Optimization of E.ON’s Power Generation Mixes
Project duration: 7/2008 - 6/2010
Funded by E.ON ERC
The liberalization of the electricity market, rapid increase in electricity demand, the aging stock of existing power plants and resource limitations induces substantial (re-)investment needs in power generation capacity in the coming years. Energy planners and providers need to modify their allocation strategies and use a robust analytical framework for selecting suitable technologies to be added to the existing generation mix. Theoretical and empirical research indicates that Mean-Variance Portfolio Theory is a consistent framework for such kind of analysis. In this framework, financial risks and the technical, economic and societal aspects of the various generation technologies are explicitly considered.
In this thematic research area we consider different aspects of E.ON’s power generation mix within the framework of two projects: “Optimization of E.ON’s Power Generation Portfolio with a Special Focus on Renewables” (completed) and “CO2 Free Power Generation from Nuclear Energy and Renewables: Perceived and Actual Risks” (ongoing). The main goal of the research project “Optimization of E.ON’s Power Generation Portfolio with a Special Focus on Renewables” was to analyze the efficiency of E.ON’s current power generation portfolios in three markets (United Kingdom, Sweden and Germany) and to evaluate feasible investment opportunities in new electricity generation technologies from an expected risk-return perspective. We used Markowitz’s well-established Mean-Variance Portfolio (MVP) theory (Markowitz, 1952) to investigate existing power generation assets in the considered markets. Power generation portfolios were optimized under the assumption that a rational investor was trying to obtain either the power generation mix with the highest annual expected return per unit of electricity output or the mix with the lowest risk, measured as standard deviation of expected annual return. When investigating existing power generation mixes we used an unrestricted portfolio optimization model. When evaluating the feasibility of new investments we used restricted portfolio selection models to avoid technically infeasible solutions. In all cases efficient portfolios are well diversified and exhibit a significant share of renewable energy technologies (Madlener et al. 2009).
We investigated the impact of different new investment options for achieving a risk-return optimized future production portfolio, by using Net Present Value per unit of installed capacity as a proxy for return (Madlener and Glensk, 2010). The investigation indicates that new renewable investments have a positive impact on existing portfolios in the three countries under consideration (Fig.1).
Due to certain limitations connected with the Markowitz approach, we also considered a fuzzy portfolio selection model with semi-mean absolute deviation as a proxy for risk (Glensk and Madlener, 2010). A fuzzy approach increases the likelihood of integrating expert knowledge and the investors’ subjective opinions with regard to return and risk into the decision problem, which improves quantitative and qualitative analysis. Preliminary results indicate that better descriptions of an energy provider’s subjective expectations, as embodied in the fuzzy approach, could significantly improve the portfolio selection process. Also, including downside risk measures to the optimization model can positively impact the decision making process and investors’ risk expectations.
The second project connected with optimization of power generation mixes “CO2 Free Power Generation from Nuclear Energy and Renewables: Perceived and Actual Risks” is a logical extension of the first project. First, this research project aims at moving beyond the static portfolio optimization approaches suggested in the literature by utilizing 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. Second, we aim to investigate public acceptance and, in particular, possible discrepancies between perceived and actual (technical and financial) risks related to new renewable and nuclear electricity generation technologies. This latter investigation is conducted through desk research and also by means of a questionnaire survey that will be conducted in 2011 in Germany.
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 put special attention to the potential portfolio benefits of concentrating solar thermal power generation and cogeneration as new and potentially important additions to the power generation portfolio of the European electricity supply system. Different problems and aspects related to this project are considered and investigated in a number of doctoral, diploma and study theses. They include the impact of distributed generation on centralized power generation plants and portfolios, the economics of offshore wind and solar power technologies, risk management of offshore wind power projects, public acceptance of large-scale power generation investments in Germany, and the portfolio impact of various cogeneration, wind and solar power technologies.
Glensk B., Madlener R. (2010). Fuzzy Portfolio Optimization for Power Generation Assets, FCN Working Paper No. 10/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Madlener R., Glensk B. (2010). Portfolio Impact of New Power Generation Investments of E.ON in Germany, Sweden and the UK, FCN Working Paper No. 17/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Madlener R., Glensk B., Raymond P. (2009). Investigation of E.ON’s Power Generation Assets by Using Mean-Variance Portfolio Analysis, FCN Working Paper No. 12/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Madlener R., Glensk B., Weber V. (2011). Fuzzy Portfolio Optimization of Onshore Wind Power Plants, FCN Working Paper No. 10/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Westner G., Madlener R. (2010a). Investment in New Power Generation under Uncertainty: Benefits of CHP vs Condensing Plants in a Copula-Based Analysis, FCN Working Paper No. 12/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Westner G., Madlener R. (2010b). The Benefit of Regional Diversification of Cogeneration Investments in Europe: A Mean-Variance Portfolio Analysis, Energy Policy, 38(12): 7911–7920.
Madlener R., Glensk B., Westner G. (2010). Optimization of E.ONs Power Generation with a Special Focus on Renewables, E.ON Energy Research Center Series, Vol. 2, Issue 2, December (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). Diploma thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University, December.
Kott I. (2010). Öffentliche Akzeptanz von Kern- und Kohlekraftwerken: Methodiken und empirische Forschungsergebnisse (Public Acceptance of Nuclear and Coal Power Plants: Methods and Empirical Research Results; in German). Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University, April.
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, April.
Weber V. (2010). Fuzzy Portfoliooptimierung von Onshore-Windkraftwerken (Fuzzy Portfolio Optimization of Onshore Wind Power Plants; in German). Study thesis, Chair of Energy Economics and Management, Faculty of Business and Economics, RWTH Aachen University, December.
Dr. Barbara Glensk
- +49 (0)241 80 49833
- Send Email