Kies de Nederlandse taal
Course module: 202200263
Optimisation of Sustainable Energy Systems
Course infoSchedule
Course module202200263
Credits (ECTS)5
Course typeStudy Unit
Language of instructionEnglish
Contact persondr. A. Trivella
dr. D. Guericke
Contactperson for the course
dr. A. Trivella
dr. A. Trivella
Academic year2023
Starting block
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes

The aim of this course is to familiarize students with models and techniques for tackling optimization problems that arise in the energy sector. The students get insights into how energy systems and markets work, how to formulate operational, planning, and investment problems as mathematical optimization problems, and how to solve them using an appropriate method. Special emphasis is placed on modern challenges involving the transition to renewable energies, including the management of intermittent units (e.g., wind and solar) and energy storage. The course addresses 8 learning objectives (linked to the intended learning outcomes of the IEM master program by the numbers in brackets). At the end of the course a student:

  1. Can explain the key components and mechanisms underlying energy systems [A3, B3]
  2. Can formulate and solve planning problems in energy systems (e.g., economic dispatch, unit commitment, and capacity expansion) as mathematical optimization problems [A1, B2]
  3. Can explain the typical structure and general functioning of electricity markets [A3, B3]
  4. Can formulate and solve the market clearing problem and the market bidding problem faced by different types of power producers (e.g., conventional, renewable, and virtual power plants) [A1, B2]
  5. Can formulate and solve energy real option problems for the management and valuation of energy assets or projects (e.g., storage operations, investment in a renewable energy project) [A1, B2]
  6. Can explain the challenges associated with the integration of renewable energy sources, and their implications towards solving optimization problems, including the key role of stochasticity [A2, B1]
  7. Can select and apply appropriate techniques for solving energy optimization problems, including mathematical programming, stochastic programming, and approximate dynamic programming [A4, B1]
  8. Can implement models and algorithms in a programming language (e.g., Python and Gurobi), and examine the solutions in the context of the considered energy application [A5, A6, B2]
In this course, we study a set of important problems that arise in the energy sector and that require the use of optimization techniques to be solved. We consider the classical problems as well as modern challenges, e.g., related to the integration of renewable energy sources. The course is structured into three parts on: (i) energy systems, (ii) energy markets, and (iii) energy real options. We deal with both deterministic and stochastic optimization problems, and use operations research techniques to formulate and solve them. In particular, we use integer programming, stochastic programming, Markov decision processes, and approximate dynamic programming.
Bachelor students who have the possibility to follow a master course during their bachelor programme and would like to take this course can submit a motivated request no later than 14 days before the start of the quartile, containing:
  • Study progress overview from Osiris
  • Description of how the student meets the course’s prerequisites
  • Approval of the programme director (or a delegate from the student’s bachelor programme) for following this master course

The request should be sent either to Niek van der Veen (email: or Ellemijn Ensink (email:
Assumed previous knowledge
No prior knowledge of the energy sector is required.
Operations Research Techniques 1 (201800003) or an equivalent.
Since the course heavily relies on stochastic optimization, having followed also Operations Research Techniques 2 (201800004) is a significant advantage.
The students must be familiar with coding using a programming language (e.g. Python).
Participating study
Master Industrial Engineering and Management
Required materials
Sides provided by the lecturers
Recommended materials
Scientific papers
Instructional modes
Presence dutyYes


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Exam and assignments

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