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ELEC5203: Topics in Power Engineering (2018 - Semester 2)

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Unit: ELEC5203: Topics in Power Engineering (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Electrical & Information Engineering
Unit Coordinator/s: Dr Verbic, Gregor
Session options: Semester 2
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This unit of study provides an introduction to engineering optimisation, focusing specifically on practical methods for formulating and solving linear, nonlinear and mixed-integer optimization problems that arise in science and engineering. The course is general enough to be of interest also for students from other engineering disciplines, not only for power engineering students. The course covers conventional optimisation techniques, including unconstrained and constrained single- and multivariable optimisation, convex optimisation, linear and nonlinear programming, mixed-integer programming, and sequential decision making using dynamic programming. The emphasis is on building optimisation models, understanding their structure and using off-the-shelf solvers to solve them. The application focus is on the optimisation problems arising in smart grids and electricity markets, including economic dispatch, unit commitment, home energy management and device scheduling. The course will use Matlab and AMPL as modelling tools and a range of state-of-the-art solvers, including Cplex, Gurobi, Knitro and Ipopt.
Assumed Knowledge: ELEC3203 AND ELEC3204. Familiarity with basic mathematics and physics; competence with basic circuit theory and understanding of electricity grid equipment such as transformers, transmission lines and associated modeling; and fundamentals of power electronic technologies.
Lecturer/s: Dr Mhanna, Sleiman
Timetable: ELEC5203 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Tutorial 2.00 1 12
3 Project Work - own time 3.00 13
4 Independent Study 2.00 13
T&L Activities: Tutorial: Tutorials are devoted to practising basic concepts covered in the lectures and understanding how more complex tasks can be handled by putting these basic concepts together.

Project Work - own time: Each student will be given four homework problems which will be solved in student`s own time. One hour of the weekly tutorial will be used to answer homework-related questions.

Independent Study: Students need to do some preparation for tutorials and need to read the references to fully master the basic concepts covered in the lectures.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Homework problems and tutorials are designed to enable students to develop design skills. Design (Level 4)
Gain an understanding of the basic concepts of applied optimisation to be able to solve power engineering problems. Engineering/IT Specialisation (Level 4)
Gain an ability to apply their knowledge of linear algebra and differential calculus to solve applied optimisation problems. Maths/Science Methods and Tools (Level 4)
Students need to read scientific papers related to the topics discussed in the class to be able to gain a deeper understanding of the material. Information Seeking (Level 2)
Students need to solve four homework problems. For each problem, they need to produce a written report. Communication (Level 2)
Group work in tutorials. Project and Team Skills (Level 2)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Design (Level 4)
1. Ability to formulate engineering problems as optimisation problems and solve them using appropriate solvers.
Engineering/IT Specialisation (Level 4)
2. Ability to demonstrate an understanding of applied optimisation to be able to solve practical power engineering problems.
3. Proficiency in using dedicated modelling tools and solvers for solving optimisation problems arising in power systems.
Maths/Science Methods and Tools (Level 4)
4. Ability to apply linear algebra and differential calculus to solve applied optimisation problems.
Information Seeking (Level 2)
5. Ability to undertake inquiry and knowledge development by identifying the limits of the available information on applied optimisation in the context of power systems by drawing on relevant research papers and articles on the topic.
Communication (Level 2)
6. Ability to make written and oral presentations using varied media aids and tools, to convey complex engineering material concisely and accurately.
Project and Team Skills (Level 2)
7. Ability to work in a team by assuming appropriate roles and balancing responsibilities to achieve the goals of a project in a timely manner.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Final Exam No 60.00 Exam Period 1, 2, 4,
2 Mid-Semester Exam No 10.00 Week 9 1, 2, 4,
3 Homework Problems No 30.00 Multiple Weeks 1, 2, 3, 4, 5, 6, 7,
Assessment Description: Final exam: test of knowledge learned in lecture, tutorial, assignment, and lab (60%)

Mid-semester exam: test of knowledge learned in lecture and tutorial (10%)

Homework: application of knowledge gained in lectures and tutorials to solve practical power engineering problems (30%)
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
Online Course Content: Canvas
Note on Resources: IEEE, IET and Elsevier research papers and articles (ieeexplore.ieee.org, www.sciencedirect.com)

