Flexible interconnection photovoltaic low-voltage transformer area adjustable resource regulation and control method and system based on genetic algorithm

Abstract

The invention discloses a flexible interconnection photovoltaic low-voltage transformer area adjustable resource regulation and control method and system based on a genetic algorithm, and the method comprises the steps: carrying out the power flow solving of a multi-transformer area interconnection system through a forward-backward substitution method, and obtaining the final power flow information; based on the final power flow information, establishing an optimization model taking power loss minimization as an optimization target, and obtaining a plurality of corresponding low-voltage transformer area operation constraint conditions; and carrying out optimization solution processing on the optimization model by utilizing a genetic algorithm to obtain an optimal power instruction scheme of the flexible interconnection device, the static var compensator and the photovoltaic inverter. According to the invention, the voltage regulation capabilities of the flexible interconnection device, the static var compensator and the photovoltaic inverter can be exerted at the same time, and the maximum consumption of photovoltaic resources is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization control technology, and in particular to a method and system for regulating and controlling adjustable resources in flexible interconnected photovoltaic low-voltage areas based on genetic algorithms. Background Art

[0002] Distributed photovoltaic power generation is affected by many factors such as weather, season, and temperature, which makes photovoltaic power generation have obvious volatility and intermittency. This fluctuation can easily affect the voltage of the distribution network, especially when the photovoltaic output is greater than the load, which may cause the voltage to exceed the limit. The rural distribution network has a weak network structure and limited pressure resistance, so the voltage problem will be more prominent. Secondly, when the photovoltaic output is greater than the load, photovoltaic power cannot be consumed nearby and may be sent back to the surrounding loads or even the substation. When the photovoltaic capacity is too large and the substation has a phased reverse power supply, it will not only cause certain damage to the substation transformer, but also greatly increase the transmission power loss.

[0003] Traditional voltage regulation strategies, such as photovoltaic inverter "active-reactive" coordinated voltage regulation, additional on-load voltage-regulating transformers, and static VAR compensators, mainly focus on voltage regulation, while ignoring the deep development and utilization of optical resources within the substation. In recent years, with the construction of flexible AC / DC distribution networks, more and more flexible interconnected devices have been gradually applied to the voltage control process of distribution networks. Flexible interconnected devices can not only realize power transmission between different substations and make full use of photovoltaic resources, but also adjust voltage and power flow within a certain range and enhance the flexibility and reliability of the power grid. Compared with traditional regulation methods, existing studies have shown that flexible interconnected devices can accurately and continuously adjust active power and reactive power, solve the problem of reverse power, improve the voltage quality of distribution networks, and effectively reduce network losses. However, due to the limitations of factors such as the capacity of flexible interconnected devices and the number and location of installation, the regulation effect of flexible interconnected devices on voltage is also limited. Most of the current research on flexible interconnected devices focuses on the research of their own control strategies and multi-substation collaborative algorithms, and there is little research on the collaborative optimization control of photovoltaic inverters, static VAR compensators, and flexible interconnected devices. Through the research on coordinated optimization control of photovoltaic inverters, static VAR compensators and flexible interconnected devices, the global controllable resources can be regulated to further improve the voltage regulation capability of the substation. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for regulating adjustable resources of flexible interconnected photovoltaic low-voltage substations based on genetic algorithms, which can realize energy interconnection and mutual supply among substations, improve the power supply level of substations, reduce the risk of voltage exceeding the limit in substations and improve the photovoltaic absorption level.

[0005] The technical solution of the present invention is: a method for regulating and controlling adjustable resources of flexible interconnected photovoltaic low-voltage areas based on genetic algorithms, comprising the following steps:

[0006] S1. Use the forward-backward method to solve the power flow of the multi-station interconnected system and obtain the final power flow information.

[0007] S2. Based on the final power flow information of step S1, an optimization model with minimization of power loss as the optimization goal is established, and corresponding operating constraints of multiple low-voltage substations are obtained.

