## 5 Conclusions and Future Work  
The wake effect is among the major concerns in wind farm control, because it influences the overall power production. To develop and test wind farm control strategies, developing an accurate dynamic wind farm model is essential to capture the varying conditions and wake effects occurring at wind farms. Such a dynamic wind farm model, i.e., WFSim, developed by Delft University of Technology, is reported in this paper. To reduce the wake effect, active wake control methods such as WRC and AIC are used. The goal of this work is to design a wind farm controller that incorporates WRC and AIC to maximize the wind farm power output. The wake effect on a virtual two-turbine wind farm and a real-life offshore wind farm is explored in this study using WRC, AIC, or both. In the two-turbine wind farm, without and with WRC, the overall power production is compared for various distances between turbines. The overall power output by the virtual two-turbine wind farm demonstrates improvement in power when the WRC is in operation. It is also worth noting that the power output improves further when the spacing between the turbines increases from 3D to 10D. In the real-life wind farm, WRC or AIC or both methods are also applied to reduce wake and thereby enhance the wind farm power output. In this paper, constant wind directions are assumed, but in the future, more realistic, varying wind directions will be considered. For determining the optimal value of the yaw angle or axial induction factor, a power production optimizer is used, which considers ambient wind conditions, wind farm layout, wake effect, wind direction probability of occurrence, and so on. When WRC or a combination of WRC and AIC is applied to the real-life wind farm model, the simulation results show an increase in power. The results show a 17% increase in power production. An optimization-based wake redirection strategy tested on an operational wind farm in [16] has shown a power increment of 7-13% for site average wind speed and 28-47% for low wind speed. In this work, the percentage power increment is about 17% for the site average wind speed (6.85 m/s). It is also worth noting that when WRC is used, the power produced by upstream turbines is lowered, but the performance of the remaining downstream turbines improves, resulting in a rise in overall wind farm power production. As demonstrated in [17], AIC resulted in a less significant increase in power than WRC; that is, the result show that WRC is more effective than AIC. In the future, all possible wind directions need to be considered to analyze the effectiveness of these control strategies more realistically. These control strategies may have a negative effect on structural loadings, which should be thoroughly investigated. The study carried out in [35] illustrates that the turbulence intensity and atmospheric stability conditions have a significant impact on wind farm power production, and these factors will also be taken into account. Moreover, the real-time adaptive optimization discussed in this paper will be improved further to be more suitable for larger wind farms. Finally, the real-time optimization algorithms will be validated using SOWFA followed by actual wind farm data that will be available towards the end of this ongoing project. Conceptualization, S.-H.H. and R.K.B. ; methodology, S.-h.H. ; software, R.K.B. ; validation, S.-h.H. and R.K.B. ; formal analysis, S.-h.H. ; investigation, R.K.B. and S.-h.H. ; resources, S.-h.H. and R.K.B. ; data curation, R.K.B. ; writing--original draft preparation, R.K.B. and S.-h.H. ; writing--review and editing, R.K.B. ; visualization, S.-h.H. and R.K.B. ; supervision, S.-h.H. ; project administration, S.-h.H. ; funding acquisition, S.-h.H. All authors have read and agreed to the published version of the manuscript.