Optimization and Integration of Vertical Axis Wind Turbines in Hybrid Renewable Energy Systems: A Systematic Review
Keywords:
Vertical Axis Wind Turbine, Hybrid Energy System, Aerodynamic Optimization, CFD, Artificial Intelligence, PRISMA, Renewable EnergyAbstract
Vertical axis wind turbines (VAWTs) are also receiving a new wave of interest as an effective element of a hybrid renewable energy system, especially in situations with low-wind and turbulent conditions where horizontal axis wind turbines (HAWTs) fail to perform effectively. This is a systematic review that evaluates the aerodynamic optimization methods, control methods, and integration systems used in VAWTs within hybrid renewable energy systems, and determines the performance trends, challenges, and gaps in the research. Based on Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA), 80 peer-reviewed articles (2018-2025) were collected on the platforms of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and MDPI. The quality of studies was evaluated in terms of the validation of simulations, the strength of an experiment, and the relevance of scaling. The efficiency was enhanced by up to 1020 percent through innovations in aerodynamics, including helical blades, tailored solidity ratios, and computational fluid dynamics (CFD)-based optimization, which reduced torque ripple and improved low-speed performance. Incorporated into hybrid systems — particularly PV-VAWT, wind-hydro, and wind-battery systems —capacity factors improved by 20-35 percent, and the levelized cost of energy (LCOE) dropped to as low as 18 percent. The optimization strategies based on aero science and AI together lead to improved VAWT performance, its reliability, and flexibility. To ensure scalability and alignment in terms of policy, future studies ought to focus on CFD-AI coupling, standardized testing protocols, and compatibility with smart grids to promote the development of renewable transitions in the world.
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Copyright (c) 2025 Sarmeje C.P, Medugu D.W, Ali Danladi

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