## 5 Conclusions  
This paper proposed a DES optimization model to determine the locations of energy stations, energy supply range, and distributions of networks to minimize the total annual cost of DES. The method mainly included the design process of determining the locations of energy stations with the help of the Voronoi diagram and the optimization process of \"relative-load-distance\". The feasibility and reliability of the method were verified by a case study. The following conclusions were obtained. Firstly, based on the Voronoi diagram and improved P-median optimization method, the station location optimization method can not only find the locations of energy stations in the new planning of DES, but also realize the optimal locations of energy stations according to the spatial distribution of load. This method can be applied to planning scenes in which it is difficult to pre-determine the sites to be selected, so as to enhance the concentration of energy stations and pipelines in the region. Secondly, considering the load characteristics in the division of energy supply range will slightly increase the construction length of the pipeline, but can effectively improve the utilization efficiency of the pipe network, so as to improve the concentration of load in the region, and then improve the economy of the station-network layout scheme. Finally, the proposed method is verified and compared by vertical and horizontal comparison. Compared with the minimum distance principle, relative-load-distance can correctly deal with the relationship between the cost of the energy station and the cost of the network, and reasonably divide the ownership of users, so as to reduce the total cost by 0.92%. In addition, the sensitivity analysis shows that the coefficient \(k\) in relative-load-distance significantly affects the optimal scheme. In the actual planning, the load characteristics in the region should be comprehensively evaluated to reduce the loss. It should be noted that the accurate locations of the energy stations cannot be determined only by the optimization results of the method proposed in this paper. We also need to consider the geographical conditions, environment, resource distribution, and other factors in the station network planning in DESs. In future research, the scope of optimization should consider different types of customers in each node and the location of the node can also be considered as a variable, which will add more dimensions to the optimization problem. Conceptualization, Y.R. and J.Y. ; methodology, J.W. and J.Y. ; software, F.Q. and J.Y. ; validation, H.M., F.Q. and J.Y. ; formal analysis, H.M. and J.Y. ; investigation, J.W. and J.Y. ; \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Value**} & **Cost of Energy Stations** & **Cost of Pipelines** & **Total Cost** \\  & **(10\({}^{6}\) RMB)** & **(10\({}^{6}\) RMB)** & **(10\({}^{6}\) RMB)** \\ \hline \(k=3\) & 953.82 & 13.66 & 967.48 \\ \(k=4\) & 961.82 & 14.13 & 975.95 \\ \(k=5\) & 962.12 & 14.39 & 976.52 \\ Scheme 1 & 962.90 & 13.57 & 976.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results under different \(k\) values. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimum dynamic distribution network reconfiguration using minimum spanning tree algorithm. In Proceedings of the 2017 5 th International Conference on Electrical Engineering--Boumerdes (ICEE-B), Boumerdes, Algeria, 29-31 October 2017. [CrossRef]
* _[PERSON] (2008)_ [PERSON]; [PERSON] Optimisation of tree path pipe network with nonlinear optimisation method. _Appl. Therm. Eng._**2008**, _29_, 1584-1591. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Novel planning methodology for energy stations and networks in regional integrated energy systems. _Energ. Convers. Manag._**2020**, _205_, 112441. [CrossRef]
* _[PERSON] et al.