## 1 Introduction
In Reference [15], using the minimum operating cost of 24-h VPP as the objective function, the teaching-learning based optimization (TLBO) heuristic algorithm is proposed to solve the model. In Reference [16], after considering the electric boiler in the VPP operation mode of the integrated thermoelectric system, a multi-objective model is established, including the carbon emission cost, the total operating cost of VPP, and the transaction electricity between the VPP and the main transaction power. In Reference [17], a mixed-integer linear programming model is proposed, which targets the maximum weekly profit of VPP and is subject to long-term bilateral contracts and technical conditions. In Reference [18], the actual location and specific functions of each DER in the public network can be considered, and a large-scale VPP optimization model has been established. In Reference [19], the technical VPP (TVPP) takes into account the real-time influence of the local network on the DER-aggregated profile. In Reference [20], for EES units with integer constraints, a mixed integer linear programming (MILP) algorithm, which takes into account the grid constraints, is proposed. Based on the above-mentioned references, the research on the related scheduling of VPP is more mature. However, there are few studies on how to construct VPPs. The disadvantages and deficiencies of the aforementioned references are as follows: the types of involved DERs are few, the differences between different types of DERs in different functional areas are less considered, the complementarity between various DERs is less considered, and the proposed construction indicators are also relatively single. In addition, the geographical information and resource characteristics are rarely combined to consider. Combined with the existing research results and shortcomings in the references, the contributions of this paper are as follows: (1) the idea of decision area division for determining decision variables is proposed. (2) A multi-objective VPP construction model with low daily average costs, fine load characteristics, a high degree of DG consumption and a high degree of resource aggregation is established. (3) The complementary constraints between various DERs have been considered and a fusion space distance that accounts for geographical information and load density has also been introduced. (4) The improved bat algorithm based on the preference degree is used to solve the multi-objective 0-1 programming model. Therefore, the purpose of this paper is to propose a reasonable plan for selecting distributed generation (DG), energy storage (ES) and demand response (DR). The rest of this paper is organized as follows: in Section 1, some kinds of DERs are introduced, the decision area according to the relevant criterion is divided and then the decision variables are also determined. In Section 2, a multi-objective VPP construction model based on decision area division is established and various constraints including geographic information are also considered. In Section 3, an improved bat algorithm based on the preference degree, which is to solve this multi-objective 0-1 programming model, is introduced. In Section 4, the example analysis are conducted. In Section 5, summary and relevant analysis are given.