Distributed photovoltaic and intelligent soft switching collaborative planning method based on operation risk

Abstract

According to the power distribution network distributed photovoltaic and intelligent soft switch planning method based on the operation risk, a distributed photovoltaic random processing probability model and a demand response model are established, and power distribution network random load flow calculation considering distributed photovoltaic and demand response load correlation is put forward. And an intelligent soft switching and distributed photovoltaic rolling planning model considering the operation risk of the power distribution network is established. According to the method, the operation cost of the power distribution network can be effectively reduced, the photovoltaic consumption rate of the power distribution network is improved, and help can be provided for research on planning and operation of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning and operation, and in particular to a distributed photovoltaic and intelligent soft switch collaborative planning method based on operation risk. Background Art

[0002] Compared with wind turbines, photovoltaics do not require strict environments when connected to distribution networks. This has resulted in a large number of distributed photovoltaics in distribution networks, further blurring the "source-load" boundary of distribution networks, and exposing distribution networks to a series of risks such as bidirectional power flows and node voltage exceeding limits, which limits the improvement of photovoltaic penetration rates in distribution networks. Intelligent soft switches usually replace normally open switches on interconnection lines and are one of the effective means to deal with the above risks. They can adjust the power flow of the target feeder according to the settings, change the power flow distribution of the entire distribution system, and thus promote the photovoltaic absorption rate of the distribution network.

[0003] However, power electronic devices have high cost and power shortage. Reasonable planning of intelligent soft switches can save resources and reduce waste. At the same time, we should also start from distributed photovoltaics and reasonably plan their installation locations to further reduce investment costs and increase the photovoltaic penetration rate of the distribution network. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a distributed photovoltaic and intelligent soft switch collaborative planning method, device and storage medium based on operation risk, which can effectively reduce the operating cost of the system and improve the economy of the distribution network and the photovoltaic absorption rate.

[0005] A distribution network distributed photovoltaic and intelligent soft switch planning method based on operation risk, comprising the following steps:

[0006] S1. Establish a random output probability model of distributed photovoltaics and a load model that takes demand response into account;

[0007] S2. Carry out random power flow calculation of distribution network based on distributed photovoltaic and load correlation considering demand response;

[0008] S3. Calculate the node voltage and branch power flow over-limit risk indicators and conduct operation risk assessment of the distribution network;

[0009] S4. Based on the results of S3, an intelligent soft switch planning model considering active power balance constraints and capacity constraints is established;

[0010] S5. Based on the results of S3 and S4, calculate the limit penetration rate of photovoltaic access to the distribution network and plan large-scale decentralized photovoltaics;

[0011] S6. Determine the operating risk of the distribution network. If it is within an acceptable range, return to S2; otherwise, the planning process ends.

[0012] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0013] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0014] Compared with the prior art, the beneficial effects are:

[0015] The present invention proposes a distributed photovoltaic and intelligent soft switch planning method for distribution network based on operation risk, establishes a distributed photovoltaic random processing probability model and a demand response model, proposes a distribution network random flow calculation considering the correlation between distributed photovoltaic and demand response load, and establishes an intelligent soft switch and distributed photovoltaic rolling planning model considering the operation risk of the distribution network. The role of intelligent soft switch and distributed photovoltaic is fully considered, which can effectively reduce the operation cost of the system and improve the economy of the distribution network and the photovoltaic absorption rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a distributed photovoltaic and intelligent soft switch collaborative planning method based on operation risk of the present invention;

[0017] Figure 2 This is a load model that takes demand response into account. DETAILED DESCRIPTION

[0018] The following is a detailed description of a specific implementation method of a distributed photovoltaic and intelligent soft switch collaborative planning method based on operation risk of the present invention in conjunction with the accompanying drawings.

[0019] like Figure 1 As shown, the method for coordinated planning of distributed photovoltaic and intelligent soft switches in a distribution network based on operation risk described in this embodiment includes the following steps:

[0020] Step 1: Establish a distributed photovoltaic random output probability model and a load model that takes demand response into consideration;

[0021] Step 2: Based on distributed photovoltaics and load correlation considering demand response, random power flow calculation of distribution network is carried out;

[0022] Step 2.1, input the original data of the distribution network, including the original data required for general power flow calculation, such as node voltage and branch parameters;

[0023] Step 2.2, using the copula function to establish a joint probability distribution model of the distributed photovoltaic connected to each node of the distribution network and the load considering demand response, and obtain random distribution data of the joint model;

[0024] Step 2.3, based on deterministic power flow calculation, obtain the node state variable X0, Jacobian matrix J0 and sensitivity matrix T0;

[0025] Step 2.4, calculate the moments of each order of the injected power at each node and its semi-invariant;

[0026] Step 2.5, add up all the semi-invariants of each node to obtain the semi-invariants of each order of the node injection power;

[0027] Step 2.6, obtain the semi-invariants of each order of the node state variable ΔX and the branch power flow variable ΔZ;

[0028] Step 2.7, use the Gram-Charlier series to obtain the probability density function and cumulative distribution function of ΔX and ΔZ;

[0029] Step 3: Calculate the node voltage and branch power flow over-limit risk indicators and conduct operation risk assessment of the distribution network;

[0030] Step 4: According to the operation risk assessment results of the distribution network, an intelligent soft switch planning model considering active power balance constraints and capacity constraints is established;

[0031] Step 5: According to the planning results of the intelligent soft switch, the operation risk assessment of the distribution network is carried out based on the method in step 3, the limit penetration rate of photovoltaic access to the distribution network is calculated, and the large-scale decentralized photovoltaic access to the distribution network is planned;

[0032] Step 6: Determine the operating risk of the entire distribution network. If it is within an acceptable range, return to step 2; otherwise, the entire planning process ends.

[0033] The distributed photovoltaic random output probability model in step 1 adopts the kernel density estimation method, and the load model considering demand response adopts the normal distribution function modified by the triangular membership fuzzy variable (see Figure 2 ) for description;

[0034] The distributed photovoltaic output value is P, and its corresponding probability density is f pv (P). Let the sample space of P be (P1, P2, ..., P n ).

[0035] The kernel density estimation is shown in formula (1):

[0036]

[0037] Where: is the kernel density estimation, K is the kernel function of the kernel density estimation, n is the sample size, h is the smoothing parameter of the kernel density estimation, P i represents the i-th sample in the sample space.

[0038] The kernel function is selected as shown in formula (2):

[0039]

[0040] The cumulative probability distribution of the distributed photovoltaic output is shown in formula (3):

[0041]

[0042] The active power probability density function in the load model considering demand response is shown in formula (4):

[0043]

[0044] Where: are the expected value and variance of the load active power respectively; is the triangular membership fuzzy variable correction coefficient of power; L is the active load;

[0045]

[0046] Where: ρ1, ρ2, P DR , P N They are all important parameters of triangular membership fuzzy variables.

[0047] The node voltage and branch power flow exceeding limit probability in step 3 are obtained by the cumulative distribution function of the random variable obtained in step 2.7;

[0048] The node voltage over-limit risk index is shown in formula (6):

[0049]

[0050] Where: is the probability that the node voltage exceeds the upper limit, P r ( V i ) is the probability that the node voltage exceeds the lower limit; is the severity index of the node voltage exceeding the upper limit, Sev( V i ) is the severity index of the node voltage exceeding the lower limit.

[0051] The probability calculation formula of the node voltage exceeding the upper limit and the lower limit is shown in formula (7):

[0052]

[0053] Where: V i Represents the voltage amplitude of the current node i, V imax and V imin are the upper and lower limits of the voltage amplitude allowed at node i, and F() represents the cumulative distribution function of the node voltage.

[0054] The calculation formula of the severity index of the node voltage exceeding the upper limit and the lower limit is shown in formula (8):

[0055]

[0056] Where: V i Represents the voltage amplitude of the current node i, V imax and V imin are the upper and lower limits of the voltage amplitude allowed at node i.

[0057] The branch power flow over-limit risk index is shown in formula (9):

[0058] Rs k =Pr(S ij )Sev(S ij ) (9)

[0059] Where: Rs k is the risk index of the k-th branch power flow exceeding the limit; P r (S ij ) is the probability of branch ij power flow exceeding the limit; Sev(S ij ) is the severity index of the branch ij flow exceeding the limit; S ij is the active power flow of branch ij.

[0060] The probability calculation formula of the branch power flow exceeding the upper limit is shown in formula (10):

[0061] P r (S ij )=P r (S ij >S ijmax )=1-F(S ijmax ) (10)

[0062] Where: S ij is the active power flow of branch ij, S ijmax is the upper limit of the active power flow allowed by branch ij, F(S ijmax ) is the cumulative distribution function of branch power flow.

[0063] The calculation formula of the branch overload severity function is shown in formula (11):

[0064]

[0065] Where: S ij is the active power flow of branch ij, S ijmax It is the upper limit of the active power flow allowed in branch ij.

[0066] In step 4, based on the operation risk assessment results of the distribution network, an intelligent soft switch planning model considering active power balance constraints and capacity constraints is established, which specifically includes:

[0067] Step 4.1 Take the weighted minimum of economic cost and operational risk as the planning objective;

[0068]

[0069] C COST =C SOP +C LOSS +C BUY (13)

[0070]

[0071]

[0072]

[0073] Where: Ω b ,Ω l are the node set and branch set of the distribution network respectively; Rv i , Rs k are the operation risks of nodes and branches respectively; C COST is the comprehensive economic cost; α1 and α2 are weight coefficients; C SOP , C SOP,inv , C SOP,om is the total cost, investment cost and maintenance cost of the intelligent soft switch; SOP 、r ESS are the discount rates of smart soft switch and energy storage system respectively; y SOP is the service life of the intelligent soft switch; c SOP , η SOP is the unit capacity cost and maintenance coefficient of the intelligent soft switch; C LOSS is the loss cost; c e,t is the electricity price at time t; r ij is the resistance of the branch; is the square of the current of the ij branch at the time of the typical day s; p(s) is the probability of the typical day s; N is the power purchased from the upper grid at time t on a typical day s; S is the number of typical days; T is 24 hours; C BUY The cost of purchasing electricity from the upper power grid; Ω cIt is the location set where the SOP is to be installed; SOP capacity installed for the ij branch; The active power injected into the i-node by SOP.

[0074] Step 4.2 sets the distribution network power flow constraints and intelligent soft switch constraints;

[0075] 1) Distribution network power flow constraints

[0076] The Distflow model is adopted, as follows:

[0077]

[0081] In the formula, Φ(i) and Ψ(i) are the parent node and child node of node i respectively; U s,t,i is the voltage of node i at time t on typical day s; are the square of branch current and node voltage respectively; r ij 、x ij are the resistance and reactance of the distribution network branch respectively; P s,t,ji , Q s,t,ji They are respectively the branch active power and reactive power at time t on typical day s; are the active power and reactive power injected by distributed photovoltaic output of node i at time t on typical day s, respectively; are the active power and reactive power injected by the wind turbine output of node i at time t on typical day s, respectively; are the active power and reactive power injected by the intelligent soft switch of node i at time t on typical day s, respectively; are respectively the active load and reactive load of node i at time t on typical day s; Ω l is the branch set, Ω b is a node set; s,t,i is the load factor; are respectively the active and reactive power injected into node i by energy storage; P s,t,ij , Q s,t,ij are the active and reactive power of the ij branch respectively.

[0082] 2) Intelligent soft switching constraints

[0083] The intelligent soft switch model includes power balance constraints, capacity constraints, and voltage support constraints, namely, equations (21) to (26);

[0084] Considering the energy loss of the intelligent soft switch during operation, the active power balance is as follows:

[0085]

[0086] In the formula, Ω C It is the set of locations to be installed of SOP; are the active power and reactive power injected by the intelligent soft switch of node i at time t on typical day s, respectively; are the active power and reactive power injected by the intelligent soft switch of node j at time t on typical day s; A SOP is the loss coefficient of the intelligent soft switch;

[0087] The capacity constraints of the intelligent soft switch are established as follows:

[0088]

[0089] In the formula, Ω C Branches to be selected for installation of intelligent soft switches; The capacity of the intelligent soft switch connected to the ij branch;

[0090] When there are multiple smart soft switches connected to the power failure area, there is only one smart soft switch whose VSC connected to the power failure area can work in V / f mode to provide voltage support for the power failure area. Therefore, the voltage support constraint of the smart soft switch is established as follows:

[0091]

[0092] Z i =β i α ij ,i∈(Ω l,out IΩ SOP ),ij∈Ω C (twenty four)

[0093]

[0094] U i -U0≥-M(1-Z i ),i∈(Ω l,out ∩Ω SOP ) (26)

[0095] In the formula, Ω SOP is the node set connected to the intelligent soft switch; α ij is a 0-1 variable. ij =1, it indicates that the ij branch is equipped with an intelligent soft switch. ij = 0, indicating that the ij branch is not equipped with an intelligent soft switch; c SOP,min Intelligent soft switch with minimum unit capacity; m ij is a positive integer; Z i is a 0-1 variable. When Z i =1, indicating that the intelligent soft switch connected to node i provides voltage support; βi is an intermediate variable; U0 is the voltage constraint value of the fault side of the intelligent soft switch power supply recovery, which takes the reference voltage value; M is a larger number; Ω l，out U is the node set accessed by SOP; i is the node voltage.

[0096] In step 5, according to the planning results of Sop, based on the method of step 3, the operation risk assessment of the distribution network is carried out, the limit penetration rate of photovoltaic access to the distribution network is calculated, and the large-scale decentralized photovoltaic access to the distribution network is planned; the specific steps are as follows:

[0097] Step 5.1 Calculate photovoltaic penetration

[0098] The distributed photovoltaic penetration rate is shown in formula (27):

[0099]

[0100] Where: H is the photovoltaic penetration rate, S PV is the total installed capacity of photovoltaic power generation, S LOAD It is the peak apparent power of all loads in the distribution network.

[0101] The limiting permeability H limit R is the maximum voltage over-limit risk value of all nodes in the distribution network within one day vs_max Approaching the given distribution network voltage over-limit risk limit R in actual operation vs_limit The permeability value at .

[0102] The maximum voltage over-limit risk value R of all nodes in the distribution network within one day vs_max Based on the power flow calculation in step 2, as shown in equations (28)-(29):

[0103] R vs_max =max{R vs (1),R vs (2),…,R vs (t),…,R vs (24)} (28)

[0104]

[0105] Where: R vs_max is the maximum voltage over-limit risk value of all nodes in the distribution network within one day, R vs (t) is the sum of the voltage over-limit risk values ​​of all nodes in the distribution network within the time period t, V max 、V min are the upper and lower limits of node voltage, respectively, f(V i t ) is the probability density function of the voltage at node i in time period t, U(Vi t ) is the severity function of voltage over-limit.

[0106] The calculation formula of the voltage over-limit severity function is shown in formula (9):

[0107]

[0108] Where: U x (V i t ), U s (V i t ) are the severity functions of the voltage exceeding the lower limit and the upper limit, respectively.

[0109] Step 5.2: Optimize the number, capacity and location of photovoltaic distribution within the limit penetration rate range to minimize the risk of the distribution network. The optimization plan uses a particle swarm algorithm to optimize the configuration of large-scale decentralized photovoltaics, with the minimum risk of the distribution network as the objective function and the power flow equation and photovoltaic penetration rate as constraints;

[0110] The objective function is shown in formula (31):

[0111]

[0112] Where: Ω l is the branch set, Ω b is a node set, Rv i is the voltage over-limit risk value of the ith node, Rs k is the flow over-limit risk value of the k-th branch;

[0113] The power flow equation constraint condition is shown in formula (32):

[0114]

[0115] Where: P i and Q i is the injected active and reactive power of node i; V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; G ij is the branch conductance; B ij is the branch susceptance; θ ij is the voltage phase angle difference between nodes i and j;

[0116] The photovoltaic penetration constraint condition is shown in formula (33):

[0117] H <H limit (33)

[0118] Where: H is the photovoltaic penetration rate; H limit is the photovoltaic limit penetration rate.

[0119] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0120] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0121] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer is executed on a computer, the computer executes any of the intelligent soft switch and energy storage coordination planning methods for improving the resilience of the distribution network in the above embodiments.

[0122] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0123] The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0124] Memory, used to store computer programs;

[0125] The processor is used to implement the above-mentioned intelligent soft switch and energy storage coordination planning method for improving the resilience of the distribution network when executing the program stored in the memory.

[0126] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0127] The communication interface is used for communication between the above electronic device and other devices.

[0128] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0129] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.

[0130] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0131] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0132] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed photovoltaic and intelligent soft switch collaborative planning method based on operation risk, characterized in that: The following steps are involved: Step 1: Establish a distributed photovoltaic random output probability model and a load model that takes demand response into consideration; Step 2: Based on distributed photovoltaics and load correlation considering demand response, random power flow calculation of distribution network is carried out; Step 3: Calculate the node voltage and branch power flow over-limit risk indicators and conduct operation risk assessment of the distribution network; Step 4: According to the operation risk assessment results of the distribution network, an intelligent soft switch planning model considering active power balance constraints and capacity constraints is established; Step 5: According to the planning results of the intelligent soft switch, the operation risk assessment of the distribution network is carried out based on the method in step 3, the limit penetration rate of photovoltaic access to the distribution network is calculated, and the large-scale decentralized photovoltaic access to the distribution network is planned; Step 6: Determine the operating risk of the entire distribution network. If it is within an acceptable range, return to step 2; otherwise, the entire planning process ends.

2. The method for coordinated planning of distributed photovoltaic and intelligent soft switches based on operational risk according to claim 1 is characterized in that: The distributed photovoltaic random output probability model in step 1 adopts the kernel density estimation method, and the load model considering demand response is described by a normal distribution function corrected by a triangular membership fuzzy variable; The distributed photovoltaic output value is P, and its corresponding probability density is f pv (P); Let the sample space of P be (P1, P2, ..., P n ); The kernel density estimation is shown in formula (1): Where: is the kernel density estimation, K is the kernel function of the kernel density estimation, n is the sample size, h is the smooth parameter of the kernel density estimation, and Pi represents the i-th sample in the sample space; The kernel function is selected as shown in formula (2): The cumulative probability distribution of the distributed photovoltaic output is shown in formula (3): The active power probability density function in the load model considering demand response is shown in formula (4): Where: are the expected value and variance of the load active power respectively; K DR is the correction coefficient of the triangular membership fuzzy variable of power; PL is the active load; Where: ρ1, ρ2, P DR , P N They are all important parameters of triangular membership fuzzy variables.

3. The method for coordinated planning of distributed photovoltaic and intelligent soft switches based on operational risk according to claim 2 is characterized in that: Step 2: Based on distributed photovoltaics and load correlation considering demand response, random power flow calculation of distribution network is carried out, including: Step 2.1, input the original data of the distribution network, including the original data required for general power flow calculation such as node voltage and branch parameters; Step 2.2, using the copula function to establish a joint probability distribution model of the distributed photovoltaic connected to each node of the distribution network and the load considering demand response, and obtain random distribution data of the joint model; Step 2.3, based on deterministic power flow calculation, obtain the node state variable X0, Jacobian matrix J0 and sensitivity matrix T0; Step 2.4, calculate the moments of each order of the injected power at each node and its semi-invariant; Step 2.5, add up all the semi-invariants of each node to obtain the semi-invariants of each order of the node injection power; Step 2.6, obtain the semi-invariants of each order of the node state variable ΔX and the branch power flow variable ΔZ; Step 2.7: Use the Gram-Charlier series to obtain the probability density function and cumulative distribution function of ΔX and ΔZ.

4. The method for coordinated planning of distributed photovoltaic and intelligent soft switches based on operational risk according to claim 3 is characterized in that: The node voltage and branch power flow exceeding limit probability in step 3 are obtained by the cumulative distribution function of the random variable obtained in step 2.7; The node voltage over-limit risk index is shown in formula (6): Where: is the probability that the node voltage exceeds the upper limit, P r ( V i ) is the probability that the node voltage exceeds the lower limit; is the severity index of the node voltage exceeding the upper limit, Sev( V i ) is the severity index of the node voltage exceeding the lower limit; The probability calculation formula of the node voltage exceeding the upper limit and the lower limit is shown in formula (7): Where: V i Represents the voltage amplitude of the current node i, V imax and V imin are the upper and lower limits of the voltage amplitude allowed by node i, respectively, and F() represents the cumulative distribution function of the node voltage; The calculation formula of the severity index of the node voltage exceeding the upper limit and the lower limit is shown in formula (8): Where: V i Represents the voltage amplitude of the current node i, V imax and V imin are the upper and lower limits of the voltage amplitude allowed at node i respectively; The branch power flow over-limit risk index is shown in formula (9): Rs k =Pr(S ij )Sev(S ij ) (9) Where: Rs k is the risk index of the k-th branch power flow exceeding the limit; P r (S ij ) is the probability of branch ij power flow exceeding the limit; Sev(S ij ) is the severity index of the branch ij flow exceeding the limit; S ij is the active power flow of branch ij; The probability calculation formula of the branch power flow exceeding the upper limit is shown in formula (10): P r (S ij )=P r (S ij >S ijmax )=1-F(S ijmax ) (10) Where: S ij is the active power flow of branch ij, S ijmax is the upper limit of the active power flow allowed by branch ij, F(S ijmax ) is the cumulative distribution function of branch power flow; The calculation formula of the branch overload severity function is shown in formula (11): Where: S ij is the active power flow of branch ij, S ijmax It is the upper limit of the active power flow allowed in branch ij.

5. The method for coordinated planning of distributed photovoltaic and intelligent soft switches based on operational risk according to claim 1 is characterized in that: In step 4, based on the operation risk assessment results of the distribution network, an intelligent soft switch planning model considering active power balance constraints and capacity constraints is established, which specifically includes: Step 4.1 Take the weighted minimum of economic cost and operational risk as the planning objective; C COST =C SOP +C LOSS +C BUY (13) Where: Ω b ,Ω l are the node set and branch set of the distribution network respectively; Rv i , Rs k are the operation risks of nodes and branches respectively; C COST is the comprehensive economic cost; α1 and α2 are weight coefficients; C SOP , C SOP,inv , C SOP,om is the total cost, investment cost and maintenance cost of the intelligent soft switch; SOP 、r ESS are the discount rates of smart soft switch and energy storage system respectively; y SOP is the service life of the intelligent soft switch; c SOP , η SOP is the unit capacity cost and maintenance coefficient of the intelligent soft switch; C LOSS is the loss cost; c e,t is the electricity price at time t; r ij is the resistance of the branch; is the square of the current of the ij branch at the time of the typical day s; p(s) is the probability of the typical day s; N is the power purchased from the upper grid at time t on a typical day s; S is the number of typical days; T is 24 hours; C BUY The cost of purchasing electricity from the upper power grid; Ω c It is the location set where the SOP is to be installed; SOP capacity installed for the ij branch; Inject active power of node i into SOP; Step 4.2 sets the distribution network power flow constraints and intelligent soft switch constraints; 1) Distribution network power flow constraints The Distflow model is adopted, as follows: In the formula, Φ(i) and Ψ(i) are the parent node and child node of node i respectively; U s,t,i is the voltage of node i at time t on typical day s; are the square of branch current and node voltage respectively; r ij 、x ij are the resistance and reactance of the distribution network branch respectively; P s,t,ji , Q s,t,ji They are respectively the branch active power and reactive power at time t on typical day s; are the active power and reactive power injected by distributed photovoltaic output of node i at time t on typical day s, respectively; are the active power and reactive power injected by the wind turbine output of node i at time t on typical day s, respectively; are the active power and reactive power injected by the intelligent soft switch of node i at time t on typical day s, respectively; are respectively the active load and reactive load of node i at time t on typical day s; Ω l is the branch set, Ω b is a node set; s,t,i is the load factor; are respectively the active and reactive power injected into node i by energy storage; P s,t,ij , Q s,t,ij are the active and reactive power of the ij branch respectively; 2) Intelligent soft switching constraints The intelligent soft switch model includes power balance constraints, capacity constraints, and voltage support constraints, namely, equations (21) to (26); Considering the energy loss of the intelligent soft switch during operation, the active power balance is as follows: In the formula, Ω C It is the set of locations to be installed of SOP; are the active power and reactive power injected by the intelligent soft switch of node i at time t on typical day s, respectively; are the active power and reactive power injected by the intelligent soft switch of node j at time t on typical day s; A SOP is the loss coefficient of the intelligent soft switch; The capacity constraints of the intelligent soft switch are established as follows: In the formula, Ω C Branches to be selected for installation of intelligent soft switches; The capacity of the intelligent soft switch connected to the ij branch; When there are multiple smart soft switches connected to the power failure area, there is only one smart soft switch whose VSC connected to the power failure area can work in V / f mode to provide voltage support for the power failure area. Therefore, the voltage support constraint of the smart soft switch is established as follows: Z i =b i a ij ,i∈(Ω l,out IΩ SOP ),ij∈Ω C (24) U i -U0≥-M(1-Z i ),i∈(Ω l,out ∩Ω SOP ) (26) In the formula, Ω SOP is the node set connected to the intelligent soft switch; α ij is a 0-1 variable. ij =1, it indicates that the ij branch is equipped with an intelligent soft switch. ij = 0, indicating that the ij branch is not equipped with an intelligent soft switch; c SOP,min It is the intelligent soft switch with the minimum unit capacity; m ij is a positive integer; Z i is a 0-1 variable. When Z i =1, indicating that the intelligent soft switch connected to node i provides voltage support; β i is an intermediate variable; U0 is the voltage constraint value of the fault side of the intelligent soft switch power supply recovery, which takes the reference voltage value; M is a larger number; Ω l，out is the node set accessed by SOP; U i is the node voltage.

6. The method for coordinated planning of distributed photovoltaic and intelligent soft switches based on operational risk according to claim 1 is characterized in that: In step 5, according to the planning results of Sop, based on the method of step 3, the operation risk assessment of the distribution network is carried out, the limit penetration rate of photovoltaic access to the distribution network is calculated, and the large-scale decentralized photovoltaic access to the distribution network is planned; the specific steps are as follows: Step 5.1 Calculate photovoltaic penetration The distributed photovoltaic penetration rate is shown in formula (27): Where: H is the photovoltaic penetration rate, S PV is the total installed capacity of photovoltaic power generation, S LOAD is the peak apparent power of all loads in the distribution network; The limiting permeability H limit R is the maximum voltage over-limit risk value of all nodes in the distribution network within one day vs_max Approaching the given distribution network voltage over-limit risk limit R in actual operation vs_limit The permeability value at ; The maximum voltage over-limit risk value R of all nodes in the distribution network within one day vs_max Based on the power flow calculation in step 2, as shown in equations (28)-(29): R vs_max =max{R vs (1),R vs (2),…,R vs (t),…,R vs (24)} (28) Where: R vs_max is the maximum voltage over-limit risk value of all nodes in the distribution network within one day, R vs (t) is the sum of the voltage over-limit risk values ​​of all nodes in the distribution network within the time period t, V max 、V min are the upper and lower limits of node voltage respectively, is the probability density function of the voltage at node i in time period t, is the severity function of voltage over-limit; The calculation formula of the voltage over-limit severity function is shown in formula (9): Where: are the severity functions of the voltage exceeding the lower limit and the upper limit respectively; Step 5.2: Optimize the number, capacity and location of photovoltaic distribution within the limit penetration rate range to minimize the risk of the distribution network. The optimization plan uses a particle swarm algorithm to optimize the configuration of large-scale decentralized photovoltaics, with the minimum risk of the distribution network as the objective function and the power flow equation and photovoltaic penetration rate as constraints; The objective function is shown in formula (31): Where: Ω l is the branch set, Ω b is a node set, Rv i is the voltage over-limit risk value of the ith node, Rs k is the flow over-limit risk value of the k-th branch; The power flow equation constraint condition is shown in formula (32): Where: P i and Q i is the injected active and reactive power of node i; V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; G ij is the branch conductance; B ij is the branch circuit admittance; θ ij is the voltage phase angle difference between nodes i and j; The photovoltaic penetration constraint condition is shown in formula (33): H<H limit (33) Where: H is the photovoltaic penetration rate; H limit is the photovoltaic limit penetration rate.

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