## 6 Conclusions  
Aiming at the problem that the aggregation scheme obtained in a longer time scale can not achieve good resource aggregation effect, this paper establishes a single-season DERs aggregation model. Firstly, the daily eigenvectors of DG are extracted, and the contour coefficients are introduced. According to the improved K-means algorithm, the multiple probability scenarios in a single-season are obtained. Then, considering the daily average cost, daily output, curve volatility and aggregation degree, a DERs aggregation model based on multi-scenario and multi-objective is established, and FMD-CBA is used to solve the model.  
According to the example analysis, the following conclusions can be obtained: (1) It is feasible to obtain scenarios with different probability by the clustering algorithm, and the typical output curves of WP and PV in each scenario have obvious differences. (2) The validity of the aggregation model based on single-season and multi-scenario is reflected in two aspects: one is that the aggregation results of different seasons are different, and the other is that the aggregation results of multi-scenario partition are better than that of single-scenario partition. (3) By choosing the Pareto solution sets through fuzzy compromise programming, an optimal compromise scheme can be obtained, which can provide a reference for planners.  
In future work, the risk loss caused by the uncertainty of DERs can be considered and it also can be planned as a constraint. Traditional power plants can be added to the aggregation model and the environmental impact of thermal power can be considered. In addition, the study of single RA can be extended to multiple RAs. Through the establishment of the corresponding index evaluation system, it will be determined that several RAs should be established in the whole region.  
The authors would like to acknowledge funding by the National Natural Science Foundation of China (51607068).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Multi-Scenario in Spring** & **Single-Scenario in Spring** & **Single-Scenario in a Year** \\ \hline Unaggregated resources & DG\({}_{1}\), DG\({}_{2}\), DG\({}_{5}\), DG\({}_{18}\), ES\({}_{2}\), ES\({}_{7}\), ES\({}_{12}\), ES\({}_{4}\), ES\({}_{17}\), DR\({}_{4}\) & DG\({}_{1}\), DG\({}_{5}\), DG\({}_{5}\), DG\({}_{18}\), ES\({}_{4}\), ES\({}_{10}\), \\  & ES\({}_{10}\), ES\({}_{12}\), ES\({}_{14}\), ES\({}_{17}\), DR\({}_{4}\) & BS\({}_{7}\), ES\({}_{12}\), ES\({}_{14}\), ES\({}_{17}\), DR\({}_{4}\) & DS\({}_{5}\), DG\({}_{10}\), DG\({}_{18}\), ES\({}_{4}\), ES\({}_{10}\), \\ Number of aggregated resources & 30 & 29 & 31 \\ Average daily cost (\(10^{3}\) USD) & 5.18 & 5.41 & 6.28 \\ Average daily output (MWh) & 46.5 & 43.01 & 50.23 \\ Curve volatility & 0.49 & 0.46 & 0.54 \\ Aggregation degree (MW/km) & 6.22 & 7.69 & 7.52 \\ Resource utilization rate & 0.78 & 0.73 & 0.76 \\ \hline \end{tabular}
\end{table}
Table 6: Single-scenario aggregation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Multi-Scenario in Spring** & **Multi-Scenario in Summer** & **Multi-Scenario in** & **Multi-Scenario in** \\ \hline Unaggregated resources & DG\({}_{1}\), DG\({}_{2}\), DG\({}_{5}\), DG\({}_{18}\), & ES\({}_{1}\), DG\({}_{1}\), DG\({}_{5}\), DG\({}_{5}\), DG\({}_{10}\), & DG\({}_{2}\), DG\({}_{5}\), DG\({}_{14}\) \\  & ES\({}_{2}\), ES\({}_{10}\), ES\({}_{21}\), ES\({}_{14}\), ES\({}_{12}\), ES\({}_{17}\), DR\({}_{4}\) & DG\({}_{18}\), ES\({}_{2}\), ES\({}_{10}\), ES\({}_{17}\), ES\({}_{18}\), DB\({}_{5}\) \\ Number of aggregated resources & 30 & 33 & 31 & 30 \\ Average daily cost & 5.16 & 6.33 & 6.05 & 5.74 \\ (\(10^{3}\) USD) & 5.16 & 6.33 & 6.05 & 5.74 \\ Average daily & 46.5 & 59.16 & 55.42 & 50.25 \\ Curve volatility & 0.49 & 0.53 & 0.51 & 0.5 \\ Aggregation degree (MW/km) & 6.22 & 7.32 & 7.01 & 6.73 \\ Resource utilization rate & 0.78 & 0.82 & 0.79 & 0.83 \\ \hline \end{tabular}
\end{table}
Table 5: Resource aggregation results in each season.