Distribution network optimization configuration method, system and terminal based on elastic mechanics mapping

Abstract

The present invention provides a method and system for optimizing the configuration of a distribution network based on elastic mechanics mapping, including: constructing an elastic mapping model of the power system; constructing an intelligent soft switch equivalent model based on the elastic mapping model; optimizing the position, capacity and control strategy of the intelligent soft switch equivalent model using a double-layer optimization algorithm to complete the optimal configuration of the distribution network. A corresponding terminal and medium are also provided. This method establishes an equivalent model for the normal operation and power supply recovery status of the intelligent soft switch; in order to maximize the effect of the intelligent soft switch on improving the energy supply elasticity, the optimal configuration of the normal operation and power supply recovery status is considered at the same time; in order to solve the optimal configuration style of the distribution network, a double-layer optimization algorithm is proposed for solution.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a distribution network optimization configuration method, system, terminal and medium based on elastic mechanics mapping. Background Art

[0002] Disasters such as hurricanes, earthquakes, and floods occur frequently, and the uncertainty, openness, and complexity of power system operation are gradually increasing, bringing unprecedented challenges to the continuous and stable power supply of the power grid. With the development of smart grid technology, microgrids, distributed power sources, and power electronic equipment have made distribution networks more vulnerable and susceptible to disturbances. Distribution networks urgently need to improve their energy supply elasticity. In recent years, improving the resilience of power systems has become a top priority for many countries. In distribution network dispatching, measures to improve the resilience of power systems mainly focus on preventive measures before disturbances and recovery measures after disturbances. Since it is difficult for the control center to obtain real-time operation data and take immediate measures when disturbances occur, the system needs to defend and recover according to its own characteristics and operating status.

[0003] As a fully controlled power electronic device, the intelligent soft open points (SOP) usually replaces the tie switch, effectively controls the active power distribution, compensates the reactive power, regulates the voltage, and adjusts the power distribution in the normal operation state. During the power supply restoration process, the control mode is changed to limit the short-circuit current and provide voltage support, thereby shortening the time required for power supply and reducing the scope of the fault impact. The use of smart grid technology enables the power system to operate and restore the load more efficiently to improve its resilience, which conforms to the characteristics of SOP. Therefore, its role in normal operation and after faults can be used to improve the power system's resistance to interference and its ability to recover from faults, thereby enhancing the resilience of the distribution network. Therefore, an optimal configuration scheme for distribution network optimization for resilience improvement is proposed, aiming to maximize resilience.

[0004] Compared with the transmission network, the distribution network is closely connected to the load and has a weaker ability to cope with interference. In addition, the complexity of the distribution network increases the difficulty of studying the resilience of the distribution network. Therefore, a reasonable assessment of the resilience of the distribution network can effectively reduce the scope of power outages, which is of great significance to safety, economy, environment and society. However, there are few studies on the resilience of distribution networks in the prior art. At present, no description or report of similar technology to the present invention has been found, and similar information at home and abroad has not yet been collected. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a distribution network optimization configuration method, system, terminal and medium based on elastic mechanics mapping.

[0006] According to one aspect of the present invention, a method for optimizing distribution network configuration based on elastic mechanics mapping is provided, comprising:

[0007] Based on the relationship between spring force, displacement and elastic coefficient in the mechanical spring model, mapping is performed according to the power system flow calculation formula to construct a power system energy supply elasticity mapping model;

[0008] Based on the power system energy supply elasticity mapping model, an equivalent model of normal operation and power supply recovery state of the intelligent soft switch is constructed;

[0009] A double-layer optimization algorithm is used to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model to complete the optimal configuration of the distribution network and improve the energy supply elasticity of the power system.

[0010] Preferably, the relationship between the spring force, displacement and elastic coefficient in the mechanical spring model is mapped according to the power system flow calculation formula to construct the power system energy supply elasticity mapping model, including:

[0011] According to the static power flow equation of the power system, combined with the magnitude and direction of the force in the mechanical spring model, based on the mapping criterion, the elastic network branch of the power system is mapped into a two-degree-of-freedom elastic branch; where:

[0012] The mapping criterion is: the x and y axis directions of each elastic network branch of the power system correspond to the xy axis of the plane coordinate system of the two-degree-of-freedom elastic branch; the xy direction component S of the power of each elastic network branch of the power system x , S y The elastic force F corresponding to the two-degree-of-freedom elastic branch x 、F y ; The xy-direction component U of the voltage drop of each elastic network branch of the power system x , U y Corresponding to the deformation variables Δx and Δy of the two-degree-of-freedom elastic branch;

[0013] The static power flow equation of the power system is:

[0014]

[0015]

[0016] U x =e i -e j

[0017] U y =f i -f j

[0018]

[0019] Among them, Pij Indicates the active power transmitted by the line, Q ij Represents the reactive power transmitted by the line, r ij represents the line resistance, x ij Represents line reactance, S ij represents the apparent power, z ij represents the line impedance, e i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively, represents the voltage vector at node i, represents the voltage vector at node j, U x Indicates voltage drop The component in the direction, U y The voltage drop The component in the y direction;

[0020] The apparent power S ij The voltage is decomposed into S in the x and y directions x and S y ,get:

[0021] S ij 2 =P ij 2 +Q ij 2 =S x 2 +S y 2 ;

[0022] Map the elastic network branches of the power system to the physical spring system:

[0023] In the x direction

[0024] In the y direction

[0025] Among them, F x represents the x-direction force mapped from the power coupling according to the mapping relationship, Δx represents the x-direction displacement mapped from the x-direction component of the voltage drop in the rectangular coordinate system, and F y represents the y-direction force mapped from the power coupling according to the mapping relationship, and Δy represents the y-direction displacement mapped from the y-direction component of the voltage drop in the rectangular coordinate system;

[0026] Since the elastic potential energy generated by the spring deformation is the integral of the force over the displacement, the elastic potential energy E of the two-degree-of-freedom spring is calculated by this mapping l for:

[0027]

[0028] Calculate the mapped elastic potential energy E of a two-degree-of-freedom spring branch ij for:

[0029]

[0030] Assuming that the elastic network of the power system consists of n branches, the total elastic potential energy E of the power system is l∑ for:

[0031]

[0032] Calculate the resultant force F of the equivalent force on the power system eq for:

[0033]

[0034] Constructing the equivalent elasticity coefficient k of the power system energy supply elasticity mapping model eq for:

[0035]

[0036] The obtained equivalent elasticity coefficient of the power system functional elasticity mapping model is evaluated in multiple stages to obtain an evaluation index of the equivalent elasticity coefficient of the power system functional elasticity mapping model.

[0037] Preferably, the constructing of an equivalent model of normal operation and power supply recovery state of an intelligent soft switch based on the power system energy supply elasticity mapping model includes:

[0038] When the intelligent soft switch is in normal operation, the PQ-V dc The Q control mode realizes the control of power between feeders;

[0039] Based on the transmission power of the VSC (voltage source converter), in combination with the wide area measurement system (WAMS), the equivalent forces in the x and y directions of the power system energy supply elasticity mapping model are calculated;

[0040] Based on the equivalent stress and equivalent deformation of the mapping relationship of the power system energy supply elasticity mapping model, the equivalent elastic coefficient k0 of the power system under normal operating conditions is calculated by superimposing the stress and energy of the branch containing the intelligent soft switch and the other branches;

[0041] When a fault occurs in the power system, the intelligent soft switch responds to restore power supply; based on the control voltage measured by the wide area measurement system (WAMS), the force and energy of the branch containing the intelligent soft switch are obtained; the equivalent elastic potential energy of the intelligent soft switch branch and the remaining branches is superimposed, and the equivalent elastic coefficient under the current power supply restoration state is calculated through force decomposition and synthesis;

[0042] Under all N-1 faults, calculate the equivalent elastic coefficient k1 of the power system in the power supply restoration state according to the above steps;

[0043] Based on the equivalent elastic coefficients k0 and k1, the energy supply elasticity of the power system containing the intelligent soft switch is calculated, and then an equivalent model of the normal operation and power supply recovery state of the intelligent soft switch is constructed.

[0044] Preferably, the use of a double-layer optimization algorithm to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model includes:

[0045] Converting the optimization configuration problem of the intelligent soft switch equivalent model into a mixed integer nonlinear problem, wherein the capacity and position variables of the intelligent soft switch equivalent model are both integer values, and the control variable is any value within a given range;

[0046] Construct an objective function to maximize the effect of intelligent soft switches on improving the energy supply flexibility of the power system;

[0047] Constructing a two-layer optimization algorithm, wherein the upper layer of the two-layer optimization algorithm is used to solve the optimization problem of allocating intelligent soft switches from candidate locations and a given total capacity, and the lower layer of the two-layer optimization algorithm is used to solve the optimization problem of the operation control strategy of the intelligent soft switches subject to the constraints of the upper layer;

[0048] Based on the two-layer optimization algorithm, after the operation control strategy of the lower layer is determined, the elasticity value at this time is returned to the upper layer, and this process is iterated until the objective function reaches the optimal result, thereby completing the optimization of the intelligent soft switch equivalent model.

