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Measuring Performance of Long-Term Power Generating Portfolios

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Green Energy and Efficiency

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

We propose a model for assessing the performance of generation mixes in a mean-variance context. In particular, we focus on the expected price of electricity and the price volatility that result from different generating portfolios that change over time (because of investments and retirements). Our valuation model rests on solving an optimization problem. At any time it minimizes the total costs of electricity generation and delivery. A distinctive feature of our model is that the optimization process is subject to the behavior of stochastic variables (e.g. load, wind generation, fuel prices). Thus we deal with a problem of stochastic optimal control. The model combines optimization techniques, Monte Carlo simulation over the decades-long planning horizon, and market data from futures contracts on commodities. It accounts for uncertain dynamics on both the demand side and the supply side. The aim is to assist decision makers in trying to assess electricity portfolios or supply strategies regarding generation infrastructures. To demonstrate the model by example we consider the case of Great Britain’s generation mix over the next 20 years. In particular, we compare three future energy scenarios and the contracted background, i.e. four time-varying generating portfolios. Major British power producers are covered by the EU Emissions Trading Scheme (ETS), so they operate under binding greenhouse gas (GHG) emission constraints. Further, the UK Government has announced a floor price for carbon in the power sector from 1 April 2013. The generation mix is optimally managed every period by changing input fuel and electricity output as required.

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Notes

  1. 1.

    This is in contrast to related papers that usually perform economic dispatch on an hourly (or shorter) basis with a time horizon extending over one (or a few) year(s). For example, Delarue et al. [12] take hourly load patterns into account (over 7 weeks) and corresponding dispatch issues as ramping constraints. There would be no major problem in using our model for a yearly period on an hourly basis (8,760 steps) apart from the increase in the time required for computation. Unfortunately, our long-term simulation comes at the cost of framing the optimization problem on a longer time span (for example, a week instead of an hour).

  2. 2.

    See Chamorro et al. [11], Appendix C.

  3. 3.

    This does not mean that investors are risk neutral.

  4. 4.

    When there is a floor price for carbon in place (as in the UK), the carbon price (A) can be different from the allowance price on the EU ETS.

  5. 5.

    As shown in National Grid [28], both peak and baseload electricity prices more or less track natural gas prices at National Balancing Point (which does not happen with coal or oil, for instance). This is relevant when we deal with the profit margin included in generation costs; see Chamorro et al. [11], Appendix C.

  6. 6.

    The wholesale electricity market is operated within the British Electricity Trading and Transmission Arrangements (BETTA). It is based on voluntary bilateral agreements between generators, suppliers, traders and customers. In practice BETTA does not set a unique price: the actual price generators are paid or customers have to pay is different if there is underproduction (for generators) or overconsumption (for consumers).

  7. 7.

    UK Department of Energy and Climate Change [35], Table 5.5, support MC Excel spreadsheet.

  8. 8.

    The Climate Change Act of 2008 introduced a legally binding target to reduce GHG emissions by at least 80 % below the 1990 baseline by 2050, with an interim target to reduce emissions by at least 34 % in 2020. It also introduced ‘carbon budgets’, which set the trajectory to ensure these targets are met. These budgets represent legally binding limits on the total amount of GHG that can be emitted in the UK for a given 5-year period. The fourth carbon budget covers the period up to 2027 and should ensure that emissions will be reduced by around 60 % by 2030.

  9. 9.

    Renewables are governed by the 2009 Renewable Energy Directive which sets a target for the UK to achieve 15 % of its total energy consumption from renewable sources by 2020.

  10. 10.

    http://www.nationalgrid.com/uk/Electricity/ten-year-statement/current-elec-tys/.

  11. 11.

    In models where optimal dispatch takes place on an hourly basis the underlying model is able to determine the effective number of operating hours (ENOH). The load factor equals ENOH/8,760. For instance the model in Delarue et al. [12] determines technology specific load factors by optimization. In our case, such a direct calculation cannot be made. Instead, we can calculate the effective electricity output from each technology in a given period and the maximum possible output in that period. Dividing the former by the latter we could get an indirect measure of technology specific load factors similarly by optimization.

  12. 12.

    These prices can be substantially lower than actual prices under market power [22].

  13. 13.

    This overlap is by no means new in the related literature. Even radically different mixes can have nearly identical risk-return characteristics. As Awerbuch and Yang [5] put it: “There are many ways to combine ingredients to produce a given quantity of salad at a given price”.

  14. 14.

    Lynch et al. [24] calculate (hourly) electricity prices from the (hourly) marginal cost of electricity provision and determine the return of each power technology under least-cost dispatch and marginal-cost pricing.

