Low-voltage distributed photovoltaic group dispatching and group control method and system

Abstract

The invention relates to the technical field of low-voltage distributed photovoltaic regulation and control, in particular to a low-voltage distributed photovoltaic group regulation and group control method and system, and the method is realized based on the low-voltage distributed photovoltaic group regulation and group control system, and comprises the following steps: a fusion terminal collects the operation parameters of a photovoltaic unit in a transformer area and the operation parameters of a transformer area power grid through a preset communication link; filtering the acquired parameters to obtain standardized data; and the master station receives the standardized data, calculates the photovoltaic cluster permeability and the voltage deviation value, and judges the power grid operation risk level in combination with the line overload coefficient and the transformer load rate. According to the method, the optimization coefficient is calculated through the multi-dimensional control deviation value, closed-loop dynamic adjustment of the control parameters is achieved, and the application scene coverage range of the method is widened; a terminal local decision module is fused to guarantee continuous operation of a control function when communication of the master station is interrupted; by standardizing the data format and unifying the control logic, the data linkage efficiency of all modules of the system is improved, and the problems existing in a traditional scheme are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-voltage distributed photovoltaic regulation, in particular to a low-voltage distributed photovoltaic cluster regulation method and system. BACKGROUND

[0002] With the rapid development of the photovoltaic industry, low-voltage distributed photovoltaics have continued to increase their penetration in distribution networks due to their flexibility in installation and their ability to be consumed locally. Low-voltage distributed photovoltaic units are mostly connected to 35 kV and below voltage level distribution networks in a decentralized manner. Although the capacity of a single grid-connected point is small, the overall cluster size is large. The output of low-voltage distributed photovoltaics has intermittent and random characteristics, which poses challenges to voltage stability, load balancing, and safe operation of equipment in the substation.

[0003] Existing low-voltage distributed photovoltaic control schemes can be divided into three categories. The first category is decentralized control, where each photovoltaic unit adjusts its output based on its own operating parameters without the need for unified dispatch instructions. This type of scheme has simple control logic and fast response speed, but lacks cluster coordination capabilities, which can lead to voltage deviations exceeding the standard in the substation, line overload, and other issues, and it cannot achieve global power optimization and distribution. The second category is centralized control, which involves a master station that collects all photovoltaic unit and grid parameters, generates control instructions, and then distributes them to each terminal device. This type of scheme can achieve global dispatch, but it is highly dependent on communication links. When there is a delay or interruption in communication, control instructions cannot be transmitted in a timely manner, which can lead to control failure. Additionally, it is difficult to adapt to the control needs of different types of terminal devices. The third category is semi-centralized control, which involves setting up regional fusion terminals to assist the master station in decomposing instructions. However, existing control strategies are single and only use one of the following methods: flexible adjustment or rigid switching. This approach cannot dynamically adjust based on the matching relationship between the adjustable capacity of the photovoltaic cluster and the control demand, resulting in insufficient control precision and flexibility.

[0004] Furthermore, the existing control schemes rely solely on single parameter threshold judgments for situation analysis and do not establish a risk assessment mechanism for multiple parameter coordination, making it difficult to fully reflect the state of the power grid. The dynamic optimization capability is weak, and there is a lack of closed-loop adjustment mechanism after the control instructions are issued, which cannot effectively compensate for control deviations, resulting in difficulty in meeting the actual application requirements for power grid operation stability and control reliability.

[0005] In summary, a low-voltage distributed photovoltaic cluster regulation method and system are needed to address the aforementioned issues. SUMMARY

