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According to the Vision 2023 agenda, the Turkish government plans to produce 30% of Turkey's electricity demand from renewable energy sources in 2023. This means hydroelectric, wind and solar energy capacities would increase to 36,000 MW, 20,000 MW and 3000 MW, respectively. Increased hydroelectric capacity would indeed benefit stability and flexibility of the existing energy infrastructure. However, increased wind and solar capacity could bring intermittency problems due to nature of these energy sources. Recently, pumped hydroelectric energy storage (PHES) has become a hot topic in Turkey. And, its potential applications via synergistic utilisation with wind and solar energy to solve the aforementioned intermittency problems is being discussed. Yet, detailed information in the literature about PHES in Turkey is scarce. Therefore, this paper was intended to provide this crucial information. Detailed analysis showed even if all obstacles are overcome and all potential PHES capacity is used PHES could only provide a storage capacity worth equivalent to 0.6-0.8% of Turkey's annual electricity demand in 2023. It is estimated that the overnight capital cost required to develop this PHES potential is between 3.6 and 6.5 billion US$. Even so, there would be serious economic risks of PHES plants. Because, PHES systems are currently net consumers of energy. Current study also showed that instead of focusing on PHES, flexibility of Turkish electricity market could be sustained by diversifying energy sources and increasing nuclear energy capacity. It is estimated that an additional 490 MW of nuclear capacity could provide the same potential PHES capacity. By this way, intermittency problems associated with increased share of solar and wind energy can be easily tackled without imperilling the Vision 2023 energy targets. [ABSTRACT FROM AUTHOR]

Energy production in a safe and hazard free manner is one of the world׳s greatest concern. Since the inception of Kyoto Protocol, which was adopted in 1997 and entered into force in February 2005, countries have started to adopt different measures for emission reduction such as electricity generation from renewable energy sources; as the source is free from green house gas (GHG) or CO 2 emission. Legislations and financial incentives have been provided by some governments for encouragement and ensuring good returns to the investors in renewable energy sector. This paper analyzes how a 500 kWp solar photovoltaic (PV) system for electricity generation contributes significantly in the GHG emission reduction and also the potential impact of introducing CO 2 emission reduction cost in the solar PV electricity generation. The result shows that the emission reduction is of the order of hundreds of tons of CO 2 and CO 2 emission reduction cost has a positive impact on the cumulative cash flow of the system. [ABSTRACT FROM AUTHOR]

This paper presents an energy performance analysis of three different photovoltaic (PV) modules technologies under İzmit, Kocaeli weather conditions in Northwest of Turkey. Some comparative analyzes were conducted for showing solar energy potential of İzmit, Kocaeli. For this purpose, by using crystalline (c-Si), multi crystalline (mc-Si) and cadmium–telluride (Cd–Te) modules, three on-grid photovoltaic power systems (PPS) were installed on the rooftop of the Engineering Faculty at Kocaeli University. These PPSs were monitored from October, 2013 to December, 2014. In order to determine the energy performances of PV modules and PPSs, normalized energy of systems, performance ratios (PRs), mean array efficiencies (MAEs) and capacity factors (CFs) were calculated. The results show that due to the low dependency on weather changes, MAE of Cd–Te arrays result in higher value than that of the other arrays. Changes in PRs of c-Si and mc-Si arrays are lower than Cd–Te arrays within the measurement period and mean values of PRs are 83.8%, 82.05% and 89.76% for mc-Si, c-Si and Cd–Te arrays, respectively. Since Cd–Te array has the highest CF during all months, Cd–Te array can be accepted as more reliable array under İzmit climatic condition. On the other hand, one of the outcomes of this study is to reveal the solar energy potential of İzmit by on-site measurements. So, it is shown that contrary to measurement taken between 1985 and 2006 from 156 stations by General Directorate of Renewable Energy (GDRE) and Turkish State Meteorological Service (DMI), İzmit has more solar energy potential by rate of 23.79%. [ABSTRACT FROM AUTHOR]

The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kırklareli, Tokat, Nevşehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018–31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m2, from 2.273 to 2.820 MJ/m2, respectively. At all cases, k-NN exhibited the worst result in terms of R2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively. • Comparison of four machine learning algorithms in the prediction of daily global solar radiation. • Discussion of the performance success of the algorithms with seven metrics. • Different regions come different algorithms to the fore in terms of prediction success. • ANN is generally presenting better prediction results in comparison with those of DL, SVM, and k-NN. • k-NN algorithm is giving the worst prediction results for almost all metrics. [ABSTRACT FROM AUTHOR]