  15. 15.

    http://www.lowcarbonprogramme.org.

References

  1. Abadie LM, Chamorro JM (2008) European CO2 prices and carbon capture investments. Energy Econ 30(6):2992–3015

    Article  Google Scholar 

  2. Arnesano M, Carlucci AP, Laforgia D (2012) Extension of portfolio theory application to energy planning problem—the Italian case. Energy 39:112–124

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Awerbuch S, Berger M (2003) Energy security and diversity in the EU: a mean-variance portfolio approach. IEA Research Paper, International Energy Agency, Paris

    Google Scholar 

  5. Awerbuch S, Yang S (2008) Efficient electricity generating portfolios for Europe: Maximizing energy security and climate change mitigation. In: Bazilian M, Roques F (eds) Analytical methods for energy diversity and security. Elsevier, Amsterdam

    Google Scholar 

  6. Berger M (2003) Portfolio analysis of EU electricity generating mixes and its implications for renewables. Ph.D. Dissertation, Technische Universität Wien, Vienna

    Google Scholar 

  7. Bazilian M, Roques F (2008) Analytical methods for energy diversity and security. Elsevier, North-Holland

    Google Scholar 

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

    Article  Google Scholar 

  9. Blyth W, Bradley R, Bunn D, Clarke C, Wilson T, Yang M (2007) Investment risk under uncertain climate policy. Energy Policy 35:5766–5773

    Article  Google Scholar 

  10. Bohn RE, Caramanis MC, Schweppe FC (1984) Optimal pricing in electrical networks over space and time. Rand J Econ 15(3):360–376

    Article  Google Scholar 

  11. Chamorro JM, Abadie LM, de Neufville R, Ilić M (2012) Market-based valuation of transmission network expansion: a heuristic application in GB. Energy 44:302–320

    Article  Google Scholar 

  12. Delarue E, de Jonghe C, Belmans R, D’haeseleer W (2011) Applying portfolio theory to the electricity sector: energy versus power. Energy Econ 33:12–23

    Google Scholar 

  13. de Neufville R, Scholtes S (2011) Flexibility in engineering design. The MIT Press, Cambridge

    Google Scholar 

  14. Dixit AK, Pindyck RS (1994) Investment under Uncertainty. Princeton University Press, Princeton

    Google Scholar 

  15. Foley AM, Ó Gallachóir BP, Hur J, Baldick R, McKeogh EJ (2010) A strategic review of electricity systems models. Energy 35:4522–4530

    Google Scholar 

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

    Article  Google Scholar 

  17. HM Treasury, HM Revenue & Customs (2010) December

    Google Scholar 

  18. HM Treasury, HM Revenue & Customs (2011) March

    Google Scholar 

  19. Hill M (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54(2):427–432

    Article  Google Scholar 

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

    Google Scholar 

  21. Jansen J, Beurskens L, van Tilburg X (2006) Application of portfolio analysis to the Dutch generating mix. Report C-05-100. Energy Research Center at the Netherlands

    Google Scholar 

  22. Kotsan S, Douglas S (2008) Application of mean-variance analysis to locational value of generation assets. In: Bazilian M, Roques F (eds) Analytical methods for energy diversity and security. Elsevier, Amsterdam

    Google Scholar 

  23. Krey B, Zweifel P (2008) Efficient and secure power for the USA and Switzerland. In: Bazilian M, Roques F (eds) Analytical methods for energy diversity and security. Elsevier, Amsterdam

    Google Scholar 

  24. Lynch MA, Shortt A, Tol RSJ, O’Malley MJ (2013) Risk-return incentives in liberalised electricity markets. Energy Econ 40:598–608

    Article  Google Scholar 

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

    Google Scholar 

  26. Murto P, Nese G (2002) Input price risk and optimal timing of energy investment: choice between fossil and biofuels. WP 25/02, Institute for Research in Economics and Business Administration, Bergen

    Google Scholar 

  27. Näsäkkälä E, Fleten S-E (2005) Flexibility and technology choice in gas fired power plant investments. Rev Financ Econ 14:371–393

    Article  Google Scholar 

  28. National Grid (2010) Winter Outlook Report 2010–2011 October

    Google Scholar 

  29. National Grid (2012) Electricity Ten Year Statement (ETYS) 2012 November

    Google Scholar 

  30. Newbery D (2005) Electricity liberalisation in Britain: the quest for a satisfactory wholesale market design. Energy J 26:43–70

