## 4 Research Needs and Trends in Wind Farm Design and Optimization
### Performance Metrics  
Previous research works [135,199] have demonstrated that the outputs of the WFDO process are highly sensitive to the choice of the objective function (_i.e._, there is high dependency of the best wind farm layout on chosen performance metric). However, a complete sensitivity analysis describing the effects of using different performance metrics and including different adverse factors during the optimization procedures has not yet been performed. In addition, a complete sensitivity analysis ofthe resultant optimized layouts considering different atmospheric conditions (e.g., for a wide range of inflow conditions and different thermal stratification conditions) has not yet been performed.  
The general trend of the optimized results obtained among the different approaches adopted in the literature is to install few and large capacity WTs, in contrast to installing many small capacity WTs, in irregular arrays in which wake effects are minimized. An interesting trend observed in all the reviewed works, without regard to the chosen objective metric and optimization procedure, is that significant improvements (in terms of the selected performance metric) can be obtained due to the application of systematic optimization methods over micro-siting methods based on rules-of-thumb.  
The energy yield alone has proven not to be the best objective function for practical purposes, since the net economic benefits obtained from the wind farm may not always increase monotonically with increasing converted energy. This is mainly due to the effects of wakes and turbulence on the power conversion and loading and/or by the required costs for the acquisition of WTs and the construction of civil engineering and electrical infrastructure. Therefore, the wind farm with the largest energy conversion does not necessarily have to be the one that produces the best economic profits. The need for all-encompassing design variables requires the costs to be computed besides the energy conversion yield. However, significant improvements in the traditional way to calculate the annual energy production of wind farms (that may include the inclusion robust multivariate probability density functions and the inclusion of additional factors such as turbulence, wind shear or WT dynamic loading) are required in order to improve the energy yield predictions and lower the impacts of risk and uncertainty on the expected economic returns.  
The most suitable performance metrics are the NPV and the FB, since the wind farm purpose is to produce the largest possible economic profit. The CoE alone cannot provide conclusions about the viability of a particular project, thus it should be incorporated as a complementary economic metric. The inclusion of additional finance metrics such as benefit cost ratio (the present value of all benefits divided by the present value of all costs), the internal rate of return (IRR, is the value of the discount rate that gives a NPV of zero), and discounted value indicators such as the payback period (the capital cost of the project divided by the annual average return) might improve the understanding of the absolute financial prospections in the WFDO problem and will include the possibility of performing additional analysis that might be necessary when considering different energy markets. In this regard, the inclusion of dynamic models for energy price setting and the integration of regulation models for energy marketing remain complex problems yet to be solved.