Flexible regulation and control method and system for transformer area load

Abstract

The invention provides a flexible regulation and control method and system for court loads, and the method comprises the steps: obtaining the operation data of a distribution transformer court; inputting the operation data of the distribution transformer district into a preset low-voltage distribution district source load commutation optimization control model to obtain a corresponding flexible regulation and control strategy; wherein the low-voltage distribution area source load commutation optimization control model is a distribution network load optimization control model for setting a multi-objective function based on a commutation strategy that the three-phase unbalance degree of an area transformer node is lowest and the number of action times of a commutation unit is least; and determining a corresponding transformer area regulation strategy according to the flexible regulation strategy, issuing a commutation command to a commutation switch based on the transformer area regulation strategy, and dynamically adjusting the load equipment in the transformer area to realize real-time response to the load demand. According to the invention, intelligent optimization regulation and control of the operation state of the power distribution network are realized; the operation efficiency of the power distribution network is improved, the equipment loss and fault risk are reduced, and the stability and reliability of power supply are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and in particular to a flexible control method and system for loads in a substation. Background Art

[0002] In modern power systems, the distribution network is an important link between power sources and end users, and its stability and reliability directly affect the quality of power supply. However, with the access of emerging loads such as distributed energy (such as photovoltaic power generation) and electric vehicles, the distribution network is facing an increasingly serious three-phase imbalance problem. Three-phase imbalance not only reduces the effective utilization rate of electric energy, but also may cause equipment loss, overheating and failure, thereby increasing the operating cost of the power system. Therefore, the optimization and regulation strategy for the three-phase imbalance problem of the distribution network has important practical significance. Summary of the invention

[0003] The purpose of the present invention is to propose a flexible control method and system for load in an area to solve the technical problem of three-phase imbalance faced by the distribution network in a modern power system.

[0004] On the one hand, a flexible control method for a load in a substation is provided, comprising:

[0005] Obtain the operation data of the distribution transformer area;

[0006] Input the operation data of the distribution transformer area into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model that sets a multi-objective function based on the phase-change strategy with the lowest three-phase imbalance of the transformer node in the area and the least number of phase-change unit actions;

[0007] The corresponding substation control strategy is determined according to the flexible control strategy, and a phase change command is issued to the reversing switch based on the substation control strategy to dynamically adjust the load equipment in the substation to achieve real-time response to load demand.

[0008] Preferably, the operation data of the distribution transformer area at least includes the three-phase current and voltage on the low-voltage side of the distribution transformer, the load branch current and the switch status.

[0009] Preferably, the low-voltage distribution station area source-load switching optimization control model specifically includes:

[0010] δ 1 =min(σ(K))

[0011] δ 2 =min(D(K))

[0012] Among them, σ(K) is the unbalance degree of the three-phase current in the distribution network, D(K) is the number of commutation times, δ1 is the first commutation strategy, δ 2 This is the second commutation strategy.

[0013] Preferably, the low-voltage distribution station area source-load switching optimization control model also includes:

[0014] δ(K)≤C num

[0015] K∈{[1,0,0] T ,[0,1,0] T ,[0,0,1] T}

[0016] I A ,I B ,I C al

[0017] Among them, C num is the number of commutation unit actions set according to prior knowledge, I av is the average value of the three-phase current, I A ,I B ,I C is the three-phase current, [1,0,0] T To access phase A, [0,1,0] T To access phase B, [0,0,1] T To access phase C.

[0018] Preferably, the method further comprises optimizing the load optimization control model of the distribution network according to the following steps:

[0019] Generating an initial population according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme;

[0020] The fitness value of the multi-objective function is calculated according to the source-load phase-changing optimization control model, and the entire initial population is divided into multiple microhabitats according to the chromosome similarity and fitness value, each microhabitat contains a preset number of chromosomes;

[0021] Selection operations are performed independently in each niche, and chromosomes produce offspring through crossover operations;

[0022] Perform mutation operation on the offspring after the crossover operation, and perform mutation operation on the individual with the highest fitness in the current population after the mutation operation;

[0023] ​According to the results of crossover and mutation, a new generation of population is formed; the selection, crossover and mutation operations are repeated, and the performance of the new population is evaluated by updating the imbalance and the number of commutations in each iteration until the maximum number of iterations is reached or the optimal solution is obtained. The number of commutations is output and the optimal access phase of the commutation unit is different, and the final distribution network load optimization control model is obtained.

[0024] On the other hand, a flexible control system for loads in a substation is also provided, which is used to implement the flexible control method for loads in a substation, including:

[0025] The load control terminal is used to obtain the operation data of the distribution transformer area; and the operation data of the distribution transformer area is input into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model with a multi-objective function set based on the phase-change strategy with the lowest three-phase imbalance degree of the transformer node in the area and the least number of phase-change unit actions;

[0026] It is also used to determine the corresponding substation control strategy according to the flexible control strategy, and send a phase change command to the reversing switch based on the substation control strategy, dynamically adjust the load equipment in the substation, and realize real-time response to load demand.

[0027] Preferably, the operation data of the distribution transformer area at least includes the three-phase current and voltage on the low-voltage side of the distribution transformer, the load branch current and the switch status.

[0028] Preferably, the low-voltage distribution station area source-load switching optimization control model specifically includes:

[0029] δ 1 =min(σ(K))

[0030] δ 2 =min(D(K))

[0031] Among them, σ(K) is the unbalance degree of the three-phase current in the distribution network, D(K) is the number of commutation times, δ 1 is the first commutation strategy, δ 2 This is the second commutation strategy.

[0032] Preferably, the low-voltage distribution station area source-load switching optimization control model also includes:

[0033] δ(K)≤C num

[0034] K∈{[1,0,0] T ,[0,1,0] T ,[0,0,1] T}

[0035] IA ,I B ,I C al

[0036] Among them, C num is the number of commutation unit actions set according to prior knowledge, I av is the average value of the three-phase current, I A ,I B ,I C is the three-phase current, [1,0,0] T To access phase A, [0,1,0] T To access phase B, [0,0,1] T To access phase C.

[0037] Preferably, the load control terminal is further used to optimize the load optimization control model of the distribution network according to the following steps, and generate an initial population according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme;

[0038] The fitness value of the multi-objective function is calculated according to the source-load phase-changing optimization control model, and the entire initial population is divided into multiple microhabitats according to the chromosome similarity and fitness value, each microhabitat contains a preset number of chromosomes;

[0039] Selection operations are performed independently in each niche, and chromosomes produce offspring through crossover operations;

[0040] Perform mutation operation on the offspring after the crossover operation, and perform mutation operation on the individual with the highest fitness in the current population after the mutation operation;

[0041] According to the results of crossover and mutation, a new generation of population is formed; the selection, crossover and mutation operations are repeated, and the performance of the new population is evaluated by updating the imbalance and the number of commutations in each iteration until the maximum number of iterations is reached or the optimal solution is obtained. The number of commutations is output and the optimal access phase of the commutation unit is different, and the final distribution network load optimization control model is obtained.

[0042] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0043] The flexible control method and system of the substation load provided by the present invention aims to minimize the three-phase current imbalance and the action of the commutation switch execution unit, establish a three-phase unbalanced commutation model of the power grid, and simultaneously apply the niche technology to design a genetic algorithm based on a similar fitness value selection mechanism and an optimal individual retention mechanism to achieve intelligent optimization and control of the operating state of the distribution network; improve the operating efficiency of the distribution network, reduce equipment loss and failure risks, and thus enhance the stability and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS​

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0045] Figure 1 It is a schematic diagram of the main process of a flexible control method of a substation load in an embodiment of the present invention.

[0046] Figure 2 The present invention is a schematic diagram of a data collection and load control architecture of a substation in an embodiment of the present invention.

[0047] Figure 3 Schematic diagram of the logic of the three-phase unbalanced commutation strategy in an embodiment of the present invention.

[0048] Figure 4 Schematic diagram of a (2+2) variation selection operation in an embodiment of the present invention.

[0049] Figure 5 Schematic diagram of a (1+1) variation selection operation in an embodiment of the present invention.

[0050] Figure 6 The figure is a schematic diagram of a flexible control system for substation loads in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 FIG. 1 is a schematic diagram of an embodiment of a flexible control method for load in a substation provided by the present invention. In this embodiment, the method includes the following steps:

[0053] Step S1, obtain the operation data of the distribution transformer area; it should be noted that before obtaining the data, it is necessary to set up the communication architecture between the load control terminal and other devices, such as Figure 2As shown. The public mobile communication network (GPRS) is used for uplink communication to realize data reporting from the load control terminal to the main station. Micro-power wireless network communication is used for downlink communication to realize data interaction between the load control terminal and more phase-changing units. RS485 communication is used for local communication to realize communication between the load control terminal and multiple local models such as leakage protector and electric energy meter three-phase current acquisition module. The measurement unit is used to collect the three-phase current and voltage data on the low-voltage side of the distribution transformer in real time, and the data is uploaded to the load control terminal through the RS485 communication module. At the same time, the single-phase load current and switch status information are uploaded to the load control terminal using micro-power wireless communication, providing a reliable data basis for subsequent model construction.

[0054] In one embodiment, the operation data of the distribution transformer area at least includes the three-phase current and voltage on the low-voltage side of the distribution transformer, the load branch current and the switch status.

[0055] Step S2, input the operation data of the distribution transformer area into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein, the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model with a multi-objective function set based on the phase-change strategy with the lowest three-phase imbalance of the transformer node in the area and the least number of phase-change unit actions; it can be understood that, as Figure 3 As shown in the figure, the low-voltage distribution substation source-load commutation optimization control model is constructed based on the commutation strategy with the lowest three-phase imbalance of the substation transformer node and the least number of commutation unit actions. The multi-objective function is designed to establish the distribution network load optimization control model. The load optimization model of the niche technology and genetic algorithm is solved. The genetic algorithm is used to solve the multi-objective optimization problem and obtain the optimal control plan.

[0056] In one embodiment, the low-voltage distribution station area source-load switching optimization control model specifically includes:

[0057] δ 1 =min(σ(K))

[0058] δ 2 =min(D(K))

[0059] Among them, σ(K) is the unbalance degree of the three-phase current in the distribution network, D(K) is the number of commutation times, δ 1 is the first commutation strategy, δ 2 This is the second commutation strategy.

[0060] The low voltage distribution station area source-load switching optimization control model also includes:

[0061] δ(K)≤C num

[0062] K∈{[1,0,0]T ,[0,1,0] T ,[0,0,1] T}

[0063] I A ,I B ,I C al

[0064] Among them, C num is the number of commutation unit actions set according to prior knowledge, I av is the average value of the three-phase current, I A ,I B ,I C is the three-phase current, [1,0,0] T To access phase A, [0,1,0] T To access phase B, [0,0,1] T To access phase C.

[0065] It should be noted that a multi-objective optimization function is designed based on the three-phase imbalance of the transformer node and the number of phase-changing unit actions. The optimization objectives include: reducing the three-phase imbalance, reducing the number of phase-changing unit actions, improving the energy utilization rate and reducing the operating cost. By analyzing the characteristics of the distribution network load, a load optimization control model is established.

[0066] First, the phase switching unit is connected to the matrix representation. A distribution station is equipped with N phase switching units. The phase sequence corresponding to each phase switching unit is binary coded, and the N phase switching units can be represented by a column vector K. Before phase switching, the phase sequence connected to the N phase switching units can be represented by the matrix K X Indicates that K X =[K 1 ,K 2 ,...,K N ], K X All elements in the system correspond to one of the three phases A, B, and C.

[0067] Secondly, establish the minimum objective function of the three-phase unevenness of the substation. Assume that the current measured by each switching unit is I, then I A ,I B ,I C The three-phase current can be represented by the following matrix:

[0068]

[0069] Before commutation, the three-phase currents of A, B, and C are

[0070]

[0071] After phase switching, the three-phase currents of A, B, and C are​

[0072]

[0073] When calculating the three-phase current imbalance of the distribution network, in order to facilitate the calculation of the collected power grid data, this patent uses the commonly used MAX (phase current-three-phase average current / three-phase average current) to represent the three-phase current imbalance of the distribution network. After phase switching, the three-phase current imbalance of the distribution network A, B, and C can be expressed as:

[0074]

[0075] In the above formula, σ(K) is the unbalance degree of three-phase current in the distribution network, σ A , σ B , σ C is the current imbalance of each phase, I av is the average value of three-phase current.

[0076] Secondly, establish the objective function of the minimum number of phase-changing actions in the substation area; from the perspective of the governance process, improving three-phase imbalance is actually a process of making the three-phase current reach a balanced state through the phase-changing action of the phase-changing unit when the three-phase current in the distribution network is not balanced or seriously unbalanced within a period of time. In addition to considering the reliability and safety of the power grid operation, the overall economy of the power grid must also be considered. As a part of the power supply network, the phase-changing unit must also serve the overall economy and safety of the power grid. The relationship between the service life of the switching device of the phase-changing unit and its action frequency is positively correlated. Frequent action of the switch will inevitably lead to a reduction in service life. Therefore, it is particularly important to achieve the minimum number of switch actions, no high-frequency action in a short time, and ensure that all switches of the phase-changing unit can act as evenly as possible. In order to increase the service life of the switch, in the phase-changing strategy model, the number of switch actions should be set to be the minimum, all switches should be operated within a certain period of time, and continuous action of the switch should be avoided as much as possible to achieve the purpose of extending the service life of the phase-changing unit and saving economic costs accordingly. Use K(x Ai ,x Bi ,x Ci )∈{0,1} indicates whether the i-th switch is switched, 0 indicates no switching, 1 indicates switching. Considering the economic cost of switching, the switching action should be minimized, so the following matrix model can be established:

[0077] D=[K(x A1 ,x B1 ,x C1 ),K(x A2 ,x B2 ,x C2 ),...,K(x AN ,x BN ,xCN )]

[0078]

[0079] in, For the phase before commutation, the calculation of commutation times is as follows:

[0080]

[0081] Then the objective function of minimizing the number of commutation times in the distribution station area can be expressed as

[0082] δ 2 =min(D(K)).

[0083] Finally, according to the phase change requirements of the distribution network, after phase change, the phase current of each phase should be within the allowable load of the phase conductor. In summary, when solving the three-phase imbalance based on the phase change unit, in order to achieve the optimal phase change strategy, the following distribution network load optimization control model can be established.

[0084] δ 1 =min(σ(K))

[0085] δ 2 =min(D(K))

[0086] Constraints:

[0087] δ(K)≤C num

[0088] K∈{[1,0,0] T ,[0,1,0] T ,[0,0,1] T}

[0089] I A ,I B ,I C al

[0090] C num Indicates the number of commutation unit actions set based on prior knowledge.

[0091] ​In one embodiment, after the load optimization control model of the distribution network is established, the load optimization model is solved based on the microhabitat technology and the genetic algorithm. First, the initial population is generated, and it is continuously evolved through genetic operations such as selection, crossover, and mutation to optimize the objective function. The microhabitat technology is introduced to divide the population into multiple microhabitats, so that the competition and cooperation between individuals can be carried out more effectively, and the global search ability of the algorithm is enhanced. At the same time, the similar fitness value selection mechanism and the optimal individual retention mechanism are adopted to ensure the retention and propagation of excellent solutions, improve the convergence speed of the algorithm and the quality of the solution, obtain the optimal control plan, and ensure the best balance between multiple objectives.

[0092] Specifically, the load optimization control model of the distribution network is optimized according to the following steps, and an initial population is generated according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme; the fitness value of the multi-objective function is calculated according to the source-load phase switching optimization control model, and the entire initial population is divided into multiple small habitats according to the chromosome similarity and the fitness value, and each small habitat contains a preset number of chromosomes; the selection operation is independently performed in each niche, and the chromosome generates offspring through the crossover operation; the offspring subjected to the crossover operation are mutated, and the individuals with the highest fitness in the current population after the mutation operation are mutated; a new generation of population is formed according to the results of the crossover and mutation; the selection, crossover and mutation operations are repeated, and the performance of the new population is evaluated by updating the imbalance and the number of phase switching times in each iteration until the maximum number of iterations is reached or the optimal solution is obtained, and the number of phase switching times and the optimal access phase difference of the phase switching unit are output to obtain the final distribution network load optimization control model.

[0093] It should be noted that the initial population is generated according to the access phase of the load, and each chromosome represents a possible access phase configuration scheme. In this process, the construction of the initial population involves a systematic analysis of the access phases of various load devices to ensure that the generated configuration schemes are diverse and representative. The structure of each chromosome can reflect the specific phase switching strategy, including the access status of each load in the three-phase system. The multi-objective function fitness value is calculated according to the source-load phase switching optimization control model:

[0094] F i =w 1 δ 1 +w 2 δ 2 =w 1 σ(K Xi )+w 2 (D(K Xi ))

[0095] Among them, K Xi represents the i-th chromosome, w 1 represents the imbalance weight, w2 Represents the commutation switch weight.

[0096] After the initial population is generated, the entire population is divided into multiple microhabitats according to chromosome similarity and fitness. Each microhabitat contains a certain number of chromosomes, which can compete and cooperate under specific environmental conditions.

[0097] After the initial population is generated, the tournament selection operation is performed independently in each niche. The selected chromosomes are crossovered to generate offspring. The crossover process can be performed within a niche or across niches to maintain genetic diversity. The offspring after the crossover are subjected to (2+2) mutation selection, that is, the offspring after the crossover operation are subjected to (2+2) mutation operation to add new genetic characteristics. At the same time, the fitness of the individual after the mutation is improved.

[0098] Among them, Figure 4 As shown, the (2+2) mutation selection method is as follows:

[0099] The (2+2) mutation operation refers to selecting two chromosomes from the population for mutation and generating two new mutant chromosomes, and then selecting the two chromosomes with the highest fitness from the four chromosomes (the original two plus the two after mutation) as the surviving individuals, thereby increasing the genetic diversity of the population and improving the search range.

[0100] The best individual is selected by (1+1) mutation, that is, the individual with the highest fitness in the current population after processing is subjected to (1+1) mutation operation, so that the algorithm can jump out of the local optimum and explore new possible solution space.

[0101] Among them, Figure 5 As shown in Figure 1, the (1+1) mutation selection method is as follows: The (1+1) mutation operation usually refers to selecting a single chromosome for mutation, then comparing it with the original chromosome, and selecting the chromosome with higher fitness to continue to remain in the population. This strategy can be regarded as a "trial and error" method, in which the chromosome tries a new mutation version, and if the new version is better than the current version, the mutation is accepted.

[0102] Based on the results of crossover and mutation, a new generation of population is formed. Repeat the selection, crossover, and mutation operations until the maximum number of iterations is reached. Repeat the selection, crossover, and mutation operations, and evaluate the performance of the new population by updating the imbalance and the number of commutations in each iteration until the maximum number of iterations is reached or the optimal solution is obtained. The output commutation number is different from the optimal access phase of the commutation unit.

[0103] Step S3, determining the corresponding substation control strategy according to the flexible control strategy, and issuing a commutation command to the reversing switch based on the substation control strategy, dynamically adjusting the load equipment in the substation, and realizing real-time response to load demand.

[0104] Embodiments of the present invention, such as Figure 6 As shown, a flexible control system for the load in the substation is also provided, which is used to implement the flexible control method for the load in the substation, including:

[0105] The load control terminal is used to obtain the operation data of the distribution transformer area; and the operation data of the distribution transformer area is input into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model with a multi-objective function set based on the phase-change strategy with the lowest three-phase imbalance degree of the transformer node in the area and the least number of phase-change unit actions;

[0106] It is also used to determine the corresponding substation control strategy according to the flexible control strategy, and send a phase change command to the reversing switch based on the substation control strategy, dynamically adjust the load equipment in the substation, and realize real-time response to load demand.

[0107] In a specific embodiment, the load control terminal is installed at the low-voltage outlet line on the secondary side of the distribution transformer, and can collect data information such as voltage and current of each phase of the distribution transformer for calculating the three-phase imbalance; at the same time, it receives the load current data and its phase sequence uploaded by the phase-changing unit, and calculates the three-phase imbalance in real time. When the three-phase current imbalance exceeds the set threshold, the genetic algorithm is used to solve the load optimization model based on the niche technology and the genetic algorithm, generate the optimal control plan, and send it to the phase-changing unit through micro-power wireless communication to perform phase-changing operations, so as to dynamically adjust the load equipment in the substation.

[0108] The load control terminal is also used to optimize the distribution network load optimization control model according to the following steps, generate an initial population according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme; calculate the fitness value of the multi-objective function according to the source-load phase switching optimization control model, and divide the entire initial population into multiple small habitats according to the chromosome similarity and fitness value, each small habitat contains a preset number of chromosomes; independently perform selection operations in each niche, and the chromosomes generate offspring through crossover operations; perform mutation operations on the offspring that have undergone the crossover operation, and perform mutation operations on the individuals with the highest fitness in the current population after the mutation operation; form a new generation of population according to the results of crossover and mutation; repeat the selection, crossover and mutation operations, and evaluate the performance of the new population by updating the imbalance and the number of phase switching times in each iteration until the maximum number of iterations is reached or the optimal solution is obtained, and the number of phase switching times and the optimal access phase difference of the phase switching unit are output to obtain the final distribution network load optimization control model.

[0109] It should be noted that the system described in the above embodiment corresponds to the method described in the above embodiment. Therefore, the undetailed parts of the system described in the above embodiment can be obtained by referring to the contents of the method described in the above embodiment, and will not be repeated here.

[0110] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0111] The flexible control method and system of the substation load provided by the present invention aims to minimize the three-phase current imbalance and the action of the commutation switch execution unit, establish a three-phase unbalanced commutation model of the power grid, and simultaneously apply the niche technology to design a genetic algorithm based on a similar fitness value selection mechanism and an optimal individual retention mechanism to achieve intelligent optimization and control of the operating state of the distribution network; improve the operating efficiency of the distribution network, reduce equipment loss and failure risks, and thus enhance the stability and reliability of power supply.

[0112] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A flexible control method for load in a substation, characterized in that: include: Obtain the operation data of the distribution transformer area; Input the operation data of the distribution transformer area into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model that sets a multi-objective function based on the phase-change strategy with the lowest three-phase imbalance of the transformer node in the area and the least number of phase-change unit actions; The corresponding substation control strategy is determined according to the flexible control strategy, and a phase change command is issued to the reversing switch based on the substation control strategy to dynamically adjust the load equipment in the substation to achieve real-time response to load demand.

2. The method according to claim 1, characterized in that The operation data of the distribution transformer area at least includes the three-phase current and voltage on the low-voltage side of the distribution transformer, the load branch current and the switch status.

3. The method according to claim 2, characterized in that The low-voltage distribution station area source-load switching optimization control model specifically includes: δ1=min(σ(K)) δ2=min(D(K)) Among them, σ(K) is the unbalance degree of the three-phase current in the distribution network, D(K) is the number of commutation times, δ1 is the first commutation strategy, and δ2 is the second commutation strategy.

4. The method according to claim 3, characterized in that The low voltage distribution station area source-load switching optimization control model also includes: δ(K)≤C num K∈{[1,0,0] T ,[0,1,0] T ,[0,0,1] T} I A ,I B ,I C <I al Among them, C num is the number of commutation unit actions set according to prior knowledge, I av is the average value of the three-phase current, I A ,I B ,I C is the three-phase current, [1,0,0] T To access phase A, [0,1,0] T To access phase B, [0,0,1] T To access phase C.

