In recent years, integrating renewable energy sources (RESs) has achieved significant attention due to the growing demand for sustainable energy solutions. Inverter-interfaced Islanded Microgrids (IGs) have appeared as an advantageous approach to integrating RESs into the power grid. Grid-forming inverters (GFIs) are a critical component of IGs, and their synchronization is essential for stable and reliable operation. The literature has widely proposed soft transition, pre-synchronization, and re-synchronization to synchronize IGs to the main grid. However, methods for synchronizing GFIs in the islanded microgrid are restricted. Parallel operation of GFIs is required to guarantee the high power demand of IG and improve voltage-frequency stability. For parallel operation, GFIs must be synchronized with each other. In the conventional synchronization control systems that are highly nonlinear, the linear proportional-integral (PI) controllers are commonly used in synchronization loops without considering the nonlinearity resulting from the initial condition dependency and cross-coupling. Thus, conventional synchronization methods can be exposed to concerns of stable operation, narrow operation area, and performance degradation. This study proposes a new linearized synchronization control system for GFIs in IGs. In this way, it is possible to analytically design robust linear controllers and ensure a stable operation, high performance, and wide (full) operation area. In addition, a new soft-commissioning method is proposed to deactivate synchronization loops and soft-start the synced GFI. The proposed system has been tested in real-time and CHIL hardware setups for two 550 kW GFIs operating in parallel, and the results in the perfect agreement are presented in this study.

The production of electric and hybrid vehicles on land and at sea is now widely used to reduce carbon emissions. While charging the batteries of electric vehicles can be done manually, studies to automate this process are increasing. Recently, many studies based on computer vision have been carried out to provide real-time and more accurate detection of charging systems. For automatic charging, the position and distance of the socket on the ship approaching the shore can be determined by processing the image taken by the camera. For this purpose, in this study, an interface has been developed for the detection system of electrical charging system sockets by using classical image processing and the YOLO technique, which is one of the deep learning methods. With the developed interface, the socket's position can be detected and monitored in real-time through the image taken from the camera. Thus, automatic charging can be performed successfully.

Maritime transport, responsible for over 90% of global trade, is a significant contributor to greenhouse gas emissions due to its reliance on fossil fuels. Achieving international decarbonization targets set by the International Maritime Organization and the European Green Deal requires not only advancements in vessel technologies but also the sustainable transformation of port infrastructures. In this context, this study examines current trends in shore-to-ship (STS) charging technologies as a key enabler of sustainable port electrification. It provides a comprehensive review of AC, DC, and hybrid charging architectures, along with recent developments in robotic and autonomous charging systems. The study also explores industrial standards and energy management strategies for smart port applications, and identifies renewable integration and grid interaction models as key directions for future research. Furthermore, the study proposes an Onshore Power Supply (OPS) Readiness Index (OPSRI), a multi-criteria assessment framework that evaluates ship types based on their operational, technical, infrastructural, and regulatory suitability for onshore power integration. The main contribution of this review lies in integrating international standards, robotic charging technologies, and ship-type-specific readiness assessment into a unified analytical framework. The results indicate that ferries and tugboats are highly suitable for OPS deployment, while long-haul cargo vessels face greater limitations under current conditions. Overall, the findings offer practical insights for port authorities, policymakers, and energy planners, highlighting key challenges and future directions toward smart, sustainable, and low-carbon port ecosystems.