Understanding electricity price evolution – day-ahead market competitiveness in Romania
Abstract
The unexpected pandemic eruption in March 2020, the European efforts to diminish the gas house emissions, prolonged drought, higher inflation and the war in Ukraine clearly have had a strong impact on the electricity price. In this paper, we analyze the electricity prices on the Romanian Day-Ahead Market (DAM) along with other variables (inflation, consumption and traded volume of gas on DAM) over the last three and a half years in an attempt to understand its evolution and future trend in the economic and geopolitical context. Autoregressive Distributed Lag models are proposed to analyze the causality among variables on short- and long-term perspective, whereas Quantile Regression (QR) is proposed to enhance the results of the Ordinary Least Squares (OLS) regression. Furthermore, using market concentration metrics – Herfindahl-Hirschman Index (HHI), C1 and C3 ratio, we analyze the competitiveness on the Romanian DAM and correlate it with the electricity price evolution. The concentration indicators on this market reflect the degree of competition manifested between sellers and buyers respectively, their dynamics being able to influence the price level. The higher concentration on the sellers’ side (HHI = 1500) indicates a potential speculative behavior on this market that led to higher prices on DAM.
Keyword : market concentration metrics, day-ahead market, electricity price, causality, autoregressive distributed lag, quantile regression
How to Cite
Bâra, A., Oprea, S.-V., & Georgescu, I. A. (2023). Understanding electricity price evolution – day-ahead market competitiveness in Romania. Journal of Business Economics and Management, 24(2), 221–244. https://doi.org/10.3846/jbem.2023.19050
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chaikumbung, M. (2021). Institutions and consumer preferences for renewable energy: A meta-regression analysis. Renewable and Sustainable Energy Reviews, 146, 111143. https://doi.org/10.1016/j.rser.2021.111143
Clodnițchi, R., & Chinie, A. C. (2015). Factors of impact on the evolution of electricity markets from renewable energy sources: A comparison between Romania and Germany. Management and Marketing, 10(1), 34–52. https://doi.org/10.1515/mmcks-2015-0003
Díaz, G., Coto, J., & Gómez-Aleixandre, J. (2019). Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression. Applied Energy, 239, 610–625. https://doi.org/10.1016/j.apenergy.2019.01.213
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
Dobrowolski, Z., Sułkowski, Ł., & Panait, M. (2022). Using the business model canvas to improve audit processes. Problems and Perspectives in Management, 20(3), 142–152. https://doi.org/10.21511/ppm.20(3).2022.12
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Forbes, K. F., & Zampelli, E. M. (2014). Do day-ahead electricity prices reflect economic fundamentals? Evidence from the california ISO. Energy Journal, 35(3). https://doi.org/10.5547/01956574.35.3.6
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Keles, D., Scelle, J., Paraschiv, F., & Fichtner, W. (2016). Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Applied Energy, 162, 218–230. https://doi.org/10.1016/j.apenergy.2015.09.087
Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89. https://doi.org/10.1016/j.jmva.2004.05.006
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives,15(4), 143–156. https://doi.org/10.1257/jep.15.4.143
Lago, J., Marcjasz, G., De Schutter, B., & Weron, R. (2021). Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy, 293, 116983. https://doi.org/10.1016/j.apenergy.2021.116983
Ma, C., Rogers, A. A., Kragt, M. E., Zhang, F., Polyakov, M., Gibson, F., Chalak, M., Pandit, R., & Tapsuwan, S. (2015). Consumers’ willingness to pay for renewable energy: A meta-regression analysis. Resource and Energy Economics, 42, 93–109. https://doi.org/10.1016/j.reseneeco.2015.07.003
Maciejowska, K., Nitka, W., & Weron, T. (2021). Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices. Energy Economics, 99, 105273. https://doi.org/10.1016/j.eneco.2021.105273
Menegaki, A. N. (2019). The ARDL method in the energy-growth nexus field; Best implementation strategies. Economies, 7(4), 105. https://doi.org/10.3390/economies7040105
