Carbon-Aware Load Shifting Using Real Electricity Consumption Data: The Impact of Static and Dynamic Emission Factors on Operational Savings

Öz

This study compares the static and dynamic emission factor methodologies within the Turkey electricity grid, utilizing a 2025 projection framework. The analysis is based on a 168-hour period using high-resolution real consumption data integrated with EPİAŞ datasets. The results indicate that the application of a static emission coefficient (0.442 kgCO2/kWh) leads to an average underreporting of emissions by 13.6% compared to dynamic modeling. By using dynamic emission factor, a load-shifting simulation demonstrated that shifting consumption from carbon-intensive periods to cleaner intervals can yield a 25.6% reduction in emissions (144 kgCO2/MWh).

Anahtar Kelimeler

• Dynamic Emission Factors
• Carbon Footprint
• Load Shifting

Etik Beyan

The author of this article declares that the materials and methods used in their studies did not require ethical committee approval and/or any legal or special permission.

Teşekkür

The author would like to acknowledge the data provided by the EPİAŞ Transparency Platform for supplying high-resolution electricity production data used in this study. The availability of reliable and transparent real-time data has been essential for conducting the dynamic emission factor analysis. The author also appreciates the institutional support of Atılım University for providing the academic environment necessary to carry out this research.

Gerçek Tüketim Verileriyle Karbon-Duyarlı Yük Kaydırma: Statik ve Dinamik Emisyon Faktörlerinin Operasyonel Tasarruf Üzerindeki Etkisi

Öz

Bu çalışma, Türkiye elektrik şebekesinde statik ve dinamik emisyon faktörü hesaplama metodolojilerini 2025 yılı projeksiyonu üzerinden karşılaştırmalı olarak incelemektedir. Gerçek zamanlı tüketim verileri ve EPİAŞ verileriyle yapılan 168 saatlik analiz sonucunda, statik katsayı (0,442 kgCO2/kWh) kullanımının emisyonları ortalama %13,6 oranında eksik raporladığı saptanmıştır. Dinamik modelleme ile yapılan yük kaydırma simülasyonu, tüketimin en kirli saatten en temiz saate kaydırılmasıyla %25,6 oranında (144 kgCO2/MWh) tasarruf sağlanabileceğini ortaya koymuştur.

Anahtar Kelimeler

• Dinamik Emisyon Faktörü
• Karbon Ayak İzi
• Yük Kaydırma

Kaynakça

1. [1] I. Ari and M. Aydinalp Koksal, ‘Carbon dioxide emission from the Turkish electricity sector and its mitigation options,’ *Energy Policy*, vol. 39, no. 10, pp. 6120–6135, Oct. 2011, doi: 10.1016/j.enpol.2011.07.012.
2. [2] H. Dulkadı̇roğlu, Ç. Mühendisliği Bölümü, M.-M. Fakültesi, N. Hacı Bektaş Veli Üniversitesi, and T. Geliş, ‘TÜRKİYE’DE ELEKTRİK ÜRETİMİNİN SERA GAZI EMİSYONLARI AÇISINDAN İNCELENMESİ,’ *Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi*, vol. 7, no. 1, pp. 67–74, Jan. 2018, doi: 10.28948/ngumuh.369948.
3. [3] M. Ernur Akiner, ‘Structural Obstacles to Energy Transition in Türkiye and Holistic Solution Proposals: A Political, Economic and Social Dimensional Analysis,’ *Energies 2025, Vol. 18, Page 2591*, vol. 18, no. 10, p. 2591, May 2025, doi: 10.3390/en18102591.
4. [4] I. Ari, ‘A Low Carbon Pathway for the Turkish Electricity Generation Sector,’ *Green and Low-Carbon Economy*, vol. 1, no. 3, pp. 147–153, Jan. 2023, doi: 10.47852/bonviewglce3202552.
5. [5] S. T. Dağlıoğlu, ‘Carbon Footprint Analysis of Ege University within the Scope of Environmental Sustainability,’ *Commagene J. Biol.*, vol. 5, no. 1, pp. 51–58, Jun. 2021, doi: 10.31594/commagene.865194.
6. [6] A. Üreden, and S. Özden, ‘Kurumsal Karbon Ayak İzi Nasıl Hesaplanır: Teorik Bir Çalışma,’ *Anadolu Orman Araştırmaları Dergisi*, vol. 4, no. 2, pp. 98–108, Dec. 2018
7. [7] M. M. Yatarkalkmaz and M. B. Özdemir, ‘The calculation of greenhouse gas emissions of a family and projections for emission reduction,’ *Journal of Energy Systems*, vol. 3, no. 3, pp. 96–110, Sep. 2019, doi: 10.30521/jes.566516.
8. [8] N. A. Ryan, J. X. Johnson, and G. A. Keoleian, ‘Comparative Assessment of Models and Methods To Calculate Grid Electricity Emissions,’ *Environ. Sci. Technol.*, vol. 50, no. 17, pp. 8937–8953, Sep. 2016, doi: 10.1021/acs.est.5b05216.

