Search

• The city of Burdur has been selected as the target city. • Solar power target of Turkey has been analyzed at urban level for Burdur. • The results show that the solar share of the total installed capacity will reach 49%. The energy profile of a city, with all of the relevant details about production and consumption phases, is a significant part of its sustainability rating. Therefore, municipalities have initiated the development of Sustainable Energy Action Plans as a key document to introduce the respective course of actions in order to define the technology adaptation process, identify the principal sources of CO 2 emissions and their respective reduction potentials for the designated target years. A complete energy-strategy plan is required to handle the financial, technical, and environmental dimensions, not just for the country, but also for cities. As a powerful energy model generator among the energy planning and optimization tools, TIMES utilizes the energy, economy, and environmental parameters simultaneously. This paper aims to oversee and indicate the possible implications of "2023 energy targets" of Turkey in a city-level energy modeling. In this perspective, the city of Burdur is selected as the target to implement the designated solar energy policy targets. With this paper, nationwide official targets are evaluated for a city level by calculating long-term effects and costs to the system. A total of 5000 MW of solar energy utilization target of Turkey has been reduced to the urban level for Burdur, depending on its share of solar energy, and then applied as an alternative scenario on the energy model developed for this analysis. The results show that the solar share of the total installed capacity of Burdur city will reach 49% while tripling the solar-based electricity generation in its energy mix. [ABSTRACT FROM AUTHOR]

This paper aims to provide a scientific approach that indicates the need to focus on renewable energy potential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy technologies a few years ago. Accordingly, the issue of determining the best renewable resources for the country has been brought to the agenda in recent years. The issue was considered to be a limited multi-objective optimization problem allowing us to achieve a reliable result. However, the parameter of each resource was considered as a range value, rather than traditionally expressed as an exact value, and a multi-objective decision problem was developed with an interval coefficient. This method allows us to obtain more accurate and reliable results without the need to resort to normalization methods used to eliminate unit differences. According to the results of this study, the most convenient alternatives for Turkey are hydro, wind, and solar power. The findings also support decision policies aimed at reaching targets for the electricity sector in 2023, as put forth by the Ministry of Energy and Natural Resources (MENR). • Multi-dimensional analysis of optimum renewable energy resources for a sustainable economy. • Selection of renewable energy sources under uncertainty. • More sensitive selection of resources by representing the instability in expert opinions with interval numbers. • The most optimum renewable energy resources for Turkey: "geothermal, solar, and wind.". [ABSTRACT FROM AUTHOR]

• Presenting of the solar PV potential in Turkey and its future prospects. • Performing the PESTLE analysis to study the solar energy potential in Turkey. • A comparative analysis of the solar energy potential using the PVsyst program. Renewable energy sources have a tremendous amount of potential in Turkey. In the previous year, 43.2% of the country's electricity was generated from renewable energy sources. Solar photovoltaic (PV) energy accounted for 4.7% of the electricity generation and the installed capacity was 9.425 GW with 9353 solar power plants of various types. This paper provides an overview of the current state of solar PV potential in Turkey, evaluates its capacity to meet the country's energy demand, and discusses its future prospects. The PESTLE analysis is performed based on political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) factors to study the sustainable development of solar energy potential in Turkey. Furthermore, a comparative analysis of the solar energy potential between the Konya and Diyarbakır regions which are recognized as promising areas for harnessing the country's abundant solar resources has been conducted using the PVsyst software. The electricity consumption in Turkey will be 380.2 TWh in 2025, 455.3 TWh in 2030, and 510.5 TWh in 2035, according to the Turkey National Energy Plan. Hence, it is essential to maximize the use of solar energy capacity in the production of electricity to meet the increased energy demand. The main objective of this study is to help strategic and systematic evaluation of the solar energy resource potential affecting large and small-scale solar power projects in Turkey. [ABSTRACT FROM AUTHOR]

This paper aims to assess the renewable energy capacity of Turkey in order to consider main priorities in the energy policy of Turkey. In this paper, renewable energy potential and regulatory conditions are discussed in Turkey in comparison with European Union. The results of the study implemented within the framework of EnviroGRIDS project indicated a promising yet very susceptible future for the implementation of renewable energy power plants in Turkey. The forecasts have shown that the solar power potential utilization is becoming more significant after 2020. The projections for 2050 indicate that electricity consumption from small and medium renewable energy sources including solar and wind will constitute 15% of the total, whereas the solar thermal will constitute around 16%. Geothermal and other renewables will remain around 3%. According to the high demand scenario, in 2050 the share of hydropower in overall electricity generation will be 12%, followed by solar power at 7% and wind power at 3%. Additionally, renewable energy policy and regulations in Turkey and in EU are overviewed in this study. On the contrary to EU, the constant feed-in tariff amount does not consider capital investments of specific energy sources in Turkey that brings disadvantage to the implementation. However, new regulations published and currently applied should be accepted as milestones in acquisition period of Turkey in EU. [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]

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]

Data created by typical weather year approaches, like typical meteorological year, are widely used in building performance analysis studies. In the literature, there are only two studies on typical weather year for Turkey. The present paper presents the generation of typical meteorological years for selected sites representing the distinct climatic zones of Turkey. The Finkelstein–Schafer statistical method was applied to analyze measured weather data which include mean, maximum and minimum daily dry-bulb temperatures, mean and maximum daily wind speeds, mean daily global solar radiation, and mean daily relative humidity. The results show that the difference between TMY and climatological averages is small for the selected sites. Monthly profiles of TMY and climatological averages are more similar for dry-bulb temperature and global solar radiation than relative humidity and wind speed. The results of this study may be used in building energy simulations, heating-cooling load calculations and solar energy studies for the selected sites. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10, 20, 30 and 40 °C, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc. [ABSTRACT FROM AUTHOR]