Lisansüstü Eğitim Enstitüsü

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Browsing Lisansüstü Eğitim Enstitüsü by Department "Enstitüler, Lisansüstü Eğitim Enstitüsü, Enerji ve Sürdürülebilir Kalkınma Ana Bilim Dalı"

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Now showing 1 - 4 of 4
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    Master Thesis
    Analysis of the liberalization of the Turkish natural gas market
    (Kadir Has Üniversitesi, 2023) UĞUR, ETHEM; Volkan Ediger, Şevket
    Natural gas is used as a bridge fuel during the ongoing transition from fossil fuels to renewables because it produces less carbon emissions than oil and coal. In addition, countries, which are aiming to become more carbon neutral, are replacing coal with natural gas. These reasons have led the natural gas industry to grow and the gas business has gained an international dimension. In order to keep up with these developments, countries liberalize their gas markets by opening them to competition. The two important pillars of liberalization are third-party access to the physical infrastructure and the demolition of monopolies in the market. The European Union (EU) has implemented a series of reforms to be able to fully liberalize its internal gas markets. Turkey, the fourth largest gas-consuming country in Europe, has also made a series of reforms in order to harmonize with Europe during the EU accession process since 2001. However, Turkey’s goals to open its internal gas market to competition have only been partially achieved. The main purpose of this study is to analyze the performance of the Turkish natural gas market and to determine to what extent gas market reforms have been successful. The results of a detailed examination of the market and the survey carried out among the major market players have shown that the Turkish natural gas market should be improved in transparency, competitiveness, and cost-based pricing.
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    Master Thesis
    The renewable energy transition in the United Arab Emirates
    (Kadir Has Üniversitesi, 2020) Abusaada, Eman Bassam
    Enerji rejimleri tarih boyunca bir kaynaktan diğerine doğru yer değiştirmiştir. Dünya şu anda fosil yakıtların ağırlıklı olduğu bir rejimden daha sürdürülebilir bir rejime geçiş döneminin ortasındadır. Geçmiş deneyimler, enerji geçişlerinin zor olduğunu ve ciddi çaba gerektirdiğini ortaya koymaktadır. Bugünkü geçiş, bütçe gelirlerinin çoğu hidrokarbon ihracat gelirlerinden oluşan, petrol ve gaz ihraç eden Körfez Arap Ülkeleri İşbirliği Konseyi (KİK) ülkeleri için daha da zor olacaktır, bu ülkelerin iç tüketimi hızla artmakta, ve kaynakları sürekli tükenmektedir. Birleşik Arap Emirlikleri (BAE), Suudi Arabistan ile birlikte enerji rejimi geçişinde KİK'te en başarılı ülkedir. BAE sadece Uluslararası Yenilenebilir Enerji Ajansının (IRENA) daimi ev sahibi olarak değil, aynı zamanda bilimsel araştırma ve geliştirmeyi destekleyen faaliyetler yürüterek, sürdürülebilir yenilenebilir enerji kaynaklarına geçişi teşvik ederek özel bir yere sahip olmuştur. Bu çalışma, Scopus veritabanına dayanarak, BAE'de 1988 ve 2018 yılları arasında bibliyometrik analizler yapan yenilenebilir enerji hakkındaki bilimsel çalışmaların ana eğilimlerini incelemektedir. Orta Doğu'da yenilenebilir enerji ile ilgili yayınlanan 1.908 makalenin yaklaşık %48'i BAE'yi ilgilendirirken, bu makalelerin yazarlarının %63'ü de BAE'dedir. Bu çalışma aynı zamanda ülke vatandaşlarının yeşil teknolojiye geçiş konusundaki kabul ve farkındalık düzeylerini ölçütleri birleştiren rastgele bir örnek kullanarak araştırmaktadır. Bu çalışmada üretilen bilgiler, devlet kurumlarının ülkedeki çeşitli güçleri nasıl daha etkin bir şekilde bağladığını anlamalarını, araştırma merkezleri ve enstitülerinin BAE'nin geçişi ile ilgili alanlarda koordine edebilecek stratejik ortaklar bulmasını sağlamaktadır. Bu tez, uluslararası akademik literatürdeki KİK ülkelerinde yenilenebilir enerji kaynakları hakkında mevcut bilgiyi artıracaktır, ancak olguyu ve gelecekteki gelişimini anlamak için daha büyük çaba sarf edilmesi gerekmektedir.
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    Master Thesis
    The role of hydrogen in the energy mix: A scenario analysis for Turkey using OSeMOSYS
    (Kadir Has Üniversitesi, 2022) TETİK, HEPNUR; KIRKIL, GOKHAN
    The urgent need to tackle climate change drives the research on new technologies to help the transition of energy systems. Hydrogen is under significant consideration by many countries as a means to reach zero-carbon goals. Turkey has also started to develop hydrogen projects. In this thesis, hydrogen’s role in the energy system of Turkey is assessed through energy modeling in the cost optimization analytical tool OSeMOSYS (Open Source energy Modelling SYStem). The hydrogen is produced via PEM electrolysis by the use of renewable electricity. Specifically, by scenario development, potential effects of hydrogen blending into natural gas network in Turkish energy system have been displayed. As a result, by using hydrogen, a significant amount of reduction in carbon dioxide emissions is observed; however, the accumulated capital investment value has increased. Furthermore, hydrogen has the potential to reduce Turkey’s energy import dependency by decreasing natural gas demand. To understand hydrogen’s full potential, continued efforts in other production methods and end-uses of hydrogen are necessary.
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    Master Thesis
    Short-term forecast for Turkey's electricity demand DNN VS LSTM
    (Kadir Has Üniversitesi, 2023) DEDE, BERK; Kirkil, Gökhan; Kirkil, Gökhan
    This study aims to estimate Turkey's short-term electricity demand using artificial intelligence algorithms. Electrical systems are complex structures; therefore, many details must be considered for the prediction. Electricity demand forecasting depends on many conditions such as climate, calendar effect ( holidays, day of the week, etc.), demographic data, and economic data. Turkey is a relatively large and crowded country, whose population distribution is concentrated in some regions and climatic conditions, population-weighted meteorological data were used as independent variables. Predicting the future is challenging machine learning can help us understand how systems behave by identifying and analyzing patterns in data. Two advanced artificial neural network models were deployed in this study: a deep neural network (DNN) model, and stacked (deep) long short-term memory (LSTM) model. Their outputs provided estimates of hourly electricity consumption compared with the actual data. For this comparison, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics were used. It was observed that the DNN model predicted more accurately than the stacked LSTM model.