9. [9] İ. Ve, İ. Bilimler, F. Dergisi, A. Arifoğlu, and T. Kandemir, ‘ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES,’ *Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty*, vol. 9, no. 2, pp. 1433–1458, Jul. 2022, doi: 10.30798/makuiibf.1097686.
10. [10] İ. İlaslaner and H. İbrahim Variyenli, ‘Türkiye’de Elektrik Üretim Santrallerinin İç İhtiyaç Tüketimlerinin Yenilenebilir Kaynaklardan Karşılanabilmesi ve Emisyona Sağlayabileceği Katkı,’ *Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji*, vol. 12, no. 2, pp. 615–624, Jun. 2024, doi: 10.29109/gujsc.1399978.
11. [11] F. Braeuer, R. Finck, and R. McKenna, ‘Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany,’ J. Clean. Prod., vol. 266, p. 121588, Sep. 2020, doi: 10.1016/j.jclepro.2020.121588.
12. [12] M. Fleschutz, M. Bohlayer, M. Braun, G. Henze, and M. D. Murphy, ‘The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices,’ Appl. Energy, vol. 295, p. 117040, Aug. 2021, doi: 10.1016/j.apenergy.2021.117040.
13. [13] J. Kono, Y. Ostermeyer, and H. Wallbaum, ‘The trends of hourly carbon emission factors in Germany and investigation on relevant consumption patterns for its application,’ The International Journal of Life Cycle Assessment 2017 22:10, vol. 22, no. 10, pp. 1493–1501, Feb. 2017, doi: 10.1007/s11367-017-1277-z.
14. [14] N. A. Ryan, J. X. Johnson, and G. A. Keoleian, ‘Comparative Assessment of Models and Methods To Calculate Grid Electricity Emissions,’ Environ. Sci. Technol., vol. 50, no. 17, pp. 8937–8953, Sep. 2016, doi: 10.1021/acs.est.5b05216.
15. [15] A. G. N. Elenes, E. Williams, E. Hittinger, and N. S. Goteti, ‘How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?,’ Environ. Sci. Technol., vol. 56, no. 20, pp. 14701–14712, Oct. 2022, doi: 10.1021/acs.est.2c02344.
16. [16] S. Ben Amor, S. Sgarciu, T. BatzLineiro, and F. Muesgens, ‘Advanced models for hourly marginal CO2 emission factor estimation: A synergy between fundamental and statistical approaches,’ Appl. Energy, vol. 397, Nov. 2025, doi: 10.1016/j.apenergy.2025.126265.
17. [17] L. Fiorini and M. Aiello, ‘Household CO2-efficient energy management,’ Energy Informatics 2018 1:1, vol. 1, no. 1, Oct. 2018, doi: 10.1186/s42162-018-0021-7.
18. [18] S. Hamels et al., ‘The use of primary energy factors and CO2 intensities for electricity in the European context - A systematic methodological review and critical evaluation of the contemporary literature,’ Renewable and Sustainable Energy Reviews, vol. 146, p. 111182, Aug. 2021, doi: 10.1016/j.rser.2021.111182.
19. [19] B. Nilges, A. Haese, C. Reinert, and N. von der Assen, ‘Validity ranges of locational marginal emission factors for the environmental assessment of electric load shifting,’ Appl. Energy, vol. 380, p. 125069, Feb. 2025, doi: 10.1016/j.apenergy.2024.125069.
20. [20] O. T. Bişkin and A. Çifci, ‘Forecasting of Turkey’s Electrical Energy Consumption using LSTM and GRU Networks,’ Bilecik Seyh Edebali University Journal of Science, vol. 8, no. 2, pp. 656–667, Dec. 2021, doi: 10.35193/bseufbd.935824.
21. [21] Ü. Ağbulut, ‘Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms,’ Sustain. Prod. Consum., vol. 29, pp. 141–157, Jan. 2022, doi: 10.1016/j.spc.2021.10.001.
22. [22] G. Göktaş Balkır and N. Ö. Önal Tuğrul, ‘Türkiye’s GHG Emissions Towards the 2030 Target: MDAM and LSTM-Based Analysis with Key Energy Factors,’ Energies (Basel)., vol. 18, no. 21, p. 5828, Nov. 2025, doi: 10.3390/en18215828.
