The Effects of Electricity Generation from Solar and Wind Energy on the Day Ahead Market-Clearing Prices and Price Volatility: The Turkish Case

Year 2023, Volume: 7 Issue: 2, 1067 - 1100, 25.05.2023

Mustafa Çağrı Peker , Ayşen Sivrikaya

https://doi.org/10.25295/fsecon.1215578

Cited By: 1

Abstract

Solar and wind generation are the primary tools to prevent climate change and high carbon emissions. Due to their intermittent generation characteristics, solar and wind power plants have a different impact on the market-clearing price formation compared to conventional generation sources. The paper investigates the solar and wind generation effect on the day ahead market-clearing price in Turkey between the 2016 and 2022. We used a 2nd order polynomial learner model to analyze the impact of solar and wind generation level on the market-clearing price level and volatility. We find that Dutch TTF increases market-clearing price with a coefficient of 0.24. An increase in wind and solar generation reduce the market-clearing price. Solar generation is ineffective on the market-clearing price below a certain demand level. Wind generation reduces market-clearing price with a 37.78 coefficient at low demand levels and a 6.55 coefficient at high demand levels. Solar generation has a price-reducing effect with 5.55 at high demand levels. Finally, Dutch TTF and wind generation increased volatility with coefficients of 0.04 and 0.69; solar generation reduced volatility with a coefficient of 0.83.

Keywords

Solar, Wind, Market-clearing Price, Wholesale Electricity Markets, Day Ahead Market

Güneş ve Rüzgar Enerjisinden Elektrik Üretiminin Gün Öncesi Piyasa Takas Fiyatlarına ve Fiyat Volatilitesine Etkisi: Türkiye Örneği

Abstract

Güneş ve rüzgardan elektrik üretimi; iklim değişikliği ve yüksek karbon emisyonunu önlemenin önde gelen araçlarındandır. Güneş ve rüzgar santralleri kesintili elektrik üretim karakterlerinden dolayı piyasa takas fiyatı oluşumunda konvansiyonel üretim kaynaklarına göre farklı bir etkiye sahiptir. Bu makale, 2016 ve 2022 yılları arasında Türkiye'de güneş ve rüzgardan elektrik üretiminin gün öncesi piyasa fiyatı üzerindeki etkisini incelemektedir. Güneş ve rüzgardan elektrik üretim seviyesinin piyasa takas fiyat seviyesi ve oynaklığı üzerindeki etkisini analiz etmek için makine öğrenmesi ile 2. derece polinom öğrenmesi kullanılmıştır. Modellerin sonucu olarak, Hollanda TTF gaz fiyatının, piyasa takas fiyatını 0,24 katsayısı ile artırdığı bulunmuştur. Rüzgâr ve güneş enerjisi üretimindeki artışın, piyasa takas fiyatını düşürdüğü gözlemlenmiştir. Güneşten elektrik üretimi, belirli bir elektrik talep seviyesinin altında piyasa takas fiyatı üzerinde etkisizdir. Rüzgar üretimi, düşük talep seviyelerinde 37,78 katsayısı ve yüksek talep seviyelerinde 6,55 katsayısı ile piyasa takas fiyatını düşürmektedir. Güneşten elektrik üretimi yüksek talep seviyelerinde 5,55 ile fiyat düşürücü etkiye sahiptir. Son olarak, Hollandalı TTF gaz fiyatı ve rüzgar üretimi, sırasıyla 0,04 ve 0,69 katsayılarıyla oynaklığı artırmaktadır; güneş enerjisi üretimi 0,83 katsayı ile oynaklığı azaltmaktadır.

