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An integrated model for risk management in electricity trade

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  • Dagoumas, Athanasios S.
  • Koltsaklis, Nikolasos E.
  • Panapakidis, Ioannis P.

Abstract

This paper presents an integrated model for risk management of electricity traders. It integrates the Unit Commitment (UC) problem, which provides the power generation units' dispatch and the electricity price forecasting of a power system, with artificial neural network (ANN) models, which provide electricity price forecasting of a neighbouring power system by incorporating a clustering algorithm. The integrated model is further extended to estimate the traders' profitability and risk, incorporating risk provisions. The integrated model is applied in bi-directional trading between the Italian and Greek day-ahead electricity markets. The UC and neural network models provide forecasts of the wholesale electricity price in Greece and Italy respectively. The model attributes a confidence level of the price forecasts, depending on the data clustering and the forecasting performance of each model. The integrated model identifies periods with high price margins for trading for each power flow, aligned with a forecasting confidence and a risk level. The integrated model can provide price signals on the profitability of traders and useful insights into the risk of traders.

Suggested Citation

  • Dagoumas, Athanasios S. & Koltsaklis, Nikolasos E. & Panapakidis, Ioannis P., 2017. "An integrated model for risk management in electricity trade," Energy, Elsevier, vol. 124(C), pages 350-363.
    • Handle: RePEc:eee:energy:v:124:y:2017:i:c:p:350-363
    DOI: 10.1016/j.energy.2017.02.064
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    References listed on IDEAS

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    Cited by:

    1. Gao, Xiang & Chan, Ka Wing & Xia, Shiwei & Zhou, Bin & Lu, Xi & Xu, Da, 2019. "Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator," Energy, Elsevier, vol. 177(C), pages 183-191.
    2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    3. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "Incorporating unit commitment aspects to the European electricity markets algorithm: An optimization model for the joint clearing of energy and reserve markets," Applied Energy, Elsevier, vol. 231(C), pages 235-258.
    4. Panagiotis Adraktas & Athanasios Dagoumas, 2019. "Integration of Electric Vehicles in the Unit Commitment Problem with Uncertain Renewable Electricity Generation," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 315-333.
    5. repec:eco:journ2:2017-04-08 is not listed on IDEAS
    6. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
    7. Ehsani, Behdad & Pineau, Pierre-Olivier & Charlin, Laurent, 2024. "Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks," Applied Energy, Elsevier, vol. 359(C).
    8. Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).
    9. Zorana Božić & Dušan Dobromirov & Jovana Arsić & Mladen Radišić & Beata Ślusarczyk, 2020. "Power Exchange Prices: Comparison of Volatility in European Markets," Energies, MDPI, vol. 13(21), pages 1-15, October.
    10. Dagoumas, Athanasios & Polemis, Michael, 2018. "Analysing Carbon Pass-Through Rate Mechanism in the Electricity Sector: Evidence from Greece," MPRA Paper 91067, University Library of Munich, Germany.
    11. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    12. Halužan, Marko & Verbič, Miroslav & Zorić, Jelena, 2022. "An integrated model for electricity market coupling simulations: Evidence from the European power market crossroad," Utilities Policy, Elsevier, vol. 79(C).
    13. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    14. Nikolaos Koltsaklis & Athanasios Dagoumas, 2018. "Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece," Energies, MDPI, vol. 11(10), pages 1-26, October.
    15. Athanasios Dagoumas, 2019. "Assessing the Impact of Cybersecurity Attacks on Power Systems," Energies, MDPI, vol. 12(4), pages 1-23, February.
    16. Nikolaos E. Koltsaklis & Athanasios S. Dagoumas, 2021. "A power system scheduling model with carbon intensity and ramping capacity constraints," Operational Research, Springer, vol. 21(1), pages 647-687, March.
    17. Athanasios Dagoumas & Nikolaos Koltsaklis, 2020. "Zonal Pricing in Kazakhstan Power System with a Unit Commitment Model," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 24-36.
    18. Ioannis Panapakidis & Nikolaos Asimopoulos & Athanasios Dagoumas & Georgios C. Christoforidis, 2017. "An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures," Energies, MDPI, vol. 10(9), pages 1-42, September.

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