[0049] Preferably, the objective function of constructing the intelligent soft switch to maximize the effect of improving the energy supply elasticity of the power system includes:

[0050] Performing a multi-stage evaluation on the equivalent elasticity coefficient of the obtained power system functional elasticity mapping model to obtain an evaluation index of power system elasticity, and taking maximization of the evaluation index as an objective function;

[0051] The objective function that maximizes the effect of intelligent soft switches on improving the flexibility of distribution networks is constructed as follows:

[0052] max Res(x)(1)

[0053] Where x is the set of decision variables for the position and capacity of the intelligent soft switch;

[0054] Establish constraint equations of the objective function, including: power flow constraint equations, safety constraint equations and intelligent soft switch operation constraint equations; wherein:

[0055] The flow constraint equation is expressed in rectangular coordinates as:

[0056]

[0057]

[0058] e i 2 +f i 2 =U i 2 (4)

[0059] Where N(i) represents the nodes connected to node i; i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively; G ij and B ij represents the conductance and susceptance of the branch; P i and Q i are the active power and reactive power injected into each node;

[0060] The safety constraint equation is:

[0061] U imin <U i <U imax (5)

[0062] I l 2 <(I lmax ) 2 (6)

[0063] Among them, U imin and U imax are the lower and upper limits of the voltage amplitude at node i; I l is the current amplitude of branch ij; I lmax is the upper limit of the current in branch ij;

[0064] Assume that the intelligent soft switch operates normally at PQ-V dc In Q control mode, when power is restored, it runs at V dc In the Q-Vf control mode, the intelligent soft switch operation constraint equation is:

[0065] P i,SOP +P j,SOP =0 (7)

[0066]

[0067]

[0068] U j,SOP ≥U0 (10)

[0069] Where i and j are the node numbers of the power system to which the intelligent soft switch is connected; P i,SOP , P j,SOP , Q i,SOP , Q j,SOP are the active power and reactive power injected by the two converters of the intelligent soft switch respectively; S i,SOP , S j,SOP is the capacity of the intelligent soft switch connected to the node; U j,SOP is the control voltage of node j of the intelligent soft switch on the fault side; U0 is the minimum voltage amplitude of the constant voltage control terminal node in the set intelligent soft switch using the voltage-frequency control method.

[0070] Preferably, the upper layer of the two-layer optimization algorithm adopts a genetic algorithm, including:

[0071] The location, capacity and control variables of the intelligent soft switch are encoded into chromosomes, wherein the chromosomes contain the location and capacity information of the intelligent soft switch; the length of the integer variable is determined by the candidate installation location; the number on the candidate location represents the capacity of the intelligent soft switch installed at the corresponding location;

[0072] The optimized configuration scheme of the smart soft switches with energy supply elasticity higher than the set threshold a is replaced by the optimized configuration scheme of the same number of smart soft switches with energy supply elasticity lower than the set threshold b;

[0073] Based on the unified crossover operator and recombination probability, the capacity of two random individuals at the same position is exchanged, so that the total capacity of the intelligent soft switch corresponding to each scheme is constant;

[0074] If any number between 0 and 1 randomly generated during the mutation process is less than the set mutation probability, the capacities of any two smart soft switch positions in the optimal configuration scheme of the exchanged smart soft switches;

[0075] For a chromosome of the first generation, the elasticity value under the current control strategy of the lower layer is used as the objective function value; if the number of iterations does not reach the maximum number of iterations, the iteration continues, otherwise the result is output.

[0076] Preferably, the capacities of two random individuals at the same position are exchanged based on a unified crossover operator and recombination probability so that the total capacity of the intelligent soft switch corresponding to each scheme is constant, including:

[0077] When the total capacity is greater than the specified capacity, the capacity of the intelligent soft switch that is less than the absolute value of the capacity difference is randomly reduced, and the intelligent soft switch is randomly installed at the corresponding position of the individual until the total capacity is equal to the specified capacity;

[0078] When the total capacity is less than the specified capacity, a smart soft switch capacity that is less than the absolute value of the capacity difference is randomly selected and the smart soft switch is randomly installed in the currently selected individuals until the total capacity is equal to the specified capacity.

[0079] Preferably, the lower layer of the two-layer optimization algorithm adopts a particle swarm algorithm, including:

[0080] In normal operation, the intelligent soft switch operates at PQ-V dc In the Q control mode, the control variables are active power and reactive power. In the power recovery phase, the intelligent soft switch will operate at V dc In the Q-Vf mode, the control variables are active power, reactive power and voltage;

[0081] An intelligent soft switch has 4 variables X during normal operation nPi , X nQi , X nPj and X nQj , there are 6 variables X in the power recovery phase rPi , X rQi , X rPj , X rQj , X rUi and X rUj , whose particles are encoded according to the number of smart soft switches as follows:

[0082] X l-ij =[X nPi ,X nQi ,X nPj ,X nQj ,X rPi ,X rQi ,X rPj ,X rQj ,X rUi ,X rUj ]

[0083] X=[X l1 ,X l2 ,…,X ln ]

[0084] Among them, X l-ij is the variable set of branch l with nodes i and j as endpoints, X nPi , X nQi , X nPj and X nQj represents the transmission power control variable under normal operating conditions, corresponding to the intelligent soft switch operating at PQ-V dc In Q control mode, the active power and reactive power transmitted by the VSCs on both sides; X rPi , X rQi , X rPj and XrQj Represents the control variable in the power recovery phase, corresponding to the operation at V dc The active power and reactive power at end i or end j in Q mode (because it is uncertain which end the non-fault side is located at, the active power and reactive power at both ends are listed as control variables. When a specific fault occurs, only the control variables on the non-fault side are optimized) X rUi and X rUj represents the voltage control variable, corresponding to the control voltage at the fault side i or j when running in Vf mode during the power supply recovery phase. The variable X nPi , X nQi , X nPj , X nQj , X rPi , X rQi , X rPj , X rQj , X rUi and X rUj constituent particles;

[0085] The method for obtaining the operation control strategy of the intelligent soft switch through the particles is specifically as follows:

[0086] Under the position and capacity conditions of the intelligent soft switch given by the upper layer, the control variables of the normal operation and power supply recovery states are initialized, and the equivalent elastic coefficients k0 and k1 of the corresponding power system functional elasticity mapping model are calculated at this time, and the fitness corresponding to the operation control strategy under the normal operation and power supply recovery states is obtained. The fitness curves corresponding to different operation control strategies are compared respectively, and the operation control strategy corresponding to the energy supply elasticity is higher than the set threshold c. The speed and position of the particle are updated before the next operation control strategy is determined. If the number of iterations has been met, the optimal operation control strategy under the given position and capacity configuration is obtained; if the number of iterations has not been reached, the energy supply elasticity at this time is calculated under the new particle speed and position conditions, and the fitness corresponding to the new strategy and the fitness corresponding to the optimal result obtained by the previously run strategy are calculated.

[0087] According to another aspect of the present invention, there is provided a distribution network optimization configuration system based on elastic mechanics mapping, comprising:

[0088] Elastic mapping model module, which is based on the relationship between spring force, displacement and elastic coefficient in the mechanical spring model, and maps according to the power system flow calculation formula to construct a power system energy supply elastic mapping model;

[0089] An equivalent model module, which constructs an equivalent model of normal operation and power supply recovery state of the intelligent soft switch based on the power system energy supply elasticity mapping model;

[0090] The optimization module uses a double-layer optimization algorithm to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model to complete the optimal configuration of the distribution network.

[0091] According to a third aspect of the present invention, a terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute any of the above-mentioned methods or to run the above-mentioned system when executing the program.

[0092] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the above methods, or to run the above system.

[0093] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0094] The invention provides a distribution network optimization configuration method, system, terminal, and medium based on elastic mechanics mapping, and constructs an elastic mapping model based on the equivalent elastic coefficient of elastic mechanics mapping to evaluate the elasticity of the distribution network and establish a mixed integer nonlinear optimization problem for the optimal configuration of the distribution network. The mixed integer nonlinear optimization problem of the distribution network is solved by a double-layer optimization algorithm, wherein a genetic algorithm determines the position and capacity of the SOP, and a particle swarm algorithm obtains the optimal control strategy of the SOP, thereby realizing an optimization scheme for the elasticity of the distribution network.

[0095] The distribution network optimization configuration method, system, terminal, and medium based on elastic mechanics mapping provided by the present invention innovatively convert the system energy supply elasticity problem into the equivalent elasticity coefficient of the calculation system according to the mapping relationship between elastic mechanics and power system flow calculation, and return the problem to the mechanism characteristics such as the calculation system parameters.

[0096] The distribution network optimization configuration method, system, terminal, and medium based on elastic mechanics mapping provided by the present invention simultaneously consider the improvement effect of SOP on the system energy supply elasticity during normal operation and power supply restoration, establish equivalent models under two scenarios, and further quantify the improvement effect.

[0097] The distribution network optimization configuration method, system, terminal, and medium based on elastic mechanics mapping provided by the present invention adopt a double-layer optimization algorithm to achieve the optimization configuration for improving the energy supply elasticity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0099] Figure 1The present invention is a flowchart of a method for optimizing distribution network configuration based on elastic mechanics mapping in one embodiment of the present invention.

[0100] Figure 2 It is a schematic diagram of an equivalent branch model in a preferred embodiment of the present invention.

[0101] Figure 3 A schematic diagram of equivalent branches with virtual nodes in a preferred embodiment of the present invention.

[0102] Figure 4 Schematic diagram of an equivalent model of a branch for installing SOP in a preferred embodiment of the present invention.

[0103] Figure 5 Schematic diagram of an equivalent branch model with SOP in a preferred embodiment of the present invention.

[0104] Figure 6 Flowchart of a two-layer optimization algorithm in a preferred embodiment of the present invention.

[0105] Figure 7 A schematic diagram of an IEEE 33-node test system in a preferred embodiment of the present invention.

[0106] Figure 8 A schematic diagram of elasticity indicators (using height to reflect the size of elasticity) corresponding to all planning schemes in a preferred embodiment of the present invention.

[0107] Fig. 9 A schematic diagram of elasticity indicators (using height to reflect the size of elasticity) corresponding to all planning schemes in a preferred embodiment of the present invention.

[0108] Fig.10 A fitness curve diagram of different scenarios in a preferred embodiment of the present invention.

[0109] Fig.11 A schematic diagram of active power and reactive power distribution of a node in a preferred embodiment of the present invention; wherein (a) is a schematic diagram of active power distribution, and (b) is a schematic diagram of reactive power distribution.