[0008] S3. Utilize a genetic algorithm to optimize and solve the optimization model in step S2, obtain the optimal power instruction scheme of the flexible interconnection device, static VAR compensator and photovoltaic inverter, and complete resource regulation.

[0009] Furthermore, in step S1, the final flow information obtained includes the following contents:

[0010] According to the load power and initial voltage of the branch, the current of each node is calculated. The specific formula is:

[0011]

[0012] Among them, I i represents the current flowing into the i-th node; S i represents the complex power of the ith node; V i represents the voltage of the i-th node.

[0013] The voltage is derived from the source node to the end node, and the node voltage drop of the branch (i, j) from the i-th node to the j-th node is calculated according to Ohm's law. The specific formula is:

[0014] V j =V i -Z ij I j

[0015] Among them, Z ij represents the line impedance from the i-th node to the j-th node, I j represents the current flowing into the jth node, V j represents the voltage of the jth node.

[0016] Starting from the end node of the multi-station interconnection system, back-substitution is performed step by step to the source node to calculate the branch current. The specific formula is:

[0017]

[0018] Among them, I k represents the current flowing into the kth child node, and children of i represents the set of child nodes belonging to the i-th node.

[0019] After the derivation and back substitution are completed, check the convergence of the voltage. When the voltage of all nodes satisfies When , the voltage change satisfies the convergence condition, and the final power flow information is obtained; otherwise, the derivation and back substitution process is repeated.

[0020] in, represents the voltage of the ith node at the tth iteration, represents the voltage of the i-th node at the t+1-th iteration, and ε represents the tolerance.

[0021] Furthermore, in step S2, the optimization model includes the following contents:

[0022] The optimization model is established with the minimization of power loss as the optimization goal. The specific expression is:

[0023] min P total =min P lossDC +P lossAC

[0024]

[0025] Among them, P total Represents the total power transmission loss of the multi-zone interconnection system, P lossDC P represents the total DC power transmission loss of the multi-zone interconnection system. lossAC represents the total AC side power transmission loss of the multi-zone interconnected system, k DC represents the DC side loss coefficient, represents the active power flowing from the DC side to the AC side of the flexible interconnection device in the u-th area, represents the active power flowing from the AC side to the DC side of the flexible interconnection device in the u-th area, k AC represents the AC loss coefficient, It represents the active power on the low-voltage side of the AC transformer in the u-th substation, and n represents the total number of substations.

[0026] The constraints for the operation of multiple low-voltage areas interconnected by flexible interconnection devices are:

[0027] -7%V N ≤ΔV i ≤7%V N

[0028]

[0029] 0≤Q SVG,u ≤Q SVG,max

[0030] Among them, V N Indicates the standard voltage rating, ΔV iIndicates the deviation of each node voltage from the standard voltage rating, P x Indicates the active power generated by the photovoltaic inverter numbered x, Q x Indicates the reactive power generated by the photovoltaic inverter numbered x, S xN Indicates the rated capacity of the photovoltaic inverter numbered x, Q u represents the reactive power generated by the flexible interconnection device in the u-th substation, S u represents the rated capacity of the flexible interconnection device in the u-th station, Q SVG,u represents the reactive power amplitude output by the static VAR compensator in the u-th substation, Q SVG,max It represents the maximum reactive power amplitude that the static VAR compensator of the u-th substation can output.

[0031] Furthermore, in step S3, the power instruction scheme is obtained including the following contents:

[0032] S301. Randomly generate an initial optimization solution to form an initial population, where each chromosome represents a command setting scheme for a flexible interconnection device, a static VAR compensator, and a photovoltaic inverter power.

[0033] S302, check whether each chromosome in the initial population meets the total constraint of the optimization model. If all chromosomes do not meet the total constraint, there is no feasible solution and the operation is terminated. If there is a chromosome that meets the total constraint, the chromosomes that meet the total constraint constitute a population and enter step S303 for operation.

