In this paper frequency response, one of the most critical ancillary services, provided by grid-scale battery energy storage systems is studied with manipulating the optimum battery state of charge control area. An algorithm has been developed that primarily provides frequency response service and at the same time optimizes the battery state of charge. The difference between the algorithm developed in this study and the other algorithms is that, apart from the maximum and minimum points of the state of charge level, it has also the optimum highest and optimum lowest state of charge points. Thus, it is aimed that the battery will support the grid at the most extreme points in urgent cases and decides the power output it will provide according to the battery state of charge in the case of non-urgent cases. The algorithm used the one-second resolution 1-day frequency data which has the highest standard deviation in a month obtained from Turkey National Transmission System Operator's database as input data. Assuming a facility in Turkey which has a battery energy storage system that participates in the grid frequency response ancillary service, the output power and the battery state of charge graph are examined.

The electricity demand associated with charging electric vehicles (EVs) is significant and is rapidly expanding in terms of quantity, power capacity requirements, and waveform distortion. This growth is particularly critical regarding deploying EV charging infrastructure on the public low-voltage distribution networks, where the available short-circuit power may be limited, creating a risk of sensitive load coinciding within the same area of the grid. The current standards address these issues but predominantly focus on a certain frequency range; therefore, the high-frequency range needs extension and improvement. This article proposes a modeling technique for predicting the effects of conducted emissions (CEs) caused by a single-phase AC–DC converter for a grid-to-vehicle application within a single-phase system. This work includes a measurement design technique and exhibiting standard limits for conducted noise-separated signals into common mode and differential mode components while presenting a comprehensive investigation of the electromagnetic compatibility issues for a grid-connected, bidirectional converter operating at 4.4 kW and 18 kHz switching frequency. For the investigation, the converter is modelled in MATLAB/Simulink, and a statistical approach in Minitab software is also used to evaluate the circuit and frequency domain data. The developed model is also used to investigate the impact of a battery’s state of charge on CEs. The presented results show that the proposed emission standards are exceeded by a bidirectional converter under nonlinear load conditions.

The increasing global population, combined with the impacts of climate change and global warming, has made the production of high-quality agricultural products at lower costs a critical necessity. Addressing these challenges requires leveraging emerging technologies. This study presents a prototype system that integrates digital twin technology into greenhouse farming. A physical greenhouse was equipped with a Raspberry Pi and various environmental sensors to collect real-time data. This data is visualized and managed through a Unity 3D-based digital twin, enabling automated control via rule-based decision logic. Monitored parameters include temperature, humidity, light intensity, and soil moisture, which dynamically trigger irrigation, ventilation, and lighting mechanisms. The system was tested over a seven-day period under different lighting scenarios. Results showed consistent system performance, demonstrating its viability for educational use and small-scale agricultural applications. Furthermore, the flexible architecture of the system suggests potential for future extension toward predictive modeling and adaptive control strategies.