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Investment in electric energy storage under uncertainty: a real options approach

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Abstract

In this paper we develop a real options approach to evaluate the profitability of investing in a battery bank. The approach determines the optimal investment timing under conditions of uncertain future revenues and investment cost. It includes time arbitrage of the spot price and profits by providing ancillary services. Current studies of battery banks are limited, because they do not consider the uncertainty and the possibility of operating in both markets at the same time. We confirm previous research in the sense that when a battery bank participates in the spot market alone, the revenues are not sufficient to cover the initial investment cost. However, under the condition that the battery bank also can receive revenues from the balancing market, both the net present value (NPV) and the real options value are positive. The real options value is higher than the NPV, confirming the value of flexible investment timing when both revenues and investment cost are uncertain.

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Notes

  1. Launch of battery project in Germany Press release by Statkraft 27.07.15. http://statkraft.com/media/news/20151/launch-of-battery-project-in-germany/.

  2. The Chan-Karolyi-Longstaff-Sanders differential equation nests several processes commonly used to represent commodity prices, including the GBM, Vasicek, Merton and Brennan-Schwartz model. It can assume the form of any of these processes by altering the parametrization, without changing the solution of the equation. This allows us to incorporate elements such as mean reversion and inverse leverage effects in our model. We refer to Chan et al. (1992) for further details.

  3. We refer to Janczura and Weron (2009, 2010) for a more in depth analysis of the shifted lognormal distribution and how this better fits electricity prices than other alternatives.

  4. Investment costs is an economic variable that is highly affected by the business cycle. It exhibits long-term variation, and changes in the cost level be expected to persist for a long while. It is also natural to expect that changes in costs are normally distributed when considering relative changes (as opposed to absolute EUR/kWh changes). These features point toward a geometric Brownian motion for these costs.

References

  • Arvesen Ø, Medbø V, Fleten SE, Tomasgard A, Westgaard S (2013) Linepack storage valuation under price uncertainty. Energy 52:155–164

    Article  Google Scholar 

  • Bradbury K, Pratson L, Patiño-Echeverri D (2014) Economic viability of energy storage systems based on price arbitrage potential in real-time US electricity markets. Appl Energy 114:512–519

    Article  Google Scholar 

  • Byrne RH, Silva-Monroy CA (2012) Estimating the maximum potential revenue for rid connected electricity storage: arbitrage and regulation. Sandia National Laboratories

  • Carriere JF (1996) Valuation of the early-exercise price for options using simulations and nonparametric regression. Insur Math Econ 19(1):19–30

    Article  Google Scholar 

  • Cartea A, Figueroa MG (2005) Pricing in electricity markets: a mean reverting jump diffusion model with seasonality. Appl Math Financ 12(4):313–335

    Article  Google Scholar 

  • Chan KC, Karolyi GA, Longstaff FA, Sanders AB (1992) An empirical comparison of alternative models of the short-term interest rate. J Financ 47(3):1209–1227

    Article  Google Scholar 

  • Chen H, Cong TN, Yang W, Tan C, Li Y, Ding Y (2009) Progress in electrical energy storage system: a critical review. Prog Nat Sci 19(3):291–312

    Article  Google Scholar 

  • Cho J, Kleit AN (2015) Energy storage systems in energy and ancillary markets: a backwards induction approach. Appl Energy 147:176–183

    Article  Google Scholar 

  • Contreras J, Espinola R, Nogales F, Conejo A (2003) ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18(3):1014–1020

    Article  Google Scholar 

  • Del Granado PC, Wallace SW, Pang Z (2016) The impact of wind uncertainty on the strategic valuation of distributed electricity storage. Comput Manage Sci 13(1):5–27

    Article  Google Scholar 

  • Denholm P, Jorgenson J, Hummon M, Jenkin T, Palchak D, Kirby B, Ma O, Malley M (2013) The value of energy storage for grid applications. Contract 303:275–3000

    Google Scholar 

  • Dixit AK, Pindyck RS (1994) Investment Under Uncertainty. Princeton University Press

  • Dunn B, Kamath H, Tarascon JM (2011) Electrical energy storage for the grid: a battery of choices. Science 334(6058):928–935

    Article  Google Scholar 

  • Commission European (2013) Generation adequacy in the internal electricity market-guidance on public interventions. Tech rep, European Commission

  • Faria E, Fleten SE (2011) Day-ahead market bidding for a Nordic hydropower producer: taking the Elbas market into account. Comput Manage Sci 8(1–2):75–101

    Article  Google Scholar 

  • Fertig E, Heggedal AM, Doorman G, Apt J (2014) Optimal investment timing and capacity choice for pumped hydropower storage. Energy Syst 5(2):285–306

    Article  Google Scholar 

  • Fleten SE, Heggedal AM, Siddiqui A (2011) Transmission capacity between Norway and Germany: a real options analysis. J Energy Mark 4(1):121–147

    Article  Google Scholar 

  • González V, Contreras J, Bunn DW (2012) Forecasting power prices using a hybrid fundamental-econometric model. IEEE Trans Power Syst 27(1):363–372