[0110] Fig.12 The figure is a schematic diagram of component modules of a distribution network optimization configuration system based on elastic mechanics mapping in one embodiment of the present invention. DETAILED DESCRIPTION

[0111] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0112] Figure 1A flow chart of a distribution network optimization configuration method based on elastic mechanics mapping provided in one embodiment of the present invention.

[0113] like Figure 1 As shown, the distribution network optimization configuration method based on elastic mechanics mapping provided in this embodiment may include the following steps:

[0114] S100, based on the relationship between the spring force, displacement and elastic coefficient in the mechanical spring model, mapping is performed according to the power system flow calculation formula to construct a power system energy supply elasticity mapping model;

[0115] S200, based on the power system energy supply elasticity mapping model, builds an equivalent model of normal operation and power supply recovery status of intelligent soft switches;

[0116] S300 uses a two-layer optimization algorithm to optimize the location, capacity and control strategy of the intelligent soft switch equivalent model, completes the optimal configuration of the distribution network, and realizes the improvement of the energy supply flexibility of the power system.

[0117] In S100 of this embodiment, as a preferred embodiment, based on the relationship between the spring force, displacement and elastic coefficient in the mechanical spring model, mapping is performed according to the power system flow calculation formula to construct a power system energy supply elasticity mapping model, which may include the following steps:

[0118] S101, according to the static power flow equation of the power system, combined with the magnitude and direction of the force in the mechanical spring model, based on the mapping criterion, the elastic network branch of the power system is mapped into a two-degree-of-freedom elastic branch; wherein:

[0119] The mapping criteria are: the x and y axis directions of each elastic network branch of the power system correspond to the xy axis of the plane coordinate system of the two-degree-of-freedom elastic branch; the xy direction component S of the power of each elastic network branch of the power system x , S y The elastic force F corresponding to the two-degree-of-freedom elastic branch x 、F y ; The xy-direction component U of the voltage drop of each elastic network branch of the power system x , U y Corresponding to the deformation variables Δx and Δy of the two-degree-of-freedom elastic branch;

[0120] The static power flow equation of the power system is:

[0121]

[0122]

[0123] U x =e i -e j

[0124] U y =f i -f j

[0125]

[0126] Among them, P ij Indicates the active power transmitted by the line, Q ij Represents the reactive power transmitted by the line, r ij represents the line resistance, x ij Represents line reactance, S ij represents the apparent power, z ij represents the line impedance, e i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively, represents the voltage vector at node i, represents the voltage vector at node j, U x Indicates voltage drop The component in the direction, U y The voltage drop The component in the y direction;

[0127] S102, the apparent power S ij The voltage is decomposed into S in the x and y directions x and S y ,get:

[0128] S ij 2 =P ij 2 +Q ij 2 =S x 2 +S y 2 ;

[0129] S103, mapping each elastic network branch of the power system to the elastic spring system:

[0130] In the x direction

[0131] In the y direction

[0132] Among them, F x represents the x-direction force mapped from the power coupling according to the mapping relationship, Δx represents the x-direction displacement mapped from the x-direction component of the voltage drop in the rectangular coordinate system, and F yrepresents the y-direction force mapped from the power coupling according to the mapping relationship, and Δy represents the y-direction displacement mapped from the y-direction component of the voltage drop in the rectangular coordinate system;

[0133] S104, since the elastic potential energy generated by the spring deformation is the integral of the force over the displacement, the elastic potential energy E of the two-degree-of-freedom spring is calculated by this mapping l for:

[0134]

[0135] S105, calculate the mapped elastic potential energy E of the two-degree-of-freedom spring branch ij for:

[0136]

[0137] S106, assuming that the elastic network of the power system consists of n branches, then the total elastic potential energy E of the power system l∑ for:

[0138]

[0139] S107, calculate the resultant force F of the equivalent force on the power system eq for:

[0140]

[0141] S108, construct the equivalent elasticity coefficient k of the power system energy supply elasticity mapping model eq for:

[0142]

[0143] S109, performing a multi-stage evaluation on the equivalent elastic coefficient of the obtained power system function elasticity mapping model to obtain an evaluation index of the equivalent elastic coefficient of the power system function elasticity mapping model, and the evaluation index will be used to construct a subsequent objective function.

[0144] In S200 of this embodiment, as a preferred embodiment, based on the power system energy supply elasticity mapping model, constructing an equivalent model of normal operation and power supply recovery state of the intelligent soft switch may include the following steps:

[0145] S201, intelligent soft switch in normal operation, using PQ-V dc The Q control mode realizes the control of power between feeders;

[0146] S202, based on the transmission power of the VSC (the transmission power of the VSC is known), in combination with a wide area measurement system (WAMS), calculating the equivalent forces in the x and y directions of the power system energy supply elasticity mapping model;

[0147] S203, based on the equivalent stress and equivalent deformation of the mapping relationship of the power system energy supply elasticity mapping model, by superimposing the stress and energy of the branch containing the intelligent soft switch and the other branches, calculating the equivalent elastic coefficient k0 of the power system under normal operating conditions;

[0148] S204, when a fault occurs in the power system, the intelligent soft switch responds to restore power supply; based on the control voltage measured by the wide area measurement system (WAMS), the force and energy of the branch containing the intelligent soft switch are obtained; the equivalent elastic potential energy of the intelligent soft switch branch and the remaining branches are superimposed, and the equivalent elastic coefficient under the current power supply restoration state is calculated through force decomposition and synthesis;

[0149] S205, under all N-1 faults, calculate the equivalent elastic coefficient k1 of the power system in the power supply restoration state according to the above steps;

[0150] S206, based on the equivalent elastic coefficients k0 and k1, the energy supply elasticity of the power system including the intelligent soft switch is calculated, and then an equivalent model of the normal operation and power supply recovery state of the intelligent soft switch is constructed.

[0151] In S300 of this embodiment, as a preferred embodiment, a two-layer optimization algorithm is used to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model, which may include the following steps:

[0152] S301, converting the optimization configuration problem of the intelligent soft switch equivalent model into a mixed integer nonlinear problem, wherein the capacity and position variables of the intelligent soft switch equivalent model are both integer values, and the control variable is any value within a given range;

[0153] S302, constructing an objective function for maximizing the effect of intelligent soft switches on improving the energy supply flexibility of the power system;

[0154] S303, constructing a two-layer optimization algorithm, wherein the upper layer of the two-layer optimization algorithm is used to solve the optimization problem of allocating intelligent soft switches from candidate locations and a given total capacity, and the lower layer of the two-layer optimization algorithm is used to solve the optimization problem of the operation control strategy of the intelligent soft switch subject to the constraints of the upper layer;

[0155] S304, based on the two-layer optimization algorithm, after determining the operation control strategy of the lower layer, the elasticity value at this time is returned to the upper layer, and this process is iterated until the objective function reaches the optimal result, completing the optimization of the intelligent soft switch equivalent model.

[0156] In S302 of this embodiment, as a preferred embodiment, constructing an objective function for maximizing the effect of the intelligent soft switch on improving the energy supply flexibility of the power system may include the following steps:

[0157] S3021, performing a multi-stage evaluation on the equivalent elasticity coefficient of the obtained power system functional elasticity mapping model to obtain an evaluation index of power system elasticity, and taking maximization of the evaluation index as an objective function;

[0158] S3022: Construct an objective function to maximize the effect of intelligent soft switches on improving the flexibility of the distribution network:

[0159] max Res(x)(1)

[0160] Where x is the set of decision variables for the position and capacity of the intelligent soft switch;

[0161] S3023, establishing constraint equations of the objective function, including: power flow constraint equations, safety constraint equations, and intelligent soft switch operation constraint equations; wherein:

[0162] The power flow constraint equation is expressed in rectangular coordinates as:

[0163]

[0164]

[0165] e i 2 +f i 2 =U i 2 (4)

[0166] Where N(i) represents the nodes connected to node i; i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively; G ij and B ij represents the conductance and susceptance of the branch; P i and Q i are the active power and reactive power injected into each node;

[0167] The safety constraint equation is:

[0168] U imin <U i <U imax (5)

[0169] I l 2 <(I lmax ) 2 (6)

[0170] Among them, U imin and U imaxare the lower and upper limits of the voltage amplitude at node i; I l is the current amplitude of branch ij; I lmax is the upper limit of the current in branch ij;

[0171] Assume that the intelligent soft switch operates normally at PQ-V dc In Q control mode, when power is restored, it runs at V dc In the Q-Vf control mode, the intelligent soft switch operation constraint equation is:

[0172] P i,SOP +P j,SOP =0 (7)

[0173]

[0174]

[0175] U j,SOP ≥U0(10)

[0176] Where i and j are the node numbers of the power system to which the intelligent soft switch is connected; P i,SOP , P j,SOP、 Q i,SOP , Q j,SOP are the active power and reactive power injected by the two converters of the intelligent soft switch respectively; S i,SOP , S j,SOP is the capacity of the intelligent soft switch connected to the node; U j,SOP is the control voltage of node j of the intelligent soft switch on the fault side; U0 is the minimum voltage amplitude of the constant voltage control terminal node in the set intelligent soft switch using the voltage-frequency control method.

[0177] In S303 of this embodiment, as a preferred embodiment, the upper layer of the two-layer optimization algorithm adopts a genetic algorithm, which may include the following steps:

[0178] S3031, encoding the location, capacity and control variables of the intelligent soft switch into a chromosome, wherein the chromosome contains the location and capacity information of the intelligent soft switch; the length of the integer variable is determined by the candidate installation location; the number on the candidate location represents the capacity of the intelligent soft switch installed at the corresponding location;

[0179] S3032, replacing the optimized configuration scheme of the smart soft switches with energy supply elasticity higher than the set threshold a with the optimized configuration scheme of the same number of smart soft switches with energy supply elasticity lower than the set threshold b;

[0180] S3033, based on a unified crossover operator and recombination probability, the capacities of two random individuals at the same position are exchanged, so that the total capacity of the intelligent soft switch corresponding to each scheme is constant;

[0181] S3034, if any number between 0 and 1 randomly generated during the mutation process is less than the set mutation probability, the capacities of any two smart soft switch positions in the optimal configuration scheme of the exchanged smart soft switches;

[0182] S3035, for a generation of chromosomes, the elasticity value under the current control strategy of the lower layer is used as the objective function value; if the number of iterations does not reach the maximum number of iterations, continue to iterate, otherwise output the result.

