Testing the Factors Affecting Intraday Market Electricity Prices by Connectedness Approach

Year 2023, Volume: 31 Issue: 57, 179 - 194, 26.07.2023

Arif Arifoğlu , Halilibrahim Gökgöz , Tuğrul Kandemir

https://doi.org/10.17233/sosyoekonomi.2023.03.09

Cited By: 1

Abstract

This study aims to dynamically analyse the relationship between intraday market electricity prices and day-ahead market electricity prices and the amount of electricity generated based on the primary energy resource in Turkey. In this context, the data set consisting of electricity prices in the day-ahead market, electricity prices in the day-ahead market, and electricity generation amount based on primary energy resources, covering the period from 1 January 2018 to 19 June 2022, was analysed with TVP-VAR. Findings reveal that the relationship between variables changes over time and is affected by global events. Furthermore, it has been determined that the intraday market has moved from a general receiver of volatility to a general transmitter in the post-Covid 19 period.

Keywords

Intraday Market, Electricity Price, Connectedness

Gün İçi Piyasası Elektrik Fiyatlarını Etkileyen Faktörlerin Bağlantılılık Yaklaşımı ile Test Edilmesi

Abstract

Bu çalışma, Türkiye'de birincil enerji kaynağına dayalı olarak üretilen elektrik miktarı ve gün içi piyasası elektrik fiyatları ile gün öncesi piyasası elektrik fiyatları arasındaki ilişkiyi dinamik olarak analiz etmeyi amaçlamaktadır. Bu kapsamda, 1 Ocak 2018-19 Haziran 2022 dönemini kapsayan, gün içi piyasası elektrik fiyatları, gün öncesi piyasası elektrik fiyatları ve birincil enerji kaynaklarına dayalı elektrik üretim miktarından oluşan veri seti TVP-VAR ile analiz edilmiştir. Bulgular, değişkenler arasındaki ilişkinin zaman içinde değiştiğini ve küresel olaylardan etkilendiğini ortaya koymaktadır. Ayrıca Covid 19 sonrası dönemde gün içi piyasasının volatilitenin genel alıcısı konumundan genel yayıcısı konumuna geçtiği tespit edilmiştir.

