Renewable energy systems such as photovoltaic (PV) and wind energy systems are widely designed grid connected or autonomous. This is a problem especially in small powerful system due to the restriction on the inverter markets. Inverters which are utilised in these kinds of energy systems operate on grid or off grid. In this study, a novel power management strategy has been developed by designing a wind‐PV hybrid system to operate both as an autonomous system and as a grid‐connected system. The inverter used in this study has been designed to operate both on‐grid and off‐grid. Due to the continuous demand for energy, gel batteries are used in the hybrid system. The designed Power Management Unit performs measurement from various points in the system and in accordance with this measurement; it provides an effective energy transfer to batteries, loads and grid. The designed control unit provided the opportunity to work more efficiently up to 10% rate.

In recent years, PV power plants have been widely used on the roofs of commercial buildings with grid connections, primarily to enhance self-consumption in distributed energy systems. In addition, installing PV plants on commercial buildings’ roofs is becoming increasingly important, especially in crowded cities where land is limited. Since the Sun is an intermittent energy source, PV power plants cause frequency and voltage fluctuations in the grid. The way to avoid this problem is to install PV plants together with battery storage systems. Battery storage systems prevent frequency and voltage fluctuations in the grid and provide economic benefits. This article presents the sizing and techno-economic analysis of a factory building’s rooftop PV system with a battery. The amount of energy produced by the PV plant, PV temperature, and irradiation were recorded in a data logger obtained by various sensors. These real-time measurements were continuously collected and analyzed to evaluate system performance and assess seasonal variations.Load demand data were collected through an automatic meter reading system. The installed capacity of the PV power plant is 645 kW. The optimum battery capacity determined for this factory is 130 kW for 5 h. Techno-economic analysis was carried out using metrics such as the payback period, net present value, and levelized cost of energy. As a result of the analysis using various input variables, LCOE, NPV, and PBP were determined as 0.1467 $/kWh, 4918.3 $, and 7.03 years, respectively.

Nowadays, modern power systems are facing increasing complexity with the rapid growth of renewable energy sources (RESs), the expansion of small and distributed energy resources (DERs) and the evolution towards cyber-physical smart grids. These developments have made the need for intelligent and flexible systems to support the decision-making processes. Advanced large linguistic models (LLM), as a new generation of artificial intelligence (AI), can understand and process complex text data and have been proposed as an innovative solution in the energy industry. These technologies can create a fundamental transformation in power systems management and optimization. This paper analyzes in depth the application of LLM in power systems. The current research tracks the evolution of AI in this field from simple methods, based on basic rules, to more advanced technologies, such as deep learning and transformer models. Within the paper, the basic principles of transformer models are described, and three important capabilities of this technology are emphasized: the ability to interpret the results, the ability to expand for different applications, and the flexibility to face different conditions. The conducted analysis highlights how the new generation of AI can bring a fundamental transformation in the management of power systems.