Restructuring in the Electricity Markets and Structural Transformation in Turkish Electricity Market

Çetintaş, Hakan; Bicil, İbrahim Murat

doi:10.17541/oeybd.33366

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…With the balancing and settlement regulation published in November 2004, the Day-Ahead Balancing system was adopted to facilitate real-time balancing and improve system security in Turkey [8]. Since then, Balancing and settlement regulation have undergone various changes as a result of the studies carried out in line with the demands of the market participants to ensure the healthy execution of real-time balancing in the electricity market and to fully respond to the needs of the market participants.…”

Section: Introductionmentioning

confidence: 99%

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Guven

Ozozen²,

Kayakutlu

et al. 2021

IFIP Advances in Information and Communication Technology

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Energy market models are generally focused on energy balancing using the optimum energy mix. In countries where the energy markets are not fully liberalised, the State Regulators reflect any cost of being off-balance on the utility companies and this affects the consumers as well. The right short term prediction of the market trends is beneficial both to optimise the physical energy flow and commercial revenue balance for suppliers and utility companies. This study is aimed to predict the sign trends in the power market by selecting the influencing factors adaptive to the conditions of the day ahead, 10 hours, 5 hours, 2 hours and 1 hour before the electricity balance is active. There are numerous factors consisting of weather conditions, resource costs, operation costs, renewable energy conditions, regulations, etc. with a considerable impact on the predictions. The contribution of this paper is to choose the factors with the highest impacts using the Genetic Algorithm (GA) with Akaike Information Criteria (AIC), which are then used as input of a Recursive Neural Network (RNN) model for forecasting the deviation trends. The proposed hybrid method does not only reduce the prediction errors but also avoid dependency on expert knowledge. Hence this paper will allow both the market regulator and the suppliers to take precautions based on a confident prediction.

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Section: Introductionmentioning

confidence: 99%

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Guven

Ozozen²,

Kayakutlu

et al. 2021

IFIP Advances in Information and Communication Technology

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Forecasting and Analysis in Turkish Electricity Market With Multiple Regression, Arima and Artificial Neural Network Methods

ARSLAN

Ertuğrul

2022

Yönetim Ve Ekonomi Araştırmaları Dergisi

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Türkiye'de EPİAŞ aracılığıyla işletilen ve operasyon süreçlerinden birisi olarak elektrik piyasalarında arza çıkan her birim elektrik için arz ve talep dengesine göre piyasa takas fiyatı oluşmaktadır. Piyasa takas fiyatı saatlik olarak oluşmakta katılımcılara ve halka açık olarak şeffaf platformlarda bildirilmektedir. Bu çalışmada R istatistik paket programı kullanılarak Çoklu Regresyon yöntemi ve Yapay Sinir Ağı modelleri; Eviews paket programı ile ise ARIMA yöntemi kullanılarak geçmiş piyasa fiyatının analizi ve tahmini gerçekleştirilmiştir. Çoklu Regresyon ve Yapay Sinir Ağları yöntemleri ile piyasa fiyatını doğrudan etkileyen etmenler bağımsız değişkenler olarak ele alınmıştır. ARIMA yönteminde ise verinin geçmiş değerleri referans olarak alınarak analizler gerçekleştirilmiştir. Çalışmada kullanılan analiz yöntemleri sonucunda en iyi performansın sırasıyla Yapay Sinir Ağları, Çoklu Regresyon yöntemi ve ARIMA yönteminden elde edildiği belirlenmiştir.

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Determining the Factors Affecting the Market Clearing Price by Using Multiple Linear Regression Method

Kalfa

ARSLAN

Ertuğrul

2021

Alphanumeric Journal

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The aim of this study is to determine the factors affecting the market clearing price by the multiple linear regression method. In order to achieve this goal, hourly data for 2019 were obtained from the website of Energy Exchange Istanbul (EXIST). Due to the multiple linear regression analysis assumptions, only January and June data were included in the analysis. The analysis results show that the variables affecting the market clearing price are statistically determined as the amount of natural gas production, the amount of hydroelectric energy, the amount of energy produced in thermal power plants, and the amount of wind energy (only in January) at the significance level of 0.05. Methods with high specificity coefficient, low mean absolute percentage error (MAPE) and mean absolute deviation are known as the methods that adapt to the data better. In this study, artificial neural network method was used along with the multiple linear regression method in order to determine which prediction model fit the data better. Coefficient of determination (R2), mean absolute percentage error, and mean absolute deviation were used to compare the methods. In this study, it can be concluded that the artificial neural network method is a better predictive than the multiple linear regression method due to its high R2 and low MAPE and the mean absolute deviation values.

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Restructuring in the Electricity Markets and Structural Transformation in Turkish Electricity Market

Cited by 8 publications

References 3 publications

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Forecasting and Analysis in Turkish Electricity Market With Multiple Regression, Arima and Artificial Neural Network Methods

Determining the Factors Affecting the Market Clearing Price by Using Multiple Linear Regression Method

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