Abstract
Virtual power plants (VPPs) have become a revolutionary concept in both the IT sector and the energy industry, particularly in the context of integrating smart grids and renewable energy sources. These systems combine distributed energy resources, such as solar panels, wind turbines, battery storage and demand response systems operating as a single coordinated system. Data collection, transmission and processing systems for VPP operating modes are key components for their effective management and planning. They provide real-time monitoring, analysis and management of distributed energy resources. This operating principle enables VPPs to participate in electricity markets, provides additional services and enhances grid resilience. The paper is aimed at conducting a systematic analysis of modern data storage, processing and forecasting methods in the terms of virtual power plants including their effectiveness, limitations and applicability assessment in a dynamically developing energy system. Particular attention is paid to machine learning (ML) methods and optimization algorithms, which enable not only effective monitoring but also prediction of future loads and dynamics of renewable sources. Modern data storage solutions are considered and their advantages and limitations are evaluated. Papers describing key aspects of VPPs are also reviewed, including their data architecture, communication and information transmission systems, optimization methods and market integration, in terms of operational decision-making and ensuring high energy system resilience. The methods evaluation includes both theoretical aspects and practical examples of the technology application in the framework of research to improve the modern VPPs efficiency and reliability. This review was used to form promising directions for future research including the development of hybrid forecasting methods and the refinement of real-time heterogeneous data processing mechanisms.
Keywords
virtual power plants, data processing, data storage, forecasting, machine learning, distributed energy resources, smart grids
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