[0006] The present application aims to provide a low-voltage distributed photovoltaic cluster regulation method and system to address the issues raised in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The application provides a low-voltage distributed photovoltaic group regulation and control method, which is realized based on a low-voltage distributed photovoltaic group regulation and control system and comprises the following steps. S1. Data acquisition: a fusion terminal collects photovoltaic unit operation parameters and substation power grid operation parameters in a substation through a preset communication link, and filters the collected parameters to obtain standardized data; S2. Situation judgment: a master station receives the standardized data, calculates a photovoltaic cluster penetration rate and a voltage deviation value, and determines a power grid operation risk level in combination with a line overload coefficient and a transformer load rate; S3. Strategy generation: the master station generates a cluster-level control target according to the risk level, and a fusion terminal decomposes the cluster-level control target into individual control instructions through a power distribution algorithm, wherein the individual control instructions comprise flexible adjustment instructions and rigid switching instructions; S4. Instruction issuing: the fusion terminal issues the individual control instructions to corresponding terminal devices through a preset communication link, and adopts an instruction verification mechanism to guarantee transmission integrity; S5. Execution control: terminal devices receive and execute the individual control instructions, photovoltaic inverters execute the flexible adjustment instructions, intelligent grid-connected circuit breakers execute the rigid switching instructions, and operation parameters after execution are collected in real time; S6. Dynamic optimization: a fusion terminal uploads the operation parameters after execution, a master station calculates a control deviation value, adjusts power distribution algorithm parameters through an optimization coefficient, generates a new cluster-level control target, and repeats steps S3 to S6.

[0008] Preferably, the implementation process of step S1 is as follows: The preset communication link comprises wireless public networks, wireless private networks or optical fiber communication between the master station and the fusion terminal, RS485 communication between the fusion terminal and the photovoltaic inverter, and HPLC communication between the fusion terminal and the intelligent grid-connected circuit breaker. The photovoltaic unit operation parameters comprise active power, reactive power, output voltage, output current, operation state, adjustable margin and device type, and the substation power grid operation parameters comprise substation head voltage, line current, transformer load rate and regional maximum load. The filtering process adopts a sliding average filtering algorithm, as shown in formula (1): (1); In the formula, is filtered data, is original data collected for the ith time, is a filtering window length, and the value is an integer from 5 to 10; The standardized data adopts a fixed field structure, and the fields comprise data identification, collection time, parameter name, parameter value and device number.

[0009] Preferably, the implementation process of step S2 is as follows: Photovoltaic cluster penetration rate calculation, see formula (2): (2); In the formula, is the photovoltaic cluster penetration rate, is the total installed capacity of the photovoltaic cluster, is the maximum load of the region; Voltage deviation value calculation, see formula (3): (3); In the formula, is the voltage deviation value, is the real-time voltage of the transformer head, is the rated voltage of the transformer head, taking the value of 0.4kV; Line overload coefficient calculation, see formula (4): (4); In the formula, is the line overload coefficient, is the real-time current of the line, is the rated current of the line; Transformer load rate calculation, see formula (6): (6); In the formula, is the transformer load rate, is the real-time apparent power of the transformer, is the rated apparent power of the transformer; Risk level is determined by risk coefficient, see formula (7): (7); In the formula, is the risk coefficient, determined as high risk, determined as medium risk, determined as low risk.

[0010] Preferably, the flexible adjustment instruction generation process in step S3 is: Photovoltaic cluster total adjustable capacity calculation, see formula (8): (8); In the formula, is the total adjustable capacity of the photovoltaic cluster, is the adjustable capacity of the i-th photovoltaic unit, is the total number of photovoltaic units in the transformer area; Power distribution coefficient calculation, see formula (9): (9); In the formula, is the power allocation factor of the i-th photovoltaic unit, is the state factor of the i-th photovoltaic unit, when normally operating , when in a fault state , is the voltage sensitivity factor of the i-th photovoltaic unit, when in a voltage out-of-limit region , when in other regions ; Active power set value calculation, see equation (10): (10); wherein, is the active power set value of the i-th photovoltaic unit, is the upper limit of the active power regulation amount of the cluster-level control target; Reactive power set value calculation, see equation (11): (11); wherein, is the reactive power set value of the i-th photovoltaic unit, is the reactive power regulation amount of the cluster-level control target; Power factor set value calculation, see equation (12): (12); wherein, is the power factor set value of the i-th photovoltaic unit.

[0011] Preferably, the rigid switching instruction generation process in step S3 is: Switching decision value calculation, see equation (13): (13); wherein, is the switching decision value of the j-th intelligent grid-connected circuit breaker, is the voltage deviation value, is the line overload coefficient, is the transformer load rate; preset switching threshold , generate a tripping instruction, generate a closing instruction; The intelligent grid-connected circuit breaker performs switching actions in the order of the electrical distance from the transformer head from near to far.

