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Improving the strategies of the market players using an AI-powered price forecast for electricity market

    Adela Bâra Affiliation
    ; Simona-Vasilica Oprea Affiliation
    ; Cristian-Eugen Ciurea Affiliation

Abstract

This paper analyses the recent evolution of the electricity price of one of the East-European countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.


First published online 14 November 2023

Keyword : electricity price forecast, balancing market, machine learning, classification, bidding strategy, trading probabilities

How to Cite
Bâra, A., Oprea, S.-V., & Ciurea, C.-E. (2024). Improving the strategies of the market players using an AI-powered price forecast for electricity market. Technological and Economic Development of Economy, 30(1), 312–337. https://doi.org/10.3846/tede.2023.20251
Published in Issue
Feb 27, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aasgård, E. K. (2022). The value of coordinated hydropower bidding in the Nordic day-ahead and balancing market. Energy Systems, 13, 53–77. https://doi.org/10.1007/s12667-020-00388-7

Aasgård, E. K., Fleten, S.-E., Kaut, M., Midthun, K., & Perez-Valdes, G. A. (2019). Hydropower bidding in a multi-market setting. Energy Systems, 10(3), 543–565. https://doi.org/10.1007/s12667-018-0291-y

Adefarati, T., & Bansal, R. C. (2016). Integration of renewable distributed generators into the distribution system: A review. IET Renewable Power Generation, 10(7), 873–884. https://doi.org/10.1049/iet-rpg.2015.0378

Bobo, L., Delikaraoglou, S., Vespermann, N., Kazempour, J., & Pinson, P. (2018). Offering strategy of a flexibility aggregator in a balancing market using asymmetric block offers. In 20th Power Systems Computation Conference, PSCC 2018. https://doi.org/10.23919/PSCC.2018.8443038

Boomsma, T. K., Juul, N., & Fleten, S. E. (2014). Bidding in sequential electricity markets: The Nordic case. European Journal of Operational Research, 238(3), 797–809. https://doi.org/10.1016/j.ejor.2014.04.027

Brijs, T., De Vos, K., De Jonghe, C., & Belmans, R. (2015). Statistical analysis of negative prices in European balancing markets. Renewable Energy, 80, 53–60. https://doi.org/10.1016/j.renene.2015.01.059

Bringedal, A. S., Søvikhagen, A. M. L., Aasgård, E. K., & Fleten, S. E. (2023). Backtesting coordinated hydropower bidding using neural network forecasting. Energy Systems, 14, 847–86. https://doi.org/10.1007/s12667-021-00490-4

Bunn, D. W., Inekwe, J. N., & Macgeehan, D. (2021). Analysis of the fundamental predictability of prices in the British balancing market. IEEE Transactions on Power Systems, 36(2), 1309–1316. https://doi.org/10.1109/TPWRS.2020.3015871

Dimoulkas, I., Amelin, M., & Hesamzadeh, M. R. (2016). Forecasting balancing market prices using Hidden Markov Models. In 2016 13th International Conference on the European Energy Market (EEM) (pp. 1–5). IEEE. https://doi.org/10.1109/EEM.2016.7521229

Dinler, A. (2021). Reducing balancing cost of a wind power plant by deep learning in market data: A case study for Turkey. Applied Energy, 289, Article 116728. https://doi.org/10.1016/j.apenergy.2021.116728

Dumas, J., Boukas, I., De Villena, M. M., Mathieu, S., & Cornelusse, B. (2019, September). Probabilistic forecasting of imbalance prices in the Belgian context. In International Conference on the European Energy Market, EEM. IEEE. https://doi.org/10.1109/EEM.2019.8916375

Fleten, S.-E., & Pettersen, E. (2005). Constructing bidding curves for a price-taking retailer in the Norwegian electricity market. IEEE Transactions on Power Systems, 20(2), 701–708. https://doi.org/10.1109/TPWRS.2005.846082

Hameed, Z., Hashemi, S., & Traholt, C. (2021, March). Applications of AI-Based forecasts in renewable based electricity balancing markets. In Proceedings of the IEEE International Conference on Industrial Technology. IEEE. https://doi.org/10.1109/ICIT46573.2021.9453469

Kartal, G. (2022). The effects of positive and negative shocks in energy security on economic growth: Evidence from asymmetric causality analysis for Turkey. Economic Computation and Economic Cybernetics Studies and Research, 56, 223–239. https://doi.org/10.24818/18423264/56.1.22.14

Klæboe, G., Braathen, J., Eriksrud, A., & Fleten, S.-E. (2019). Day-Ahead market bidding taking the balancing power market into account. SSRN. https://doi.org/10.2139/ssrn.3434318

