Skip to main content
SpringerLink
Log in

Portfolio Optimization of Power Generation Assets

  • Chapter
  • First Online:
Handbook of CO₂ in Power Systems

Part of the book series: Energy Systems ((ENERGY))

Abstract

In this chapter I provide an overview of the theoretical and applied literature dealing with mean-variance portfolio analysis used to study the efficiency of portfolios of power generation assets. The relevant literature focuses on the risk-mitigating benefits of technological diversification vis-a-vis single-technology analysis with conventional levelized cost analysis, to varying degrees taking into account real-world constraints. Part of the cutting-edge research deals with the benefits that accrue from intra-technology diversification and geographical dispersion. Some studies also take into account country-specific differences in national regulatory framework conditions and local resource potentials (esp. in the case of wind power). Other research has focused on technical and system-related aspects, such as load dispatch and portfolio restrictions, e.g., arising from grid constraints and the intermittent nature of many renewable energy sources. Complementary approaches to mean-variance portfolio analysis, such as real options analysis and fuzzy modeling, as well as alternative measures of risk (e.g. Value at Risk – VaR, Conditional Value at Risk – CVaR, and (semi-) mean absolute deviation), are briefly discussed as well, thus acknowledging some of the most important recent developments in this research area that have not been reviewed elsewhere yet.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A formal presentation of these membership functions is provided in Glensk and Madlener [27].

  2. 2.

    More information about this model and other fuzzy portfolio optimization problems proposed and analyzed can be found in Glensk and Madlener [27].

References

  1. Acerbi C (2002) Spectral measures of risk: a coherent representation of subjective risk aversion. J Bank Financ 26(7):1487–1503

    Article  Google Scholar 

  2. Artzner P, Delbaen F, Eber J-M, Heath D (1999) Coherent measures of risk. Math Financ 9(3):203–228

    Article  MathSciNet  MATH  Google Scholar 

  3. Awerbuch S (2000) Investing in photovoltaics: risk, accounting and the value of new technology. Energy Policy 28(14):1023–1035

    Article  Google Scholar 

  4. Awerbuch S, Berger M (2003) Applying portfolio theory to EU electricity planning and policy-making. IEA/EET Working Paper No. EET/2003/03, IEA/OECD, Paris

    Google Scholar 

  5. Awerbuch S (2006) Portfolio-based electricity generation planning: policy implications for renewables and energy security. Mitig Adapt Strat Glob Chang 11(3):693–710

    Article  Google Scholar 

  6. Awerbuch S, Jansen JC, Beurskens L (2008) The Role of wind generation in enhancing Scotland’s energy diversity and security: a mean-variance portfolio optimization of Scotland’s energy mix. In: Bazilian M and Roques F (eds) op cit., Chap 7, pp 139–150

    Google Scholar 

  7. Bar-Lev D, Katz S (1976) A portfolio approach to fossil fuel procurement in the electric utility industry. J Financ 31(3):933–947

    Article  Google Scholar 

  8. Bawa VS (1975) Optimal rules for ordering uncertain prospects. Rev Financ Stud 2(1):95–121

    MathSciNet  Google Scholar 

  9. Bazilian M, Roques F (eds) (2008) Analytical methods for energy diversity and security – portfolio optimization in the energy sector: a tribute to the work of Dr. Shimon Awerbuch, 1st edn. Elsevier, Oxford/Amsterdam

    Google Scholar 

  10. Bazilian M, Roques F (2008b). Using portfolio theory to value power generation investments. In: Bazilian M and Roques F (eds) op cit., Chap 3, pp 61–68

    Google Scholar 

  11. Borchert J, Schemm R (2007) Einsatz der Portfoliotheorie im Asset Allokations-Prozess am Beispiel eines fiktiven Anlageraums von Windkraftstandorten. Z Energie 31(4):311–322

    Google Scholar 

  12. Cornell JA (2002) Experiments with mixtures. John Wiley & Sons, New York

    Book  MATH  Google Scholar 

  13. Denault M, Dupuis D, Couture-Cardinal S (2009) Complementarity of hydro and wind power: improving the risk profile of energy inflows. Energy Policy 37(12):5376–5384