[0012] Preferably, the hybrid control strategy generation process in step S3 is: Synergy coefficient calculation, see equation (14): (14); wherein, is a coordination coefficient, is a total adjustable capacity of the photovoltaic cluster, is a reactive power adjustment amount of the cluster-level control target; all the flexible adjustment instructions are adopted; the flexible adjustment instructions bear the reactive power adjustment amount, and the rigid switching instructions cut off the photovoltaic units corresponding to the remaining reactive power adjustment amount; the flexible adjustment instructions bear all the reactive power adjustment amounts corresponding to the total adjustable capacity, and the rigid switching instructions cut off the photovoltaic units corresponding to the remaining reactive power adjustment amount.

[0013] Preferably, the implementation process of the step S4 is as follows: The instruction transmission protocol includes the IEC61850 protocol between the master station and the fusion terminal, the Modbus / TCP-104 protocol between the fusion terminal and the photovoltaic inverter, and the HPLC protocol between the fusion terminal and the intelligent grid-connected circuit breaker; The instruction format includes instruction identification, target device number, control type, control parameter, execution time, and check code. The control type is divided into flexible adjustment and rigid switching, and the control parameter corresponds to a power set value or a switching instruction; After the terminal device receives the instruction, the check code is checked. If the check is passed, an acknowledgement signal is returned. If the fusion terminal does not receive the acknowledgement signal within 500 milliseconds, it is reissued, and the reissuing frequency is not more than 3 times.

[0014] Preferably, the dynamic optimization process of the step S6 is as follows: The active power control deviation value is calculated, as shown in formula (15): (15); In the formula, is the active power control deviation value, is the actual total active power after adjustment of all the photovoltaic units, is the upper limit of the active power of the cluster-level control target; The reactive power control deviation value is calculated, as shown in formula (16): (16); In the formula, is the reactive power control deviation value, is the actual total reactive power after adjustment of all the photovoltaic units, is the reactive power adjustment amount of the cluster-level control target; The voltage control deviation value is calculated, as shown in formula (17): (17); In the formula, is a voltage control deviation value, is a real-time voltage of the head end of the background area after adjustment, is a voltage stability target of the cluster level control target, and is 1.03pu; Optimization coefficient calculation, see formula (18): (18); In the formula, is an optimization coefficient; The adjusted power distribution coefficient is , the new active power upper limit is , and the new reactive power adjustment amount is .

[0015] The application also provides a low-voltage distributed photovoltaic cluster group control system for the low-voltage distributed photovoltaic cluster group control method, and the system comprises: A master station system, the master station system comprises a data receiving module, a situation judgment module, a strategy generating module, an instruction issuing module, a dynamic optimization module, a data storage module and a communication module, and is respectively used for receiving standardized data, judging a risk level, generating a cluster level control target, issuing a control target, calculating an optimization coefficient, storing related data and communicating with a fusion terminal; A fusion terminal, the fusion terminal comprises a data acquisition module, a data uploading module, an instruction receiving module, an instruction decomposing module, an instruction forwarding module, a feedback acquisition module, an algorithm built-in module and a local decision module, and is respectively used for acquiring filtered data, uploading standardized data, receiving a cluster level control target, generating an individual control instruction, issuing an individual control instruction, acquiring parameters after execution, storing various algorithms and generating a local control instruction when communication is interrupted; Terminal equipment, the terminal equipment comprises a photovoltaic inverter and an intelligent grid-connected circuit breaker, the photovoltaic inverter is provided with a flexible execution module and a state reporting module, and the intelligent grid-connected circuit breaker is provided with a rigid execution module and a state reporting module, and is respectively used for executing a corresponding control instruction and uploading running parameters.

[0016] Based on the method, the application also provides application of the low-voltage distributed photovoltaic cluster group control method in a master station-fusion terminal-photovoltaic inverter architecture, a master station-fusion terminal-intelligent grid-connected circuit breaker architecture and a master station-fusion terminal-intelligent grid-connected circuit breaker-photovoltaic inverter hybrid architecture.