Klæboe, G., Eriksrud, A. L., & Fleten, S. E. (2015). Benchmarking time series based forecasting models for electricity balancing market prices. Energy Systems, 6, 43–61. https://doi.org/10.1007/s12667-013-0103-3

Krkošková, R. (2021). Causality between energy consumption and economic growth in the V4 countries. Technological and Economic Development of Economy, 27(4), 900–920. https://doi.org/10.3846/tede.2021.14863

Krstevski, P., Borozan, S., & Krkoleva Mateska, A. (2021). Electricity balancing markets in South East Europe – Investigation of the level of development and regional integration. Energy Reports, 7, 7955–7966. https://doi.org/10.1016/j.egyr.2021.05.082

Lazaroiu, G. C., & Roscia, M. (2022). Fuzzy logic strategy for priority control of electric vehicle charging. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19236–19245. https://doi.org/10.1109/TITS.2022.3161398

Lucas, A., Pegios, K., Kotsakis, E., & Clarke, D. (2020). Price forecasting for the balancing energy market using machine-learning regression. Energies, 13(20), Article 5420. https://doi.org/10.3390/en13205420

Martini, A., Pelacchi, P., Pellegrini, L., Cazzol, M. V., Garzillo, A., & Innorta, M. (2001). A simulation tool for short term electricity markets. In IEEE power industry computer applications conference. IEEE. https://doi.org/10.1109/PICA.2001.932331

Mazzi, N., Trivella, A., & Morales, J. M. (2019). Enabling active/passive electricity trading in dual-price balancing markets. IEEE Transactions on Power Systems, 34(6), 1980–1990. https://doi.org/10.1109/TPWRS.2018.2888937

Nasrolahpour, E., Kazempour, J., Zareipour, H., & Rosehart, W. D. (2018). A bilevel model for participation of a storage system in energy and reserve markets. IEEE Transactions on Sustainable Energy, 9(2), 582–598. https://doi.org/10.1109/TSTE.2017.2749434

Olsson, M., & Söder, L. (2008). Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Transactions on Power Systems, 23(2), 443–450. https://doi.org/10.1109/TPWRS.2008.920046

Oprea, S.-V., Bara, A., Preotescu, D., Bologa, R. A., & Coroianu, L. (2020). A trading simulator model for the wholesale electricity market. IEEE Access, 8, 184210–184230. https://doi.org/10.1109/ACCESS.2020.3029291

Poplavskaya, K., Lago, J., & de Vries, L. (2020). Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets. Applied Energy, 270, Article 115130. https://doi.org/10.1016/j.apenergy.2020.115130

Schäfer, P., Westerholt, H. G., Schweidtmann, A. M., Ilieva, S., & Mitsos, A. (2019). Model-based bidding strategies on the primary balancing market for energy-intense processes. Computers and Chemical Engineering, 120, 4–14. https://doi.org/10.1016/j.compchemeng.2018.09.026

Soava, G., Mehedintu, A., Sterpu, M., & Raduteanu, M. (2018). Impact of renewable energy consumption on economic growth: Evidence from European Union countries. Technological and Economic Development of Economy, 24(3), 914–932. https://doi.org/10.3846/tede.2018.1426

Stathakis, E., Papadimitriou, T., & Gogas, P. (2021). Forecasting price spikes in electricity markets. Review of Economic Analysis. https://doi.org/10.15353/rea.v13i1.1822

Stratigakos, A., Michiorri, A., & Kariniotakis, G. (2021). A value-oriented price forecasting approach to optimize trading of renewable Generation. 2021 IEEE Madrid PowerTech (pp. 1–6). IEEE. https://doi.org/10.1109/PowerTech46648.2021.9494832

van der Veen, R. A. C., & Hakvoort, R. A. (2016). The electricity balancing market: Exploring the design challenge. Utilities Policy, 43, 186–194. https://doi.org/10.1016/j.jup.2016.10.008

Yilanci, V., Ozgur, O., & Altinsoy, A. (2022). The dependence of clean energy stock prices on the oil and carbon prices: A nonlinear perspective. Economic Computation and Economic Cybernetics Studies and Research, 56(2), 115­–132. https://doi.org/10.24818/18423264/56.2.22.08

Yin, K., Liu, Z., Huang, C., & Liu, P. (2020). Topological structural analysis of China’s new energy stock market: A multi-dimensional data network perspective. Technological and Economic Development of Economy, 26(5), 1030–1051. https://doi.org/10.3846/tede.2020.12723