[0034] Among them, the total constraints of the optimization model are:

[0035] g t (x)≤0

[0036] h r (x) = 0

[0037] Where x represents the instruction setting scheme of the power of the flexible interconnection device, static VAR compensator and photovoltaic inverter, g t () represents the inequality constraint function, h r () represents the inequality constraint function, t represents the inequality constraint function number, and r represents the equality constraint function number.

[0038] S303: Input the population in step S302 into the optimization model in step S2 to obtain the total power transmission loss P of the multi-station interconnection system corresponding to all chromosomes. total , which is the fitness value.

[0039] S304, selecting the chromosomes with the largest fitness values ​​in the first m to enter the next generation, and improving the population composed of the chromosomes by using selection, crossover, mutation and crossover mutation inheritance to obtain a new generation of population.

[0040] S305: Compare the average fitness value of the new generation population with the average fitness value of the previous generation to obtain a change in the average fitness value.

[0041] The expression for the degree of constraint violation is:

[0042]

[0043] Among them, Violation represents the degree of constraint violation, and max() represents the maximum value.

[0044] Determine whether the change in the average fitness value and the degree of constraint violation both meet the set tolerance. If the change in the average fitness value is less than the set tolerance for the change in the average fitness value and the degree of constraint violation is less than the set tolerance for the degree of constraint violation, it indicates that the genetic algorithm has converged. Then stop and select the chromosome with the highest fitness in the new generation population as the optimal solution. Otherwise, adjust the active power instruction of the photovoltaic inverter with the largest active power output in the multi-station interconnection system, reduce the active power instruction by 5%, and return to step S302.

[0045] S306: According to the obtained optimal solution, a flexible interconnection device, a static VAR compensator and a photovoltaic inverter power instruction scheme are set.

[0046] Furthermore, the present invention also proposes a flexible interconnected photovoltaic low-voltage area adjustable resource control system based on genetic algorithm, comprising:

[0047] The power flow information acquisition module is used to solve the power flow of the multi-station interconnection system using the forward and backward method to obtain the final power flow information.

[0048] The optimization model building module is used to establish an optimization model with power loss minimization as the optimization goal based on the final power flow information, and obtain the corresponding operating constraints of multiple low-voltage substations.

[0049] The power instruction scheme acquisition module is used to optimize and solve the optimization model in the optimization model construction module using a genetic algorithm to obtain the optimal power instruction scheme for the flexible interconnection device, static VAR compensator and photovoltaic inverter.

[0050] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for regulating adjustable resources of flexible interconnected photovoltaic low-voltage stations based on genetic algorithms are implemented.

[0051] Furthermore, the present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is run by a processor, the flexible interconnected photovoltaic low-voltage area adjustable resource control method based on a genetic algorithm is executed.

[0052] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0053] 1. The method proposed in the present invention aims at the line characteristics of low-voltage distribution stations, and considers the regulating effect and restriction conditions of the flexible interconnection device, static VAR compensator and photovoltaic inverter on the system voltage, and constructs a low-voltage multi-area interconnection system model that coordinates all adjustable resources in different areas.

[0054] 2. The method proposed in the present invention can simultaneously exert the voltage regulation capabilities of the flexible interconnection device, static VAR compensator and photovoltaic inverter itself, and achieve the maximum consumption of photovoltaic resources by "active-reactive" coordinated voltage regulation. At the same time, it can completely avoid the voltage over-limit phenomenon at the node of the substation area. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is an overall implementation flow chart of the present invention.

[0056] Figure 2 It is a schematic diagram of the structure of a low-voltage multi-zone system according to an embodiment of the present invention.

[0057] Figure 3 It is a voltage distribution diagram of the station area 1 during the optimization process of the embodiment of the present invention.

[0058] Figure 4 3 is a voltage distribution diagram before and after optimization of an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0060] To achieve the above objectives, the present invention proposes a method for regulating and controlling adjustable resources in a flexible interconnected photovoltaic low-voltage area based on a genetic algorithm, such as Figure 1 As shown, the specific steps are as follows:

[0061] S1. Use the forward-backward method to solve the power flow of the multi-station interconnected system and obtain the final power flow information; the specific content is:

[0062] The forward-backward substitution method is used to calculate the voltage and current of each node in the multi-station interconnected system.

[0063] Among them, the current of each node is calculated according to the load power and initial voltage of the branch. The specific formula is:

[0064]

[0065] Among them, I i represents the current flowing into the i-th node; S i represents the complex power of the ith node; V i represents the voltage of the i-th node.

[0066] The voltage is derived from the source node to the end node, and the node voltage drop of the branch (i, j) from the i-th node to the j-th node is calculated according to Ohm's law. The specific formula is:

[0067] V j =V i -Z ij I j

[0068] Among them, Z ij represents the line impedance from the i-th node to the j-th node, I j represents the current flowing into the jth node, V j represents the voltage of the jth node.

[0069] Starting from the end node of the multi-station interconnection system, back-substitution is performed step by step to the source node to calculate the branch current. The specific formula is:

[0070]

[0071] Among them, I k represents the current flowing into the kth child node, and children of i represents the set of child nodes belonging to the i-th node.

[0072] After derivation and back substitution, check the convergence of voltage. When the voltage of all nodes satisfies When , the voltage change satisfies the convergence condition, and the final power flow information is obtained; if the voltage change does not meet the convergence condition, the derivation and back substitution process is repeated.

[0073] in, represents the voltage of the ith node at the tth iteration, represents the voltage of the i-th node at the t+1-th iteration, and ε represents the tolerance.

[0074] S2. Based on the final power flow information of step S1, an optimization model with power loss minimization as the optimization goal is established, and corresponding operating constraints of multiple low-voltage areas are obtained; the specific contents are:

[0075] The specific expression of the optimization model with power loss minimization as the optimization goal is:

[0076] min P total =min P lossDC +P lossAC

[0077]

[0078] Among them, P total Represents the total power transmission loss of the multi-zone interconnection system, P lossDC P represents the total DC power transmission loss of the multi-zone interconnection system. lossAC represents the total AC side power transmission loss of the multi-zone interconnected system, k DC represents the DC side loss coefficient, represents the active power flowing from the DC side to the AC side of the flexible interconnection device in the u-th area, represents the active power flowing from the AC side to the DC side of the flexible interconnection device in the u-th area, k AC represents the AC loss coefficient, It represents the active power on the low-voltage side of the AC transformer in the u-th substation, and n represents the total number of substations.

[0079] The constraints for the operation of multiple low-voltage areas interconnected by flexible interconnection devices are:

[0080] -7%V N ≤ΔV i ≤7%V N

[0081]

[0082]

[0083] 0≤Q SVG,u ≤Q SVG,max

[0084] Among them, V N Indicates the standard voltage rating, ΔV i Indicates the deviation of each node voltage from the standard voltage rating, P x Indicates the active power generated by the photovoltaic inverter numbered x, Q x Indicates the reactive power generated by the photovoltaic inverter numbered x, S xN Indicates the rated capacity of the photovoltaic inverter numbered x, Q u represents the reactive power generated by the flexible interconnection device in the u-th substation, S u represents the rated capacity of the flexible interconnection device in the u-th station, Q SVG,u represents the reactive power amplitude output by the static VAR compensator in the u-th substation, QSVG,max It represents the maximum reactive power amplitude that the static VAR compensator of the u-th substation can output.

[0085] S3, using genetic algorithm to optimize and solve the optimization model in step S2, and obtain the optimal flexible interconnection device, static VAR compensator and photovoltaic inverter power instruction scheme; the specific content is:

[0086] GA (Genetic Algorithm) is an optimization search method based on natural selection and genetic mechanism. It imitates the evolution process in nature and continuously improves candidate solutions through operations such as selection, crossover, and mutation to find the global optimal solution. The specific optimization solution process includes the following steps:

[0087] S301. Randomly generate an initial optimization solution to form an initial population, where each chromosome represents a command setting scheme for a flexible interconnection device, a static VAR compensator, and a photovoltaic inverter power.

[0088] S302. When generating the initial population, check whether each chromosome satisfies the total constraint of the optimization model. If all chromosomes do not meet the total constraint, there is no feasible solution and the algorithm is terminated. If there are chromosomes that meet the total constraint, these chromosomes are formed into a population and enter step S303 for operation.

[0089] Among them, the total constraints of the optimization model are:

[0090] g t (x)≤0(inequality constraint)

[0091] h r (x) = 0 (equality constraint)

[0092] Where x represents the instruction setting scheme of the power of the flexible interconnection device, static VAR compensator and photovoltaic inverter, g t () represents the inequality constraint function, h r () represents the inequality constraint function, t represents the inequality constraint function number, and r represents the equality constraint function number.

[0093] S303: Input the population chromosome in step S302 into the optimization model in step S2 to obtain the total power transmission loss P of the multi-station interconnection system corresponding to all chromosomes. total , which is the fitness value.

[0094] S304, select 200 chromosomes with the highest fitness values ​​to enter the next generation, and use selection, crossover, mutation and crossover mutation inheritance to improve the population composed of the chromosomes to obtain a new generation of population. To prevent falling into the local optimum and maintain the diversity of genes, operations such as crossover mutation inheritance are introduced here to ensure global convergence while exploring more possibilities of optimal solutions.

[0095] S305: Compare the average fitness value of the new generation population with the average fitness value of the previous generation to obtain a change in the average fitness value.

[0096] The expression for the degree of constraint violation is:

[0097]

[0098] Among them, Violation represents the degree of constraint violation, and max() represents the maximum value.

[0099] Determine whether the change in the average fitness value and the degree of constraint violation both meet the set tolerance. If the change in the average fitness value is less than the set tolerance for the change in the average fitness value and the degree of constraint violation is less than the set tolerance for the degree of constraint violation, indicating that the algorithm has converged, the algorithm is stopped and the individual with the highest fitness in the new generation population is selected as the optimal solution. Otherwise, adjust the active power instruction of the photovoltaic inverter with the largest active power output in the multi-station interconnection system, reduce the active power instruction by 5%, and return to step S302.

[0100] S306: According to the final optimal solution obtained, a flexible interconnection device, a static VAR compensator and a photovoltaic inverter power instruction scheme are set.

[0101] Based on the above content, in order to verify the effectiveness of the solution of the present invention, the above solution is applied as an example in this embodiment, as follows:

[0102] use Figure 2 The multi-zone interconnected system structure is analyzed, and the example data is shown in Table 1.

[0103] Table 1 Parameters and values ​​of the flexible interconnection system in multiple distribution areas

[0104]

[0105]

[0106]

[0107] The capacity of the flexible interconnection device is limited, and the remaining capacity of the photovoltaic inverter is also limited. The voltage regulation capabilities of the two are combined to adjust the voltage. When the voltage of node 2 of area 1 is reduced to the minimum value of 204.6V within the allowable voltage deviation range, the voltage distribution diagram of area 1 is as follows: Figure 3 As shown. Figure 3 It can be seen that at this time, the voltage of node 2 has reached the minimum value within the allowable voltage deviation range, and the voltage still exceeds the upper limit in the substation. Therefore, it is impossible to solve the appropriate power command of the flexible interconnection device, static VAR compensator and photovoltaic inverter reactive command through genetic algorithm to make the voltage of the entire multi-substation interconnection system within the standard deviation range, that is, there is no feasible solution. Therefore, it is necessary to adjust the active power command of the photovoltaic inverter with the largest active power output in the system to complete the optimization and obtain the optimal solution, that is, the final setting scheme of the power command of the flexible interconnection device, static VAR compensator and photovoltaic inverter.