Miranian, A., Abdollahzade, M., & Hassani, H. (2013). Day-ahead electricity price analysis and forecasting by singular spectrum analysis. IET Generation, Transmission and Distribution, 7(4), 337–346. https://doi.org/10.1049/iet-gtd.2012.0263
Mišnić, N., Pejović, B., Jovović, J., Rogić, S., & Đurišić, V. (2022). The economic viability of PV power plant based on a neural network model of electricity prices forecast: A case of a developing market. Energies, 15(17). https://doi.org/10.3390/en15176219
Narayan, P. K., & Smyth, R. (2005). Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests. Energy Policy, 33(9), 1109–1116. https://doi.org/10.1016/j.enpol.2003.11.010
Özen, K., & Yıldırım, D. (2021). Application of bagging in day-ahead electricity price forecasting and factor augmentation. Energy Economics, 103, 105573. https://doi.org/10.1016/j.eneco.2021.105573
Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371– 413). Cambridge University Press. https://doi.org/10.1017/CCOL0521633230.011
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
Philips, A. Q. (2018). Have your cake and eat it too? Cointegration and dynamic inference from autoregressive distributed lag models. American Journal of Political Science,62(1), 230–244. https://doi.org/10.1111/ajps.12318
Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58(1), 165–193. https://doi.org/10.2307/2938339
Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with i(1) processes. Review of Economic Studies, 57(1), 99–125. https://doi.org/10.2307/2297545
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Raimi, L., Panait, M., Grigorescu, A., & Vasile, V. (2022). Corporate social responsibility in the telecommunication industry-driver of entrepreneurship. Resources, 11(9). https://doi.org/10.3390/resources11090079
Romero, Á., Dorronsoro, J. R., & Díaz, J. (2019). Day-ahead price forecasting for the Spanish electricity market. International Journal of Interactive Multimedia and Artificial Intelligence, 5(4), 42–50. https://doi.org/10.9781/ijimai.2018.04.008
Sandhu, H. S., Fang, L., & Guan, L. (2016). Forecasting day-ahead price spikes for the Ontario electricity market. Electric Power Systems Research, 141, 450–459. https://doi.org/10.1016/j.epsr.2016.08.005
Streimikiene, D., & Kyriakopoulos, G. L. (2023). Energy poverty and low carbon energy transition. Energies, 16(2). https://doi.org/10.3390/en16020610
Streimikiene, D., Kyriakopoulos, G. L., Lekavicius, V., & Siksnelyte-Butkiene, I. (2021). Energy poverty and low carbon just energy transition: Comparative study in Lithuania and Greece. Social Indicators Research, 158, 319–371. https://doi.org/10.1007/s11205-021-02685-9
Streimikiene, D., Lekavičius, V., Baležentis, T., Kyriakopoulos, G. L., & Abrhám, J. (2020). Climate change mitigation policies targeting households and addressing energy poverty in European Union. Energies, 13(13), 3389. https://doi.org/10.3390/en13133389
Tanizaki, H. (1995). Asymptotically exact confidence intervals of cusum and cusumsq tests: A numerical derivation using simulation technique. Communications in Statistics – Simulation and Computation, 24(4), 1019–1036. https://doi.org/10.1080/03610919508813291
Variyam, J. N., Blaylock, J., & Smallwood, D. (2002). Characterizing the distribution of macronutrient intake among U.S. Adults: A quantile regression approach. American Journal of Agricultural Economics, 84(2), 454–466. https://doi.org/10.1111/1467-8276.00310
Wang, B., Yuan, Z., Liu, X., Sun, Y., Zhang, B., & Wang, Z. (2021). Electricity price and habits: Which would affect household electricity consumption? Energy and Buildings, 240, 110888. https://doi.org/10.1016/j.enbuild.2021.110888
Wang, K., Wang, H., & Li, S. (2022). Renewable quantile regression for streaming datasets. Knowledge-Based Systems, 235, 107675. https://doi.org/10.1016/j.knosys.2021.107675
Ziel, F., & Steinert, R. (2016). Electricity price forecasting using sale and purchase curves: The X-Model. Energy Economics, 59, 435–454. https://doi.org/10.1016/j.eneco.2016.08.008
Bigerna, S. (2018). Estimating temperature effects on the Italian electricity market. Energy Policy, 118, 257–269. https://doi.org/10.1016/j.enpol.2018.03.068
Boloș, M.-I., Bradea, I.-A., & Delcea, C. (2023). Modeling the covariance of financial assets using neutrosophic fuzzy numbers. Symmetry, 15(2). https://doi.org/10.3390/sym15020320
