35 | Max 2000 words |
| Report | 1 | M | 35 | Max 2000 words |
Assessment Rationale And Relationship
The examination is an appropriate way to assess both theoretical knowledge and understanding and problem solving skills under time-constraint as required in industry.
The coursework involving a case study will enable a more realistic engineering problem to be set and will also assess data and information acquisition and evaluation skills.
The coursework will assess student’s ability in developing programming and technical skills as well as their understanding of how smart grid operates.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EPE8207's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- EPE8207's past Exam Papers
General Notes
N/A
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Disclaimer
The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.