Keywords

Gün İçi Piyasası, Elektrik Fiyatı, Bağlantılılık

References

• Antonakakis, N. & D. Gabauer (2017), “Refined Measures of Dynamic Connectedness based on TVP-VAR”, MPRA Paper 78282, University Library of Munich, Germany.
• Antonakakis, N. et al. (2019), “Oil And Asset Classes Implied Volatilities: Dynamic Connectedness and Investment Strategies”, <https://ssrn.com/abstract=3399996>, 30.06.2022.
• Apergis, N. et al. (2020), “Dependence Structure in the Australian Electricity Markets: New Evidence from Regular Vine Copulae”, Energy Economics, 90, 1-14.
• Asceric, A. et al. (2022), “Correlation of Day-Ahead Electric Energy Market Price with Renewable Energy Sources Generation and Load Forecast, Advanced Technologies”, in: Advanced Technologies, Systems, and Applications VI (99-109).
• Bento, P.M.R. et al. (2018), “A Bat Optimized Neural Network and Wavelet Transform Approach for Short Term Price Forecasting”, Applied Energy, 210, 88-97.
• Berk, İ. & E. Torun (2019), “Testing Merit-Order Effect in Turkey’s Electricity Market: The Effect of Wind Penetration on Day-Ahead Electricity Prices”, Akdeniz IIBF Journal, 19(1), 133-156.
• Cervone, A. et al. (2014), “Electricity Price Forecast: A Comparison of Different Models to Evaluate the Single National Price in the Italian Energy Exchange Market”, International Journal of Energy Economics and Policy, 4(4), 744-758.
• Chan, K.F. et al. (2008), “A new approach to characterizing and forecasting electricity price volatility”, International Journal of Forecasting, 24, 728-743.
• Ciarreta, A. & A. Zarraga (2016), “Modeling Realized Volatility on the Spanish Intra-Day Electricity Market”, Energy Economics, 58, 152-163.
• Clo, S. et al. (2015), “The merit-order effect in the Italian power market: The impact of solar and wind generation on national wholesale electricity prices”, Energy Policy, 77, 79-88.
• Devir, K. (2017), Türk Elektrik Piyasasının İşleyişi, Bursa: Dora Publishing.
• Diebold, F.X. & K. Yılmaz (2014), “On the network topology of variance decompositions: measuring the connectedness of financial firms”, Journal of Econometrics, 182(1), 119-134.
• Dong, S. et al. (2019), “Volatility of electricity price in Denmark and Sweden”, Energy Procedia, 158, 4331-4337.
• EPİAŞ (N/A), <https://seffaflik.epias.com.tr/transparency/index.xhtml>, 21.06.2022.
• EXIST (2022), Participant Count Based Upon License Type, <https://seffaflik.epias.com.tr/transparency/piyasalar/genel-veriler/lisans-tipine-gore-katilimci-sayisi.xhtml>, 30.06.2022.
• EXIST (2022a), Power Futures Market Introduction, <https://www.epias.com.tr/en/introduction/>, 30.06.2022.
• EXIST (2022b), Intraday Market Introduction, <https://www.epias.com.tr/en/intra-day-market/introduction/>, 30.06.2022.
• EXIST (2022c), Intraday Market General Principles, <https://www.epias.com.tr/en/intra-day-market/general-principles/>, 30.06.2022.
• Fernandez, J.M.R. et al. (2021), “Impact of Domestic PV Systems in the Day-Ahead Iberian Electricity Market”, Solar Energy, 217, 15-24.
• Gianfreda, A. et al. (2019), The RES-Induced Switching Effect Across Fossil Fuels: An Analysis of Day-Ahead and Balancing Prices, <https://www.iaee.org/energyjournal/article/3316>, 30.06.2022.
• Girish, G.P. & S. Vijayalakshmi (2013), “Determinants of Electricity Price in Competitive Power Market”, International Journal of Business and Management, 8(21), 70-75.
• Guo, X. et al. (2020), “A Short-Term Load Forecasting Model of Multi-Scale CNN-LSTM Hybrid Neural Network Considering The Real-Time Electricity Price”, Energy Reports, 6, 1046-1053.
• Gürtler, M. & T. Paulsen (2018), “The Effect of Wind and Solar Power Forecasts on Day-Ahead and Intraday Electricity Prices in Germany”, Energy Economics, 75, 150-162.
• Huang, C.J. et al. (2020), “A Novel Hybrid Deep Neural Network Model for Short-Term Electricity Price Forecasting”, International Journal of Energy Research, 45(2), 2511-2532.
• Karakatsani, N.V. & D.W. Bunn (2010), “Fundamental and Behavioural Drivers of Electricity Price Volatility”, Studies in Nonlinear Dynamics & Econometrics, 14(4), 1-42.
• Ketterer, J.C. (2014), “The impact of wind power generation on the electricity price in Germany”, Energy Economics, 44, 270-280.
• Koop, G. & D. Korobilis (2014), “A new index of financial conditions”, European Economic Review, 71, 101-116.
• Koop, G. et al. (1996), “Impulse Response Analysis in Nonlinear Multivariate Models”, Journal of Econometrics, 74(1), 119-147.