    Article  Google Scholar 

  14. Delarue E, De Jonghe C, Belmans R, D’haeseleer W (2010) Applying portfolio theory to the electricity sector: energy versus power. Energy Econ 33(1):12–23

    Article  Google Scholar 

  15. Delbaen F (2002) Coherent risk measures on general probability spaces. In: Sandmann K, Schönbucher P (eds) Advances in finance and statistics: essays in honour of dieter sondermann. Springer, Berlin, pp 1–38

    Google Scholar 

  16. Dembo RS, Rosen D (1999) The practice of portfolio replication: a practical overview of forward and inverse problems. Ann Oper Res 85:267–284

    Article  MathSciNet  MATH  Google Scholar 

  17. de Oliveira FA, de Paiva AP, Marangon Lima JW, Balestrassi PP, Amaury Mendes RR (2010) Portfolio optimization using mixture design of experiments: scheduling trades within electricity markets. Energy Econ 33(1):24–32

    Article  Google Scholar 

  18. Doherty R, Outhred H, O’Malley M (2006) Establishing the role that wind generation May have in future generation portfolios. IEEE T Power Syst 21(3):1415–1422

    Article  Google Scholar 

  19. Doherty R, Outhred H, O’Malley M (2008) Generation portfolio analysis for a carbon constrained and uncertain future. In: Bazilian M and Roques F (eds) op cit., Chap 8, pp 151–166

    Google Scholar 

  20. Drake B, Hubacek K (2007) What to expect from a greater geographic dispersion of wind farms? – a risk portfolio approach. Energy Policy 35(8):3999–4008

    Article  Google Scholar 

  21. Duffie D, Pan J (1997) An overview of value-at-risk. J Derivatives 4:7–49

    Article  Google Scholar 

  22. Elton EJ, Gruber MJ (1997) Modern portfolio theory, 1950 to date. J Bank Financ 21(11–12):1743–1759

    Article  Google Scholar 

  23. Fabozzi G, Gupta F, Markowitz H (2002) The legacy of modern portfolio theory. J Invest, Inst Investor (Fall) 11:7–22

    Article  Google Scholar 

  24. Favre-Perrod P, Kienzle F, Andersson G (2010) Modeling and design of future multi-energy generation and transmission systems. Eur T Electr Power 20(8):994–1008

    Article  Google Scholar 

  25. Fuss S, Szolgayová J, Khabarov N, Obersteiner M (2012) Renewables and climate change mitigation: Irreversible energy investment under uncertainty and portfolio effects. Energy Policy 40:59–68

    Google Scholar 

  26. Galvani V, Plourde A (2010) Portfolio diversification in energy markets. Energy Econ 32(2):257–268

    Article  Google Scholar 

  27. 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

    Google Scholar 

  28. Glensk B, Madlener R (2011) Portfolio selection methods and their empirical applicability to real assets in energy markets. FCN Working Paper No. 11/2011. Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May (in prep.)

    Google Scholar 

  29. Gotham D, Muthuraman K, Preckel P, Rardin R, Ruangpattana S (2009) A load factor based mean-variance analysis for fuel diversification. Energy Econ 31(2):249–256

    Article  Google Scholar 

  30. Hickey EA, Carlson LJ, Loomis D (2010) Issues in the determination of the optimal portfolio of electricity supply options. Energy Policy 38(5):2198–2207

    Article  Google Scholar 

  31. Huang Y-H, Wu J-H (2008) A portfolio risk analysis on electricity supply planning. Energy Policy 36(2):627–641

    Article  MathSciNet  Google Scholar 

  32. Huismann R, Mahieu R, Schlichter F (2007) Hedging exposure to electricity price risk in a value at risk framework. ERIM Report Series Reference No. ERS-2007-013-F&A (available at SSRN: http://ssrn.com/abstract = 968855)

    Google Scholar 

  33. Huisman R, Mahieu R, Schlichter F (2009) Electricity portfolio management: optimal peak/off-peak allocations. Energy Econ 31(1):169–174

    Article  Google Scholar 

  34. Humphreys H, McClain K (1998) Reducing the impacts of energy price volatility through dynamic portfolio selection. Energy J 19(3):107–131

