This work focuses on the design and analysis of wind flow modifier (WFM) modeling of a vertical axis wind turbine (VAWT) for low wind profile urban areas. A simulation is carried out to examine the performance of an efficient low aspect ratio C-shaped rotor and a proposed involute-type rotor. Further, the WFM model is adapted with a stack of decreased diameter tubes from wind inlet to outlet. It accelerates the wind velocity, and its effectiveness is examined on the involute turbine. Numerical analysis is performed with a realizable K-ε model to monitor the rotor blade performance in the computational fluid dynamics (CFD) ANSYS Fluent software tool. This viscous model with an optimal three-blade rotor with 0.96 m2 rotor swept area is simulated between the turbine rotational speeds ranging from 50 to 250 rpm. The parameters, such as lift–drag coefficient, lift–drag forces, torque, power coefficient, and power at various turbine speeds, are observed. It results in a maximum power coefficient of 0.071 for the drag force rotor and 0.22 for the lift force involute rotor. Moreover, the proposed WFM with an involute rotor extensively improves the maximum power coefficient to an appreciable value of 0.397 at 5 m/s wind speed, and this facilitates efficient design in the low wind profile area.

This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.