[0183] In S3031 of this embodiment, the control variable refers to the intelligent soft switch operating at PQ-V in normal operation. dc In the Q control mode, the control variables are active power and reactive power. In the power recovery phase, the intelligent soft switch will operate at V dc In Q-Vf mode, the controlled variables are active power, reactive power and voltage.

[0184] In S3032 of this embodiment, when the energy supply elasticity is higher than the set threshold a, it is determined that the energy supply elasticity at this time has a high energy supply elasticity, which means that when the load changes or the system is disturbed, the system energy supply capacity is less affected, corresponding to the calculated energy supply elasticity index is high. Correspondingly, when the energy supply elasticity is lower than the set threshold b, it is determined that the energy supply elasticity at this time has a low energy supply elasticity, which means that when the load changes or the system is disturbed, the system energy supply capacity is greatly affected, corresponding to the calculated energy supply elasticity index is low.

[0185] Furthermore, both the threshold a and the threshold b can be set according to the actual operation of the power system.

[0186] In S3033 of this embodiment, as a preferred embodiment, exchanging the capacities of two random individuals at the same position based on a unified crossover operator and recombination probability so that the total capacity of the intelligent soft switch corresponding to each scheme is constant may include the following steps:

[0187] S30331, when the total capacity is greater than the specified capacity, randomly reduce the capacity of the intelligent soft switch that is less than the absolute value of the capacity difference, and randomly install the intelligent soft switch at the corresponding position of the individual until the total capacity is equal to the specified capacity;

[0188] S30332, when the total capacity is less than the specified capacity, randomly select an intelligent soft switch capacity that is less than the absolute value of the capacity difference, and randomly install the intelligent soft switch in the currently selected individual until the total capacity is equal to the specified capacity.

[0189] In S303 of this embodiment, as a preferred embodiment, the lower layer of the two-layer optimization algorithm adopts a particle swarm algorithm, which may include the following steps:

[0190] S303i, in normal operation, the intelligent soft switch operates at PQ-V dc In the Q control mode, the control variables are active power and reactive power. In the power recovery phase, the intelligent soft switch will operate at V dc In the Q-Vf mode, the control variables are active power, reactive power and voltage;

[0191] S303ii, an intelligent soft switch has 4 variables X in normal operation nPi , X nQi , X nPj and X nQj , there are 6 variables X in the power recovery phase rPi , X rQi , X rPj , X rQj , X rUi and X rUj , whose particles are encoded according to the number of smart soft switches as follows:

[0192] X l-ij =[X nPi ,X nQi ,X nPj ,X nQj ,X rPi ,X rQi ,X rPj ,X rQj ,X rUi ,X rUj ]

[0193] X=[X l1 ,X l2 ,…,X ln ]

[0194] Among them, X l-ij is the variable set of branch l with nodes i and j as endpoints, X nPi , X nQi , X nPj and X nQj represents the transmission power control variable under normal operating conditions, corresponding to the intelligent soft switch operating at PQ-V dc In Q control mode, the active power and reactive power transmitted by the VSCs on both sides; X rPi , X rQi , X rPj and X rQj Represents the control variable in the power recovery phase, corresponding to the operation at V dc The active power and reactive power at end i or end j in Q mode (because it is uncertain which end the non-fault side is located at, the active power and reactive power at both ends are listed as control variables. When a specific fault occurs, only the control variables on the non-fault side are optimized) XrUi and X rUj represents the voltage control variable, corresponding to the control voltage at the fault side i or j when running in Vf mode during the power supply recovery phase. The variable X nPi , X nQi , X nPj , X nQj , X rPi , X rQi , X rPj , X rQj , X rUi and X rUj constituent particles;

[0195] S303iii, a method for obtaining an operation control strategy of an intelligent soft switch through particles, specifically:

[0196] Under the position and capacity conditions of the intelligent soft switch given by the upper layer, the control variables of the normal operation and power supply recovery states are initialized, and the equivalent elastic coefficients k0 and k1 of the corresponding power system functional elasticity mapping model are calculated at this time, and the fitness corresponding to the operation control strategy under the normal operation and power supply recovery states is obtained. The fitness curves corresponding to different operation control strategies are compared, and the corresponding operation control strategy when the energy supply elasticity is higher than the set threshold a (that is, the energy supply elasticity is higher) is determined. Before the next operation control strategy is determined, the speed and position of the particle are updated. If the number of iterations has been met, the optimal operation control strategy under the given position and capacity configuration is obtained; if the termination condition (that is, the number of iterations) is not reached, the energy supply elasticity at this time is calculated under the new particle speed and position conditions, and a comparison is made, that is, the fitness corresponding to the new strategy and the fitness corresponding to the optimal result obtained by the previously run strategy are calculated.

[0197] The working principle and technical solution of the distribution network optimization configuration method based on elastic mechanics mapping provided by the above embodiment of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0198] 1. Problem Construction

[0199] According to the multi-stage characteristics of resilience, all N-1 fault conditions can be considered in advance to calculate the resilience of the power system. The objective function (1) represents the maximization of the SOP's effect on resilience.

[0200] max Res(x)(1)

[0201] where x is the set of decision variables for location and capacity.

[0202] The problem is solved under power flow constraints, operation constraints and SOP operation constraints.

[0203] A. System flow limitation:

[0204] The flow equations in (2)-(4) are expressed in rectangular coordinates.

[0205]

[0206]

[0207] e i 2 +f i 2 =U i 2 (4)

[0208] Where N(i) represents the nodes connected to node i. i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively. ij and B ij Represents the conductance and susceptance of the branch. i and Q i are the active power and reactive power injected into each node.

[0209] B. System security constraints:

[0210] U imin and U imax are the lower and upper limits of the voltage amplitude at node i, respectively. l is the current amplitude of branch ij, I lmax is the upper limit of the current of branch ij. Then the safety constraint equation is:

[0211] U imin <U i <U imax (5)

[0212] I l 2 <(I lmax ) 2 (6)

[0213] C.SOP operation constraints:

[0214] Under normal operation, SOP is usually in PQ-V dc The model is described in (7)-(9), where active power and reactive power are the control variables of SOP. Since the loss of SOP is related to the injected power, the model can be expressed as a lossless SOP by the injected power. In order to simplify the calculation process, the model does not consider the loss. Then the intelligent soft switch operation constraint equation is:

[0215] P i,SOP+P j,SOP =0 (7)

[0216]

[0217]

[0218] Where i and j are the node numbers of the power system to which the intelligent soft switch is connected; P i,SOP , P j,SOP、 Q i,SOP、 Q j,SOP are the active power and reactive power injected by the two converters of the intelligent soft switch respectively; S i,SOP , S j,SOP is the capacity of the intelligent soft switch connected to the node; U j,SOP is the control voltage of node j of the intelligent soft switch on the fault side; U0 is the minimum voltage amplitude of the constant voltage control terminal node in the set intelligent soft switch using the voltage-frequency control method.

[0219] During power recovery, SOP is at V dc Q-Vf control mode operation. V dc The -Q control mode can be equivalent to the PQ control. Assuming that the fault occurs in the region of node j, the control variables are the active power and reactive power of VSC1 and the output voltage of VSC2. In addition to (7)-(9), the voltage on the fault side of the SOP also needs to satisfy (10).

[0220] U j,SOP ≥U0 (10)

[0221] 2. Quantification of Distribution Network Resilience

[0222] 2.1 Power System Resilience Mapping Model

[0223] According to Hooke's law in elastic mechanics, the relationship between the force and deformation of the elastic branch is measured by the elastic coefficient of the spring. Based on the static flow equations (11) and (12), considering the magnitude and direction of the force, the network branch can be mapped into a two-degree-of-freedom elastic branch.

[0224]

[0225]

[0226] Among them U x (U x =e i -e j ) represents the voltage drop The component in the x direction, and (U y =f i -f j) represents the component of the voltage drop in the y direction. In (13), the apparent power S ij The voltage is decomposed into S in the x and y directions x and S y :

[0227] S ij 2 =P ij 2 +Q ij 2 =S x 2 +S y 2 (13)

[0228] The power system is mapped onto an elastic spring system according to the following analogy:

[0229] In the x direction

[0230] In the y direction

[0231] like Figure 2 As shown in the figure, the electrical branch can be equivalent to a spring branch that is stressed in the direction of the voltage drop. Figure 2 Medium, F eq The apparent power S of the corresponding branch ij Mapped; F P and F Q They correspond to the active power and reactive power of the corresponding branches respectively. The nodes and branches of the power system correspond to the nodes and branches in the elastic network model respectively. The position of each node is determined by the amplitude and phase angle of the voltage and is distributed in the xy coordinate system of the voltage vector. The mapping structure corresponding to the series-parallel structure of the circuit remains unchanged, corresponding to the series-parallel connection of the spring branches.

[0232] In elastic mechanics, the elastic potential energy generated by spring deformation is the integral of force over displacement. Based on this mapping, the elastic potential energy of a two-degree-of-freedom spring can be calculated, see formula (14).

[0233]

[0234] The force direction of each equivalent spring branch is different. In order to realize the topological mapping from the branch to the network, the force is decomposed into the x and y directions according to the composition and decomposition of the force described in formula (13), and the mapped elastic potential energy of the spring branch with two degrees of freedom defined in formula (15) is calculated.

