Unit outline_

ELEC5208: Active Distribution Systems

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Power distribution systems are undergoing a significant transformation, driven by the growing integration of distributed energy resources (DERs)—such as rooftop solar, home batteries, electric vehicles, and flexible loads. At the same time, advances in information and communication technologies are enabling the shift from traditional passive systems, which feature unidirectional power flow and limited control, to active distribution systems that are decentralised, data-rich, and dynamically managed. This unit explores the technical, computational, and regulatory dimensions of this transition. The unit has a strong emphasis on computational modelling and optimisation techniques used in active distribution systems to deal with the new emerging challenges introduced by the widespread DER adoption, including voltage rise, reverse power flows and thermal loading. In this unit, students will learn to simulate DER behaviour using power flow algorithms designed specifically for multi-wire unbalanced distribution networks, model network components (lines, transformers, voltage regulators), and apply mathematical optimisation for DER scheduling and network operation. Topics such as optimal power flow, hosting capacity assessment, and inverter-based control (Volt–VAR, Volt–Watt) are explored in depth. The course also addresses interoperability standards and data modelling principles. Through practical exercises and case studies, students will gain the analytical and computational skills needed to manage modern distribution systems and contribute to the transition toward net-zero, resilient, customer-driven energy systems.

Unit details and rules

Academic unit School of Electrical and Computer Engineering
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Fundamentals of Electricity Networks, Control Systems and Telecommunications

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Gregor Verbic, gregor.verbic@sydney.edu.au
The census date for this unit availability is 31 March 2026
Type Description Weight Due Length Use of AI
Interactive oral Oral assessment of problem-based learning assignment #1
Oral examination
20% Week 07 10 minutes AI prohibited
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Experimental design group assignment Problem-based learning assignment #1
Written report
20% Week 07 n/a AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Interactive oral Oral assessment of problem-based learning assignment #2
Oral examination
20% Week 10 10 minutes AI prohibited
Outcomes assessed: LO11 LO12 LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Experimental design group assignment Problem-based learning assignment #2
Written report
20% Week 10 n/a AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
Interactive oral Oral assessment of problem-based learning assignment #3
Oral examination
10% Week 13 10 minutes AI prohibited
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
Experimental design group assignment Problem-based learning assignment #3
Written report
10% Week 13 n/a AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
group assignment = group assignment ?

Assessment summary

Assessment consists of two components:

  • Written report on the problem-based learning assignment (three parts)
  • Oral examination of the assignment results (three parts)

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 guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Multiple weeks Weekly tutorial and computing workshop (Weeks 2-13) Practical (36 hr) LO1 LO2 LO3 LO4 LO5 LO7 LO9 LO10 LO11 LO12 LO8 LO6
Self study Self-directed learning (39 hr)  
Week 01 Background and context Lecture (2 hr) LO1 LO2
Week 02 Distribution systems fundamentals Lecture (2 hr) LO1 LO2
Week 03 Distribution line modelling Lecture (2 hr) LO3
Week 04 Transformer modelling Lecture (2 hr) LO3
Week 05 Power flow analysis in distribution systems Lecture (2 hr) LO4
Week 06 Voltage regulation in distribution systems Lecture (2 hr) LO5
Week 07 Distributed energy resources Lecture (2 hr) LO6
Week 08 Mathematical optimisation basics Lecture (2 hr) LO7
Week 09 Home energy management Lecture (2 hr) LO7 LO8
Week 10 Optimal power flow in distribution systems Lecture (2 hr) LO7 LO9 LO8
Week 11 Hosting capacity management Lecture (2 hr) LO7 LO9 LO8
Week 12 Standards and interoperability Lecture (2 hr) LO10
Week 13 Future trends and course wrap-up Lecture (2 hr) LO1 LO2

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.

Required readings

Distribution System Modelling and Analysis, fourth edition. by William H. Kersting

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.

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

  • LO1. Understand the evolution from passive to active distribution systems.
  • LO2. Identify key drivers for the transition (decarbonisation, DER uptake, technology, policy)
  • LO3. Model unbalanced three-phase network components
  • LO4. Solve power flow problems in multi-wire unbalanced distribution networks
  • LO5. Develop a practical and analytical understanding of voltage regulation in distribution systems
  • LO6. Develop an understanding of distributed energy resources (DER), including rooftop PV, batteries, electric vehicles, and flexible loads
  • LO7. Understand the basics of mathematical optimisation for solving practical engineering problems
  • LO8. Develop an understanding of modelling and control of distributed energy resources via a home energy management system (HEMS)
  • LO9. Formulate and solve optimisation problems for DER scheduling and hosting capacity management
  • LO10. Explain the role of communication and control protocols for DER control and management
  • LO11. Collaborate in teams for scientific investigations and the learning process
  • LO12. Effectively communicate scientific concepts through oral and written presentations

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

N/A because the content of the unit has been changed.

Disclaimer

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