    Google Scholar 

  35. Jansen JC, Beurskens L (2008) Portfolio analysis of the future dutch generating mix. In: Bazilian M and Roques F (eds) op cit., Chap 6, pp 117–138

    Google Scholar 

  36. Jorion P (2006) Value at risk: the benchmark for controlling market risk, 3rd edn. McGraw-Hill, Maidenhead

    Google Scholar 

  37. Morgan JP (1994) Riskmetrics. JP Morgan, New York

    Google Scholar 

  38. Kienzle F, Andersson G (2008) Efficient multi-energy generation portfolios for the future, paper presented at the 4th annual Carnegie Mellon conference on the electricity industry, Pittsburgh, USA, 2008

    Google Scholar 

  39. Konno H, Koshizuka T (2005) Mean-absolute deviation model. IIE T 37(10):893–900

    Article  Google Scholar 

  40. Konno H, Suzuki K (1995) A mean-variance skewness optimization model. J Oper Res Soc Jpn 38:137–187

    Google Scholar 

  41. Konno H, Yamazaki H (1991) Mean-absolute deviation portfolio optimization model and its application to Tokyo stock market. Manage Sci 37(5):519–531

    Article  Google Scholar 

  42. Kotsan S, Douglas S (2008) Application of mean-variance analysis to locational value of generation assets. In: Bazilian M and Roques F (eds) op cit., Chap 13, pp 263–274

    Google Scholar 

  43. Krey B, Zweifel P (2006) Efficient electricity portfolios for Switzerland and the United States. Working Paper No. 0602. Socioeconomic Institute, University of Zurich

    Google Scholar 

  44. Krey B, Zweifel P (2008a) Efficient and secure power for the USA and Switzerland. In: Bazilian M and Roques F (eds) op cit., Chap 10, pp 193–118

    Google Scholar 

  45. Krey B, Zweifel P (2008) The impact of liberalization on the scope of efficiency improvement in electricity-generating portfolios for the United States and Switzerland. Z Energie 32(3):203–209

    Article  Google Scholar 

  46. Krokhmal P (2007) Higher moment coherent risk measures. Quantitative Finance 7(4): 373–387

    Article  MathSciNet  MATH  Google Scholar 

  47. Krokhmal P, Palmquist J, Uryasev S (2002) Portfolio optimization with conditional value-at-risk objective and constraints. J Risk 4(2):43–68

    Google Scholar 

  48. Kroll Y, Levy H, Markowitz HM (1984) Mean-variance versus direct utility maximization. J Financ 39(1):47–61

    Article  Google Scholar 

  49. Lang J, Madlener R (2010) Portfolio optimization for power plants: the impact of credit risk mitigation and margining. FCN Working Paper No. 11/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University

    Google Scholar 

  50. Liu MK, Wu F (2007) Portfolio optimization in electricity markets. Electr Power Syst Res 77(8):1000–1009

    Article  Google Scholar 

  51. 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

    Google Scholar 

  52. 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

    Google Scholar 

  53. 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

    Google Scholar 

  54. Madlener R, Wenk C (2008) Efficient investment portfolios for the swiss electricity supply sector. FCN Working Paper No. 2/2008. Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University

    Google Scholar 

  55. Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91

    Google Scholar 

  56. Markowitz HM (1959) Portfolio selection, 1st edn. Wiley & Sons, New York

    Google Scholar 

  57. Muñoz JI, Sánchez de la Nieta AA, Contreras J, Bernal-Augustín JL (2009) Optimal investment portfolio in renewable energy: the Spanish case. Energy Policy 37(12):5273–5284

    Article  Google Scholar 

  58. Nitsch J (2008) Weiterentwicklung der Ausbaustrategie Erneuerbare Energien – Leitstudie 2008, Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, Reihe Umweltpolitik, Berlin

    Google Scholar 

  59. Nguene GN, Finger M (2007) A fuzzy-based approach for strategic choices in electric energy supply: the case of a swiss power provider on the eve of electricity market opening. Eng Appl Artif Intell 20(1):37–48

    Article  Google Scholar 

  60. Pindoriya NM, Singh SN, Singh SK (2010) Multi-objective mean–variance–skewness model for generation portfolio allocation in electricity markets. Electr Power Syst Res 80(10):1314–1321

