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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

ELEC5203: Topics in Power Engineering

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.

Code ELEC5203
Academic unit Electrical and Information Engineering
Credit points 6
Assumed knowledge:
Competence with linear algebra, differential calculus, numerical methods and Matlab; basic programming skills (Python or Matlab); familiarity with basic physics

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

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.