Budulan, P., Rugina, V., & Bogzianu, R. (2003). Electricity market development in Romania. 2003 IEEE Bologna Power Tech Conference Proceedings, 4. https://doi.org/10.1109/PTC.2003.1304780
Carmona, R., Coulon, M., & Schwarz, D. (2013). Electricity price modeling and asset valuation: A multi-fuel structural approach. Mathematics and Financial Economics, 7, 167–202. https://doi.org/10.1007/s11579-012-0091-4
Chaikumbung, M. (2021). Institutions and consumer preferences for renewable energy: A meta-regression analysis. Renewable and Sustainable Energy Reviews, 146, 111143. https://doi.org/10.1016/j.rser.2021.111143
Clodnițchi, R., & Chinie, A. C. (2015). Factors of impact on the evolution of electricity markets from renewable energy sources: A comparison between Romania and Germany. Management and Marketing, 10(1), 34–52. https://doi.org/10.1515/mmcks-2015-0003
Díaz, G., Coto, J., & Gómez-Aleixandre, J. (2019). Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression. Applied Energy, 239, 610–625. https://doi.org/10.1016/j.apenergy.2019.01.213
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
Dobrowolski, Z., Sułkowski, Ł., & Panait, M. (2022). Using the business model canvas to improve audit processes. Problems and Perspectives in Management, 20(3), 142–152. https://doi.org/10.21511/ppm.20(3).2022.12
Engle, R. F., & Granger, C. W. J. (1987). Co-integración y corrección de error: representación, estimación y prueba [Co-integration and error correction: Representation, estimation, and testing]. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
Fávero, L. P., & Belfiore, P. (2019). Simple and multiple regression models. In Data Science for Business and Decision Making (pp. 443–538). Academic Press. https://doi.org/10.1016/B978-0-12-811216-8.00013-6
Fernández-González, R., Puime-Guillén, F., & Panait, M. (2022). Multilevel governance, PV solar energy, and entrepreneurship: The generation of green hydrogen as a fuel of renewable origin. Utilities Policy, 79, 101438. https://doi.org/https://doi.org/10.1016/j.jup.2022.101438
Forbes, K. F., & Zampelli, E. M. (2014). Do day-ahead electricity prices reflect economic fundamentals? Evidence from the california ISO. Energy Journal, 35(3). https://doi.org/10.5547/01956574.35.3.6
Fragkioudaki, A., Marinakis, A., & Cherkaoui, R. (2015). Forecasting price spikes in European day-ahead electricity markets using decision trees. International Conference on the European Energy Market, EEM. https://doi.org/10.1109/EEM.2015.7216672
Haben, S., Caudron, J., & Verma, J. (2021). Probabilistic day-ahead wholesale price forecast: A case study in Great Britain. Forecasting, 3(3), 596–632. https://doi.org/10.3390/forecast3030038
Hildmann, M., Ulbig, A., & Andersson, G. (2015). Empirical analysis of the Merit-Order effect and the missing money problem in power markets with high RES shares. IEEE Transactions on Power Systems, 30(3), 1560–1570. https://doi.org/10.1109/TPWRS.2015.2412376
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration – with applications to the demand for money. Oxford Bulletin of Economics and Statistics,52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
Jordan, S., & Philips, A. Q. (2018). Cointegration testing and dynamic simulations of autoregressive distributed lag models. Stata Journal, 18(4), 902–923. https://doi.org/10.1177/1536867x1801800409
Keles, D., Scelle, J., Paraschiv, F., & Fichtner, W. (2016). Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Applied Energy, 162, 218–230. https://doi.org/10.1016/j.apenergy.2015.09.087
Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89. https://doi.org/10.1016/j.jmva.2004.05.006
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. https://doi.org/10.2307/1913643
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives,15(4), 143–156. https://doi.org/10.1257/jep.15.4.143
Lago, J., Marcjasz, G., De Schutter, B., & Weron, R. (2021). Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy, 293, 116983. https://doi.org/10.1016/j.apenergy.2021.116983
Ma, C., Rogers, A. A., Kragt, M. E., Zhang, F., Polyakov, M., Gibson, F., Chalak, M., Pandit, R., & Tapsuwan, S. (2015). Consumers’ willingness to pay for renewable energy: A meta-regression analysis. Resource and Energy Economics, 42, 93–109. https://doi.org/10.1016/j.reseneeco.2015.07.003