23. [23] ‘Carbon Border Adjustment Mechanism (CBAM).’ Accessed: May 17, 2026. [Online]. Available: https://www.ab.gov.tr/carbon-border-adjustment-mechanism-cbam-_53747_en.html
24. [24] H. Gülçin Beken and A. Cebeci, ‘Çatışmalı bir Süreç Olarak Sınırda Karbon Düzenleme Mekanizması Uygulaması,’ Paradigma İktisadi ve İdari Araştırmalar Dergisi, vol. 1, no. 1, pp. 61–78, 2022. Accessed: Mar. 30, 2026. [Online]. Available: https://dergipark.org.tr/tr/pub/paradigmaiiad
25. [25] A. Makalesi, Z. Aktaş Çimen, Ö. Gör, A. Üniversitesi, S. Bilimler, and M. Yüksekokulu, ‘Sınırda Karbon Düzenlemesi ve Seçilmiş Sektörlerde Türkiye’nin Küresel Rekabet Gücü,’ Politik Ekonomik Kuram, vol. 8, no. 1, pp. 1–17, Mar. 2024, doi: 10.30586/pek.1378742.
26. [26] H. Karbon Vergi̇si̇ni̇n Türki̇ye, D. Yeni̇lenebi̇li̇r Enerji̇ye Yönlendi̇rme Araci Olarak Kullanilmasi, and Ö. Üyesi Emine Sevcan ARTUN, ‘KARBON VERGİSİNİN TÜRKİYE’DE YENİLENEBİLİR ENERJİYE YÖNLENDİRME ARACI OLARAK KULLANILMASI,’ Yaşar Hukuk Dergisi, vol. 6, no. 1, pp. 15–48, Jan. 2024. Accessed: Mar. 30, 2026. [Online]. Available: https://dergipark.org.tr/tr/pub/yhd/article/1424256
27. [27] E. İmer-Ertunga and Ö. K. Seyhun, ‘SINIRDA KARBON DÜZENLEME MEKANİZMASI VE TÜRKIYE’NİN İHRACATINA OLASI ETKİLERİ,’ Ege Stratejik Araştırmalar Dergisi, vol. 13, no. 1, pp. 1–13, Jul. 2022, doi: 10.18354/esam.1119230.
28. [28] E. Ercan, M. Mustafa Yatarkalkmaz, and O. Fatih Bulut, ‘The Impact of Income Level on Household Greenhouse Gas Emissions: A Case Study for Turkey,’ The Journal of International Scientific Researches, vol. 8, no. 2, pp. 187–199, 2023.
29. [29] D. Guven, M. O. Kayalica, and O. L. Sen, ‘The Impact of Electricity Generation on CO2 Emissions in Türkiye: An Agent-Based Simulation Approach,’ Energies (Basel)., vol. 18, no. 3, p. 655, Feb. 2025, doi: 10.3390/en18030655.
30. [30] ‘Anasayfa | EPİAŞ ŞEFFAFLIK PLATFORMU.’ Accessed: Mar. 30, 2026. [Online]. Available: https://seffaflik.epias.com.tr/home
31. [31] M. Ozcan, ‘Increasing voluntary carbon credits potential via renewable energy projects in Turkey,’ Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 5, pp. 710–719, Oct. 2022, doi: 10.5505/pajes.2022.06882.
32. [32] A. Çoruh Üniversitesi Doğal Afetler Uygulama ve Araştırma Merkezi Doğal Afetler ve Çevre Dergisi et al., ‘Yerel Yönetimler İçin Karbon Ayak İzinin Belirlenmesi: Ümraniye Belediyesi Örneği,’ Doğal Afetler ve Çevre Dergisi, vol. 6, no. 2, pp. 319–333, Jul. 2020, doi: 10.21324/dacd.639370.
33. [33] C. Coskun, ‘A time-varying carbon intensity approach for demand-side management strategies with respect to CO2 emission reduction in the electricity grid,’ International Journal of Global Warming, vol. 19, no. 1–2, pp. 3–23, 2019, doi: 10.1504/ijgw.2019.101768.
34. [34] E. Tetik Kollugil, K. Sarıca, and Y. I. Topcu, ‘Electric vehicles as an emission mitigation option: expectations and reality,’ Clean Technologies and Environmental Policy 2024 27:3, vol. 27, no. 3, pp. 1093–1114, May 2024, doi: 10.1007/s10098-024-02873-4.
35. [35] M. Schäfer, F. Cerdas, and C. Herrmann, ‘Towards standardized grid emission factors: methodological insights and best practices,’ Energy Environ. Sci., vol. 17, no. 8, pp. 2776–2786, Apr. 2024, doi: 10.1039/d3ee04394k.
36. [36] M. Fleschutz, M. Bohlayer, M. Braun, G. Henze, and M. D. Murphy, ‘The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices,’ Appl. Energy, vol. 295, p. 117040, Aug. 2021, doi: 10.1016/j.apenergy.2021.117040.