[0235]

[0236] Assuming that the elastic network consists of n branches, the total potential energy satisfies the linear superposition characteristic and is independent of the direction of the force on the branch, which is also applicable to mapping the elastic potential energy of the power system. The elastic potential energy of the system can be written as (16):

[0237]

[0238] In different equivalent spring branches, the forces in different directions cannot be directly added together. Therefore, the forces in the x and y directions of each branch are added separately and then combined to obtain the equivalent force of the power system. The steps to calculate the equivalent elastic coefficient of the power system are as follows:

[0239] First, calculate the total potential energy E of the elastic l∑ .

[0240] Next, calculate the resultant force in equation (17).

[0241]

[0242] Finally, the equivalent elasticity coefficient of the power system is constructed in (18).

[0243]

[0244] 2.2 Multi-stage assessment of grid resilience

[0245] The elastic coefficient of a spring is an indicator of its ability to recover after deformation, and can be used to compare the elasticity of different springs. According to its physical meaning, the defined equivalent elastic coefficient reflects the ability of the power grid to respond to external disturbances during the resilience assessment process. Under normal circumstances, it can measure the ability of the power system to resist disturbances, and during power restoration, it can be used as an indicator to judge the recovery ability. These two characteristics are the main features in the definition of a resilient power system.

[0246] The multi-stage process of the power system responding to disturbances is similar to the process of a spring with initial deformation recovering all or part of its deformation after a disturbance. The whole process can be divided into five stages, namely preparation, resistance, response, recovery and final operation mode.

[0247] The deformation of the equivalent elastic model represents the voltage level of the system, and the force of the spring corresponds to the load level at different stages. The elastic coefficients at different stages are used to measure the response capacity of the power system to disturbance events.

[0248] Stage 1 is the state of normal operation of the power system before being disturbed, corresponding to the stage where the spring system is not disturbed by external forces.

[0249] Phase 2 is the stage of system resistance to disturbance, which corresponds to the deformation of the spring system under the influence of external force. When subjected to the disturbance of equivalent tension, the spring needs to reduce its own load to ensure that the deformation range does not exceed the safety limit. In the process of resisting disturbance, the power grid should have strong robustness to resist disturbance, and the equivalent elastic coefficient k0 of the system phase 1 is used to measure the system's ability to resist disturbance.

[0250] Stage 3 is the stage in which the power system recovers after being disturbed, corresponding to the stage in which the spring system receives the restoring force.

[0251] Phase 4 is the recovery phase after the disturbance event. After the partial recovery force is supplemented, the system can appropriately increase the load tension to achieve the recovery of the deformation. In the power supply recovery phase, the power grid must have sufficient redundancy and recovery to face the disturbance scenario and reduce power supply losses. The equivalent elastic coefficient k1 of the system in phase 3 is used as an indicator of recovery capacity.

[0252] Considering the system's response to extreme events and its recovery capacity, the indicators for multi-stage resilience assessment are defined according to (19). k0 can be calculated based on the flow under normal conditions. k1 is calculated according to formula (20), where k i is the elasticity coefficient of the power system when scenario i occurs. Scenario i represents the fault of branch i, which is a scenario among N-1 fault sets. k1 is the average equivalent elasticity coefficient of the system under all N-1 faults, which is used to measure the average recovery capacity of the system. The larger the k0 value, the stronger the ability of the power system to maintain stable and normal power supply, indicating a more robust system. The smaller the k1 value, the easier it is for the system to respond to recovery and the easier it is to recover to the required energy supply level, which means that the system has better recovery capacity.

[0253]

[0254]

[0255] 3. Elastic model of intelligent soft switch

[0256] In the above embodiment of the present invention, the SOP topology is a back-to-back voltage source converter (B2B VSC). In normal operation, one end of the VSC is V dc -Q control scheme to maintain the DC side voltage, while the other end VSC uses PQ control scheme to control the transmitted active power. When a disturbance occurs, SOP can change the control mode according to the location of the fault [54-57] to quickly restore the power supply to the load in the power outage area. The VSC in the area connected to the power outage area follows the Vf control mode, while the other end follows the V dc -Q control mode.

[0257] 3.1 Normal situation

[0258] like Figure 3 As shown in Figure 1, virtual nodes are added to explain the SOP equivalent model. The two parts in the branch without SOP installed correspond to Figure 3 S1 and S2 in the installation SOP, the two parts of the branch correspond to S1 and S2'. Assume that VSC1 is at V dc -Q control mode, while VSC2 works in PQ control mode.

[0259] Figure 4 It is the equivalent model of the branch installed with SOP in normal operation. load 0 is the equivalent force of the branch without SOP installed. Assume that the system has reached the deformation limit boundary. The voltage at the end of S2' is close to the safety boundary without SOP installed. After installing SOP, the control scheme V dc -Q remains unchanged. This control scheme corresponds to Figure 3 Medium F SOP , which can keep the spring branch away from the safe boundary of operation without changing the effectiveness of the spring branch. load is the force on the PQ control side after installing SOP, and is F load 0 With F SOP The equivalent force of the resultant force. F load 0 With F load The same amount but different direction. Figure 4 It can be seen that the resultant force on the branch where the SOP is installed makes it move away from the safety boundary. This equivalent model is consistent with the role of SOP in controlling the active transmission power and adjusting the power distribution during the normal operation stage. The magnitude and direction of the force are determined by the control variables P and Q of the inverter. During the normal operation stage, the equivalent force at the SOP control end only changes the direction of the force but not the magnitude of the force. Therefore, after the SOP is installed, the equivalent deformation of the branch will be reduced. Based on the calculation formula (21) of the equivalent elastic coefficient of the branch, the equivalent elastic coefficient is therefore increased, indicating that the resistance of the power system is improved.

[0260]

[0261] Whether the branch contains SOP will affect the size of the denominator. Since SOP adjusts the branch flow distribution, the pressure drop difference of the branch is small, k eq The correspondingly larger value means that the branch has stronger resistance under normal operation.

[0262] 3.2 Power supply restoration phase

[0263] After the disturbance occurs, the fault occurs on the S2 side of the branch without SOP installed, but on the S2' side of the branch with SOP installed. At this time, SOP quickly switches to VSC1. dc Q control and Vf control of VSC2.

[0264] Figure 5 It is the equivalent model of installing SOP branch after fault. loadmin is the equivalent tension of the branch without SOP installed, corresponding to the power loss of the system after the fault. In the equivalent model of the branch with SOP installed, S1 corresponds to the V dc -Q control side, S2' corresponds to the Vf control side. The process of SOP control mode conversion corresponds to the process of spring recovery with help. The solid arc in the figure is mapped by the operating boundary. On the Vf control side where the SOP branch is installed, the spring S2' takes one end of S1 as the center of the circle, and the other end moves from the outer solid circle to the inner dotted circle. Within the operating range, the load force changes from F loadmin Increase to F loadmax This is consistent with the function of the SOP to restore power to important loads in power-lost areas.

[0265] Formula (22) is the calculation formula for the equivalent elastic coefficient of the branch during power supply restoration. loadmin Represents the residual load after the load is removed from the branch without SOP installed. SOP provides voltage support for the fault area to supply power to important loads in the power-off area. During this process, the force gradually increases to F loadmax When the same voltage level is restored, the equivalent elastic coefficient value corresponding to the branch with SOP installed is smaller. That is, if the branch restores the same deformation, the SOP with a smaller elastic coefficient has a stronger recovery ability.

[0266] Based on the above equivalent model, when the distribution network with SOP installed is in normal operation, its ability to resist disturbances is improved. At the same time, during the power supply restoration process, SOP reduces the system's load loss and improves the system's recovery ability, thereby improving the resilience of the power grid from the perspective of pre-disaster prevention and post-disaster recovery.

[0267] 4. Optimization Algorithm for Improving Distribution Network Elasticity

[0268] The optimization model for improving grid resilience is a mixed integer nonlinear problem, where the capacity and location variables are integer values, while other variables (such as voltage and power flow) are arbitrary values ​​within a given range. In order to solve the model, this section proposes a heuristic method to obtain the planning scheme of SOP by solving the mixed integer linear problem.

[0269] Since the installation cost of SOP is high, its site selection and capacity planning are carried out under a certain total capacity. Since SOP is usually installed at the tie switch, its installation has certain location restrictions. The above embodiments of the present invention consider all possible SOP installation locations in order to maximize the flexibility of the distribution network.

[0270] The two-level optimization is suitable for solving planning problems that consider the SOP operation strategy. The upper level problem allocates SOPs from candidate locations and given total capacity, while the lower level model is the operation strategy problem of SOPs subject to the constraints of the upper level problem. After the control scheme of the lower level problem is determined, the elasticity value at this time is returned to the upper level problem, and the iteration will continue until the objective function reaches the optimal result.

[0271] 4.1 Upper-level issues

[0272] Select genetic algorithm to solve the upper problem. The specific steps are as follows.

[0273] (1) Encoding: The decision variables are encoded into chromosomes, which contain the location and capacity information of the SOP. The length of the integer variable is determined by the candidate location of the installation. The number on the candidate location represents the capacity of the SOP installed at the corresponding location. If the number at a specific location is 0, there is no SOP at this location. The integer variable can determine the installation capacity and location at the same time. The numbers at all locations of the variable are non-negative.

[0274] (2) Selection: To ensure that the fitness of the best individual in the current group is not lower than that of the previous generation, individuals with higher fitness will replace the same number of individuals with lower fitness.

[0275] (3) Reorganization: Exchange the capacities of two random individuals at the same position based on a unified crossover operator and recombination probability. If neither individual has an SOP installed at the selected position, no exchange is required. Because the total SOP capacity corresponding to each plan is certain, the following adjustments are made based on the difference between the current total capacity and the specified amount.

