IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1508.05837.html
   My bibliography  Save this paper

Hydroassets Portfolio Management for Intraday Electricity Trading from a Discrete Time Stochastic Optimization Perspective

Author

Listed:
  • Simone Farinelli
  • Luisa Tibiletti

Abstract

Hydro storage system optimization is becoming one of the most challenging tasks in Energy Finance. While currently the state-of-the-art of the commercial software in the industry implements mainly linear models, we would like to introduce risk aversion and a generic utility function. At the same time, we aim to develop and implement a computational efficient algorithm, which is not affected by the curse of dimensionality and does not utilize subjective heuristics to prevent it. For the short term power market we propose a simultaneous solution for both dispatch and bidding problems. Following the Blomvall and Lindberg (2002) interior point model, we set up a stochastic multiperiod optimization procedure by means of a "bushy" recombining tree that provides fast computational results. Inequality constraints are packed into the objective function by the logarithmic barrier approach and the utility function is approximated by its second order Taylor polynomial. The optimal solution for the original problem is obtained as a diagonal sequence where the first diagonal dimension is the parameter controlling the logarithmic penalty and the second is the parameter for the Newton step in the construction of the approximated solution. Optimal intraday electricity trading and water values for hydro assets as shadow prices are computed. The algorithm is implemented in Mathematica.

Suggested Citation

  • Simone Farinelli & Luisa Tibiletti, 2015. "Hydroassets Portfolio Management for Intraday Electricity Trading from a Discrete Time Stochastic Optimization Perspective," Papers 1508.05837, arXiv.org, revised Aug 2017.
  • Handle: RePEc:arx:papers:1508.05837
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1508.05837
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nils Löhndorf & David Wozabal & Stefan Minner, 2013. "Optimizing Trading Decisions for Hydro Storage Systems Using Approximate Dual Dynamic Programming," Operations Research, INFORMS, vol. 61(4), pages 810-823, August.
    2. Blomvall, Jorgen & Lindberg, Per Olov, 2002. "A Riccati-based primal interior point solver for multistage stochastic programming," European Journal of Operational Research, Elsevier, vol. 143(2), pages 452-461, December.
    3. Blomvall, Jorgen & Lindberg, Per Olov, 2003. "Back-testing the performance of an actively managed option portfolio at the Swedish Stock Market, 1990-1999," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 1099-1112, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Piccirilli & Tiziano Vargiolu, 2018. "Optimal Portfolio in Intraday Electricity Markets Modelled by L\'evy-Ornstein-Uhlenbeck Processes," Papers 1807.01979, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    2. Gondzio, Jacek & Grothey, Andreas, 2007. "Solving non-linear portfolio optimization problems with the primal-dual interior point method," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1019-1029, September.
    3. Blomvall, Jörgen & Hagenbjörk, Johan, 2022. "Reducing transaction costs for interest rate risk hedging with stochastic programming," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1282-1293.
    4. Jacek Gondzio & Andreas Grothey, 2009. "Exploiting structure in parallel implementation of interior point methods for optimization," Computational Management Science, Springer, vol. 6(2), pages 135-160, May.
    5. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    6. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2020. "Integrated dynamic models for hedging international portfolio risks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 48-65.
    7. Finnah, Benedikt & Gönsch, Jochen & Ziel, Florian, 2022. "Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 301(2), pages 726-746.
    8. Daniel F. Salas & Warren B. Powell, 2018. "Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 106-123, February.
    9. Ankur Kulkarni & Uday Shanbhag, 2012. "Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms," Computational Optimization and Applications, Springer, vol. 51(1), pages 77-123, January.
    10. Alois Geyer & Michael Hanke & Alex Weissensteiner, 2009. "A stochastic programming approach for multi-period portfolio optimization," Computational Management Science, Springer, vol. 6(2), pages 187-208, May.
    11. Hohmann, Marc & Warrington, Joseph & Lygeros, John, 2020. "A moment and sum-of-squares extension of dual dynamic programming with application to nonlinear energy storage problems," European Journal of Operational Research, Elsevier, vol. 283(1), pages 16-32.
    12. Löhndorf, Nils & Wozabal, David, 2021. "Gas storage valuation in incomplete markets," European Journal of Operational Research, Elsevier, vol. 288(1), pages 318-330.
    13. Jie Sun & Xinwei Liu, 2006. "Scenario Formulation of Stochastic Linear Programs and the Homogeneous Self-Dual Interior-Point Method," INFORMS Journal on Computing, INFORMS, vol. 18(4), pages 444-454, November.
    14. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    15. Picarelli, Athena & Vargiolu, Tiziano, 2021. "Optimal management of pumped hydroelectric production with state constrained optimal control," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    16. Schrotenboer, Albert H. & Veenstra, Arjen A.T. & uit het Broek, Michiel A.J. & Ursavas, Evrim, 2022. "A Green Hydrogen Energy System: Optimal control strategies for integrated hydrogen storage and power generation with wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    17. Gao, Jianwei, 2010. "An extended CEV model and the Legendre transform-dual-asymptotic solutions for annuity contracts," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 511-530, June.
    18. Gao, Jianwei, 2009. "Optimal portfolios for DC pension plans under a CEV model," Insurance: Mathematics and Economics, Elsevier, vol. 44(3), pages 479-490, June.
    19. Miguel, Angel Víctor de & Nogales Martín, Francisco Javier, 2004. "On the relationship between bilevel decomposition algorithms and direct interior-point methods," DES - Working Papers. Statistics and Econometrics. WS ws042509, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Thomas Hubner & Gabriela Hug, 2025. "Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives," Papers 2502.09420, arXiv.org, revised Apr 2025.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1508.05837. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.