• Kuo, P.H. & C.J. Huang (2018), “An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks”, Sustainability, 10(4), 1-17.
• Li, W. & D.M. Becker (2021), “Day-Ahead Electricity Price Prediction Applying Hybrid Models of LSTM-Based Deep Learning Methods and Feature Selection Algorithms Under Consideration of Market Coupling”, Energy, 237, 121543.
• Marcos, R.A.D. et al. (2019), “Electricity Price Forecasting in the Short Term Hybridising Fundamental and Econometric Modelling”, Electric Power Systems Research, 167, 240-251.
• Matsumoto, T. & M. Endo (2021), “Electricity Price Forecast Based on Weekly Weather Forecast and Its Application to Arbitrage in the Forward Market”, 11th International Conference on Power, Energy and Electrical Engineering (CPEEE), Shiga, Japan, 104-111.
• MENR (2022), Electricity, <https://enerji.gov.tr/bilgi-merkezi-enerji-elektrik#:~:text=2021%20y%C4%B1l%C4%B1nda%20elektrik%20%C3%BCretimimizin%2C%20%31,g%C3%BCc%C3%BC%20100.667%20MW'a%20ula%C5%9Fm%C4%B1%C5%9Ft%C4%B1r>, 30.06.2022.
• Mirakyan, A. et al. (2017), “Composite Forecasting Approach, Application for Next-Day Electricity Price Forecasting”, Energy Economics, 66, 228-237.
• Mulder, M. & B. Scholtens (2013), “The Impact of Renewable Energy on Electricity Prices in the Netherlands”, Renewable Energy, 57, 94-100.
• Nieta, A.A.S.D.L. & J. Contreras (2020), “Quantifying the Effect of Renewable Generation on Day-Ahead Electricity Market Prices: The Spanish Case”, Energy Economics, 90, 104841.
• Niza, E.C. et al. (2022), “A Volatility Spillover Analysis with Realized Semi(co)variances in Australian Electricity Markets”, Energy Economics, 111, 106076.
• Paraschiv, F. et al. (2014), “The Impact of Renewable Energies on EEX Day-Ahead Electricity Prices”, Energy Policy, 73, 196-210.
• Pereira, A.J.C. & J.T. Saraiva (2013), “Long Term Impact of Wind Power Generation in the Iberian Day-Ahead Electricity Market Price”, Energy, 55, 1159-1171.
• Pesaran, H.H. & Y. Shin (1998), “Generalized Impulse Response Analysis In Linear Multivariate Models”, Economics Letters, 58(1), 17-29.
• Qiao, W. & Z. Yang (2020), “Forecast the Electricity Price of U.S. Using a Wavelet Transform-Based Hybrid Model”, Energy, 193, 116704.
• Rajan, P. & K.R.M.V. Chandrakala (2021), “Statistical Model Approach of Electricity Price Forecasting for Indian Electricity Market”, 2021 IEEE Madras Section Conference (MASCON), Chennai, India, 1-5.
• TEİAŞ (2022), About Us, <https://www.teias.gov.tr/en-US>, 30.06.2022.
• TEİAŞ (N/A), <https://www.teias.gov.tr/turkiye-elektrik-uretim-iletim-istatistikleri>, 30.06.2022.
• Terzic, I. et al. (2021), “Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX”, Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, https://doi.org/10.7595/management.fon.2021.0002.
• Thoenes, S. (2011), “Understanding The Determinants of Electricity Prices and The Impact of The German Nuclear Moratorium in 2011”, EWI Working Paper, 11/06, 1-30.
• Tschora, L. et al. (2022), “Electricity Price Forecasting on the Day-Ahead Market Using Machine Learning”, Applied Energy, 313, 118752, 1-14.
• Uğurlu, U. et al. (2018), “Electricity Price Forecasting Using Recurrent Neural Networks”, Energies, 11(5), 1-23.
• Ulgen, T. & G. Poyrazoğlu (2020), “Predictor Analysis for Electricity Price Forecasting by Multiple Linear Regression”, 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Sorrento, Italy, 618-622.
• Würzburg, K. et al. (2013), “Renewable Generation and Electricity Prices: Taking Stock and New Evidence for Germany and Austria”, Energy Economics, 40, S159-S171.
• Xie, X. et al. (2018), “The Day-Ahead Electricity Price Forecasting Based on Stacked CNN and LSTM”, in: Y. Peng et al. (eds.), International Conference on Intelligent Science and Big Data Engineering, 11266, 216-230.
• Yang, W. et al. (2022), “A Novel Machine Learning-Based Electricity Price Forecasting Model Based on Optimal Model Selection Strategy”, Energy, 238(C), 121989.
• Yarıcı, M. (2018), EPİAŞ Elektrik Piyasaları Eğitim Sunumları: Gün Öncesi Piyasası, İstanbul: EPİAŞ Yayınları.
• Yılmaz, E. & P.P. Cowley (2022), “Enerji Tüketimi ve Ekonomik Büyüme İlişkisine Ekonometrik Yaklaşım”, Journal of Academic Researches and Studies, 14(26), 59-74.
• Zareipour, H. et al. (2007), “Electricity Market Price Volatility: The Case of Ontario”, Energy Policy, 35(9), 4739-4748.
• Zhou, S. et al. (2019), “An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting”, IEEE Access, 7, 108161-108173.