[0276] When the sum is greater than the specified capacity, randomly reduce the SOP capacity that is less than the absolute value of the difference and randomly install it at the corresponding position of the individual until the sum is equal to the specified capacity.

[0277] When the sum is less than the specified capacity, a capacity less than the absolute value of the capacity difference is randomly selected and installed randomly on the currently selected individual clock until the requirement is met.

[0278] (4) Mutation: If any number between 0 and 1 is less than the mutation probability, the capacity of any two SOP positions in the individual is swapped.

[0279] (5) Fitness calculation: For a chromosome of the first generation, the objective function value is the optimal value of elasticity under this plan, which needs to be calculated based on the particle swarm algorithm. A larger value indicates that the corresponding plan is better and has better elasticity.

[0280] (6) Termination judgment: If the number of iterations does not reach the maximum number of iterations, continue to iterate, otherwise output the result.

[0281] 4.2 Lower-level issues

[0282] Once the SOP position capacity is given, its control strategy will affect the elasticity index. Due to the high dimensionality and nonlinearity of the variables in the underlying problem, it is more appropriate to use a particle swarm optimization algorithm. In normal operation, the SOP usually runs at PQ-V dc In the Q control mode, the control variables are active and reactive power. When the SOP is in the power recovery phase, it will run at V dc In the Q-Vf mode, the control variables are active power, reactive power and voltage, which are selected as particles. A SOP has 4 variables in normal operation and 6 variables in the power recovery phase. Its particles are coded as follows according to the number of SOPs.

[0283] X l-ij =[X nPi ,X nQi ,X nPj ,X nQj ,X rPi ,X rQi ,X rPj ,X rQj ,X rUi ,X rUj ]

[0284] X=[X l1 ,X l2 ,…,X ln ]

[0285] Among them, X nPi , X nQi , X nPj and X nQj represents the transmission power control variable under normal conditions. rPi , X rQi , X rPj and X rQj represents the control variable in power restoration, X rUi and X rUj Corresponding to the variables of control voltage. All these control variables constitute SOP, and particles are composed of these control variables.

[0286] The flowchart of the specific calculation process is as follows Figure 6 shown.

[0287] The technical solution provided by the above embodiment of the present invention is simulated and analyzed in conjunction with specific application examples.

[0288] In this specific application example, a modified IEEE 33-node system is used to verify the rationality of the elastic mapping model and the effectiveness of the double-layer optimization algorithm in the optimization configuration method provided in the above embodiment of the present invention.

[0289] like Figure 7 The figure shows the improved IEEE 33-bus system. Its detailed data comes from "Baran ME, Wu F. F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Transactions power delivery 1989; 4(2): 1401-1407." Its rated voltage is 12.66 kV. Five tie switch positions, 21-8, 9-15, 12-22, 18-33 and 25-29, are selected as candidate positions for SOP. Figure 7 Marked as L1-L5. The total capacity that can be allocated to the 5 SOPs is 1000kVA, and the minimum capacity that can be optimized for each SOP is 100kVA. The investment cost of installing SOPs includes installation cost and operating cost, which can be calculated in advance when determining the total capacity. According to the parameters and calculation methods in the literature "Wang C, Song G, Li P, et al. Optimal siting and sizing of soft open points in active electrical distribution networks. Applied Energy 2017; 189 (MAR. 1): 301-309.", when the total capacity is 1000kVA, the cost is $34,540.

[0290] In order to verify the rationality and effectiveness of the model, a multi-stage resilience evaluation index is used to measure the rationality of the capacity allocation of SOP at three candidate locations (i.e., 21-8, 22-12, and 25-29). In this specific application example, it is carried out on the basis of the optimization configuration method proposed in the above embodiment of the present invention, but a different resilience evaluation method is used. One is based on a multi-stage resilience evaluation model, and the other is based on the literature “Panteli M, Mancarella P. Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies. Electric Power Systems Research 2015; 127 (oct.): 259-270.”, “Simpson-Porco JW, F, Bullo F. Voltage collapse in complex power grids. Nature Communications 2016; 7: 10790. ", "Panteli M, Mancarella P. Modeling and evaluating the resilience of critical electrical power infrastructure to extreme weather events. IEEE Systems Journal 2015; pp: 1-10. " and "Panteli M, Trakas DN, Mancarella P, et al. Boosting the power grid resilience to extreme weather events using defensive islanding. IEEE Transactions on Smart Grid 2017; 7(6): 2913-2922. " The methods proposed in these methods are evaluated by the power supply shortage of the power system (such as power loss, expected load loss, etc.).

[0291] The three candidate positions have corresponding positions on the x, y and z axes, forming a plane that can be rotated into a two-dimensional plane in three-dimensional space. In order to make the results of elasticity change with the scheme more obvious, height is selected as the third dimension indicator. Fig. 9It can be seen that the differences between all the plans are greater than Figure 8 The differences in . Figure 8 There are different plans corresponding to the same elasticity index. Since different capacity allocation schemes may lead to the same load loss in N-1 failures, it is reasonable to have multiple local optimal solutions, but it is also easy to cause the genetic algorithm to fall into the local optimal solution and fail to obtain the global optimal solution.

[0292] contrast Figure 8 and Fig. 9 It can be seen that the elasticity index proposed in the above embodiment of the present invention has a wider scope of application. When the power supply capacity changes are not much different, the index of the elasticity mapping model provided by the above embodiment of the present invention still has good sensitivity. The index related to the power supply capacity is unidirectional and has a fixed range of change, which is contrary to the bidirectionality of elasticity that can be increased and decreased. Therefore, the elasticity mapping model provided by the above embodiment of the present invention has better sensitivity and directionality.

[0293] 1. Optimization results of capacity allocation:

[0294] Assuming that the initial operating states of the selected SOPs are the same and no control strategy optimization is performed, the optimized configuration of the SOPs is only for location and capacity. Table 1 lists the optimization results and the elasticity indicators corresponding to the specified configuration schemes.

[0295] Table 1 Elasticity indicators under different configuration scenarios

[0296]

[0297] The first scenario corresponds to the best installation locations 22-12, 18-33, and 25-29, and the SOP capacities are 100kVA, 100kVA, and 800kVA, respectively. The corresponding elasticity coefficient is 517.4735. In the second scenario, the total capacity of the SOP is evenly distributed to 5 locations, and in scenarios 3-7, the entire capacity of the SOP is allocated to one candidate location. The results shown in Table 1 effectively demonstrate the effectiveness of the proposed method for the optimal configuration of the SOP. It can also be concluded from the results that the various installation locations of the SOP have great differences in elasticity.

[0298] 2. Analysis of optimization results considering control strategy:

[0299] In order to fully utilize the role of SOP in improving resilience, the optimization of SOP control strategy must be considered. After considering the optimization of control strategy, the resilience can be evaluated and compared.

[0300] The elasticity results are compared by using the two-layer optimization algorithm provided by the above embodiment of the present invention. In this algorithm, the maximum number of iterations of the particle swarm algorithm is 200. The fitness of the upper and lower layer problems both correspond to the calculation of elasticity. Table 2 lists three optimization configuration schemes for scenarios 8-10. Fig.10 The fitness curves of scenarios 1-2 and 8-10 are shown. Table 3 lists the optimal control strategy of SOP in scenario 8 corresponding to the optimal elasticity value. The elasticity index is an indicator that comprehensively measures the system's ability in defense and recovery. Therefore, the optimal control strategy of the intelligent soft switch corresponds to different operation schemes under normal operation and power supply restoration. Considering all N-1 faults, the control variables in power supply restoration include all possibilities. When one end of the VSC is under the PQ control scheme, the normal operating state strategy in the optimal control strategy of the intelligent soft switch is used to control it, and the other end of the VSC is under the Vf control scheme, and its corresponding voltage can be selected.

[0301] Table 2 considers the SOP planning results under different scenarios of control strategies. Fig.10 It shows that after the control strategy optimization, the elasticity index of scenario 1 can be improved from 517.4735 to 586.9878, and the elasticity index of scenario 2 can be improved from 351.4298 to 605.3174. In scenario 10, the elasticity after double-layer optimization can reach 4.8683 times that of the elasticity without the optimal control strategy, which proves the importance and necessity of the appropriate control scheme of SOP.

[0302] Table 2 Elasticity indicators under different configuration scenarios when considering SOP control strategy

[0303]

[0304]

[0305] Table 3

[0306] like Fig.11 As shown in (a) and (b), the active and reactive power loads on both sides of candidate locations 25-29 are the highest among the five locations, while the loads at the ends of 9-15 and 12-22 are the lightest. The active loads on bus 21 and bus 8 are greater than those on bus 18 and bus 33. The load distribution corresponds to the capacity distribution of SOP because SOP has the function of adjusting the power flow distribution and quickly supplying power to the power-off area. According to the best results, the capacity distribution ratio is 2:1:1:2:4, corresponding to the load distribution of the five locations.

[0307] Table 3 Control strategies of SOP in scenario 8

[0308]

[0309] From the above results, we can see that after adopting the optimal control strategy, the elasticity has been greatly improved on the basis of the previous optimization. At the same time, the configuration result considering the control scheme optimization is not the configuration result of the given strategy. In this case, the scenario considering capacity allocation and control strategy optimization has the best elasticity.

[0310] Fig.12 A schematic diagram of component modules of a distribution network optimization configuration system based on elastic mechanics mapping provided by an embodiment of the present invention.

[0311] like Fig.12 As shown, the distribution network optimization configuration system based on elastic mechanics mapping provided in this embodiment may include: an elastic mapping model module, an equivalent model module and an optimization module; wherein:

[0312] Elastic mapping model module, which is based on the relationship between spring force, displacement and elastic coefficient in the mechanical spring model, and maps according to the power system flow calculation formula to construct a power system energy supply elastic mapping model;

[0313] Equivalent model module, which builds an equivalent model of normal operation and power supply recovery status of intelligent soft switches based on the power system energy supply elasticity mapping model;

[0314] Optimization module: This module uses a double-layer optimization algorithm to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model to complete the optimal configuration of the distribution network.

[0315] An embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the terminal can be used to execute the method provided in any one of the above embodiments of the present invention, or to run the system provided in the above embodiments of the present invention.

[0316] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, it can be used to execute the method provided in any one of the above embodiments of the present invention, or to run the system provided in the above embodiments of the present invention.

[0317] In the above two embodiments, optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic RandomAccess Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above methods), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0318] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0319] The processor is used to execute the computer program stored in the memory to implement the various steps in the method involved in the above embodiment. For details, please refer to the relevant description in the above method embodiment.

[0320] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0321] It should be noted that the steps in the method provided by the present invention can be implemented by using corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the method to realize the composition of the system, that is, the embodiments in the method can be understood as preferred examples for constructing the system, which will not be elaborated here.

[0322] Those skilled in the art know that, in addition to implementing the system and its various devices provided by the present invention in a purely computer-readable program code, the system and its various devices provided by the present invention can be made to implement the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices provided by the present invention can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component; the devices for implementing various functions can also be considered as both software modules for implementing the method and structures within the hardware component.

[0323] The distribution network optimization configuration method, system, terminal and medium based on elastic mechanics mapping provided by the above-mentioned embodiments of the present invention make full use of the effect of distribution network configuration on improving elasticity. Considering that the elasticity index can measure the multi-stage characteristics of the power system during the interference process, the elastic mapping model is used to evaluate elasticity. A two-layer optimization algorithm is used to solve the mixed integer nonlinear problem of improving the elasticity of the power grid, in which the genetic algorithm solves the selection of the position and capacity of the smart soft switch (SOP), and the particle swarm algorithm optimizes the control strategy. By comparing the elastic mapping model provided by the above-mentioned embodiments of the present invention with the existing elasticity measurement method and with the results of the single-stage elastic coefficient, the necessity of the multi-stage model is verified. The analysis of the optimization results of capacity allocation and considering the control strategy proves the effectiveness of the model and optimization algorithm provided by the above-mentioned embodiments of the present invention and the effect of the optimization configuration on improving elasticity.

[0324] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A distribution network optimization configuration method based on elastic mechanics mapping, characterized in that: include: Based on the relationship between spring force, displacement and elastic coefficient in the mechanical spring model, mapping is performed according to the power system flow calculation formula to construct a power system energy supply elasticity mapping model; Based on the power system energy supply elasticity mapping model, an equivalent model of normal operation and power supply recovery state of the intelligent soft switch is constructed; A double-layer optimization algorithm is used to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model, thereby optimizing the configuration of the distribution network and improving the energy supply flexibility of the power system. The two-layer optimization algorithm is used to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model, including: Converting the optimization configuration problem of the intelligent soft switch equivalent model into a mixed integer nonlinear problem, wherein the capacity and position variables of the intelligent soft switch equivalent model are both integer values, and the control variable is any value within a given range; Construct an objective function to maximize the effect of intelligent soft switches on improving the energy supply flexibility of the power system; Constructing a two-layer optimization algorithm, wherein the upper layer of the two-layer optimization algorithm is used to solve the optimization problem of allocating intelligent soft switches from candidate locations and a given total capacity, and the lower layer of the two-layer optimization algorithm is used to solve the optimization problem of the operation control strategy of the intelligent soft switches subject to the constraints of the upper layer; Based on the two-layer optimization algorithm, after determining the operation control strategy of the lower layer, the elasticity value at this time is returned to the upper layer, and this process is iterated until the objective function reaches the optimal result, thus completing the optimization of the intelligent soft switch equivalent model; The upper layer of the two-layer optimization algorithm adopts a genetic algorithm, including: The location, capacity and control variables of the intelligent soft switch are encoded into chromosomes, wherein the chromosomes contain the location and capacity information of the intelligent soft switch; the length of the integer variable is determined by the candidate installation location; the number on the candidate location represents the capacity of the intelligent soft switch installed at the corresponding location; The optimized configuration scheme of the smart soft switches with energy supply elasticity higher than the set threshold a is replaced by the optimized configuration scheme of the same number of smart soft switches with energy supply elasticity lower than the set threshold b; Based on the unified crossover operator and recombination probability, the capacity of two random individuals at the same position is exchanged, so that the total capacity of the intelligent soft switch corresponding to each scheme is constant; If any number between 0 and 1 randomly generated during the mutation process is less than the set mutation probability, the capacities of any two smart soft switch positions in the optimal configuration scheme of the exchanged smart soft switches; For a chromosome of the first generation, the elasticity value under the current control strategy of the lower layer is used as the objective function value; if the number of iterations does not reach the maximum number of iterations, the iteration continues, otherwise the result is output; The lower layer of the two-layer optimization algorithm adopts a particle swarm algorithm, including: In normal operation, the intelligent soft switch operates at PQ-V dc In the Q control mode, the control variables are active power and reactive power. In the power recovery phase, the intelligent soft switch will operate at V dc In the Q-Vf mode, the control variables are active power, reactive power and voltage; An intelligent soft switch has 4 variables X during normal operation nPi , X nQi , X nPj and X nQj , there are 6 variables X in the power recovery phase rPi , X rQi , X rPj , X rQj , X rUi and X rUj , whose particles are encoded according to the number of smart soft switches as follows: X l-ij =[X nPi ,X nQi ,X nPj ,X nQj ,X rPi ,X rQi ,X rPj ,X rQj ,X rUi ,X rUj ] X=[X l1 ,X l2 ,…,X ln ] Among them, X l-ij is the variable set of branch l with nodes i and j as endpoints, X nPi , X nQi , X nPj and X nQj represents the transmission power control variable under normal operating conditions, corresponding to the intelligent soft switch operating at PQ-V dc In Q control mode, the active power and reactive power transmitted by the VSCs on both sides; X rPi , X rQi , X rPj and X rQj Represents the control variable in the power recovery phase, corresponding to the operation at V dc Active power and reactive power X at end i or end j in Q mode rUi and X rUj represents the voltage control variable, corresponding to the control voltage at the fault side i or j when operating in the Vf mode during the power supply recovery phase; the variable X nPi , X nQi , X nPj , X nQj , X rPi , X rQi , X rPj , X rQj , X rUi and X rUj constituent particles; The method for obtaining the operation control strategy of the intelligent soft switch by using the particles includes: Under the position and capacity conditions of the intelligent soft switch given by the upper layer, the control variables of the normal operation and power supply recovery states are initialized, and the equivalent elastic coefficients k0 and k1 of the corresponding power system functional elasticity mapping model are calculated at this time, and the fitness corresponding to the operation control strategy under the normal operation and power supply recovery states is obtained. The fitness curves corresponding to different operation control strategies are compared respectively, and the operation control strategy corresponding to the energy supply elasticity is higher than the set threshold a is determined. The speed and position of the particle are updated before the next operation control strategy is determined. If the number of iterations has been met, the optimal operation control strategy under the given position and capacity configuration is obtained; if the number of iterations has not been reached, the energy supply elasticity at this time is calculated under the new particle speed and position conditions, and the fitness corresponding to the new strategy and the fitness corresponding to the optimal result obtained by the previously run strategy are calculated.

2. The distribution network optimization configuration method based on elastic mechanics mapping according to claim 1 is characterized in that: The relationship between the spring force, displacement and elastic coefficient in the mechanical spring model is mapped according to the power system flow calculation formula to construct the power system energy supply elasticity mapping model, including: According to the static power flow equation of the power system, combined with the magnitude and direction of the force in the mechanical spring model, based on the mapping criterion, the elastic network branch of the power system is mapped into a two-degree-of-freedom elastic branch; where: The mapping criterion is: the x and y axis directions of each elastic network branch of the power system correspond to the xy axis of the plane coordinate system of the two-degree-of-freedom elastic branch; the xy direction component S of the power of each elastic network branch of the power system x , S y The elastic force F corresponding to the two-degree-of-freedom elastic branch x 、F y ; The xy-direction component U of the voltage drop of each elastic network branch of the power system x , U y Corresponding to the deformation variables Δx and Δy of the two-degree-of-freedom elastic branch; The static power flow equation of the power system is: U x =and i -and j U y =f i -f j Among them, P ij Indicates the active power transmitted by the line, Q ij Represents the reactive power transmitted by the line, r ij represents the line resistance, x ij Represents line reactance, S ij represents the apparent power, z ij represents the line impedance, e i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively, represents the voltage vector at node i, represents the voltage vector at node j, U x Indicates voltage drop The component in the direction, U y The voltage drop The component in the y direction; The apparent power S ij The voltage is decomposed into S in the x and y directions x and Sy, we get: S ij 2 =P ij 2 +Q ij 2 =S x 2 +S y 2 ; Map the elastic network branches of the power system to the physical spring system: In the x direction In the y direction Among them, F x represents the x-direction force mapped from the power coupling according to the mapping relationship, Δx represents the x-direction displacement mapped from the x-direction component of the voltage drop in the rectangular coordinate system, and F y represents the y-direction force mapped from the power coupling according to the mapping relationship, and Δy represents the y-direction displacement mapped from the y-direction component of the voltage drop in the rectangular coordinate system; Since the elastic potential energy generated by the spring deformation is the integral of the force over the displacement, the elastic potential energy E of the two-degree-of-freedom spring is calculated by this mapping l for: Calculate the mapped elastic potential energy E of a two-degree-of-freedom spring branch ij for: Assuming that the elastic network of the power system consists of n branches, the total elastic potential energy E of the power system is l∑ for: Calculate the resultant force F of the equivalent force on the power system eq for: Constructing the equivalent elasticity coefficient k of the power system energy supply elasticity mapping model eq for: The obtained equivalent elasticity coefficient of the power system functional elasticity mapping model is evaluated in multiple stages to obtain an evaluation index of the equivalent elasticity coefficient of the power system functional elasticity mapping model.

