Urban wind energy consists of the utilization of wind energy technology in applications to the urban and suburban built environment. The paper provides some views on the progress made recently in the areas of wind resource assessment in the urban habitat; the utilization of suitable wind turbines for enhancing the exploitation of these resources; and the significant role of knowledge of building and urban aerodynamics for an optimal arrangement of interfacing augmented wind with its extraction mechanisms. The paper is not intended to be exhaustive, rather its purpose is to provide some views on the above-mentioned topics from the viewpoint of wind engineering and industrial aerodynamics in the context of buildings and cities.

There is a need to compare wind tunnel results with field measurement wind data in order to examine the validity of wind tunnel data in providing realistic estimates of urban wind energy potential and assess the probable errors involved. The paper refers to and discusses two Montreal building cases with different upstream roughness homogeneity. In the first case, field wind speed measurements are used to calculate the wind energy potential for a building with upstream rather homogeneous suburban type of terrain. In the second case, the building upstream terrain is very rough and highly nonhomogeneous. The calculated wind energy potential based on the field measurements was compared with the estimated value based on respective boundary layer wind tunnel data. In the first case, where the upstream terrain is homogeneous, the difference between the estimation of wind energy potential and the calculation using the field measurements is less than 5%. However, in the second case with the nonhomogeneous upstream terrain conditions, the difference between the estimation of the wind energy potential and the calculation using the field measurements is increased by up to 20%.

The problem of electrical power delivery is a common problem, especially in remote areas where electrical networks are difficult to reach. One of the ways that is used to overcome this problem is the use of networks separated from the electrical system through which it is possible to supply electrical energy to remote areas. These networks are called standalone microgrid systems. In this paper, a standalone micro-grid system consisting of a Photovoltaic (PV) and Wind Energy Conversion System (WECS) based Permanent Magnet Synchronous Generator (PMSG) is being designed and controlled. Fuzzy logic-based Maximum Power Point Tracking (MPPT) is being applied to a boost converter to control and extract the maximum power available for the PV system. The control system is designed to deliver the required energy to a specific load, in all scenarios. The excess energy generated by the PV panel is used to charge the batteries when the energy generated by the PV panel exceeds the energy required by the load. When the electricity generated by the PV panels is insufficient to meet the load’s demands, the extra power is extracted from the charged batteries. In addition, the controller protects the battery banks in all conditions, including normal, overcharging, and overdischarging conditions. The controller should handle each case correctly. Under normal operation conditions (20% < State of Charge (SOC) < 80%), the controller functions as expected, regardless of the battery’s state of charge. When the SOC reaches 80%, a specific command is delivered, which shuts off the PV panel and the wind turbine. The PV panel and wind turbine cannot be connected until the SOC falls below a safe margin value of 75% in this controller. When the SOC goes below 20%, other commands are sent out to turn off the inverter and disconnect the loads. The electricity to the inverter is turned off until the batteries are charged again to a suitable value.

This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.

This paper proposes a power-speed (P-V) model of the wind turbine by assuming three different functions for the first performance region; cubic, quadratic and uncorrected cubic. These three functions have been compared with the manufacturer models of five different wind turbines which were installed in five different locations in Jordan; Tafila, Hofa, Fujeij, Al Rajef, and Deahan. The wind turbine of these wind farms are considered as large scale HAWT in the range of Mw. The generated P-V models are developed by applying a new method described in this paper which is basically based on generating a multiplier factor x. In this study, the quadratic model shows the highest correlation compared with the other models. The wind energy yield for the selected wind farms has been estimated by a mathematical modelling based on Rayleigh distribution function, derived in this paper. The energy yield using this mathematical model has been compared with the measured energy output of four wind farms, Tafila, Hofa, Al Rajef, and Deahan. The measured energy were provided by the operators of these wind farms which are: Jordan Wind Project Company(JWPC), Central Electricity Generation Company (CEGC), Green Watts Renewable Energy (GWRC)and Korean Southern Power Company(KOSPO). Results show that the estimated energy using the quadratic wind turbine model for all wind farms are very close to the actual output. Accuracy analysis for the quadratic model resulted in an error of less than 10 % between the measured and estimated energy output for all wind farms. The capacity factors for the selected wind farms have been estimated using the quadratic P-V model. Results show that Tafila wind farm has the highest capacity factor which is around 47 % .