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction to optimisation. Statement of an optimisation problem. Classification of optimisation problems.
Week 2 Classical optimisation techniques. Single- and multivariable unconstrained optimisation.
Week 3 Multivariable optimisation with equality constraints. Solution by the method of Lagrange multipliers.
Week 4 Multivariable optimization with inequality constraints. Karush-Kuhn–Tucker optimality conditions.
Week 5 Convex optimisation. Duality. First and second-order optimality conditions.
Week 6 Linear programming. Geometry of linear programming problems. Duality in linear programming.
Week 7 Economic dispatch.
Week 8 Nonlinear programming. Unconstrained optimisation techniques. Indirect search (descent) methods. Newton and Quasi-Newton method.
Week 9 Nonlinear programming. Constrained optimisation techniques. Interior point methods.
Assessment Due: Mid-Semester Exam
Week 10 Mixed integer programming. Cutting plane method. Branch and bound methods.
Week 11 Unit commitment problem.
Week 12 Sequential decision making. Dynamic programming.
Week 13 Home energy management problem.
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Master of Engineering (Power) 2011, 2012
Computer Engineering (till 2010) 2010
Electrical (till 2014) 2010, 2011, 2012, 2013, 2014
Electrical Engineering / Arts 2011, 2012, 2013, 2014
Electrical Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Electrical (Bioelectronics) (till 2012) 2011, 2012
Electrical Engineering (Bioelectronics) / Arts 2011, 2012
Electrical Engineering (Bioelectronics) / Science 2011, 2012
Electrical Engineering / Medical Science 2011, 2012, 2013, 2014
Electrical Engineering / Project Management 2012, 2013, 2014
Electrical Engineering / Science 2011, 2012, 2013, 2014
Electrical (Computer) (till 2014) 2011, 2012, 2013, 2014
Electrical Engineering (Computer) / Arts 2011, 2012, 2013, 2014
Electrical Engineering (Computer) / Commerce 2012, 2013, 2014, 2011
Electrical Engineering (Computer) / Science 2011, 2012, 2013, 2014
Electrical Engineering (Computer) / Law 2012, 2013, 2014
Electrical (Power) (till 2014) 2010, 2011, 2012, 2013, 2014
Electrical Engineering (Power) / Arts 2011, 2012, 2013, 2014
Electrical Engineering (Power) / Science 2011, 2012, 2013, 2014
Electrical (Telecommunications) (till 2014) 2011, 2012, 2013, 2014
Electrical Engineering (Telecommunications) / Science 2011, 2012, 2013, 2014
Electrical Mid-Year 2016, 2017, 2018, 2019
Electrical 2015, 2016, 2017, 2018, 2019
Electrical / Arts 2016, 2017, 2018, 2019
Electrical / Commerce 2016, 2017, 2018, 2019
Electrical / Medical Science 2016, 2017
Electrical / Music Studies 2016, 2017
Electrical / Project Management 2016, 2017, 2018
Electrical / Science 2016, 2017, 2018, 2019
Electrical/Science (Health) 2018, 2019
Electrical (Computer) 2015
Electrical / Law 2016, 2017, 2018, 2019
Electrical (Power) 2015
Electrical (Telecommunications) 2015
Software Mid-Year 2016, 2017, 2018, 2019
Software 2015, 2016, 2017, 2018, 2019
Software / Arts 2016, 2017, 2018, 2019
Software / Commerce 2016, 2017, 2018, 2019
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018, 2019
Software/Science (Health) 2018, 2019
Software / Law 2016, 2017, 2018, 2019
Software Engineering (till 2014) 2010, 2011, 2012, 2013, 2014
Software Engineering / Arts 2011, 2012, 2013, 2014
Software Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Software Engineering / Medical Science 2011, 2012, 2013, 2014
Software Engineering / Project Management 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013, 2014
Telecommunications (till 2010) 2010
Bachelor of Information Technology (Computer Science) 2014 and earlier 2010, 2011, 2012
Information Technology (Computer Science)/Arts 2012
Electrical/Science (Medical Science Stream) 2018, 2019
Master of Engineering 2013, 2014, 2015, 2016, 2017, 2018, 2019
Master of Engineering (Electrical) 2011, 2012
Master of Engineering (Network) 2012
Master of Engineering (Wireless) 2012
Master of Professional Engineering (Electrical) 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019
Master of Professional Engineering (Power) 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019
Software/Science (Medical Science Stream) 2018, 2019
Computer Engineering / Commerce 2010
Electrical Engineering (Computer) / Medical Science 2011, 2013, 2014
Electrical Engineering (Telecommunications) / Arts 2011, 2012, 2013, 2014
Electrical Engineering (Telecommunications) / Medical Science 2011, 2012, 2013, 2014
Information Technology (Computer Science) / Science 2012

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Design (Level 4) Yes 32.5%
Engineering/IT Specialisation (Level 4) Yes 31.5%
Maths/Science Methods and Tools (Level 4) Yes 24%
Information Seeking (Level 2) Yes 3%
Communication (Level 2) Yes 6%
Professional Conduct (Level 2) No 0%
Project and Team Skills (Level 2) Yes 3%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.