Keywords

Güneş, Rüzgar, Piyasa Takas Fiyatı, Toptan Elektrik Piyasaları, Gün Öncesi Piyasası

References

• Adom, P. K., Minlah, M. K. & Adams, S. (2018). Impact of Renewable Energy (Hydro) on Electricity Prices in Ghana: A Tale of the Short-and Long-Run. Energy Strategy Reviews, 20, 163-178.
• Ahmad, T. & Chen, H. (2020). A Review on Machine Learning Forecasting Growth Trends and Their Real-Time Applications in Different Energy Systems. Sustainable Cities and Society, 54, 102010.
• Alolo, M., Azevedo, A. & El Kalak, I. (2020). The Effect of The Feed-In-System Policy on Renewable Energy Investments: Evidence from The EU Countries. Energy Economics, 92, 104998.
• Arıoğlu Akan, M. Ö., Selam, A. A., Oktay Fırat, S. Ü., Er Kara, M. & Özel, S. (2015). A Comparative Analysis of Renewable Energy Use and Policies: Global and Turkish Perspectives. Sustainability, 7(12), 16379-16407.
• Astaneh, M. F. & Chen, Z. (2013, July). Price Volatility in Wind Dominant Electricity Markets. Eurocon 2013 (770-776). IEEE.
• Aydin, A. D. & Cavdar, S. C. (2015). Comparison of Prediction Performances of Artificial Neural Network (ANN) And Vector Autoregressive (VAR) Models by Using the Macroeconomic Variables of Gold Prices, Borsa Istanbul (BIST) 100 Index and US Dollar-Turkish Lira (USD/TRY) Exchange Rates. Procedia Economics and Finance, 30, 3-14.
• Ballester, C. & Furió, D. (2015). Effects of Renewables on The Stylized Facts of Electricity Prices. Renewable and Sustainable Energy Reviews, 52, 1596-1609.
• Basu, R. & Ferreira, J. (2020). Understanding Household Vehicle Ownership in Singapore Through a Comparison of Econometric and Machine Learning Models. Transportation Research Procedia, 48, 1674-1693.
• Blazquez, J., Fuentes-Bracamontes, R., Bollino, C. A. & Nezamuddin, N. (2018). The Renewable Energy Policy Paradox. Renewable and Sustainable Energy Reviews, 82, 1-5.
• Bolhuis, M. A. & Rayner, B. (2020). Deus Ex Machina? A Framework for Macro Forecasting with Machine Learning. International Monetary Fund.
• Brown, P. (2012). US Renewable Electricity: How Does Wind Generation Impact Competitive Power Markets?. Congressional Research Service.
• Bushnell, J. & Novan, K. (2018). Setting With the Sun: The Impacts of Renewable Energy on Wholesale Power Markets (No. w24980). National Bureau of Economic Research.
• Chattopadhyay, D. (2014). Modelling Renewable Energy Impact on The Electricity Market in India. Renewable and Sustainable Energy Reviews, 31, 9-22.
• Chen, X., Mcelroy, M. B., Wu, Q., Shu, Y. & Xue, Y. (2019). Transition Towards Higher Penetration of Renewables: An Overview of Interlinked Technical, Environmental and Socio-Economic Challenges. Journal of Modern Power Systems and Clean Energy, 7(1), 1-8.
• Ciarreta, A., Pizarro-Irizar, C. & Zarraga, A. (2020). Renewable Energy Regulation and Structural Breaks: An Empirical Analysis of Spanish Electricity Price Volatility. Energy Economics, 88, 104749.
• Clò, S., Cataldi, A. & Zoppoli, P. (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.
• Cutler, N. J., Boerema, N. D., MacGill, I. F. & Outhred, H. R. (2011). High Penetration Wind Generation Impacts on Spot Prices in The Australian National Electricity Market. Energy Policy, 39(10), 5939-5949.
• Çakmak, N. & Gözen, M. (2021). An Analysis of Systematic Risk Factors Associated with Renewable Energy Support Mechanism Applied in Turkey. Journal of Business Innovation and Governance, 4(1), 57-81.
• de la Nieta, A. S. & Contreras, J. (2020). Quantifying The Effect of Renewable Generation on Day–Ahead Electricity Market Prices: The Spanish Case. Energy Economics, 90, 104841.