37. [37] A. Zou et al., ‘Temporal resolution matters: Evaluating carbon emission factors for accurate accounting in commercial buildings,’ Appl. Energy, vol. 412, p. 127655, Jun. 2026, doi: 10.1016/j.apenergy.2026.127655.
38. [38] P. Wiesner, I. Behnke, D. Scheinert, K. Gontarska, and L. Thamsen, ‘Let’s wait awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud,’ pp. 260–272, Dec. 2021, doi: 10.1145/3464298.3493399.
39. [39] A. Zharova and F. Creutzig, ‘An Assessment of the CO$_2$ Emission Reduction Potential of Residential Load Management in Developing and Developed Countries,’ Apr. 2025. Accessed: May 17, 2026. [Online]. Available: https://arxiv.org/pdf/2504.02811
40. [40] E. Mata, J. Ottosson, and J. Nilsson, ‘A review of flexibility of residential electricity demand as climate solution in four EU countries,’ Environmental Research Letters, vol. 15, no. 7, Jul. 2020, doi: 10.1088/1748-9326/ab7950.
41. [41] ‘ON PAIRED SAMPLES T-TEST: APPLICATIONS, EXAMPLES AND LIMITATIONS.’ Accessed: May 17, 2026. [Online]. Available: https://www.researchgate.net/publication/389788905_ON_PAIRED_SAMPLES_T-TEST_APPLICATIONS_EXAMPLES_AND_LIMITATIONS
42. [42] H. Li, Z. Wang, T. Hong, and M. A. Piette, ‘Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications,’ Advances in Applied Energy, vol. 3, p. 100054, Aug. 2021, doi: 10.1016/j.adapen.2021.100054.
43. [43] M. Hemmati, N. Bayati, K. M. Banjar-Nahor, A. Anvari-Moghaddam, and T. Ebel, ‘Optimization-based Dynamic Life Cycle Assessment on Emission-based Demand Response Strategies in Danish Households,’ Smart Grids and Sustainable Energy 2026 11:1, vol. 11, no. 1, Feb. 2026, doi: 10.1007/s40866-026-00328-x.
44. [44] L. Fiorini and M. Aiello, ‘Household CO2-efficient energy management,’ Energy Informatics 2018 1:1, vol. 1, no. 1, Oct. 2018, doi: 10.1186/s42162-018-0021-7.
45. [45] B. Nilges, A. Haese, C. Reinert, and N. von der Assen, ‘Validity ranges of locational marginal emission factors for the environmental assessment of electric load shifting,’ Appl. Energy, vol. 380, p. 125069, Feb. 2025, doi: 10.1016/j.apenergy.2024.125069.
46. [46] A. Narayan, ‘HomeSHIFT: Automated Coordination of Home Distributed Energy Resources for Minimizing Personal Energy Carbon Footprint,’ EGU25, Mar. 2025, doi: 10.5194/egusphere-egu25-14204.
47. [47] M. Barani, S. Backe, R. O’Reilly, and P. C. del Granado, ‘Residential demand response in the European power system: No significant impact on capacity expansion and cost savings,’ Sustainable Energy, Grids and Networks, vol. 38, p. 101198, Jun. 2024, doi: 10.1016/j.segan.2023.101198.
48. [48] A. G. N. Elenes, E. Williams, E. Hittinger, and N. S. Goteti, ‘How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?,’ Environ. Sci. Technol., vol. 56, no. 20, pp. 14701–14712, Oct. 2022, doi: 10.1021/acs.est.2c02344.
49. [49] E. Uzlu and T. Dede, ‘Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini,’ Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 8, no. 3, pp. 511–528, Sep. 2020, doi: 10.29109/gujsc.684334.
50. [50] M. Demirtaş, N. Akkoyun, E. Akkoyun, and İ. Çetinbaş, ‘Akıllı Şebekelerde Güneş Enerjisi Üretiminin Zamana Bağlı Olasılıksal Tahmini,’ Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 7, no. 2, pp. 411–424, Jun. 2019, doi: 10.29109/gujsc.549704.
51. [51] S. Ermiş, R. Bayindir, M. Yeşilbudak, and M. Bilgisi, ‘İyileştirilmiş Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmasıyla Güç Sistemlerinde Gerilim Kararlılığı Çalışması,’ Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 11, no. 3, pp. 695–705, Sep. 2023, doi: 10.29109/gujsc.1282188.