## 4 Research Needs and Trends in Wind Farm Design and Optimization
### Sub-Modeling of Adverse Factors  
A wide range of varying conclusions about the different effects of the adverse factors, described in Section 2.2, produce in the resultant optimized wind farms were provided in the literature. However, most of the conclusions are based on sub-models in which the effects of such factors were limited or oversimplified. In addition, the conclusions are commonly based on contrasts between pairs of factors that are usually in conflict. Only a few works were able to address the overall effects of including different factors [54, 55, 56, 199, 234, 278, 310]. Two general kinds of effects in the resultant wind farm layouts were observed; some factors (e.g., infrastructure costs) induce pressure to concentrate WTs in high dense irregular clusters within the wind farm area while other factors (e.g., wake effects,turbulence and noise) induce pressure to separate WTs. Both trends affect the optimal number of WTs and the wind farm size. These observed trends, however, are highly dependent on the chosen performance metric and further analyses are required to understand which factors, and under which conditions, dominate during the WFDO process. In addition, the inaccuracies and validity ranges of the common used sub-models may be influencing the shape of the obtained near-optimal solutions, thus the validation and extension of sub-models is considered the most important aspect to be improved in the development of the WFDO problem. Some of the most important modeling limitations are described next:  
Sub-models describing the impacts of WT wakes are limited, since crude assumptions about the wake expansion, wake merging, wake rotation, partial wake operation, wake meandering and 2-way interactions with large scale turbulence structures are commonly considered. State-of-the-art sub-models describing the structural degradation costs of the different WT members and the costs associated to O&M are still not validated, and rely on aero-elastic codes that simulate the loads that each component will undergo while operating, as described in Sections 3.2.2 and 3.2.6. Although the quantification of WT structural degradation is a complex task that depends on the local climatology, the particular WT design, and its operation, it is expected that some WT members (e.g., gear box, main shaft, and WT blades among others) will be more affected than others while operating. Thus, significant research is required to identify which components are the most affected, and under what conditions, and to improve the cost prediction of their degradation. The civil infrastructure cost models are significantly different comparing onshore and offshore wind farms as described in Sections 3.2.3 and 3.2.6. State-of-the-art infrastructure cost models for offshore wind farms consider all relevant factors during the design of the WT substructures and foundations (e.g., seabed depth, tower mass as a function of wind and wave loads among others). However, current existing models for onshore projects consider that foundation costs are proportional to the land bearing capacity. The inclusion of additional variables such as the expected WT loading and the type and capacity of the WT foundation might improve the civil infrastructure modeling in onshore sites. The most comprehensive civil and electrical infrastructure cost models are highly sensitive to time variations of the price of their constituent components. In addition, an important lack of modeled physics in power transmission was observed, which in turn affect the predictions of the effective expected electric energy conversion. An adequate combination of improved models describing electrical infrastructure costs, electrical losses, electrical components capacity and reliability is required. An important aspect that has been largely neglected is the topography of the wind farm area. The topography does not only affect the computation of the local wind field but also affects the development of other adverse factors such as noise, wakes, turbulence and electromagnetic fields.