Electricity Market Structure and Forecasting Market Clearing Prices

  • Kürşad Derinkuyu
  • Mehmet Güray GülerEmail author
Conference paper
Part of the Green Energy and Technology book series (GREEN)


Electricity markets are evolving into a complex competitive business environment with an increasing role of the private sector in production, consumption, and retailing of electricity. Even transmission and distribution activities have private share in many countries. Technology is also rapidly adding new concepts such as smart grids, batteries, and prosumers (participants that are both on the production and consumption side). This study first gives a brief history on the liberalization of electricity markets, specifically concentrates on the Turkish markets. Other European markets also had similar historical developments. Secondly, we provide the market participants and their roles as well as briefly introduce the problems they need to solve. Then the paper discusses the market types such as day-ahead market, intraday market and balancing power market. Furthermore, we explain the auction mechanism to determine prices in these markets. Finally, we give an illustration for predicting the electricity prices of next days using a forecasting methodology called ARIMA. We use a real data set from Turkish market and provide a step-by-step procedure for calculating the prices using an open-source statistical software R.


Electricity markets Day-ahead market Market-clearing prices Price forecasting ARIMA 


  1. 1.
    Boisseleau F (2004) The role of power exchanges for the creation of a single European electricity market: market design and market regulationGoogle Scholar
  2. 2.
    Kütaruk K (2013) Day ahead markets. Master thesis, Middle East Technical University, School of Natural and Applied SciencesGoogle Scholar
  3. 3.
    Hunt S (2002) Making competition work in electricity, vol 146. WileyGoogle Scholar
  4. 4.
    Maria NS (2010) Day-ahead electricity market: proposals to adapt complex conditions in OMEL. Master thesis, Universidad Pontificia Comillas, Madrid, SpainGoogle Scholar
  5. 5.
    Van Vyve M (2011) Linear prices for non-convex electricity markets: models and algorithms. In: CORE Discussion Paper 2011/50Google Scholar
  6. 6.
    Derinkuyu K (2015) On the determination of European day ahead electricity prices: the Turkish case. Eur J Oper Res 244(3):980–989Google Scholar
  7. 7.
    Korkulu Z (2008) Serbestleştirilmiş Elektrik Piyasalarında Türev Araçların Kullanılması, Vadeli İşlem ve Opsiyon Piyasaları. Proficieny thesis, EMRAGoogle Scholar
  8. 8.
    Derinkuyu K, Tanrisever F, Baytugan F, Sezgin M (2015) Combinatorial auctions in Turkish day ahead electricity market. In: Industrial engineering applications in emerging countries, p 51Google Scholar
  9. 9.
    Araoz V, Jörnsten K (2011) Semi-Lagrangean approach for price discovery in markets with non-convexities. Eur J Oper Res 214(2):411–417Google Scholar
  10. 10.
    Li T, Shahidehpour M (2005) Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming. IEEE Trans Power Syst 20(4):2015–2025Google Scholar
  11. 11.
    Phan DT (2012) Lagrangian duality and branch-and-bound algorithms for optimal power flow. Oper Res 60(2):275–285Google Scholar
  12. 12.
    Martin A, Müller JC, Pokutta S (2014) Strict linear prices in non-convex European day-ahead electricity markets. Optim Meth Softw 29(1):189–221Google Scholar
  13. 13.
    Emir T, Güler MG (2018) Production planning using day-ahead prices in a cement plant. In: Exergetic, energetic and environmental dimensions. Elsevier, pp 149–166Google Scholar
  14. 14.
    Kölmek F (2016) Türkiye Elekrik Piyasasında Fiyat Oluşumunun Analizi ve Fiyat Tahmin Modelleri. Doktora Tezi, Hacettepe Üniversitesi, AnkaraGoogle Scholar
  15. 15.
    Weron R (2006) Modeling and forecasting electricity loads and prices: a statistical approach. Wiley, p 403Google Scholar
  16. 16.
    Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast 30(4):1030–1081Google Scholar
  17. 17.
    Contreras J, Espinola R, Nogales FJ, Conejo AJ (2003) ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18(3):1014–1020Google Scholar
  18. 18.
    Cuaresma JC, Hlouskova J, Kossmeier S, Obersteiner M (2004) Forecasting electricity spot-prices using linear univariate time-series models. Appl Energy 77(1):87–106Google Scholar
  19. 19.
    Lagarto J, de Sousa J, Martins A, Ferrao P (2012) Price forecasting in the day-ahead Iberian electricity market using a conjectural variations ARIMA model. In: 2012 9th International Conference on the European Energy Market, pp 1–7Google Scholar
  20. 20.
    Shafie-Khah M, Moghaddam MP, Sheikh-El-Eslami MK (2011) Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Convers Manage 52(5):2165–2169Google Scholar
  21. 21.
    Weron R, Misiorek A (2005) Forecasting spot electricity prices with time series models. In: Proceedings of the European electricity market EEM-05 conference, pp 133–141Google Scholar
  22. 22.
    Shumway RH, Stoffer DS (2017) Time series analysis and its applications: with R examples. SpringerGoogle Scholar
  23. 23.
    Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. WileyGoogle Scholar
  24. 24.
    Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTextsGoogle Scholar
  25. 25.
    Weron R, Misiorek A (2008) Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. Int J Forecast 24(4):744–763Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Industrial EngineeringTOBB University of Economics and TechnologyAnkaraTurkey
  2. 2.Department of Industrial EngineeringYildiz Technical UniversityIstanbulTurkey

Personalised recommendations