• Depren, S. K., Kartal, M. T., Ertuğrul, H. M. & Depren, Ö. (2022). The Role of Data Frequency and Method Selection in Electricity Price Estimation: Comparative Evidence from Turkey in Pre-Pandemic and Pandemic Periods. Renewable Energy, 186, 217-225.
• Edenhofer, O., Hirth, L., Knopf, B., Pahle, M., Schlömer, S., Schmid, E. & Ueckerdt, F. (2013). On The Economics of Renewable Energy Sources. Energy Economics, 40, S12-S23.
• Figueiredo, N. C. & da Silva, P. P. (2019). The “Merit-Order Effect” of Wind and Solar Power: Volatility and Determinants. Renewable and Sustainable Energy Reviews, 102, 54-62.
• Herrera, G. P., Constantino, M., Tabak, B. M., Pistori, H., Su, J. J. & Naranpanawa, A. (2019). Data on Forecasting Energy Prices Using Machine Learning. Data in Brief, 25, 104122.
• Gallego-Castillo, C. & Victoria, M. (2015). Cost-Free Feed-In Tariffs for Renewable Energy Deployment in Spain. Renewable Energy, 81, 411-420.
• Ghoddusi, H., Creamer, G. G. & Rafizadeh, N. (2019). Machine Learning in Energy Economics and Finance: A Review. Energy Economics, 81, 709-727.
• Hall, A. S. (2018). Machine Learning Approaches to Macroeconomic Forecasting. The Federal Reserve Bank of Kansas City Economic Review, 103(63), 2.
• He, Q., Lin, Z., Chen, H., Dai, X., Li, Y. & Zeng, X. (2022). Bi-Level Optimization Based Two-Stage Market-Clearing Model Considering Guaranteed Accommodation of Renewable Energy Generation. Protection and Control of Modern Power Systems, 7(1), 1-13.
• Herrero, I., Rodilla, P. & Batlle, C. (2015). Electricity mcp and investment Incentives: The Role of Pricing Rules. Energy Economics, 47, 42-51.
• Hildmann, M., Ulbig, A. & Andersson, G. (2013). Revisiting The Merit-Order Effect of Renewable Energy Sources. arXiv preprint arXiv:1307.0444.
• Huisman, R. & Kilic, M. (2013). A History of European Electricity Day-Ahead Prices. Applied Economics, 45(18), 2683-2693.
• Janda, K. (2018). Slovak Electricity Market and The Price Merit Order Effect of Photovoltaics. Energy Policy, 122, 551-562.
• Kabak, M. & Tasdemir, T. (2020). Electricity Day-Ahead Market Price Forecasting by Using Artificial Neural Networks: An Application for Turkey. Arabian Journal for Science and Engineering, 45(3), 2317-2326.
• Karatekin, C. (2020). The Effects of Renewable Energy Sources on The Structure of The Turkish Electricity Market. 670216917.
• Kwon, T. H. (2020). Policy Mix of Renewable Portfolio Standards, Feed-In Tariffs, and Auctions in South Korea: Are Three Better Than One?. Utilities Policy, 64, 101056.
• Kyritsis, E., Andersson, J. & Serletis, A. (2017). Electricity Prices, Large-Scale Renewable Integration, and Policy Implications. Energy Policy, 101, 550-560.
• López Prol, J. & Schill, W. P. (2021). The Economics of Variable Renewable Energy and Electricity Storage. Annual Review of Resource Economics, 13, 443-467.
• Ma, T., Du, Y., Xu, T. & Chen, W. (2022). Cross-Regional Effects of Renewable Power Generation on The Electricity Market: An Empirical Study on Japan's Electricity Spot Market. Applied Economics, 1-28.
• Masini, R. P., Medeiros, M. C. & Mendes, E. F. (2021). Machine Learning Advances for Time Series Forecasting. Journal of Economic Surveys.
• Macedo, D. P., Marques, A. C. & Damette, O. (2021). The Merit-Order Effect on the Swedish Bidding Zone with The Highest Electricity Flow in The Elspot Market. Energy Economics, 102, 105465.
• Maciejowska, K. (2020). Assessing The Impact of Renewable Energy Sources on The Electricity Price Level and Variability–A Quantile Regression Approach. Energy Economics, 85, 104532.
