ELEC5203: Semester 2, 2020

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Units ELEC5203 Semester 2 2020 [Normal day]

Unit of study_

ELEC5203: Topics in Power Engineering

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit of study provides an introduction to engineering optimisation, focusing specifically on practical methods for formulating and solving linear, nonlinear and mixed-integer optimisation 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.

Unit details and rules

Unit code	ELEC5203
Academic unit	Electrical and Information Engineering
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Competence with linear algebra, differential calculus, numerical methods and Matlab; basic programming skills (Python or Matlab); familiarity with basic physics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Gregor Verbic, gregor.verbic@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Final exam.	60%	Formal exam period	2 hours
Outcomes assessed:
Small test	Homework problems	30%	Multiple weeks	n/a
Outcomes assessed:
Small test	Mid-semester exam The mid-semester exam will cover the material covered up until Week 8.	10%	Week 08	50 minutes
Outcomes assessed:
= Type C final exam ?

Assessment summary

Homework problems: Students are required to complete weekly tasks that apply knowledge gained in lectures and tutorials to solve practical power engineering problems.
In-semester exam: The in-semester exam tests students’ knowledge learned in the lectures and tutorials.
Final exam: the final exam tests students’ knowledge learned in lecture, tutorial, assignment, and lab.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to optimisation	Lecture (2 hr)
Week 02	1. Classical optimisation techniques; 2. Single and multivariable unconstrained optimisation	Lecture and tutorial (4 hr)
Week 03	1. Multivariable optimisation with equality constraints; 2. Solution by the method of Lagrange multipliers	Lecture and tutorial (4 hr)
Week 04	1. Multivariable optimisation with inequality constraints; 2. Karush-Kuhn–Tucker optimality conditions	Lecture and tutorial (4 hr)
Week 05	Convex optimisation	Lecture and tutorial (4 hr)
Week 06	Linear programming	Lecture and tutorial (4 hr)
Week 07	Economic dispatch	Lecture and tutorial (4 hr)
Week 08	Nonlinear programming	Lecture and tutorial (4 hr)
Week 09	Nonlinear programming	Lecture and tutorial (4 hr)
Week 10	Mixed integer programming	Lecture and tutorial (4 hr)
Week 11	Unit commitment problem	Lecture and tutorial (4 hr)
Week 12	1. Sequential decision making; 2. Dynamic programming	Lecture and tutorial (4 hr)
Week 13	Home energy management problem	Lecture and tutorial (4 hr)

Attendance and class requirements

Study commitment: 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. Students need to prepare for tutorials and read the references to fully master the basic concepts covered in the lectures.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. formulate engineering problems as optimisation problems and solve them using appropriate solvers
LO2. demonstrate an understanding of applied optimisation to be able to solve practical power engineering problems
LO3. demonstrate proficiency in using dedicated modelling tools and solvers for solving optimisation problems arising in power systems
LO4. apply linear algebra and differential calculus to solve applied optimisation problems
LO5. 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
LO6. deliver written and oral presentations using varied media aids and tools, to convey complex engineering material concisely and accurately

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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