    Google Scholar 

  31. Roques F, Newbery D, Nuttall W, de Neufville R, Connors S (2006) Nuclear power: a hedge against uncertain gas and carbon prices? Energy J 27(4):1–24

    Article  Google Scholar 

  32. Roques F, Newbery D, Nuttall W (2008) Fuel mix diversification incentives in liberalized electricity markets: a mean-variance portfolio theory approach. Energy Econ 30(4):1831–1849

    Article  Google Scholar 

  33. Sunderkötter M, Weber C (2012) Valuing fuel diversification in power generation capacity planning. Energy Econ 34:1664–1674

    Article  Google Scholar 

  34. Trigeorgis L (1996) Real options—managerial flexibility and strategy in resource allocation. The MIT Press, Cambridge

    Google Scholar 

  35. UK Department of Energy and Climate Change (2013) Digest of United Kingdom energy statistics, DUKES 2013

    Google Scholar 

  36. UK Department of Energy and Climate Change (2009) Special feature: generation and hydro changes to electricity tables. Energy Trends Articles; 2009, September

    Google Scholar 

  37. Valeri LM (2009) Welfare and competition effects of electricity interconnection between Ireland and Great Britain. Energy Policy 37(11):4679–4688

    Article  Google Scholar 

  38. van Zon A, Fuss S (2008) Risk, embodied technical change and irreversible investment decisions in UK electricity production. In: Bazilian M, Roques F (eds) Analytical methods for energy diversity and security. Elsevier, Amsterdam

    Google Scholar 

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Acknowledgments

Abadie and Chamorro gratefully acknowledge financial support from the Spanish Ministry of Science and Innovation through the research project ECO2011-25064, the Basque Government through the research project GIC12/177-IT-399-13, and Fundación Repsol through the Low Carbon Programme joint initiative.Footnote 15 Usual disclaimer applies.

Author information

Authors and Affiliations

  1. Department of Financial Economics II, University of the Basque Country UPV/EHU, Av. Lehendakari Aguirre 83, 48015, Bilbao, Spain

    José M. Chamorro

  2. Basque Centre for Climate Change (BC3), Gran via 35-2, 48009, Bilbao, Spain

    Luis M. Abadie

  3. MIT Engineering Systems Division, 77 Massachusetts Av, Cambridge, MA, 02139, USA

    Richard de Neufville

Authors
  1. José M. Chamorro
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  2. Luis M. Abadie
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  3. Richard de Neufville
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Corresponding author

Correspondence to José M. Chamorro .

Editor information

Editors and Affiliations

  1. University of the Basque Country, Bilbao, Spain

    Alberto Ansuategi

  2. Basque Centre for Climate Change(BC3), Bilbao, Spain

    Juan Delgado

  3. Basque Centre for Climate Change (BC3), Bilbao, Spain

    Ibon Galarraga

Appendix: Estimation

Appendix: Estimation

Load. Sample period: 2002:01–2013:08, i.e. a total of 140 monthly observations for GB. Tables A.1 and A.2 .

Table A.1 OLS estimates of load seasonality
Full size table
Table A.2 Regression analysis statistics
Full size table

Average deseasonalised load over the last 24 sample months: 24.90418 TWh. With transmission losses included: 27.14556 TWh. Load volatility: 0.1801.

Wind load factor. Sample period: 2006:04–2010:12, a total of 52 monthly observations. Tables A.3 and A.4.

Table A.3 OLS estimates of wind load seasonality
Full size table
Table A.4 Regression analysis statistics
Full size table

Average wind load factor: 0.27. Wind load volatility: 0.9088.

Pumped load factor. Sample period: 1998:01 to 2013:08, i.e. 188 monthly observations.

Hydro load factor. Sample period: 1998:01–2013:08, or 188 monthly observations. Tables A.5 and A.6.

Table A.5 OLS estimates of hydro load seasonality
Full size table
Table A.6 Regression analysis statistics
Full size table

Average hydro load factor: 0.3432. Hydro load volatility: 1.1099.

Pumped load factor. Sample period: 1998:01–2013:08, i.e. 188 monthly observations Tables A.7 and A.8.

Table A.7 OLS estimates of pumped load seasonality
Full size table
Table A.8 Regression analysis statistics
Full size table

Average pumped load factor: −0.0845. Pumped load volatility: 0.4660.

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Chamorro, J.M., Abadie, L.M., de Neufville, R. (2015). Measuring Performance of Long-Term Power Generating Portfolios. In: Ansuategi, A., Delgado, J., Galarraga, I. (eds) Green Energy and Efficiency. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-03632-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-03632-8_16

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