3. The distribution network optimization configuration method based on elastic mechanics mapping according to claim 1 is characterized in that: The constructing of an equivalent model of normal operation and power supply recovery state of an intelligent soft switch based on the power system energy supply elasticity mapping model includes: When the intelligent soft switch is in normal operation, the PQ-V dc The Q control mode realizes the control of power between feeders; Based on the transmission power of the VSC and in combination with the wide area measurement system, the equivalent forces in the x and y directions of the power system energy supply elasticity mapping model are calculated; Based on the equivalent stress and equivalent deformation of the mapping relationship of the power system energy supply elasticity mapping model, the equivalent elastic coefficient k0 of the power system under normal operating conditions is calculated by superimposing the stress and energy of the branch containing the intelligent soft switch and the other branches; When a fault occurs in the power system, the intelligent soft switch responds to restore power supply; based on the control voltage measured by the wide-area measurement system, the force and energy of the branch containing the intelligent soft switch are obtained; the equivalent elastic potential energy of the intelligent soft switch branch and the remaining branches is superimposed, and the equivalent elastic coefficient under the current power supply restoration state is calculated through force decomposition and synthesis; Under all N-1 faults, calculate the equivalent elastic coefficient k1 of the power system in the power supply restoration state according to the above steps; Based on the equivalent elastic coefficients k0 and k1, the energy supply elasticity of the power system containing the intelligent soft switch is calculated, and then an equivalent model of the normal operation and power supply recovery state of the intelligent soft switch is constructed.

4. The distribution network optimization configuration method based on elastic mechanics mapping according to claim 1 is characterized in that: The objective function of constructing the intelligent soft switch to maximize the effect of improving the power system energy supply elasticity includes: Performing a multi-stage evaluation on the equivalent elasticity coefficient of the obtained power system functional elasticity mapping model to obtain an evaluation index of power system elasticity, and taking maximization of the evaluation index as an objective function; The objective function that maximizes the effect of intelligent soft switches on improving the flexibility of distribution networks is constructed as follows: max Res(x)(1) Where x is the set of decision variables for the position and capacity of the intelligent soft switch; Establish constraint equations of the objective function, including: power flow constraint equations, safety constraint equations and intelligent soft switch operation constraint equations; wherein: The flow constraint equation is expressed in rectangular coordinates as: have been i 2 +f i 2 =U i 2 (4) Where N(i) represents the nodes connected to node i; i , e j , f i and f j are the real and imaginary parts of the voltage at nodes i and j, respectively; G ij and B ij represents the conductance and susceptance of the branch; P i and Q i are the active power and reactive power injected into each node; The safety constraint equation is: IN imin <In i <In imax (5) I l 2 <(I lmax ) 2 (6) Among them, U imin and U imax are the lower and upper limits of the voltage amplitude at node i; I l is the current amplitude of branch ij; I lmax is the upper limit of the current in branch ij; Assume that the intelligent soft switch operates normally at PQ-V dc In Q control mode, when power is restored, it runs at V dc In the Q-Vf control mode, the intelligent soft switch operation constraint equation is: P i,SOP +P j,SOP =0 (7) IN j,SOP ≥U0 (10) Where i and j are the node numbers of the power system to which the intelligent soft switch is connected; P i,SOP , P j,SOP , Q i,SOP , Q j,SOP are the active power and reactive power injected by the two converters of the intelligent soft switch respectively; S i,SOP , S j,SOP is the capacity of the intelligent soft switch connected to the node; U j,SOP is the control voltage of node j of the intelligent soft switch on the fault side; U0 is the minimum voltage amplitude of the constant voltage control terminal node in the set intelligent soft switch using the voltage-frequency control method.

5. The distribution network optimization configuration method based on elastic mechanics mapping according to claim 1 is characterized in that: Based on the unified crossover operator and recombination probability, the capacity of two random individuals at the same position is exchanged, so that the total capacity of the intelligent soft switch corresponding to each scheme is constant, including: When the total capacity is greater than the specified capacity, the capacity of the smart soft switch that is less than the absolute value of the capacity difference is randomly reduced, and the smart soft switch is randomly installed at the corresponding position of the individual until the total capacity is equal to the specified capacity; When the total capacity is less than the specified capacity, a smart soft switch capacity that is less than the absolute value of the capacity difference is randomly selected and the smart soft switch is randomly installed in the currently selected individuals until the total capacity is equal to the specified capacity.

6. A distribution network optimization configuration system based on elastic mechanics mapping, characterized in that: include: Elastic mapping model module, which is based on the relationship between spring force, displacement and elastic coefficient in the mechanical spring model, and maps according to the power system flow calculation formula to construct a power system energy supply elastic mapping model; An equivalent model module, which constructs an equivalent model of normal operation and power supply recovery state of the intelligent soft switch based on the power system energy supply elasticity mapping model; An optimization module, which uses a double-layer optimization algorithm to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model to complete the optimal configuration of the distribution network; The two-layer optimization algorithm is used to optimize the position, capacity and control strategy of the intelligent soft switch equivalent model, including: Converting the optimization configuration problem of the intelligent soft switch equivalent model into a mixed integer nonlinear problem, wherein the capacity and position variables of the intelligent soft switch equivalent model are both integer values, and the control variable is any value within a given range; Construct an objective function to maximize the effect of intelligent soft switches on improving the energy supply flexibility of the power system; Constructing a two-layer optimization algorithm, wherein the upper layer of the two-layer optimization algorithm is used to solve the optimization problem of allocating intelligent soft switches from candidate locations and a given total capacity, and the lower layer of the two-layer optimization algorithm is used to solve the optimization problem of the operation control strategy of the intelligent soft switches subject to the constraints of the upper layer; Based on the two-layer optimization algorithm, after determining the operation control strategy of the lower layer, the elasticity value at this time is returned to the upper layer, and this process is iterated until the objective function reaches the optimal result, thus completing the optimization of the intelligent soft switch equivalent model; The upper layer of the two-layer optimization algorithm adopts a genetic algorithm, including: The location, capacity and control variables of the intelligent soft switch are encoded into chromosomes, wherein the chromosomes contain the location and capacity information of the intelligent soft switch; the length of the integer variable is determined by the candidate installation location; the number on the candidate location represents the capacity of the intelligent soft switch installed at the corresponding location; The optimized configuration scheme of the smart soft switches with energy supply elasticity higher than the set threshold a is replaced by the optimized configuration scheme of the same number of smart soft switches with energy supply elasticity lower than the set threshold b; Based on the unified crossover operator and recombination probability, the capacity of two random individuals at the same position is exchanged, so that the total capacity of the intelligent soft switch corresponding to each scheme is constant; If any number between 0 and 1 randomly generated during the mutation process is less than the set mutation probability, the capacities of any two smart soft switch positions in the optimal configuration scheme of the exchanged smart soft switches; For a chromosome of the first generation, the elasticity value under the current control strategy of the lower layer is used as the objective function value; if the number of iterations does not reach the maximum number of iterations, the iteration continues, otherwise the result is output; The lower layer of the two-layer optimization algorithm adopts a particle swarm algorithm, including: In normal operation, the intelligent soft switch operates at PQ-V dc In the Q control mode, the control variables are active power and reactive power. In the power recovery phase, the intelligent soft switch will operate at V dc In the Q-Vf mode, the control variables are active power, reactive power and voltage; An intelligent soft switch has 4 variables X during normal operation nPi , X nQi , X nPj and X nQj , there are 6 variables X in the power recovery phase rPi , X rQi , X rPj , X rQj , X rUi and X rUj , whose particles are encoded according to the number of smart soft switches as follows: X l-ij =[X nPi ,X nQi ,X nPj ,X nQj ,X rPi ,X rQi ,X rPj ,X rQj ,X rUi ,X rUj ] X=[X l1 ,X l2 ,…,X ln ] Among them, X l-ij is the variable set of branch l with nodes i and j as endpoints, X nPi , X nQi , X nPj and X nQj represents the transmission power control variable under normal operating conditions, corresponding to the intelligent soft switch operating at PQ-V dc In Q control mode, the active power and reactive power transmitted by the VSCs on both sides; X rPi , X rQi , X rPj and X rQj Represents the control variable in the power recovery phase, corresponding to the operation at V dc Active power and reactive power X at end i or end j in Q mode rUi and X rUj represents the voltage control variable, corresponding to the control voltage at the fault side i or j when operating in the Vf mode during the power supply recovery phase; the variable X nPi , X nQi , X nPj , X nQj , X rPi , X rQi , X rPj , X rQj , X rUi and X rUj constituent particles; The method for obtaining the operation control strategy of the intelligent soft switch by using the particles includes: Under the position and capacity conditions of the intelligent soft switch given by the upper layer, the control variables of the normal operation and power supply recovery states are initialized, and the equivalent elastic coefficients k0 and k1 of the corresponding power system functional elasticity mapping model are calculated at this time, and the fitness corresponding to the operation control strategy under the normal operation and power supply recovery states is obtained. The fitness curves corresponding to different operation control strategies are compared respectively, and the operation control strategy corresponding to the energy supply elasticity is higher than the set threshold a is determined. The speed and position of the particle are updated before the next operation control strategy is determined. If the number of iterations has been met, the optimal operation control strategy under the given position and capacity configuration is obtained; if the number of iterations has not been reached, the energy supply elasticity at this time is calculated under the new particle speed and position conditions, and the fitness corresponding to the new strategy and the fitness corresponding to the optimal result obtained by the previously run strategy are calculated.

7. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it can be used to execute the method described in any one of claims 1 to 5, or to run the system described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it can be used to execute the method described in any one of claims 1 to 5, or to run the system described in claim 6.

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