    Article  Google Scholar 

  61. Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–41

    Google Scholar 

  62. Rockafellar RT, Uryasev S, Zabarankin M (2006) Generalized deviations in risk analysis. Stoch Financ 10(1):51–74

    Article  MathSciNet  MATH  Google Scholar 

  63. Roques FA (2008) Technology choices for new entrants in liberalised markets: the value of operating flexibility and contractual arrangements. Util Policy 16(4):245–253

    Article  Google Scholar 

  64. Roques FA, Newbery DM, Nuttall WJ (2008) Fuel mix diversification in liberalized electricity markets: a mean-variance portfolio theory approach. Energy Econ 30(4):1831–1849

    Article  Google Scholar 

  65. Roques FA, Newbery DM, Nuttall WJ (2008b) Portfolio optimization and utilities’ investment in liberalized power markets. In: Bazilian M and Roques F (eds) op cit., Chap 11, pp 219–245

    Google Scholar 

  66. Roques FA, Hiroux C, Saguan M (2010) Optimal wind power deployment in europe – a portfolio approach. Energy Policy 38(7):3245–3256

    Article  Google Scholar 

  67. Scheffe H (1958) Experiments with mixtures. J Roy Stat Soc B 20(2):344–360

    MathSciNet  MATH  Google Scholar 

  68. Sunderkötter M, Weber C (2009) Valuing fuel diversification in optimal investment policies for electricity generation portfolios. EWL Working Paper No. 6. University of Duisburg-Essen, Germany

    Google Scholar 

  69. Testuri CE, Uryasev S (2004) On relation between expected regret and conditional value-at-risk. In: Rachev Z (ed) Handbook of computational and numerical methods in finance. Birkhäuser, Boston, pp 361–373

    Chapter  Google Scholar 

  70. Klein Haneveld WK, Streutker MH, van der Vlerk MH (2010) An ALM model for pension funds using integrated chance constraints. Ann Oper Res 177(1):47–62

    Article  MathSciNet  MATH  Google Scholar 

  71. van Zon A, Fuss S (2008) Risk, Embodied technical change and irreversible investment decisions in UK electricity production: an optimum technology portfolio approach. In: Bazilian M and Roques F (eds) op cit., Chap 14, pp 275–304

    Google Scholar 

  72. Westner G, Madlener R (2009) Development of cogeneration in Germany: a dynamic portfolio analysis based on the new regulatory framework. FCN Working Paper No. 4/2009. Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised March 2010)

    Google Scholar 

  73. Westner G, Madlener R (2010) The benefit of regional diversification of cogeneration investments in europe: a mean-variance portfolio analysis. Energy Policy 38(12):7911–7920

    Article  Google Scholar 

  74. Yu Z (2003) A spatial mean-variance MIP model for energy market risk analysis. Energy Econ 25(3):255–268

    Article  Google Scholar 

  75. Zhu L, Fan Y (2009) Optimization of China’s generating portfolio and policy implications based on portfolio theory. Energy 35(3):1391–1402

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

    Reinhard Madlener

Authors
  1. Reinhard Madlener
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reinhard Madlener .

Editor information

Editors and Affiliations

  1. , Department of Industrial & Management Sy, West Virginia University, Evansdale Drive 401, Morgantown, 26506-6070, West Virginia, USA

    Qipeng P. Zheng

  2. , Division of Economics and Business, Colorado School of Mines, 15th Street 816, Golden, 80401, Colorado, USA

    Steffen Rebennack

  3. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

  4. Rio Praia de Botafogo, Centro Empresarial, -A-Botafogo 2281701, Rio de Janeiro, 22250-040, Rio de Janeiro, Brazil

    Mario V. F. Pereira

  5. Research & Development, EnerCoRD - Energy Consulting,, Plastira Street 4, Athens, 171 21, Greece

    Niko A. Iliadis

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Madlener, R. (2012). Portfolio Optimization of Power Generation Assets. In: Zheng, Q., Rebennack, S., Pardalos, P., Pereira, M., Iliadis, N. (eds) Handbook of CO₂ in Power Systems. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27431-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27431-2_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27430-5

  • Online ISBN: 978-3-642-27431-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation