PESS-considered rural power distribution network static change-over switch locating and sizing method

Abstract

The invention discloses a rural power distribution network static change-over switch locating and sizing method considering PESS. The method comprises the following steps: establishing an upper-layer static change-over switch annual comprehensive cost optimization model by taking static change-over switch annual comprehensive cost minimization as a target function and taking the static change-over switch installation number and static change-over switch equipment current as constraint conditions; establishing a lower-layer power system stabilizer operation strategy optimization model taking rural power distribution network three-phase unbalance degree operation minimization as a target function and taking three-phase power distribution system operation constraint, static change-over switch equipment operation constraint and mobile energy storage system micro-grid operation constraint as constraint conditions; and forming a static change-over switch planning-operation double-layer model by the upper-layer model and the lower-layer model, solving the static change-over switch planning-operation double-layer model by adopting a double-layer hyper-parameter alternate iteration mode, and determining a rural power distribution network locating and sizing optimal scheme. According to the method, the problems that the convergence speed is low and local optimum is easily caused in the aspect of rural power distribution network STS locating and sizing are solved.

Description

Technical Field

[0001] The present invention relates to a method for site selection and capacity determination of static transfer switches in rural distribution networks, and in particular to a method for site selection and capacity determination of static transfer switches in rural distribution networks taking PESS into consideration. Background Art

[0002] The core of site selection and capacity determination for rural microgrid photovoltaic storage complementary systems is the accurate prediction of photovoltaic power generation and the reasonable configuration of energy storage capacity. The University of Catania and other research institutions have developed a prediction model based on solar radiation intensity, which aims to predict the output power of photovoltaic systems by establishing a solar radiation model and using historical climate data to predict solar radiation values. Although this method relies on a large amount of detailed meteorological data, the improvement of prediction accuracy increases the complexity of the model and the amount of data required. These factors increase the cumbersomeness of the prediction process and affect its convenience in practical applications. The 2008 International Conference on Electrical Machines and Systems proposed a prediction method based on Markov chains, which predicts the output of photovoltaic systems in the short term by constructing a transfer matrix. Although this method is relatively simple and has certain practical value, it ignores the natural variation of photovoltaic output power with sunrise and sunset, and regards the state transition throughout the day as a uniform process, resulting in limited prediction accuracy.

[0003] In addition, the research of various universities has further expanded the depth and breadth of research in this field. Research from Hefei University of Technology shows that cluster planning can promote the self-balancing consumption of renewable energy, thereby improving the economy and reliability of the system. However, the existing cluster division methods are mostly based on fixed scenarios and load characteristics, without considering the changes in system interconnection switches, and there is room for comprehensive improvement under dynamic topology and changing loads. Nanchang University optimized the coordinated control strategy of distributed power sources such as wind, solar and water through adaptive genetic algorithms for energy storage capacity, equipment installation and operation and maintenance costs, and demonstrated the optimization of the configuration capacity and operation mode of the energy storage system. Research from South China University of Technology, Shanghai Jiaotong University, Zhejiang University and Tianjin University explored the problems of microgrid optimization configuration, energy storage site selection and capacity determination, and multi-microgrid system planning from different perspectives, and applied heuristic algorithms such as particle swarm optimization and genetic algorithm. Although these methods have improved the efficiency of planning and operation to a certain extent, they also have problems such as slow convergence and easy to fall into local optimality. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for site selection and sizing of static transfer switches in rural distribution networks taking into account PESS, in order to overcome the shortcomings of the prior art and to improve the accuracy of photovoltaic power prediction and planning results, thereby improving the system voltage level and the autonomy of each cluster during the operation phase.

[0005] The technical solution adopted by the present invention is: a method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS, characterized in that it includes the following steps:

[0006] 1) Establish an annual comprehensive cost optimization model for upper-level static transfer switches: Considering the annual comprehensive cost of static transfer switch installation and maintenance, the line loss cost of rural distribution network, the aging cost of three-phase imbalance of distribution lines, and the cost of abandoned light in rural distribution network, the annual comprehensive cost optimization model for upper-level static transfer switches is established with the minimization of the annual comprehensive cost of static transfer switches as the objective function and the number of static transfer switches installed and the current size of static transfer switch equipment as constraints;

[0007] 2) Establish an optimization model for the operation strategy of the lower-level power system stabilizer: Considering the factors of three-phase imbalance in the system and the switching of mobile energy storage systems, establish an optimization model for the operation strategy of the lower-level power system stabilizer with the objective function of minimizing the three-phase imbalance of the rural distribution network, and with the operation constraints of the three-phase distribution system, the operation constraints of the static transfer switch equipment, and the operation constraints of the mobile energy storage system microgrid as constraints;

[0008] 3) A static transfer switch planning-operation two-layer model is constructed by the upper-layer static transfer switch annual comprehensive cost optimization model and the lower-layer power system stabilizer operation strategy optimization model. The two-layer hyperparameter alternating iteration method is used to solve the static transfer switch planning-operation two-layer model to determine the optimal solution for the siting and sizing of the rural distribution network.

[0009] The method for site selection and capacity determination of static transfer switches for rural distribution networks considering PESS of the present invention solves the problems of slow convergence speed and easy falling into local optimum in the site selection and capacity determination of STS in rural distribution networks in the prior art. The present invention establishes a planning-optimization double-layer model for static phase-changing switches, that is, an upper-layer STS annual comprehensive cost optimization model with the goal of minimizing STS investment and maintenance costs, line loss costs and three-phase unbalanced costs, combines PESS with its operation scheduling, establishes a lower-layer PSS operation strategy optimization model with the goal of minimizing the abandoned light rate, and introduces a hyperparameter alternating iteration method for solving, thereby achieving the goal of reducing three-phase imbalance and photovoltaic consumption. On the one hand, the present invention can save the number of static phase-changing switches installed, and on the other hand, it can also achieve three-phase imbalance management. The method proposed in the present invention improves the accuracy of photovoltaic power prediction and the accuracy of planning results, thereby improving the system voltage level and the autonomy of each cluster in the operation stage, providing convenience for the regulation and management of the distribution network, and solving the problems of slow convergence speed and easy falling into local optimum in the site selection and capacity determination of STS in rural distribution networks in the prior art. The effect of the present invention is verified by simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1It is a flow chart of the method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS of the present invention;

[0011] Figure 2 Schematic diagram of the hyperparameter alternating iteration method used in the present invention;

[0012] Figure 3 is a structural diagram of the IEEE-13 node system of the present invention;

[0013] Figure 4 It is a schematic diagram of comprehensive cost changes;

[0014] Figure 5a is the three-phase current value of scenario S1;

[0015] Figure 5b is the three-phase current value of scenario S2;

[0016] Figure 5c is the three-phase current value of scenario S3;

[0017] Figure 5d is the three-phase current value of scenario S4;

[0018] Figure 6 It is the three-phase imbalance curve diagram of four scenarios. DETAILED DESCRIPTION

[0019] The following is a detailed description of the method for site selection and capacity determination of static transfer switches in a rural distribution network considering PESS according to the present invention, in conjunction with the embodiments and drawings.

[0020] The method for site selection and sizing of static transfer switches in rural distribution networks considering PESS of the present invention firstly establishes an upper-layer STS annual comprehensive cost optimization model with the goal of minimizing STS investment and maintenance costs, line loss costs and three-phase imbalance costs, and combines PESS with its operation scheduling; secondly, establishes a lower-layer PSS operation strategy optimization model with the goal of minimizing abandoned light rate; finally, introduces a hyperparameter alternating iteration method for solving, thereby achieving the goals of reducing three-phase imbalance and photovoltaic absorption, improving the photovoltaic power prediction accuracy and the accuracy of planning results, and further improving the system voltage level and the autonomy of each cluster in the operation stage, providing convenience for the regulation and management of the distribution network.

[0021] The present invention considers the site selection and capacity determination method of the static transfer switch of the rural distribution network of PESS, such as Figure 1 The following steps are shown:

[0022] 1) Establish an annual comprehensive cost optimization model for the upper-level static transfer switch (STS): Considering the annual comprehensive cost of static transfer switch installation and maintenance, the line loss cost of rural distribution network, the aging cost of three-phase imbalance of distribution lines, and the cost of abandoned light in rural distribution network, the annual comprehensive cost optimization model for the upper-level static transfer switch is established with the minimization of the annual comprehensive cost of the static transfer switch as the objective function and the number of static transfer switches installed and the current size of the static transfer switch equipment as the constraints; Among them,

[0023] (1) The objective function of minimizing the annual comprehensive cost of the static transfer switch is expressed as follows:

[0024] minS IN =S ins,STS +S cub,STS +S lo,line +S ab,PV (1)

[0025] Among them, S IN is the annual comprehensive cost of the static transfer switch; S in,STS The annual comprehensive cost of installing and maintaining a static transfer switch; S cub,STS The aging cost of the three-phase unbalanced distribution line; S lo,line is the line loss cost of rural distribution network; S ab,PV is the cost of abandoned solar power in rural distribution network;

[0026] (1.1) Rural low-voltage distribution networks are generally equipped with one communication terminal device and several phase-changing devices. The terminal device sends the PWM signal to the phase-changing device according to the load change control strategy, and the phase-changing device adjusts the phase to improve the three-phase imbalance in the substation area. The annual comprehensive cost of installing and maintaining a complete set of static transfer switches is S in,STS , expressed as:

[0027]

[0028] Where, χ is the annual discount rate; y is the planning period; π con The installation cost of the communication equipment for the static transfer switch; π STS,u is the control terminal cost of the static transfer switch; ns is the number of static transfer switches installed; β STS,u I is the maintenance cost factor of the static transfer switch equipment; STS,u is the current value flowing through the static transfer switch device;

[0029] (1.2) The three-phase imbalance of rural distribution networks will cause the distribution transformers and other equipment to age faster and cause other costs to increase. The increased costs depend on the three-phase imbalance of the distribution network. The aging cost S of the three-phase imbalance of the distribution linecub,STS , expressed as:

[0030]

[0031] Among them, α is the three-phase unbalance control cost coefficient of the static transfer switch; is the three-phase imbalance of line l at time t; T is the daily dispatching time;

[0032] (1.3) In addition to causing equipment aging, three-phase imbalance will also increase line losses. After installing STS equipment, through reasonable adjustment, the system flow will change and the line loss will also be reduced. Therefore, the line loss factor must be considered when calculating the comprehensive cost of rural distribution networks. The line loss cost of rural distribution networks is S lo,line , expressed as:

[0033]

[0034] Among them, λ is the line loss cost coefficient; be is the number of lines; is the X-phase impedance parameter of line l; is the X-phase current of line l at time t;

[0035] (1.4) The linkage between STS and PESS can not only reduce the operating cost of the distribution network but also absorb more photovoltaic power generation. ab,PV , expressed as:

[0036]

[0037] Among them, γ is the cost coefficient of abandoned light; is the light abandonment rate at time t.

[0038] (2) The three-phase unbalanced regulation function of the static transfer switch will reduce other costs, and the initial investment and maintenance costs will lead to increased operating costs. Therefore, the total number of devices should be limited when planning the static transfer switch. Among the constraints mentioned above:

[0039] The installation quantity constraint of the static transfer switch described in (2.1) is expressed as:

[0040] ns≤n d +n PESS (6)

[0041] Among them, n d is the number of user nodes willing to participate in three-phase unbalance; n PESS is the number of photovoltaic storage systems; ns is the number of static transfer switches installed;

[0042] (2.2) The current constraint of the static transfer switch device is expressed as:

[0043]

[0044] in, The maximum current value that the static transfer switch device can withstand; I STS,u is the current constraint value.

[0045] 2) Establish the optimization model of the operation strategy of the lower-level power system stabilizer:

[0046] The upper model is modeled around the investment of the static transfer switch and the benefits brought by its installation. After the static transfer switch is installed, it still needs to meet the system operation requirements, so the lower optimization model is established for the operation of the three-phase rural low-voltage distribution network. The lower model is the operation model of the rural three-phase distribution network after the static transfer switch is planned, which mainly considers factors such as the three-phase imbalance in the system and the switching of PESS.

[0047] The lower-level power system stabilizer operation strategy optimization model is to consider the factors of three-phase imbalance in the system and the switching of the portable energy storage system (PESS), and establish a lower-level power system stabilizer operation strategy optimization model with the minimization of the three-phase imbalance operation of the rural distribution network as the objective function, and the operation constraints of the three-phase distribution system, the operation constraints of the static transfer switch equipment and the operation constraints of the mobile energy storage system microgrid as the constraints; wherein,

[0048] The objective function of minimizing the three-phase unbalanced operation of the rural distribution network is expressed as:

[0049]

[0050] in, is the three-phase imbalance of line l at time t, and T is the daily dispatching time.

[0051] Three-phase unbalance The calculation method is:

[0052]

[0053] in, is the X-phase current of line l at time t, and X∈{A,B,C} represents any phase among the three phases A, B, and C.

[0054] Rural low-voltage distribution network has the structural characteristics of a radial network and often adopts a three-phase four-wire open-loop operation mode. The Dist-flow model is improved to establish a three-phase low-voltage distribution network operation model.

[0055] Among the constraints described:

[0056] (1) The three-phase distribution system operation constraints include:

[0057] (1.1) The energy balance constraint of the three-phase distribution network is expressed as:

[0058]

[0059] Among them, A e is the node-branch association matrix; is the X-phase active power and reactive power of distribution line ij at time t; is the X-phase resistance and reactance of the distribution line ij; is the X-phase current of the distribution line ij; is the active power and reactive power generated by the X-phase distribution transformer at time t; is the active load and reactive load of phase X at time t; is the three-phase transfer power of the X-phase static transfer switch and the mobile energy storage system at time t in the system;

[0060] The action of STS and PESS will change the three-phase power of the system. The power of the high-load phase is transferred to the low-load phase through the phase-changing device. The three-phase transfer power is expressed as:

[0061]

[0062] Active and reactive loads transferred through static transfer switch equipment; Active and reactive loads injected into the photovoltaic power generation system;

[0063]

[0064] in, is the combination matrix of the static transfer switch device adjusted from phase y to phase x at time t; is the combination matrix of static transfer switch devices adjusted from x phase to y phase at time t, is the active power and reactive power of phase y of line at time t;

[0065] (1.2) The voltage drop constraint of the three-phase distribution network line is expressed as:

[0066]

[0067] Among them, A ′ e is the transposed matrix of the node-branch association matrix, is the voltage of phase X at node u at time t;

[0068] (1.3) The line energy balance constraint of the three-phase distribution system is expressed as:

[0069]

[0070] (1.4) Node voltage safety constraint, expressed as:

[0071]

[0072] in, and Represents the lower and upper limit column vectors of node voltage;

[0073] (1.5) The output power constraint of the three-phase distribution transformer is expressed as:

[0074]

[0075] and Indicates the lower and upper limits of the active power output by the generator. is the active power of three-phase distribution transformer;

[0076]

[0077] and Indicates the lower and upper limits of the reactive power output by the generator. is the reactive power of three-phase distribution transformer;

[0078] (1.6) Line thermal stability constraint, expressed as:

[0079]

[0080] represents line current constraint;

[0081] (2) The operating constraints of the static transfer switch device are expressed as:

[0082]

[0083] (3) Operation constraints of mobile energy storage system microgrid, including

[0084] (3.1) The operating constraints of the three-phase inverter at the connection point between the mobile energy storage system and the distribution network are expressed as:

[0085]

[0086] is the switch state of the PESS connecting the PCC point at time t.

[0087]

[0088] The maximum and minimum output active power of the PCC point; The maximum and minimum output reactive power of the PCC point;

[0089] (3.2) Energy storage system charging and discharging constraints are expressed as:

[0090]

[0091] in, is the maximum charge and discharge power; is a binary variable that defines the operating state of the energy storage. is the discharge power of the energy storage device at time t; is the charging power of the energy storage device at time t;

[0092] (3.2) Battery energy constraint, expressed as:

[0093]

[0094] in, For the maximum and minimum battery storage energy, is the energy storage capacity of the battery energy storage system at time t, is the energy storage capacity of the battery energy storage system during period T, is the storage energy of the battery energy storage system at the initial time 0.

[0095] 3) The static transfer switch planning-operation two-layer model is composed of the upper-layer static transfer switch annual comprehensive cost optimization model and the lower-layer power system stabilizer operation strategy optimization model, such as Figure 2 As shown in the figure, the static transfer switch planning-operation two-layer model is solved by alternating iteration of two-layer hyperparameters, fixing the lower hyperparameters and optimizing the upper hyperparameters: the solution of the two-layer optimization problem is regarded as an iterative optimization process to determine the optimal solution for the site selection and capacity determination of the rural distribution network. Specifically, it includes:

[0096] (1) The gradient descent algorithm is used to solve the upper objective function to obtain the optimal solution, that is, to obtain the minimum number of static transfer switches to be installed. The descent gradient of the annual comprehensive cost optimization model of the upper static transfer switch takes into account the number of static transfer switches to be installed. The iterative process is expressed as:

[0097]

[0098] Among them, x k is the decision variable of the annual comprehensive cost optimization model of the upper static transfer switch, that is, the upper optimization solution, G k-1 (x,y) is the objective function of the lower model; η is the dynamic step size; Δε is a decimal close to 0;

[0099] (2) Fix the upper layer parameters and optimize the lower layer hyperparameters:

[0100] After obtaining the optimization solution of the upper layer, the branch and bound method is used to calculate the optimal solution of the lower power system stabilizer operation strategy optimization model:

[0101]

[0102] Among them, Δy k is the change of variable y during the kth iteration, is the objective function G of the lower model in the kth iteration k The variable y corresponding to the optimal solution of is the objective function G of the lower model in the k-1th iteration process k The variable y corresponding to the optimal solution.

[0103] The iterative stopping conditions of formula (30) and formula (31) must meet the following conditions:

[0104]

[0105] Where, ΔF k is the change in the upper objective function F in the kth iteration, ΔF k-1 The change in the upper objective function F in the k-1th iteration, ρ F is the convergence threshold of the change of the upper objective function, ΔG k is the change of the lower layer objective function G in the kth iteration, which is ΔG k-1 The change in the lower layer objective function G in the k-1th iteration, ρ G It is the convergence threshold of the change of the lower layer objective function.

[0106] The upper-level static transfer switch annual comprehensive cost optimization model optimizes the installation quantity and installation cost of static transfer switches by factors such as three-phase imbalance and network loss, and feeds the results back to the lower level. The lower-level model determines the installation location and calculates the new three-phase imbalance based on the optimization results of the upper-level model, ultimately achieving the optimal state for both planning and operation.

[0107] The IEEE-13 node three-phase standard system is used to verify the rationality and accuracy of the model proposed in this invention. The system topology is as follows: Figure 3 As shown in the figure, the black frame line is the node involved in three-phase unbalance management, and the user load will change the access phase during special periods according to the control signal received by the STS.

[0108] 1. Analysis of STS planning results in different scenarios

[0109] In order to prove the superiority of the static transfer switch site selection and capacity determination method for rural distribution network considering PESS of the present invention, the following operation scenarios are set:

[0110] S1: No three-phase imbalance control was performed;

[0111] S2: Governance using artificial phase change method;

[0112] S3: Use STS only for governance;

[0113] S4: STS+PESS comprehensive management;

[0114] Through simulation, the comprehensive cost changes as Figure 4 The three-phase current values ​​in the four scenarios are shown in Figure 5a to Figure 5d As shown in the figure, it can be seen that the change trends of the current values ​​of the distribution transformers in the four scenarios are similar. However, in scenarios S1 and S2, the three-phase current of the distribution transformers are unbalanced, which seriously affects the life of the equipment. The current values ​​in the distribution transformers in scenario S4 tend to be consistent. The reason for this phenomenon is that PESS participates in the three-phase imbalance regulation. It can adjust the output of the equipment according to the three-phase balance of the system, or connect it to the low-voltage distribution network as a load, and balance the three-phase load while ensuring the safe operation of the system. It reflects the advantages of using PESS and STS to jointly participate in the three-phase imbalance management proposed by the present invention. The three-phase imbalance of the four scenarios is shown in the figure. Figure 6 As shown in the figure, although the three-phase imbalance in scenario S2 is improved compared with scenario S1, it still cannot meet the safe operation requirements, while the three-phase imbalance in scenarios S3 and S4 is significantly reduced, meeting the safe operation requirements of the three-phase imbalance of the distribution network. Since PESS participates in the three-phase imbalance regulation, reducing the number of phase changes of the rural distribution network load can meet the three-phase imbalance regulation requirements.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made to the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS, characterized in that: The following steps are involved: 1) Establish an annual comprehensive cost optimization model for upper-level static transfer switches: Considering the annual comprehensive cost of static transfer switch installation and maintenance, the line loss cost of rural distribution network, the aging cost of three-phase imbalance of distribution lines, and the cost of abandoned light in rural distribution network, the annual comprehensive cost optimization model for upper-level static transfer switches is established with the minimization of the annual comprehensive cost of static transfer switches as the objective function and the number of static transfer switches installed and the current size of static transfer switch equipment as constraints; 2) Establish an optimization model for the operation strategy of the lower-level power system stabilizer: Considering the factors of three-phase imbalance in the system and the switching of mobile energy storage systems, establish an optimization model for the operation strategy of the lower-level power system stabilizer with the objective function of minimizing the three-phase imbalance of the rural distribution network, and with the operation constraints of the three-phase distribution system, the operation constraints of the static transfer switch equipment, and the operation constraints of the mobile energy storage system microgrid as constraints; 3) A static transfer switch planning-operation two-layer model is constructed by the upper-layer static transfer switch annual comprehensive cost optimization model and the lower-layer power system stabilizer operation strategy optimization model. The two-layer hyperparameter alternating iteration method is used to solve the static transfer switch planning-operation two-layer model to determine the optimal solution for the siting and sizing of the rural distribution network.

2. The method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS according to claim 1, characterized in that: The objective function of minimizing the annual comprehensive cost of the static transfer switch in step 1) is expressed as follows: minS IN =S ins,STS +S cub,STS +S lo,line +S ab,PV (1) Among them, S IN is the annual comprehensive cost of the static transfer switch; S in,STS The annual comprehensive cost of installing and maintaining a static transfer switch; S cub,STS The aging cost of the three-phase unbalanced distribution line; S lo,line is the line loss cost of rural distribution network; S ab,PV is the cost of abandoned solar power in rural distribution network; (1) Annual comprehensive cost of installation and maintenance of static transfer switches S in,STS , expressed as: Where, χ is the annual discount rate; y is the planning period; π con The installation cost of the communication equipment for the static transfer switch; π STS,u is the control terminal cost of the static transfer switch; ns is the number of static transfer switches installed; β STS,u I is the maintenance cost factor of the static transfer switch equipment; STS,u is the current value flowing through the static transfer switch device; (2) The aging cost S of the three-phase unbalanced distribution line cub,STS , expressed as: Among them, α is the three-phase unbalance control cost coefficient of the static transfer switch; is the three-phase imbalance of line l at time t; T is the daily dispatching time; (3) Line loss cost S of rural distribution network lo,line , expressed as: Among them, λ is the line loss cost coefficient; be is the number of lines; is the X-phase impedance parameter of line l; is the X-phase current of line l at time t; (4) Rural distribution network abandoned solar power cost S ab,PV , expressed as: Among them, γ is the cost coefficient of abandoned light; is the light abandonment rate at time t.

3. The method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS according to claim 1, characterized in that: Among the constraints described in step 1): The static transfer switch installation quantity constraint is expressed as: ns≤n d +n PESS (6) Among them, n d is the number of user nodes willing to participate in three-phase unbalance; n PESS is the number of photovoltaic storage systems; ns is the number of static transfer switches installed; The current constraint of the static transfer switch device is expressed as: in, The maximum current value that the static transfer switch device can withstand; I STS,u is the current constraint value.

4. The method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS according to claim 1, characterized in that: The objective function of minimizing the three-phase unbalanced operation of the rural distribution network in step 2) is expressed as: in, is the three-phase unbalance degree of line l at time t, and T is the daily dispatching time; Three-phase unbalance The calculation method is: in, is the X-phase current of line l at time t, and X∈{A,B,C} represents any phase among the three phases A, B, and C.

5. The method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS according to claim 1, characterized in that: In step 2): (1) The operating constraints of the three-phase distribution system include: (1.1) The energy balance constraint of the three-phase distribution network is expressed as: Among them, A e is the node-branch association matrix; is the X-phase active power and reactive power of distribution line ij at time t; is the X-phase resistance and reactance of the distribution line ij; is the X-phase current of the distribution line ij; is the active power and reactive power generated by the X-phase distribution transformer at time t; is the active load and reactive load of phase X at time t; is the three-phase transfer power of the X-phase static transfer switch and the mobile energy storage system at time t in the system; The power of the high-load phase is transferred to the low-load phase through the phase-commutation device. The three-phase transfer power is expressed as: Active and reactive loads transferred through static transfer switch equipment; Active and reactive loads injected into the photovoltaic power generation system; in, is the combination matrix of the static transfer switch device adjusted from phase y to phase x at time t; is the combination matrix of static transfer switch devices adjusted from x phase to y phase at time t, is the active power and reactive power of phase y of line at time t; (1.2) The voltage drop constraint of the three-phase distribution network line is expressed as: Among them, A ′ e is the transposed matrix of the node-branch association matrix, is the voltage of phase X at node i at time t; (1.3) The line energy balance constraint of the three-phase distribution system is expressed as: (1.4) Node voltage safety constraint, expressed as: in, and Represents the lower and upper limit column vectors of node voltage; (1.5) The output power constraint of the three-phase distribution transformer is expressed as: and Indicates the lower and upper limits of the active power output by the generator. is the active power of three-phase distribution transformer; and Indicates the lower and upper limits of the reactive power output by the generator. is the reactive power of three-phase distribution transformer; (1.6) Line thermal stability constraint, expressed as: represents line current constraint; (2) Static transfer switch equipment operation constraints, expressed as: (3) Operation constraints of mobile energy storage system microgrid, including (3.1) The operating constraints of the three-phase inverter at the connection point between the mobile energy storage system and the distribution network are expressed as: is the switch state of the PESS connecting the PCC point at time t; The maximum and minimum output active power of the PCC point; The maximum and minimum output reactive power of the PCC point; (3.2) Energy storage system charging and discharging constraints are expressed as: in, is the maximum charge and discharge power; is a binary variable that defines the operating state of the energy storage. is the discharge power of the energy storage device at time t; is the charging power of the energy storage device at time t; (3.2) Battery energy constraint, expressed as: in, For the maximum and minimum battery storage energy, is the energy storage capacity of the battery energy storage system at time t, is the energy storage capacity of the battery energy storage system during period T, is the storage energy of the battery energy storage system at the initial time 0.

6. The method for site selection and capacity determination of static transfer switches in rural distribution networks considering PESS according to claim 1, characterized in that: Step 3) includes: (1) The gradient descent algorithm is used to solve the upper objective function to obtain the optimal solution. The descent gradient of the annual comprehensive cost optimization model of the upper static transfer switch takes into account the number of static transfer switches installed. The iterative process is expressed as: Among them, x k is the decision variable of the annual comprehensive cost optimization model of the upper static transfer switch, that is, the upper optimization solution, G k-1 (x,y) is the objective function of the lower model; η is the dynamic step size; Δε is a decimal close to 0; (2) Fix the upper layer parameters and optimize the lower layer hyperparameters: After obtaining the optimization solution of the upper layer, the branch and bound method is used to calculate the optimal solution of the lower power system stabilizer operation strategy optimization model: Among them, Δy k is the change of variable y during the kth iteration, is the objective function G of the lower model in the kth iteration k The variable y corresponding to the optimal solution of is the objective function G of the lower model in the k-1th iteration process k The variable y corresponding to the optimal solution; The iterative stopping conditions of formula (30) and formula (31) must meet the following conditions: Where, ΔF k is the change in the upper objective function F in the kth iteration, ΔF k-1 The change in the upper objective function F in the k-1th iteration, ρ F is the convergence threshold of the change of the upper objective function, ΔG k is the change of the lower layer objective function G in the kth iteration, which is ΔG k-1 The change in the lower layer objective function G in the k-1th iteration, ρ G It is the convergence threshold of the change of the lower layer objective function.

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