5. The method according to claim 4, characterized in that It also includes optimizing the distribution network load optimization control model according to the following steps: Generating an initial population according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme; The fitness value of the multi-objective function is calculated according to the source-load phase-changing optimization control model, and the entire initial population is divided into multiple microhabitats according to the chromosome similarity and fitness value, each microhabitat contains a preset number of chromosomes; Selection operations are performed independently in each niche, and chromosomes produce offspring through crossover operations; Perform mutation operation on the offspring after the crossover operation, and perform mutation operation on the individual with the highest fitness in the current population after the mutation operation; According to the results of crossover and mutation, a new generation of population is formed; the selection, crossover and mutation operations are repeated, and the performance of the new population is evaluated by updating the imbalance and the number of commutations in each iteration until the maximum number of iterations is reached or the optimal solution is obtained. The number of commutations is output and the optimal access phase of the commutation unit is different, and the final distribution network load optimization control model is obtained.

6. A flexible control system for load in a substation, used to implement the method according to any one of claims 1 to 5, characterized in that: include, The load control terminal is used to obtain the operation data of the distribution transformer area; and the operation data of the distribution transformer area is input into the preset low-voltage distribution area source-load phase-change optimization control model to obtain the corresponding flexible control strategy; wherein the low-voltage distribution area source-load phase-change optimization control model is a distribution network load optimization control model with a multi-objective function set based on the phase-change strategy with the lowest three-phase imbalance degree of the transformer node in the area and the least number of phase-change unit actions; It is also used to determine the corresponding substation control strategy according to the flexible control strategy, and send a phase change command to the reversing switch based on the substation control strategy, dynamically adjust the load equipment in the substation, and realize real-time response to load demand.

7. The system according to claim 6, characterized in that The operation data of the distribution transformer area at least includes the three-phase current and voltage on the low-voltage side of the distribution transformer, the load branch current and the switch status.

8. The system according to claim 7, characterized in that The low-voltage distribution station area source-load switching optimization control model specifically includes: δ1=min(σ(K)) δ2=min(D(K)) Among them, σ(K) is the unbalance degree of the three-phase current in the distribution network, D(K) is the number of commutation times, δ1 is the first commutation strategy, and δ2 is the second commutation strategy.

9. The system according to claim 8, characterized in that The low voltage distribution station area source-load switching optimization control model also includes: δ(K)≤C num K∈{[1,0,0] T ,[0,1,0] T ,[0,0,1] T} I A ,I B ,I C <I al Among them, C num is the number of commutation unit actions set according to prior knowledge, I av is the average value of the three-phase current, I A ,I B ,I C is the three-phase current, [1,0,0] T To access phase A, [0,1,0] T To access phase B, [0,0,1] T To access phase C.

10. The system according to claim 9, characterized in that The load control terminal is further used to optimize the distribution network load optimization control model according to the following steps, and generate an initial population according to the access phase of the load, wherein each chromosome in the initial population represents a possible access phase configuration scheme; The fitness value of the multi-objective function is calculated according to the source-load phase-changing optimization control model, and the entire initial population is divided into multiple microhabitats according to the chromosome similarity and fitness value, each microhabitat contains a preset number of chromosomes; Selection operations are performed independently in each niche, and chromosomes produce offspring through crossover operations; Perform mutation operation on the offspring after the crossover operation, and perform mutation operation on the individual with the highest fitness in the current population after the mutation operation; According to the results of crossover and mutation, a new generation of population is formed; the selection, crossover and mutation operations are repeated, and the performance of the new population is evaluated by updating the imbalance and the number of commutations in each iteration until the maximum number of iterations is reached or the optimal solution is obtained. The number of commutations is output and the optimal access phase of the commutation unit is different, and the final distribution network load optimization control model is obtained.

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