Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey’s energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.

The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources

Photovoltaic technology harvest electrical energy by stimulating liberated electrons within the semiconductor layers using solar radiation. Photovoltaic technology produces electrical energy by collecting electrons that are liberated in a semiconductor pn-junction by solar radiation. Photovoltaic solar cells have layered semiconductor structures and this study utilised for this objective. Current researches on energy storage with solar cells, focused to optimise the utilisation of the generated energy with cell efficiency. This study offers a thorough analysis of the energy efficiency of solar cells based on their infrastructures. The study involved obtaining computational visuals and doing efficiency verification. This was done by comparing the impact of different chemical structures on energy production. The MATLAB software was used with fixed parameters and varying efficiency. The results show that the Monocrystalline N-Type IBC model exhibits the maximum efficiency in terms of PV cell structure. The MIBC structure is more efficient than polycrystalline cells and also standard monotypes with high temperatures. This allows the cell to reflect itself and passivise the cell base, resulting in a 5% or more increase in energy production. Standard monotype cell has %16.2 efficiency and Monotype IBC has %20.1 efficiency results achieved with PVsyst and Matlab softwares. The results of the calculations were applied in real time and confirmed by testing the impact of structural differences on efficiency with real climate data

Smart grids bring more electrical equipment into cities, so the fields around this equipment need to be checked. Electric field strength (E) and magnetic flux density (B) in an underground transformer room, under a 380 kV, 50 Hz transmission line and near low and medium voltage distribution lines measured and expressed each reading as an exposure ratio (ER), i.e. as a percentage of the corresponding ICNIRP public reference level. The largest values were found 1 m above ground below the center conductor of the 380 kV line, where E was 1.396 kV/m (ER 27.9%) and B was 4.191 µT (ER 4.2%). For the same line, a three phase Biot–Savart expression came within about 4% of the measured B, while an ANSYS Maxwell model gave values 7%–21% above the analytical estimate over the current cases studied. On the risk scale adopted here, most locations fell into Category I or II. The transmission corridor was placed in Category III because of its electric field, even though no reading exceeded an ICNIRP reference level. The comparison also shows that the dominant field component differs between sources: E under the 380 kV line, B next to the transformer.

The use of solar energy holds significant importance within the context of smart grid infrastructure, which is specifically designed to promote environmental preservation and long-term sustainability. Solar collectors are integral components of solar energy systems. Consistent maintenance of solar collectors will enhance energy efficiency and mitigate the use of energy resources. The aforementioned studies hold significance in the context of transitioning to a smart grid, particularly for developing nations. They provide light on how the existing energy production framework may facilitate the transformation of the conventional grid into a smart grid. This study focused specifically on the investigation of solar energy systems utilised as thermal energy sources. The energy efficiency of solar air collectors is diminished because of their low specific heat capacity and least expensive facilities to supply warm air for various applications. In addition, this study emphasizes investigating the enhancement of collector efficiency using double-layer glass coated collectors including arch-shaped obstacles. Three collectors were constructed with distinct features. The first collector, denoted as SC120, was designed without any obstacles and featured a double-layer cover as the witness. The second collector, referred to as SC121, incorporated obstacles with a width of 4 cm along with a double-layer cover. Lastly, the third collector, designated as SC122, included obstacles with a width of 7 cm arranged in a different configuration, also accompanied by a double-layer cover. A blower was installed in each collection to facilitate the forced air flow directed towards the absorbent material, ensuring that the volumetric flow rate remained consistent throughout all collectors. The trials were conducted over a span of five consecutive days, from 9 a.m. to 6 p.m., in an outdoor setting throughout the summer season. The findings indicated that the thermal efficiency of SC122 exhibited a 17% increase compared to SC121, and a 35.7% improvement relative to SC120. The presence of obstacles has a notable impact on thermal efficiency, with rough barriers leading to increased efficiency due to their ability to enhance the rate of heat absorption from the absorbent plate by the blown air.