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

Research output: Contribution to journalResearch articleContributedpeer-review

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.

Details

Original languageEnglish
Pages (from-to)1-28
JournalOperations Research Forum
Volume4
Issue number2
Publication statusPublished - 17 Apr 2023
Peer-reviewedYes

External IDs

Scopus 85153195577
dblp journals/orf/HobbieDMS23
Mendeley 27f8d0f4-2dad-3007-bd47-e950679c4a71
ORCID /0000-0001-7170-3596/work/142241641
ORCID /0000-0001-7597-8909/work/142246424

Keywords

Keywords

  • Energy modelling, Electricity markets, Energy economics, Storage optimization, Peak load pricing

Library keywords