Learning by Doing: Insights from Power Market Modelling in Energy Economics Courses

Hannes Hobbie, Constantin Dierstein, Dominik Möst () and Matthew Schmidt
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Hannes Hobbie: TU Dresden, Faculty of Business & Economics, Chair of Energy Economics
Constantin Dierstein: TU Dresden, Faculty of Business & Economics, Chair of Energy Economics
Dominik Möst: TU Dresden, Faculty of Business & Economics, Chair of Energy Economics
Matthew Schmidt: TU Dresden, Faculty of Business & Economics, Chair of Energy Economics

SN Operations Research Forum, 2023, vol. 4, issue 2, 1-28

Abstract: Abstract Much of energy economics curricula involves the study of techno-economic aspects of energy systems with an increasing focus devoted to fostering an understanding of the interactions between innovative technologies and adaptive markets. As the interplay of these dynamics and their impacts on market equilibria and outcomes is quite complex, optimization models are well-suited to facilitate their study. This paper presents two exemplary model approaches and associated case studies, which can be employed to study market developments driving long-term adaptations in the portfolio of power-generation assets as well as scheduling problems of individual plant owners with a focus on assessing the impact of changing market conditions on the profitability of investments. The combination of these two modelling approaches constitutes an innovative means of facilitating students’ understanding of how individual decisions of different market stakeholders lead to welfare-maximizing market equilibria under the assumption of perfect competition. The models are discussed along with the experiences acquired employing them in various forms as project assignments. In summary, the integration of modelling exercises and assignments into the curriculum of energy economics courses has proven to be a practical means of reinforcing and broadening lecture material that is both interesting and rewarding for students.

Keywords: Energy modelling; Electricity markets; Peak load pricing; Storage optimization; Energy economics; 90-01; 90-04; 90-10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00203-w

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