## 1 Introduction
### Literature Review  
The planning and design of DES has been widely studied because of its complex structure, including energy stations, transmission networks, and buildings [6]. The planning and design of DES is divided into two parts: energy station location and transmission network optimization.  
At present, many studies have been carried out on the optimization of the pipe network layout of DES. The network connects energy stations and consumers, and its performance is affected by factors such as location, material, diameter [7], thermal insulation, and heat loss [8]. [PERSON] et al. [9] established the cost model of the energy pipe network, considering the influence of the change in cooling and heating load on the operation cost of the pipe network, and taking the minimum cost of pipe network construction as the objective. The genetic algorithm was used to solve the problem of pipe diameter optimization. By combining the genetic algorithm (GA) with local search technology, [PERSON] et al. [10] proposed an algorithm to find the optimal layout and pipe diameter of a regional cooling network in a given candidate location. To minimize the total life cycle cost, [PERSON] et al. [11] proposed a Prim algorithm with a dynamic flow weight set to solve the cost problem, and optimized the pipe network layout based on dynamic minimum spanning tree and optimal path. [PERSON] et al. [12] used the minimum spanning tree to determine the layout of the transmission network. [PERSON] et al. [13] focused on transforming the PNs' layout into the flow distribution in the pipeline route for selection, in which a PN's layout method was proposed based on the nonlinear programming model. The studies mentioned above in the literature usually constructed linear models to optimize low span networks, but their adaptability was often limited for nonlinear factors. In addition, the shortest path method was a common path search method, such as Astar search algorithm [14] and Dijkstra algorithm [15]. The shortest path algorithm cannot consider the coupling relationship between users. Therefore, it was difficult to obtain the optimal solution.  
In the actual design, the optimal station is coupled with the network topology, so the location of the energy station must be optimized before the pipe network is optimized. However, there is still little research on the collaborative planning of energy stations and pipe networks. [PERSON] [16] put forward the concept of \"energy distance\". Taking the minimum energy distance as the objective function, combined with graph theory, the station-network layout optimization model of a regional energy system is established. However, this method must exhaust all alternative energy station combinations. [PERSON] et al. [14] determined the location of an energy station and energy storage station by kernel density estimation, and the shortest path method was used to optimize the layout of the pipe network. [PERSON] et al. [17] selected the DES site in a specific region through multi-objective analysis. [PERSON] et al. [18] employed the minimum spanning tree to obtain the DES site. [PERSON] et al. [19] put forward a decentralized optimization and comprehensive decision-making planning strategy, and used the number of energy stations to be built, the coverage rate, and the transmission load capacity of pipeline network as three objective functions. In addition, a heuristic random search method was used to determine the location of the energy station with the minimum transmission loss [20]. However, the above studies determined the sites in the candidate sites; this is not applicable when it is difficult to determine the site of the station to be selected in advance. Further, energy networks were not arranged along the road network.