## 5 Conclusions  
In this article, a comprehensive survey of aggregation modeling for wind farms was carried out. In wind speed modeling, component models can be used in conjunction with probability distribution models or time-series models when analyzing different characteristics of wind speed. In WTG modeling, a single model delivers satisfactory precision, low computational complexity, and low simulation time when modeling small-scale wind farms. However, when dealing with wind farms  
Figure 11: The equivalent circuit of the aggregated wind turbine generator (WTG) transmission system. consisting of different types of WTGs, or where WTGs work under obvious working condition differences, the single aggregation model may have big errors. The multi-WTG aggregated model should be used in these situations. The aggregation model has high precision when the parameters and working conditions of WTGs in a same aggregation group are close to each other. Meanwhile, when building an aggregation model of a wind farm with large spatial distribution, both circuit aggregation and the transformer aggregation should be considered. Aggregation modeling is an equivalent simplification for a wind farm, while its legitimate precision is maintained. Based on different aggregation purposes, different division methods of WTGs, simplified hypotheses, and the selection of aggregation types and algorithms can be applied, by which the precision of a wind farm aggregation model is influenced. With more simplified hypotheses and simpler WTG division, the aggregation model will have lower precision, as well as lower computational complexity. Otherwise, the aggregation of a WTG controller under different control strategies and working points is difficult, and the non-linear characteristic of power electronic components may also reduce the precision. Simulations in [71; 72; 73] showed that when an appropriate aggregation method is chosen, the precision of a wind farm equivalent aggregation model could be as high as 97% of the detailed model, while the complexity is greatly reduced. Meanwhile, the dynamic or transient characteristics of the wind farm for some specific incidents (such as low voltage ride-through behaviors or responses to low-frequency oscillation) are maintained in the aggregation model. Overall, the low complexity and easy application lead to the aggregation models being used more often, compared with detailed models. Apart from the typical research aforementioned, other techniques in wind farm aggregation modeling should be focused on in the future, such as the methods of actual data selection, the guarantee of the typicality and effectiveness of the collected data, the identification of the important parameters, the prevention of algorithms getting trapped in local optimizations, and the clustering methods considering the large disturbances, the low-frequency oscillations, or the subsynchronous resonance. Conceptualization F.L. and M.W. ; Writing--Original Draft, F.L. and W.Z. ; Writing--Review & Editing, J.M. and F.L. ; Investigation J.M. and W.Z. ; Supervision, M.W. ; Project administration, F.L. This research was funded by the Natural Science Foundation of Hunan Province of China grant number 2018J2529; by Natural Science Foundation of China grant number 61673398; by the Huxiang Youth Talent Program of Hunan Province grant number 2017 RS3006; by NSFC-RFBR Exchange Program grant number 6181101294. The APC was funded by the Natural Science Foundation of Hunan Province of China grant number 2018J2529. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. The following abbreviations are used in this manuscript: