Integration of Renewable Energy Sources and Prospects for Power Grid Modernization

Abstract

This article is devoted to the analysis of modern methods and technologies for power grid modernization aimed at enhancing their resilience and efficiency through the integration of renewable energy sources. The study examines models of solar and wind generation, energy storage systems, and automated control systems based on advanced information and communication technologies. Simulation and data analysis were conducted to identify key performance indicators of renewable energy potential, assess the influence of weather conditions, and develop strategies for optimizing energy flows. Particular attention is given to improving system reliability and reducing energy losses through the implementation of automated monitoring and forecasting systems. Promising directions for the development of smart grids are also discussed, including the application of artificial intelligence and machine learning to increase forecasting accuracy and process automation. Within the framework of the study, models of solar and wind generation, energy storage systems, and automated control solutions were developed and tested, providing a comprehensive picture of the potential and prospects for the deployment of these technologies. The results demonstrate that the integration of renewable energy sources and intelligent technologies can significantly increase the share of renewables in energy consumption, reduce the environmental footprint, and ensure the sustainable development of the energy system. The adoption of intelligent technologies, storage systems, and forecasting tools is identified as a key driver for an efficient and sustainable energy future. The findings confirm the relevance of further research in the field of smart grids and energy storage development. The article also presents independent recommendations for the implementation of innovative solutions and highlights their potential for enhancing the efficiency of modern energy systems.