[0017] Compared with the prior art, the beneficial effects of the present application are: the present application filters the collected parameters through a sliding average filtering algorithm, improves the data standardization accuracy, guarantees the integrity and stability of the instruction transmission, improves the comprehensiveness and accuracy of the power grid operation risk level determination, realizes the accurate decomposition of the cluster-level control target, and at the same time, through the dynamic adjustment of the coordination coefficient, the mixed control strategy of flexible adjustment and rigid switching is adjusted, the adaptability and flexibility of the control scheme are improved, and the instruction transmission failure probability is reduced; through multi-dimensional control deviation value calculation optimization coefficient, the closed loop dynamic adjustment of the control parameter is realized, the application scene coverage range of the method is widened; through the fusion of the terminal local decision module, the control function is guaranteed to run continuously when the main station communication is interrupted; through the standardized data format and unified control logic, the data linkage efficiency of each module of the system is improved, and the problems existing in the traditional scheme are solved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The low-voltage distributed photovoltaic group regulation and control system topology graph for the low-voltage distributed photovoltaic group regulation and control method of the present application is shown. Figure 2 The flow chart of the low-voltage distributed photovoltaic group regulation and control method of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] Embodiments, please refer to Figure 1 and Figure 2 The present application proposes a low-voltage distributed photovoltaic group regulation and control system for a low-voltage distributed photovoltaic group regulation and control method, which is used to execute the low-voltage distributed photovoltaic group regulation and control method, and it should be noted that the system comprises: The main station system comprises a data receiving module, a situation research and judgment module, a strategy generation module, an instruction issuing module, a dynamic optimization module, a data storage module, and a communication module, which are respectively used for receiving standardized data, determining risk level, generating cluster-level control target, issuing control target, calculating optimization coefficient, storing related data, and communicating with the fusion terminal; The fusion terminal comprises a data acquisition module, a data uploading module, an instruction receiving module, an instruction decomposing module, an instruction forwarding module, a feedback acquisition module, an algorithm built-in module and a local decision module, and is respectively used for acquiring filtered data, uploading standardized data, receiving a cluster-level control target, generating an individual control instruction, issuing the individual control instruction, acquiring parameters after execution, storing various algorithms and generating a local control instruction when communication is interrupted. The terminal device comprises a photovoltaic inverter and an intelligent grid-connected circuit breaker, the photovoltaic inverter is provided with a flexible execution module and a state reporting module, and the intelligent grid-connected circuit breaker is provided with a rigid execution module and a state reporting module, and is respectively used for executing a corresponding control instruction and uploading operation parameters. Based on this, the application provides a low-voltage distributed photovoltaic cluster regulation and control method, which is realized based on a low-voltage distributed photovoltaic cluster regulation and control system, and specifically comprises the following steps: S1. Data acquisition: The fusion terminal acquires photovoltaic unit operation parameters and substation power grid operation parameters in the substation through a preset communication link, and acquires standardized data by filtering the acquired parameters. In the embodiment, it should be further explained that in actual application, the implementation process of step S1 is as follows: The preset communication link comprises wireless public networks, wireless private networks or optical fiber communication between the master station and the fusion terminal, RS485 communication between the fusion terminal and the photovoltaic inverter, and HPLC communication between the fusion terminal and the intelligent grid-connected circuit breaker. The photovoltaic unit operation parameters comprise active power, reactive power, output voltage, output current, operation state, adjustable margin and device type, and the substation power grid operation parameters comprise substation head voltage, line current, transformer load rate and regional maximum load. The filtering process adopts a sliding average filtering algorithm, as shown in formula (1): (1); In the formula, is filtered data, is the i-th acquired original data, is a filtering window length, and is an integer from 5 to 10; The standardized data adopts a fixed field structure, and the fields comprise data identification, acquisition time, parameter name, parameter value and device number.

