IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v207y2010i2p543-556.html
   My bibliography  Save this article

A survey of stochastic modelling approaches for liberalised electricity markets

Author

Listed:
  • Möst, Dominik
  • Keles, Dogan

Abstract

Liberalisation of energy markets, climate policy and the promotion of renewable energy have changed the framework conditions of the formerly strictly regulated energy markets. Generating companies are mainly affected by these changing framework conditions as they are exposed to the different risks from liberalised energy markets in combination with huge and largely irreversible investments. Uncertainties facing generating companies include: the development of product prices for electricity as well as for primary energy carriers; technological developments; availability of power plants; the development of regulation and political context, as well as the behaviour of competitors. The need for decision support tools in the energy business, mainly based on operation research models, has therefore significantly increased. Especially to cope with different uncertain parameters, several stochastic modelling approaches have been developed in the last few years for liberalised energy markets. In this context, the present paper aims to give an overview and classification of stochastic models dealing with price risks in electricity markets. The focus is thereby placed on various stochastic methods developed in operation research with practical relevance and applicability, including the concepts of: - stochastic processes for commodity prices (especially for electricity); - scenario generation and reduction, which is important due to the need for a structured handling of large data amounts; as well as - stochastic optimising models for investment decisions, short- and mid-term power production planning and long-term system optimisation. The approaches within the energy business are classified according to the above structure. The practical relevance of the different methods and their applicability to real markets is thereby of crucial importance. Shortcomings of existing approaches and open issues that should be addressed by operation research are also discussed.

Suggested Citation

  • Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:543-556
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00857-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    2. Christoph Weber, 2005. "Uncertainty in the Electric Power Industry," International Series in Operations Research and Management Science, Springer, number 978-0-387-23048-1, December.
    3. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    4. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    5. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    6. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    7. Troncoso, Alicia & Riquelme, Jose C. & Aguilar-Ruiz, Jesus S. & Riquelme Santos, Jesus M., 2008. "Evolutionary techniques applied to the optimal short-term scheduling of the electrical energy production," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1114-1127, March.
    8. SMEERS, Yves, 1997. "Computable equilibrium models and the restructuring of the European electricity and gas markets," LIDAM Discussion Papers CORE 1997061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Yves Smeers, 1997. "Computable Equilibrium Models and the Restructuring of the European Electricity and Gas Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 1-31.
    10. Gibson, Rajna & Schwartz, Eduardo S, 1990. "Stochastic Convenience Yield and the Pricing of Oil Contingent Claims," Journal of Finance, American Finance Association, vol. 45(3), pages 959-976, July.
    11. Krey, Volker & Martinsen, Dag & Wagner, Hermann-Josef, 2007. "Effects of stochastic energy prices on long-term energy-economic scenarios," Energy, Elsevier, vol. 32(12), pages 2340-2349.
    12. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    13. Patrick Jaillet & Ehud I. Ronn & Stathis Tompaidis, 2004. "Valuation of Commodity-Based Swing Options," Management Science, INFORMS, vol. 50(7), pages 909-921, July.
    14. Finon, Dominique, 1974. "Optimisation model for the French energy sector," Energy Policy, Elsevier, vol. 2(2), pages 136-151, June.
    15. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    16. Olsina, Fernando & Roscher, Mark & Larisson, Carlos & Garces, Francisco, 2007. "Short-term optimal wind power generation capacity in liberalized electricity markets," Energy Policy, Elsevier, vol. 35(2), pages 1257-1273, February.
    17. Barreto, Leonardo & Kypreos, Socrates, 2004. "Emissions trading and technology deployment in an energy-systems "bottom-up" model with technology learning," European Journal of Operational Research, Elsevier, vol. 158(1), pages 243-261, October.
    18. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, University Library of Munich, Germany, revised 13 Nov 2003.
    19. Loren W. Tauer, 1983. "Target MOTAD," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(3), pages 606-610.
    20. Smeers, Y., 1997. "Computable equilibrium models and the restructuring of the European electricity and gas markets," LIDAM Reprints CORE 1280, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    21. Blyth, William & Bradley, Richard & Bunn, Derek & Clarke, Charlie & Wilson, Tom & Yang, Ming, 2007. "Investment risks under uncertain climate change policy," Energy Policy, Elsevier, vol. 35(11), pages 5766-5773, November.
    22. Chung-Li Tseng & Graydon Barz, 2002. "Short-Term Generation Asset Valuation: A Real Options Approach," Operations Research, INFORMS, vol. 50(2), pages 297-310, April.
    Full references (including those not matched with items on IDEAS)

    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. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    2. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    3. Möst, Dominik & Perlwitz, Holger, 2009. "Prospects of gas supply until 2020 in Europe and its relevance for the power sector in the context of emission trading," Energy, Elsevier, vol. 34(10), pages 1510-1522.
    4. repec:dui:wpaper:1502 is not listed on IDEAS
    5. Neuhoff, Karsten & Barquin, Julian & Boots, Maroeska G. & Ehrenmann, Andreas & Hobbs, Benjamin F. & Rijkers, Fieke A.M. & Vazquez, Miguel, 2005. "Network-constrained Cournot models of liberalized electricity markets: the devil is in the details," Energy Economics, Elsevier, vol. 27(3), pages 495-525, May.
    6. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    7. Angelica Gianfreda & Derek Bunn, 2018. "A Stochastic Latent Moment Model for Electricity Price Formation," BEMPS - Bozen Economics & Management Paper Series BEMPS46, Faculty of Economics and Management at the Free University of Bozen.
    8. Jan Abrell & Hannes Weigt, 2012. "Combining Energy Networks," Networks and Spatial Economics, Springer, vol. 12(3), pages 377-401, September.
    9. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    10. Florian Leuthold & Hannes Weigt & Christian Hirschhausen, 2012. "A Large-Scale Spatial Optimization Model of the European Electricity Market," Networks and Spatial Economics, Springer, vol. 12(1), pages 75-107, March.
    11. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    12. Vespucci, Maria Teresa & Innorta, Mario & Cervigni, Guido, 2013. "A Mixed Integer Linear Programming model of a zonal electricity market with a dominant producer," Energy Economics, Elsevier, vol. 35(C), pages 35-41.
    13. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    14. Keles, Dogan & Genoese, Massimo & Möst, Dominik & Fichtner, Wolf, 2012. "Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices," Energy Economics, Elsevier, vol. 34(4), pages 1012-1032.
    15. David Pozo & Enzo Sauma & Javier Contreras, 2017. "Basic theoretical foundations and insights on bilevel models and their applications to power systems," Annals of Operations Research, Springer, vol. 254(1), pages 303-334, July.
    16. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    17. Fernández, Mauricio & Muñoz, Francisco D. & Moreno, Rodrigo, 2020. "Analysis of imperfect competition in natural gas supply contracts for electric power generation: A closed-loop approach," Energy Economics, Elsevier, vol. 87(C).
    18. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    19. 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.
    20. Benjamin F. Hobbs & Fieke A.M. Rijkers & Maroeska G. Boots, 2005. "The More Cooperation, The More Competition? A Cournot Analysis of the Benefits of Electric Market Coupling," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 69-98.
    21. Bonacina, Monica & Gulli`, Francesco, 2007. "Electricity pricing under "carbon emissions trading": A dominant firm with competitive fringe model," Energy Policy, Elsevier, vol. 35(8), pages 4200-4220, August.

    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:eee:ejores:v:207:y:2010:i:2:p:543-556. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.