Maciejowska, K., Nitka, W., & Weron, T. (2021). Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices. Energy Economics, 99, 105273. https://doi.org/10.1016/j.eneco.2021.105273
Menegaki, A. N. (2019). The ARDL method in the energy-growth nexus field; Best implementation strategies. Economies, 7(4), 105. https://doi.org/10.3390/economies7040105
Miranian, A., Abdollahzade, M., & Hassani, H. (2013). Day-ahead electricity price analysis and forecasting by singular spectrum analysis. IET Generation, Transmission and Distribution, 7(4), 337–346. https://doi.org/10.1049/iet-gtd.2012.0263
Mišnić, N., Pejović, B., Jovović, J., Rogić, S., & Đurišić, V. (2022). The economic viability of PV power plant based on a neural network model of electricity prices forecast: A case of a developing market. Energies, 15(17). https://doi.org/10.3390/en15176219
Narayan, P. K., & Smyth, R. (2005). Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests. Energy Policy, 33(9), 1109–1116. https://doi.org/10.1016/j.enpol.2003.11.010
Özen, K., & Yıldırım, D. (2021). Application of bagging in day-ahead electricity price forecasting and factor augmentation. Energy Economics, 103, 105573. https://doi.org/10.1016/j.eneco.2021.105573
Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 371– 413). Cambridge University Press. https://doi.org/10.1017/CCOL0521633230.011
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
Philips, A. Q. (2018). Have your cake and eat it too? Cointegration and dynamic inference from autoregressive distributed lag models. American Journal of Political Science,62(1), 230–244. https://doi.org/10.1111/ajps.12318
Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58(1), 165–193. https://doi.org/10.2307/2938339
Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with i(1) processes. Review of Economic Studies, 57(1), 99–125. https://doi.org/10.2307/2297545
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Raimi, L., Panait, M., Grigorescu, A., & Vasile, V. (2022). Corporate social responsibility in the telecommunication industry-driver of entrepreneurship. Resources, 11(9). https://doi.org/10.3390/resources11090079
Romero, Á., Dorronsoro, J. R., & Díaz, J. (2019). Day-ahead price forecasting for the Spanish electricity market. International Journal of Interactive Multimedia and Artificial Intelligence, 5(4), 42–50. https://doi.org/10.9781/ijimai.2018.04.008
Sandhu, H. S., Fang, L., & Guan, L. (2016). Forecasting day-ahead price spikes for the Ontario electricity market. Electric Power Systems Research, 141, 450–459. https://doi.org/10.1016/j.epsr.2016.08.005
Streimikiene, D., & Kyriakopoulos, G. L. (2023). Energy poverty and low carbon energy transition. Energies, 16(2). https://doi.org/10.3390/en16020610
Streimikiene, D., Kyriakopoulos, G. L., Lekavicius, V., & Siksnelyte-Butkiene, I. (2021). Energy poverty and low carbon just energy transition: Comparative study in Lithuania and Greece. Social Indicators Research, 158, 319–371. https://doi.org/10.1007/s11205-021-02685-9
Streimikiene, D., Lekavičius, V., Baležentis, T., Kyriakopoulos, G. L., & Abrhám, J. (2020). Climate change mitigation policies targeting households and addressing energy poverty in European Union. Energies, 13(13), 3389. https://doi.org/10.3390/en13133389
Tanizaki, H. (1995). Asymptotically exact confidence intervals of cusum and cusumsq tests: A numerical derivation using simulation technique. Communications in Statistics – Simulation and Computation, 24(4), 1019–1036. https://doi.org/10.1080/03610919508813291
Variyam, J. N., Blaylock, J., & Smallwood, D. (2002). Characterizing the distribution of macronutrient intake among U.S. Adults: A quantile regression approach. American Journal of Agricultural Economics, 84(2), 454–466. https://doi.org/10.1111/1467-8276.00310
Wang, B., Yuan, Z., Liu, X., Sun, Y., Zhang, B., & Wang, Z. (2021). Electricity price and habits: Which would affect household electricity consumption? Energy and Buildings, 240, 110888. https://doi.org/10.1016/j.enbuild.2021.110888
Wang, K., Wang, H., & Li, S. (2022). Renewable quantile regression for streaming datasets. Knowledge-Based Systems, 235, 107675. https://doi.org/10.1016/j.knosys.2021.107675
Ziel, F., & Steinert, R. (2016). Electricity price forecasting using sale and purchase curves: The X-Model. Energy Economics, 59, 435–454. https://doi.org/10.1016/j.eneco.2016.08.008