    Article  Google Scholar 

  • Hadjipaschalis I, Poullikkas A, Efthimiou V (2009) Overview of current and future energy storage technologies for electric power applications. Renew Sustain Energy Rev 13(6G–7):1513–1522

  • Jaehnert S, Farahmand H, Doorman GL (2009) Modelling of prices using the volume in the Norwegian regulating power market. In: 2009 IEEE Bucharest Power Tech Conference

  • Janczura J, Weron R (2009) Regime switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions. In: IEEE Conference Proceedings (EEMG09), DOI, vol 10, pp 059–1073

  • Janczura J, Weron R (2010) An empirical comparison of alternate regime-switching models for electricity spot prices. Energy Econ 32(5):1059–1073

    Article  Google Scholar 

  • Janczura J, Weron R (2012) Efficient estimation of markov regime-switching models: an application to electricity spot prices. AStA Adv Stat Anal 96(3):385–407

    Article  Google Scholar 

  • Janczura J, Trück S, Weron R, Wolff RC (2013) Identifying spikes and seasonal components in electricity spot price data: a guide to robust modeling. Energy Econ 38:96–110

    Article  Google Scholar 

  • Kazempour SJ, Moghaddam MP, Haghifam MR, Yousefi GR (2009) Electric energy storage systems in a market-based economy: comparison of emerging and traditional technologies. Renew Energy 34(12):2630–2639

    Article  Google Scholar 

  • Kim H, Hong J, Park KY, Kim H, Kim SW, Kang K (2014) Aqueous rechargeable Li and Na ion batteries. Chem Rev 114(23):11,788–11,827

  • Kirby B (2007) Load response fundamentally matches power system reliability requirements. In: IEEE Power Engineering Society General Meeting, pp 1–6

  • Klæboe G, Eriksrud AL, Fleten SE (2015) Benchmarking time series based forecasting models for electricity balancing market prices. Energy Syst 6:43–61

    Article  Google Scholar 

  • Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Electr Power Energy Syst 25(8):599–606

  • Leadbetter J, Swan LG (2012) Selection of battery technology to support grid-integrated renewable electricity. J Power Sour 216:376–386

    Article  Google Scholar 

  • Longstaff FA, Schwartz ES (2001) Valuing American options by simulation: a simple least-squares approach. Rev Financ Stud 14(1):113–147

    Article  Google Scholar 

  • Nowotarski J, Tomczyk J, Weron R (2013) Robust estimation and forecasting of the long-term seasonal component of electricity spot prices. Energy Econ 39:13–27

    Article  Google Scholar 

  • Olsson M, Söder L (2008) Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Trans Power Syst 23(2):443–450

    Article  Google Scholar 

  • Paraschiv F, Fleten SE, Schürle M (2015) A spot-forward model for electricity prices with regime shifts. Energy Econ 47:142–153

    Article  Google Scholar 

  • Permana FJ, Weide HVD, Borovkova S (2007) A closed form approach to the valuation and hedging of basket and spread option. J Deriv 14(4):8–24

    Article  Google Scholar 

  • Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlinear Dyn Econom 6(3)

  • Reuter WH, Fuss S, Szolgayová J, Obersteiner M (2012) Investment in wind power and pumped storage in a real options model. Renew Sustain Energy Rev 16(4):2242–2248

    Article  Google Scholar 

  • Shah V, Booream-Phelps J (2015) F.I.T.T. for investors—crossing the chasm. Tech rep., Deutsche Bank

  • Sioshansi R, Denholm P, Jenkin T, Weiss J (2009) Estimating the value of electricity storage in PJM: arbitrage and some welfare effects. Energy Econ 31(2):269–277

    Article  Google Scholar 

  • Skytte K (1999) The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis. Energy Econ 21(4):295–308

    Article  Google Scholar 

  • Steffen B (2012) Prospects for pumped-hydro storage in Germany. Energy Policy 45:420–429

    Article  Google Scholar 

  • Weron R, Bierbrauer M, Trück S (2004) Modeling electricity prices: jump diffusion and regime switching. Phys A Stat Mech Appl 336(1):39–48

    Article  Google Scholar 

  • Xi X, Sioshansi R (2014) A dynamic programming model of energy storage and transformer deployments to relieve distribution constraints. Computational Management Science pp 1–28

  • Zhou Y, Scheller-Wolf AA, Secomandi N, Smith S (2016) Electricity trading and negative prices: storage vs. disposal. Manage Sci 62(3):880–898

    Article  Google Scholar 

Download references

Acknowledgments

Support from the Research Council of Norway through Project 228811 is gratefully acknowledged. Further, the work was partly supported by the Danish Council for Strategic Research through the project ’5s’—Future Electricity Markets (No. 12-132636/DSF).

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Correspondence to Stein-Erik Fleten.

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Bakke, I., Fleten, SE., Hagfors, L.I. et al. Investment in electric energy storage under uncertainty: a real options approach. Comput Manag Sci 13, 483–500 (2016). https://doi.org/10.1007/s10287-016-0256-3

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  • DOI: https://doi.org/10.1007/s10287-016-0256-3

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