CO2 Free Power Generation from Nuclear Energy and Renewables: Perceived and Actual Risks
Project duration: 7/2008 - 6/2011
Funded by E.ON ERC
In recent years, it has become increasingly popular to take financial risk considerations explicitly into account when deciding about long-term investment decisions in power supply systems. One of the financial methods used in the energy sector is Mean-Variance Portfolio (MVP) Theory. Theoretical and empirical research indicates that MVP theory is a consistent framework for such analysis. In this framework, financial risks and the technical, economic and societal aspects of the generation technologies are explicitly considered. Unfortunately, MVP theory is a static approach which allows examination of the as-is state, while dynamic approaches also consider technical change, learning effects, and other impacts that may affect the risks and returns from investment in generation technologies.
The major goal of the project “CO2 Free Power Generation from Nuclear Energy and Renewables: Perceived and Actual Risks” is to move beyond the static portfolio optimization approaches suggested in the literature and instead use a dynamic portfolio approach. To date, most applications of the MVP approach to real assets in the energy sector have examined and compared static portfolios (comparativestatic analysis). However, real world decision-making under uncertainty is dynamic, which indicates the suitability of using a dynamic approach. In this project a dynamic portfolio selection model for power generation mixes was developed (FCN Working Paper No. 16/2011) based on the multiperiod portfolio selection model proposed in the literature. Today, energy utilities around the world are heavily involved in the development of CO2-free or low-CO2 technologies. Activities connected with climate change mitigation, such as shifting energy production towards renewable energy sources, represent significant behavioral changes among energy suppliers and consumers. Hence, we included offshore wind power plants and concentrating solar thermal power to investigate the potential portfolio benefits of these technologies.
The results of our research show that the application of the multi-period portfolio selection model to the power generation mix could indeed improve the decision-making process, especially with regard to the impact of new investments (Fig.). Nevertheless, the presented conclusions as well as the application of applied solution approaches need further investigation.
The second aim of the project is to investigate public acceptance of energy projects and, in particular, possible discrepancies between perceived and actual (technical and financial) risks. A review of existing scientific literature shows highly disparate estimates of the social acceptance of different energy technologies (studies show different levels of social acceptance for nuclear power, renewable energy and Carbon Capture and Storage, CCS). In our project we also investigate the media coverage and social acceptance of a real-world state-of-the-art hard-coal-fired power plant (Datteln, Germany), and alternative ways how social acceptance can be included in MVP analysis.
Building on these analyses in future research we will combine dynamic modeling of portfolio selection with the social acceptance of technologies.
Glensk B., Madlener R. (2015). Review of Selected Methods for Portfolio Optimization of and Irreversible Investment in Power Generation Assets Under Uncertainty, in: Studia Ekonomiczne: Informatyka i Ekonometria 4 (Study of Economics: Informatics and Econometrics 4), No. 247/2015, University of Economics Katowice, Poland, pp: 20-42.
Glensk B., Madlener R. (2013). Multi-period Portfolio Optimization of Power Generation Assets, Operations Research and Decisions, 23(4): 21-38.
Glensk B., Madlener R. (2011). Dynamic Portfolio Selection Methods for Power Generation Assets, FCN Working Paper No. 16/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Glensk B., Madlener R. (2011). CO2-Free Power Generation from Nuclear Energy and Renewables: Perceived and Actual Risks, E.ON Energy Research Center Series, Vol. 3, Issue 6, October (ISSN: 1868-7415). [Download]
Supervised student research
Anton E. (2009). Wirkung öffentlicher Akzeptanz auf die Realisierbarkeit von Großkraftwerken (Effect of Public Acceptance on the Feasibility of Large-Scale Power Plants; in German), Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Godolt S. (2011). Qualitative Bewertung der sozialen Akzeptanz großer Kraftwerke am Beispiel des E.ON Steinkohlekraftwerks Datteln IV, Master thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Langstädtler J. (2010). Ökonomische Einschätzung des „Desertec“-Projekts - Energie-Oase oder Fata Morgana? (Economic assessment of the “Desertec“ Project - Energy Oasis or Mirage?; in German), Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Ortlieb C. (2010). Commercialization of Marine Renewables: Risk Management and Risk Controlling of Wave Energy Technologies, Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Schubert J. (2010). Optimization of Power Generation Portfolios Considering Innovative Power Generation Technologies. Diploma thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University.
Dr. Barbara Glensk
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