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