Application of Portfolio Theory to Optimize Power Plant Mixes in Classroom Teaching

Barbara Glensk (), Qinghan Yu and Reinhard Madlener
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Barbara Glensk: RWTH Aachen University
Qinghan Yu: RWTH Aachen University
Reinhard Madlener: RWTH Aachen University

Chapter Chapter 55 in Operations Research Proceedings 2023, 2025, pp 431-437 from Springer

Abstract: Abstract The importance and relevance of sustainability and climate neutrality is related to the transformation of energy, mobility and industry sectors. Decision-oriented, quantitative predictive or prescriptive methods (e.g., linear programming, multi-criteria decision support) are required to evaluate and design these complex systems. These are provided particularly by operations research. The relevance and need for these methods in the context of sustainable development is evidenced by many research projects, as well as the application of the operation research methods in the different industry sectors. That are also central topics in many study programs. The goal of the project “Operations Research (OR) for Sustainability: Energy, Mobility, Industry” founded by OERContent.nrw is to develop, implement and disseminate a digital, model- and application-oriented teaching/learning offer in the subject area of “Operations Research for Sustainable Development” using open source software. This project opens also new possibilities for students to learn how the real business problems can be solved with methods from finance and operations research applied to the energy domain. In the case study presented, the students are offered to learn the basics of mean-variance portfolio theory, the economics of selected power plants, and how to apply the theory to optimize power plant portfolios. In the context of real assets, the main challenge is to define the rate of return as a portfolio selection criterion and to simulate it in the modeling. The analysis is conducted in moodle learning platform by using the object-oriented programming language Python, which creates some specific challenges that are discussed in the case study as well.

Keywords: Case studies in teaching; Portfolio optimization; Energy sector; Python (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-58405-3_55

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