[0108] The voltage distribution diagram before and after optimization is as follows Figure 4 As shown by Figure 4 It can be seen that before optimization, there were multiple nodes in area 1 where voltage exceeded the limit. After optimization, the voltage over-limit was controlled through the flexible interconnection device, static VAR compensator and coordinated active-reactive control of photovoltaic inverters. The power loss before optimization was 2.943kW, and the power loss after optimization was 2.0711kW. From this embodiment, the method provided by the present invention can not only control the voltage over-limit problem in the low-voltage area, but also reduce power loss.

[0109] The embodiment of the present invention also proposes a flexible interconnected photovoltaic low-voltage area adjustable resource control system based on genetic algorithm, including a power flow information acquisition module, an optimization model construction module, a power instruction scheme acquisition module and a computer program that can be run on a processor. It should be noted that each module in the above system corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0110] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0111] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. It should be noted that when the computer program is executed by the processor, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for regulating and controlling adjustable resources in flexible interconnected photovoltaic low-voltage areas based on genetic algorithms, characterized in that: include: S1. Use the forward-backward method to solve the power flow of the multi-station interconnected system and obtain the final power flow information; S2. Based on the final power flow information of step S1, an optimization model with minimization of power loss as the optimization goal is established, and corresponding operation constraints of multiple low-voltage substations are obtained; S3. Utilize a genetic algorithm to optimize and solve the optimization model in step S2, obtain the optimal power instruction scheme of the flexible interconnection device, static VAR compensator and photovoltaic inverter, and complete resource regulation.

2. The method for regulating and controlling adjustable resources of flexible interconnected photovoltaic low-voltage areas based on genetic algorithm according to claim 1 is characterized in that: In step S1, the final power flow information obtained includes the following contents: According to the load power and initial voltage of the branch, the current of each node is calculated. The specific formula is: Among them, I i represents the current flowing into the i-th node; S i represents the complex power of the ith node; V i represents the voltage of the i-th node; The voltage is derived from the source node to the end node, and the node voltage drop of the branch (i, j) from the i-th node to the j-th node is calculated according to Ohm's law. The specific formula is: V j =V i -Z ij I j Among them, Z ij represents the line impedance from the i-th node to the j-th node, I j represents the current flowing into the jth node, V j represents the voltage of the jth node; Starting from the end node of the multi-station interconnection system, back-substitution is performed step by step to the source node to calculate the branch current. The specific formula is: Among them, I k represents the current flowing into the kth child node, and children of i represents the set of child nodes belonging to the i-th node; After the derivation and back substitution are completed, check the convergence of the voltage. When the voltage of all nodes satisfies V i t+1 -V i t When |≤ε, the voltage change satisfies the convergence condition, and the final power flow information is obtained; otherwise, the derivation and back-substitution process is repeated; Among them, V i t represents the voltage of the ith node at the tth iteration, V i t+1 represents the voltage of the i-th node at the t+1-th iteration, and ε represents the tolerance.

3. The method for regulating and controlling adjustable resources of flexible interconnected photovoltaic low-voltage areas based on genetic algorithm according to claim 1 is characterized in that: In step S2, the optimization model includes the following contents: The optimization model is established with the minimization of power loss as the optimization goal. The specific expression is: minPtotal=min PlossDC+PlossAC Among them, P total Represents the total power transmission loss of the multi-zone interconnection system, P lossDC P represents the total DC power transmission loss of the multi-zone interconnection system. lossAC represents the total AC side power transmission loss of the multi-zone interconnected system, k DC represents the DC side loss coefficient, represents the active power flowing from the DC side to the AC side of the flexible interconnection device in the u-th area, represents the active power flowing from the AC side to the DC side of the flexible interconnection device in the u-th area, k AC represents the AC loss coefficient, represents the active power on the low-voltage side of the AC transformer in the u-th substation, and n represents the total number of substations; The constraints for the operation of multiple low-voltage areas interconnected by flexible interconnection devices are: -7%VN≤ΔVi≤7%VN 0≤QSVG,u≤QSVG,max Among them, V N Indicates the standard voltage rating, ΔV i Indicates the deviation of each node voltage from the standard voltage rating, P x Indicates the active power generated by the photovoltaic inverter numbered x, Q x Indicates the reactive power generated by the photovoltaic inverter numbered x, S xN Indicates the rated capacity of the photovoltaic inverter numbered x, Q u represents the reactive power generated by the flexible interconnection device in the u-th substation, S u represents the rated capacity of the flexible interconnection device in the u-th station, Q SVG,u represents the reactive power amplitude output by the static VAR compensator in the u-th substation, Q SVG,max It represents the maximum reactive power amplitude that the static VAR compensator of the u-th substation can output.