• Maekawa, J., Hai, B. H., Shinkuma, S. & Shimada, K. (2018). The Effect of Renewable Energy Generation on The Electric Power Spot Price of The Japan Electric Power Exchange. Energies, 11(9), 2215.
• Mulder, M. & Scholtens, B. (2013). The Impact of Renewable Energy on Electricity Prices in the Netherlands. Renewable Energy, 57, 94-100.
• Oksuz, I. & Ugurlu, U. (2019). Neural Network-Based Model Comparison for Intraday Electricity Price Forecasting. Energies, 12(23), 45-57.
• Pahle, M., Schill, W. P., Gambardella, C. & Tietjen, O. (2016). Renewable Energy Support, Negative Prices, and Real-Time Pricing. The Energy Journal, 37(Sustainable Infrastructure Development and Cross-Border Coordination).
• Paraschiv, F., Erni, D. & Pietsch, R. (2014). The Impact of Renewable Energies on EEX Day-Ahead Electricity Prices. Energy Policy, 73, 196-210.
• Perez, A. & Garcia-Rendon, J. J. (2021). Integration of Non-Conventional Renewable Energy and Spot Price of Electricity: A Counterfactual Analysis for Colombia. Renewable Energy, 167, 146-161.
• Riesz, J. & Milligan, M. (2019). Designing Electricity Markets for A High Penetration of Variable Renewables. Advances in Energy Systems: The Large‐scale Renewable Energy Integration Challenge, 479-489.
• Rintamäki, T., Siddiqui, A. S. & Salo, A. (2017). Does Renewable Energy Generation Decrease the Volatility of Electricity Prices? An Analysis of Denmark and Germany. Energy Economics, 62, 270-282.
• Ríos‐Ocampo, J. P., Arango‐Aramburo, S. & Larsen, E. R. (2021). Renewable Energy Penetration and Energy Security in Electricity Markets. International Journal of Energy Research, 45(12), 17767-17783.
• Schöniger, F. & Morawetz, U. B. (2022). What Comes Down Must Go Up: Why Fluctuating Renewable Energy Does Not Necessarily Increase Electricity Spot Price Variance in Europe. Energy Economics, 111, 106069.
• Shobana, G. & Umamaheswari, K. (2021, January). Forecasting By Machine Learning Techniques and Econometrics: A Review. 2021 6th International Conference on Inventive Computation Technologies (ICICT) (1010-1016). IEEE.
• Simsek, H. A. & Simsek, N. (2013). Recent Incentives for Renewable Energy in Turkey. Energy Policy, 63, 521-530.
• Sirin, S. M. & Yilmaz, B. N. (2020). Variable Renewable Energy Technologies in The Turkish Electricity Market: Quantile Regression Analysis of The Merit-Order Effect. Energy Policy, 144, 111660.
• Vlachos, A. G. & Biskas, P. N. (2014). Embedding Renewable Energy Pricing Policies in Day-Ahead Electricity Market-Clearing. Electric Power Systems Research, 116, 311-321.
• Woo, C. K., Moore, J., Schneiderman, B., Ho, T., Olson, A., Alagappan, L., ... & Zarnikau, J. (2016). Merit-Order Effects of Renewable Energy and Price Divergence in California’s Day-Ahead and Real-Time Electricity Markets. Energy Policy, 92, 299-312.
• Wozabal, D., Graf, C. & Hirschmann, D. (2016). The Effect of Intermittent Renewables on The Electricity Price Variance. OR Spectrum, 38(3), 687-709.
• Würzburg, K., Labandeira, X. & Linares, P. (2013). Renewable Generation and Electricity Prices: Taking Stock and New Evidence for Germany and Austria. Energy Economics, 40, S159-S171.
• Li, X., Shang, W., & Wang, S. (2019). Text-Based Crude Oil Price Forecasting: A Deep Learning Approach. International Journal of Forecasting, 35(4), 1548-1560.
• Zeinalzadeh, A., Ghavidel, D. & Gupta, V. (2018, June). Pricing Energy in The Presence of Renewables. 2018 Annual American Control Conference (ACC) (3881-3886). IEEE.