## 1 Introduction  
According to the International Energy Agency (IEA), global grid-connected distributed PV capacity will reach 420 GW by the end of 2023, of which China's grid-connected capacity will reach 254 GW, the European Union 53 GW, and the United States 32 GW. In Denmark, Germany, and other European countries that focus on distributed wind power development, access to the distribution grid of wind power installed capacity accounted for approximately 86.7% of the country's total installed wind power; in China, the United States, and other countries that focus on centralized wind power development, the cumulative installed capacity of distributed wind power has reached 15.7 GW and 1.1 GW, respectively, and is increasing year by year. With the increasing penetration of DG, their inherent uncertainties and fluctuations will lead to issues such as excessive node voltage, increased network losses, and flexibility constraints on ADN, which cannot be effectively addressed by traditional network regulation methods [1]. SOP [2] and ESS [3] are effective solutions to mitigate these challenges. SOP can provide spatially flexible flow control, while ESS can address the temporal flexibility challenges associated with high penetration of DG [4]. Therefore, the key to resolving these issues lies in how to integrate SOP with ESS, design and coordinateplanning strategies effectively, and achieve mutual supplementation of flexible resources, all while balancing the flexibility and economy of ADN. In recent years, because the large-scale grid integration of DG with uncertainty affects the stable operation of power systems, scholars have conducted research on ADN flexibility. Reference [5] clarifies the definition of operational flexibility and provides a detailed explanation of flexibility supply and flexibility enhancement. Reference [6] analyzes the flexibility resources and introduces the principle of flexibility in supply-demand balance. Reference [7] constructs a flexibility indicator system with flexible resource analysis theory. Reference [8] meets the flexibility demand of the system to cope with uncertainty perturbation by reasonably scheduling flexibility of resource output and eliminates the security risks caused by insufficient flexibility. By reasonably scheduling the flexibility resources, it meets the flexibility demand of the system under the uncertainty disturbance and eliminating the safety hidden danger caused by the lack of flexibility. However, all of the above references are conducted from the perspective of flexibility resource operation optimization under the premise of flexibility resource allocation, without considering the rational planning of flexibility resources. Therefore, with the rapid development of distributed smart grids, it is necessary to study the reasonable and optimal allocation of flexible resources. SOP, as a network-based flexibility resource, can replace the traditional contact switch to achieve dynamic trend optimization and real-time power regulation in ADN. Reference [9] considered the application of network-type flexibility resource SOP at ADN level and constructed an optimal dispatch model with economy and flexibility as objectives. Reference [10] analyzed the network model and control mode of SOP in ADN and established a two-layer model in which the DG planning at the source side and the SOP operation at the network side are jointly optimized with alternating iterations. Reference [11] proposes an approach that considers the complementary nature of SOP and traditional regulation, which significantly improves the stable operation level and energy transfer capability of ADN. Reference [12] analyzes the multidimensional characteristics of the flexibility of ADN, and proposes a flexibility assessment model to quantify the flexibility resources based on SOP. ESS, as a kind of nodal flexibility resource, can realize ADN frequency regulation and peak shaving as well as reduce the output fluctuation of DG.