4. The method for regulating and controlling adjustable resources of flexible interconnected photovoltaic low-voltage areas based on genetic algorithm according to claim 1 is characterized in that: In step S3, the power instruction scheme is obtained including the following contents: S301, randomly generating an initial optimization solution to form an initial population, where each chromosome represents a command setting scheme for a flexible interconnection device, a static VAR compensator, and a photovoltaic inverter power; S302, check whether each chromosome in the initial population meets the total constraint condition of the optimization model. If all chromosomes do not meet the total constraint condition, there is no feasible solution and the operation is terminated; if there is a chromosome that meets the total constraint condition, the chromosomes that meet the total constraint condition are used to form a population and enter step S303 for operation; Among them, the total constraints of the optimization model are: g t (x)≤0 h r (x)=0 Where x represents the instruction setting scheme of the power of the flexible interconnection device, static VAR compensator and photovoltaic inverter, g t () represents the inequality constraint function, h r () represents the inequality constraint function, t represents the inequality constraint function number, and r represents the equality constraint function number; S303: Input the population in step S302 into the optimization model in step S2 to obtain the total power transmission loss P of the multi-station interconnection system corresponding to all chromosomes. total , which is the fitness value; S304, selecting the chromosomes with the largest fitness values ​​to enter the next generation, and improving the population composed of the chromosomes by using selection, crossover, mutation and crossover mutation inheritance to obtain a new generation of population; S305, comparing the average fitness value of the new generation population with the average fitness value of the previous generation to obtain a change in the average fitness value; The expression for the degree of constraint violation is: Among them, Violation represents the degree of constraint violation, and max() represents the maximum value; Determine whether the fitness average change and the constraint violation degree both meet the set tolerance. If the fitness average change is less than the set fitness average change tolerance and the constraint violation degree is less than the set constraint violation tolerance, indicating that the genetic algorithm has converged, then stop and select the chromosome with the highest fitness in the new generation population as the optimal solution; otherwise, adjust the active power instruction of the photovoltaic inverter with the largest active power output in the multi-station interconnection system, reduce the active power instruction by 5%, and return to step S302; S306: According to the obtained optimal solution, a flexible interconnection device, a static VAR compensator and a photovoltaic inverter power instruction scheme are set.

5. A system for the method for regulating and controlling the adjustable resource of the flexible interconnected photovoltaic low-voltage area based on genetic algorithm as claimed in claim 1, characterized in that: include: The power flow information acquisition module is used to solve the power flow of the multi-station interconnection system using the forward and backward method to obtain the final power flow information; The optimization model building module is used to establish an optimization model with power loss minimization as the optimization goal based on the final power flow information, and obtain the corresponding operating constraints of multiple low-voltage substations; The power instruction scheme acquisition module is used to optimize and solve the optimization model in the optimization model construction module using a genetic algorithm to obtain the optimal power instruction scheme for the flexible interconnection device, static VAR compensator and photovoltaic inverter.

6. An electronic device 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 computer program, the steps of the method for regulating adjustable resources of flexible interconnected photovoltaic low-voltage stations based on genetic algorithms as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the method for regulating adjustable resources of flexible interconnected photovoltaic low-voltage stations based on genetic algorithm described in any one of claims 1 to 4 is executed.

Images