
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:
 Can explain the key components and mechanisms underlying energy systems [A3, B3]
 Can formulate and solve planning problems in energy systems (e.g., economic dispatch, unit commitment, and capacity expansion) as mathematical optimization problems [A1, B2]
 Can explain the typical structure and general functioning of electricity markets [A3, B3]
 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]
 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]
 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]
 Can select and apply appropriate techniques for solving energy optimization problems, including mathematical programming, stochastic programming, and approximate dynamic programming [A4, B1]
 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.



 VoorkennisNo prior knowledge of the energy sector is required. It is highly recommended for IEM students to have followed Operations Research Techniques 1. Although prior knowledge of stochastic optimization may help, it is not necessary to have completed Operations Research Techniques 2 to follow this course. The students should be familiar with coding using a programming language. 
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