[0021] S2. Situation research and judgment: The master station receives the standardized data, calculates the photovoltaic cluster penetration rate and voltage deviation value, and determines the power grid operation risk level in combination with the line overload coefficient and the transformer load rate. In the embodiment, it should be further explained that in actual application, the implementation process of step S2 is as follows: The photovoltaic cluster penetration rate is calculated, as shown in formula (2): (2); In the formula, For the penetration rate of photovoltaic clusters, This represents the total installed capacity of the photovoltaic cluster. This represents the maximum load in the region. The voltage deviation value is calculated as shown in equation (3): (3); In the formula, This is the voltage deviation value. This is the real-time voltage at the beginning of the transformer substation. The rated voltage of the transformer substation is 0.4kV. The overload factor of the line is calculated as shown in equation (4): (4); In the formula, This is the line overload factor. This represents the real-time current of the line. This refers to the rated current of the line. The transformer load rate is calculated as shown in equation (6): (6); In the formula, For transformer load rate, This represents the real-time apparent power of the transformer. This refers to the transformer's rated apparent power. The risk level is determined by the risk coefficient, as shown in equation (7): (7); In the formula, For risk coefficient, Determined to be high risk Determined to be of medium risk. It was determined to be low risk.

[0022] S3. Strategy Generation: The main station generates cluster-level control targets based on the risk level. The fusion terminal decomposes the cluster-level control targets into individual control instructions through a power allocation algorithm. Individual control instructions include flexible adjustment instructions and rigid switching instructions. In this embodiment, it should also be noted that, in practical applications, the flexible adjustment command generation process in step S3 is as follows: The total adjustable capacity of the photovoltaic cluster is calculated as shown in equation (8): (8); In the formula, This represents the total adjustable capacity of the photovoltaic cluster. Let be the adjustable capacity of the i-th photovoltaic unit. This represents the total number of photovoltaic units within the distribution area. The power distribution factor is calculated as shown in equation (9): (9); In the formula, Let be the power allocation coefficient of the i-th photovoltaic unit. Let be the state coefficient of the i-th photovoltaic unit, during normal operation. In fault state , Let be the voltage sensitivity coefficient of the i-th photovoltaic unit, within the voltage over-limit region. Other areas ; The active power setpoint is calculated as shown in equation (10): (10); In the formula, Let i be the active power setpoint for the i-th photovoltaic unit. The upper limit of active power for cluster-level control targets; The reactive power setpoint is calculated as shown in equation (11): (11); In the formula, Let i be the reactive power setpoint for the i-th photovoltaic unit. The reactive power regulation amount for cluster-level control targets; The power factor setpoint is calculated as shown in equation (12): (12); In the formula, Set the power factor value for the i-th photovoltaic unit.

[0023] In this embodiment, it should also be noted that, in practical applications, the rigid switching command generation process in step S3 is as follows: The switch switching determination value is calculated as shown in equation (13): (13); In the formula, Let j be the switching decision value for the j-th intelligent grid-connected circuit breaker. This is the voltage deviation value. This is the line overload factor. Transformer load factor; Preset switching threshold , Generate trip command, Generate closing command; The intelligent grid-connected circuit breaker performs switching operations in order of electrical distance from the first end of the transformer area to the farthest.

[0024] In this embodiment, it should also be noted that, in practical applications, the hybrid control strategy generation process in step S3 is as follows: The synergy coefficient is calculated as shown in equation (14): (14); In the formula, For the synergy coefficient, This represents the total adjustable capacity of the photovoltaic cluster. The reactive power regulation amount for cluster-level control targets; All adjustments were made using flexible control commands. At that time, the flexible adjustment command takes over The reactive power regulation is used to rigidly switch off the photovoltaic unit corresponding to the remaining reactive power regulation. When the flexible adjustment command takes over the reactive power adjustment corresponding to the entire adjustable capacity, the rigid switching command cuts off the photovoltaic unit corresponding to the remaining reactive power adjustment.

[0025] S4. Command Issuance: The converged terminal issues individual control commands to the corresponding terminal devices through a preset communication link, and uses a command verification mechanism to ensure transmission integrity; In this embodiment, it should also be noted that in practical applications, the implementation process of step S4 is as follows: Command transmission protocols include the IEC61850 protocol between the master station and the fusion terminal, the Modbus / TCP-104 protocol between the fusion terminal and the photovoltaic inverter, and the HPLC protocol between the fusion terminal and the smart grid-connected circuit breaker. The instruction format includes instruction identifier, target device number, control type, control parameters, execution time, and check code. The control type is divided into flexible adjustment and rigid switching, and the control parameters correspond to the power setpoint or switching instruction. After receiving the instruction, the terminal device verifies the verification code. If the verification is successful, it returns an acknowledgment signal. If the converged terminal does not receive an acknowledgment signal within 500 milliseconds, it will resend the instruction, and the number of resends will not exceed 3.

[0026] S5. Execution Control: The terminal equipment receives and executes individual control commands, the photovoltaic inverter executes flexible adjustment commands, the smart grid-connected circuit breaker executes rigid switching commands, and the operating parameters after execution are collected in real time. S6. Dynamic optimization: After the fusion terminal uploads and executes the running parameters, the main station calculates the control deviation value, adjusts the power allocation algorithm parameters through optimization coefficients, generates a new cluster-level control target, and repeats steps S3 to S6.

[0027] In this embodiment, it should also be noted that, in practical applications, the dynamic optimization process of step S6 is as follows: The active power control deviation value is calculated as shown in equation (15): (15); In the formula, This is the active power control deviation value. This represents the actual total active power after adjustment for all photovoltaic units. The upper limit of active power for cluster-level control targets; The reactive power control deviation value is calculated as shown in equation (16): (16); In the formula, This is the reactive power control deviation value. This represents the actual total reactive power after adjustment for all photovoltaic units. The reactive power regulation amount for cluster-level control targets; The voltage control deviation value is calculated as shown in equation (17): (17); In the formula, This is the voltage control deviation value. To adjust the real-time voltage at the beginning of the back-end area, The voltage stability target for cluster-level control is set at 1.03 pu; The optimization coefficients are calculated as shown in equation (18): (18); In the formula, To optimize the coefficients; The adjusted power distribution factor is The upper limit of new active power is The new reactive power regulation is .

[0028] Based on the system and method, this invention also proposes the application of the low-voltage distributed photovoltaic group dispatch and control method in the master station-converged terminal-photovoltaic inverter architecture, the master station-converged terminal-intelligent grid-connected circuit breaker architecture, and the hybrid architecture of master station-converged terminal-intelligent grid-connected circuit breaker-photovoltaic inverter; Specifically, in the master station-converged terminal-photovoltaic inverter architecture, the photovoltaic inverter is directly connected to the converged terminal via an RS485 communication line, and the converged terminal directly collects inverter parameters and issues control commands. In the architecture of main station-converged terminal-intelligent grid-connected circuit breaker, the intelligent grid-connected circuit breaker is installed at the photovoltaic grid connection point, and the converged terminal collects the circuit breaker parameters and issues switching commands through HPLC communication; In the hybrid architecture, the intelligent grid-connected circuit breaker is directly connected to the photovoltaic inverter via an RS485 communication line. The fusion terminal sends commands to the circuit breaker via HPLC communication, and the circuit breaker forwards the commands to the inverter or executes them on its own.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-voltage distributed photovoltaic group regulation method, the method is realized based on a low-voltage distributed photovoltaic group regulation system, characterized in that, The method comprises the following steps: S1. The fusion terminal collects the operating parameters of the photovoltaic units and the operating parameters of the substation power grid in the substation through a preset communication link, filters the collected parameters to obtain standardized data; S2. The main station receives the standardized data, calculates the photovoltaic cluster penetration rate and the voltage deviation value, and determines the power grid operation risk level in combination with the line overload coefficient and the transformer load rate; S3. The main station generates a cluster-level control target according to the risk level, and the fusion terminal decomposes the cluster-level control target into individual control instructions through a power distribution algorithm, wherein the individual control instructions include flexible adjustment instructions and rigid switching instructions; S4. The fusion terminal transmits the individual control instructions to the corresponding terminal equipment through a preset communication link, and adopts an instruction verification mechanism to ensure the transmission integrity; S5. The terminal equipment receives and executes the individual control instructions, the photovoltaic inverter executes the flexible adjustment instructions, the intelligent grid-connected circuit breaker executes the rigid switching instructions, and the operating parameters after execution are collected in real time; S6. The fusion terminal uploads the operating parameters after execution, the main station calculates the control deviation value, adjusts the power distribution algorithm parameters through an optimization coefficient, generates a new cluster-level control target, and repeats steps S3 to S6. 2.The low-voltage distributed photovoltaic group regulating and controlling method according to claim 1, characterized in that, The implementation process of step S1 is as follows: The preset communication link includes wireless public network, wireless private network or optical fiber communication between the main station and the fusion terminal, RS485 communication between the fusion terminal and the photovoltaic inverter, and HPLC communication between the fusion terminal and the intelligent grid-connected circuit breaker; The operating parameters of the photovoltaic units include active power, reactive power, output voltage, output current, operating state, adjustable margin and equipment type, and the operating parameters of the substation power grid include substation head voltage, line current, transformer load rate and regional maximum load; The filtering process adopts a sliding average filtering algorithm, as shown in formula (1): （1）； In the formula, is filtered data, is the original data collected for the i-th time, is the filter window length, and is an integer from 5 to 10. The standardized data adopts a fixed field structure, and the fields include data identification, collection time, parameter name, parameter value and equipment number. 3.The low-voltage distributed PV cluster group control method according to claim 2, characterized in that, The implementation process of step S2 is as follows: The photovoltaic cluster penetration rate is calculated, as shown in formula (2): （2）； wherein is the photovoltaic cluster penetration, is the total installed capacity of the photovoltaic cluster, is the regional maximum load; The voltage deviation value is calculated, as shown in formula (3): （3）； In the formula, is a voltage deviation value, is a real-time voltage of the transformer station head end, is a rated voltage of the transformer station, and the value is 0.4 kV; The line overload coefficient is calculated, as shown in formula (4): （4）； wherein is the line overload factor, is the line real-time current, is the line rated current; The transformer load rate is calculated, as shown in formula (6): （6）； wherein is the transformer load ratio, is the transformer real-time apparent power, is the transformer rated apparent power; The risk level is determined by a risk coefficient, as shown in formula (7): （7）； wherein is a risk factor, is determined to be high risk, is determined to be medium risk, is determined to be low risk.

4. The method of claim 3, wherein, The generation process of the flexible adjustment instruction in step S3 is as follows: The total adjustable capacity of the photovoltaic cluster is calculated, as shown in formula (8): （8）； In the formula, is the total adjustable capacity of the photovoltaic cluster, is the adjustable capacity of the ith photovoltaic unit, is the total number of photovoltaic units in the transformer area; The power distribution coefficient is calculated, as shown in formula (9): （9）； wherein is the power allocation factor for the i-th photovoltaic unit, is the state coefficient for the i-th photovoltaic unit, in normal operation , in fault condition , is the voltage sensitivity coefficient for the i-th photovoltaic unit, in the voltage out-of-limit region , in other regions ; The active power set value is calculated, as shown in formula (10): （10）； wherein P*PQ,i is the setpoint value for the active power of the ith photovoltaic unit, P*PQ,max is the upper limit for the active power of the cluster level control target; The reactive power set value is calculated, as shown in formula (11): （11）； In the formula, a reactive power set value of the i-th photovoltaic unit, a reactive power adjustment amount of the cluster-level control target; The power factor set value is calculated, as shown in formula (12): （12）； wherein PFI is the power factor set point for the ith photovoltaic unit.

5. The method of claim 4, wherein, The generation process of the rigid switching instruction in step S3 is as follows: The switching switching value is calculated, as shown in formula (13): （13）； In the formula, is the switching decision value of the jth intelligent grid-connected circuit breaker, is the voltage deviation value, is the line overload coefficient, is the transformer load rate; Pre-set switching threshold , Generate a disconnection instruction, Generate a connection instruction; The intelligent grid-connected circuit breaker performs switching actions in the order from near to far according to the electrical distance from the substation head.

6. The low-voltage distributed PV flocking flocking control method according to claim 5, characterized in that, The generation process of the mixed control strategy in step S3 is as follows: The coordination coefficient is calculated, as shown in formula (14): （14）； In the formula, is a synergy coefficient, is the total adjustable capacity of the photovoltaic cluster, is the reactive power adjustment amount of the cluster-level control target; All flexible adjustment commands are used at this time; When the reactive power of the photovoltaic unit is less than the reactive power threshold, the flexible adjustment instruction assumes the reactive power adjustment amount, and the rigid switching instruction removes the photovoltaic unit corresponding to the remaining reactive power adjustment amount. When the flexible adjustment instruction is executed, the flexible adjustment instruction assumes the entire adjustable capacity corresponding to the reactive power adjustment amount, and the rigid switching instruction removes the remaining photovoltaic units corresponding to the reactive power adjustment amount. 7.The low-voltage distributed PV cluster group control method according to claim 6, characterized in that, The implementation process of step S4 is as follows: The instruction transmission protocol includes the IEC61850 protocol between the main station and the fusion terminal, the Modbus / TCP-104 protocol between the fusion terminal and the photovoltaic inverter, and the HPLC protocol between the fusion terminal and the intelligent grid-connected circuit breaker; The instruction format comprises instruction identification, target device number, control type, control parameter, execution time, check code, the control type is divided into flexible adjustment and rigid switching, and the control parameter corresponds to a power setting value or a switching instruction; After receiving the instruction, the terminal device checks the check code, returns an acknowledgement signal if the check is passed, and reissues the instruction if the acknowledgement signal is not received within 500 milliseconds, the reissuing times being no more than 3. 8.The low-voltage distributed PV cluster group control method according to claim 7, characterized in that, The dynamic optimization process of the step S6 is as follows: Active control deviation value calculation, see formula (15): （15）； In the formula, is the active control deviation value, is the actual total active power of all photovoltaic units after adjustment, is the upper limit of the active power of the cluster-level control target; Reactive control deviation value calculation, see formula (16): （16）； In the formula, is a reactive power control deviation value, is an actual total reactive power of all photovoltaic units after adjustment, is a reactive power adjustment amount of the cluster-level control target; Voltage control deviation value calculation, see formula (17): （17）； In the formula, is a voltage control deviation value, is the real-time voltage of the head of the background area after adjustment, is the voltage stability target of the cluster level control target, and the value is 1.03pu; Optimization coefficient calculation, see formula (18): （18）； In the formula, are optimization coefficients; The adjusted power distribution coefficient is , the new active power upper limit is , and the new reactive power adjustment amount is .

9. The low-voltage distributed photovoltaic group regulating and controlling system applied to the low-voltage distributed photovoltaic group regulating and controlling method of any one of claims 1-8, characterized in that, The system comprises: The main station system comprises a data receiving module, a situation judgment module, a strategy generating module, an instruction issuing module, a dynamic optimization module, a data storage module and a communication module, and is respectively used for receiving standardized data, judging a risk level, generating a cluster-level control target, issuing the control target, calculating an optimization coefficient, storing relevant data and communicating with the fusion terminal; The fusion terminal comprises a data acquisition module, a data uploading module, an instruction receiving module, an instruction decomposing module, an instruction forwarding module, a feedback acquisition module, an algorithm built-in module and a local decision module, and is respectively used for acquiring filtered data, uploading standardized data, receiving a cluster-level control target, generating an individual control instruction, issuing the individual control instruction, acquiring parameters after execution, storing various algorithms and generating a local control instruction when communication is interrupted; The terminal device comprises a photovoltaic inverter and an intelligent grid-connected circuit breaker, the photovoltaic inverter is provided with a flexible execution module and a state reporting module, and the intelligent grid-connected circuit breaker is provided with a rigid execution module and a state reporting module, and is respectively used for executing corresponding control instructions and uploading running parameters.

10. Application of the low-voltage distributed photovoltaic cluster control method according to any one of claims 1-8 in a main station-fusion terminal-photovoltaic inverter architecture, a main station-fusion terminal-intelligent grid-connected circuit breaker architecture or a main station-fusion terminal-intelligent grid-connected circuit breaker-photovoltaic inverter hybrid architecture.

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