## 1 Introduction  
With the increasing demand of electricity in the world and the serious problems of the energy shortage, environmental pollution and climate change, the shortcomings of traditional power generation are increasingly obvious [1; 2]. In this situation, distributed energy resources (DERs) have developed rapidly [3; 4; 5]. DERs are composed of distributed generation (DG), demand resource (DR), energy storage (ES), etc. DERs can improve the flexibility of the power system [6]. However, DERs are scattered, diverse and different in characteristics. It is difficult for a single DER to participate in the operation of the power market, so the resource aggregation technology has emerged [7; 8; 9]. In order to realize optimal allocation and efficient utilization of resources, aggregation can integrate DERs into the RA with simple and flexible regulation by certain technologies. Furthermore, RA can participate in the power market and grid operation as a whole. This paper aims to construct a RA by studying the resource aggregation model and improve resource utilization. The uncertainty of DG output should be considered when establishing the aggregation model. In [10], in order to deal with the uncertainty of renewable energy, a two-stage stochastic programming model is established. The K-means clustering algorithm is used to reduce the number of scenarios while maintaining the correlation between uncertain data. In [11], a scenario generation method based on Wasserstein distance measure and K-means clustering scenario reduction technology is proposed, which transforms the stochastic problem into the deterministic problem. In [12], the whole scenario sets are generated by discretizing the output of each time, and then the probability distance is used to reduce the scenario sets. RA is the aggregation of DERs, and there are different objective functions of the aggregation model. In [13], considering that ES has a great impact on the economy of RA, a two-level fuzzy stochastic expectation model is proposed to optimize the cost of ES, and the model is verified on a modified IEEE-33 test feeder. In [14], considering the comfort requirement and cost of DR, a mixed-integer linear optimization model is established, and the model can compute the optimal scheduling of a prosumer's assets. In [15], a strategy of load aggregation for central air conditioning is proposed to minimize the operating cost of load aggregator, and the particle swarm optimization algorithm is adopted to solve the optimal problem. In [16], considering the charging characteristics of EV, the aggregation of EV is realized with the objective of maximizing the profit of load aggregator. In [17], a mixed-integer linear programming model is established to maximize the profits of the virtual power plant, and the advantage of using the model to devise offering strategies becomes more apparent as the stochastic parameters become increasingly accurate. When using the clustering algorithm and multi-scenario to establish the model of DG output, the cluster object is 365 output curves in the whole year. The dimension of clustering vectors is generally 24, that is, the number of hours in a day [10; 11; 12]. For DG in multiple regions, the following issues are considered in this paper: (1) The dimension of clustering vectors will multiply with the increase of the number of regions, which can increase the complexity of the calculation. (2) The single aggregation scheme obtained in a longer time scale cannot achieve good resource aggregation effect. (3) It is necessary to objectively determine the cluster number. When establishing the aggregation models, most references focused on aggregating DERs of the same kind [13; 14; 16]. Although many kinds of DERs have been studied in some references, the objective function of the aggregation model is also simple [15; 17]. In the process of establishing an aggregation model, the following issues are considered in this paper: (1) Several realistic objective functions and constraints need to be proposed.