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Valuation of energy storage: an optimal switching approach


  • Rene Carmona
  • Michael Ludkovski


We consider the valuation of energy storage facilities within the framework of stochastic control. Our two main examples are natural gas dome storage and hydroelectric pumped storage. Focusing on the timing flexibility aspect of the problem we construct an optimal switching model with inventory. Thus, the manager has a constrained compound American option on the inter-temporal spread of the commodity prices. Extending the methodology from Carmona and Ludkovski [Appl. Math. Finance, 2008], we then construct a robust numerical scheme based on Monte Carlo regressions. Our simulation method can handle a generic Markovian price model and easily incorporates many operational features and constraints. To overcome the main challenge of the path-dependent storage levels, two numerical approaches are proposed. The resulting scheme is compared with the traditional quasi-variational framework and illustrated with several concrete examples. We also consider related problems of interest, such as supply guarantees and mines management.

Suggested Citation

  • Rene Carmona & Michael Ludkovski, 2010. "Valuation of energy storage: an optimal switching approach," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 359-374.
  • Handle: RePEc:taf:quantf:v:10:y:2010:i:4:p:359-374 DOI: 10.1080/14697680902946514

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    References listed on IDEAS

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    Cited by:

    1. Anton A. Shardin & Michaela Szolgyenyi, 2016. "Optimal Control of an Energy Storage Facility Under a Changing Economic Environment and Partial Information," Papers 1602.04662,, revised Apr 2016.
    2. Cartea, Álvaro & González-Pedraz, Carlos, 2012. "How much should we pay for interconnecting electricity markets? A real options approach," Energy Economics, Elsevier, pages 14-30.
    3. repec:wsi:ijtafx:v:20:y:2017:i:01:n:s0219024917500042 is not listed on IDEAS
    4. Secomandi, Nicola & Seppi, Duane J., 2014. "Real Options and Merchant Operations of Energy and Other Commodities," Foundations and Trends(R) in Technology, Information and Operations Management, now publishers, vol. 6(3-4), pages 161-331, July.
    5. Carl Chiarella & Les Clewlow & Boda Kang, 2016. "The Evaluation Of Multiple Year Gas Sales Agreement With Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-25, February.
    6. Giorgia Callegaro & Luciano Campi & Valeria Giusto & Tiziano Vargiolu, 2017. "Utility indifference pricing and hedging for structured contracts in energy markets," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(2), pages 265-303, April.
    7. Anton A. Shardin & Michaela Szölgyenyi, 2016. "Optimal Control Of An Energy Storage Facility Under A Changing Economic Environment And Partial Information," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-27, June.
    8. Trotter, Ian M. & Gomes, Marília Fernandes Maciel & Braga, Marcelo José & Brochmann, Bjørn & Lie, Ole Nikolai, 2016. "Optimal LNG (liquefied natural gas) regasification scheduling for import terminals with storage," Energy, Elsevier, vol. 105(C), pages 80-88.
    9. Giovanni Mottola, 2014. "A stochastic switching control model arising in general OTC contracts with contingent CSA in presence of CVA, collateral and funding," Papers 1412.1469,
    10. Felix, Bastian Joachim & Weber, Christoph, 2012. "Gas storage valuation applying numerically constructed recombining trees," European Journal of Operational Research, Elsevier, vol. 216(1), pages 178-187.
    11. Bastian Felix, 2012. "Gas Storage Valuation: A Comparative Simulation Study," EWL Working Papers 1201, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2014.
    12. Owen Q. Wu & Derek D. Wang & Zhenwei Qin, 2012. "Seasonal Energy Storage Operations with Limited Flexibility: The Price-Adjusted Rolling Intrinsic Policy," Manufacturing & Service Operations Management, INFORMS, pages 455-471.
    13. Thompson, Matt, 2016. "Natural gas storage valuation, optimization, market and credit risk management," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 26-44.
    14. Nemat Safarov & Colin Atkinson, 2016. "Natural gas-fired power plants valuation and optimisation under Levy copulas and regime-switching," Papers 1607.01207,, revised Jul 2016.
    15. Mason, Charles F. & A. Wilmot, Neil, 2014. "Jump processes in natural gas markets," Energy Economics, Elsevier, vol. 46(S1), pages 69-79.
    16. Moriarty, John & Palczewski, Jan, 2017. "Real option valuation for reserve capacity," European Journal of Operational Research, Elsevier, vol. 257(1), pages 251-260.
    17. Nicola Secomandi & Mulan X. Wang, 2012. "A Computational Approach to the Real Option Management of Network Contracts for Natural Gas Pipeline Transport Capacity," Manufacturing & Service Operations Management, INFORMS, pages 441-454.
    18. Nadarajah, Selvaprabu & Margot, François & Secomandi, Nicola, 2017. "Comparison of least squares Monte Carlo methods with applications to energy real options," European Journal of Operational Research, Elsevier, vol. 256(1), pages 196-204.
    19. repec:eee:ejores:v:263:y:2017:i:2:p:698-706 is not listed on IDEAS
    20. Thompson, Matt & Barr, Drew, 2014. "Cut-off grade: A real options analysis," Resources Policy, Elsevier, vol. 42(C), pages 83-92.
    21. Nicole Bauerle & Viola Riess, 2014. "Gas Storage valuation with regime switching," Papers 1412.1298,
    22. Hanfeld, Marc & Schlüter, Stephan, 2016. "Operating a swing option on today's gas markets: How least squares Monte Carlo works and why it is beneficial," FAU Discussion Papers in Economics 10/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    23. Kitapbayev, Yerkin & Moriarty, John & Mancarella, Pierluigi, 2015. "Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems," Applied Energy, Elsevier, pages 823-831.


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