DPV cluster multi-time scale reactive power coordination auxiliary decision-making control method and system

Abstract

The invention provides a DPV cluster multi-time scale reactive power coordination aided decision control method and system, and the method comprises the steps: solving active power loss and voltage deviation according to a daily load prediction curve, and building a day-ahead reactive power optimization objective function according to a solving result; obtaining a minimum value weight factor, constructing an entropy-AHP weighted harmonic model, and solving an optimal switch action combination of the slow action reactive power equipment in 24 hours before the day by taking a first preset time length as a time scale; active power at each moment before the day is obtained, and a photovoltaic output fluctuation threshold value is obtained through calculation; and the maximum number of DPV cluster photovoltaic inverters in a free state in the day is solved, an objective function about the reactive adjustable variable of the DPV cluster is constructed, the objective function is solved, and a control instruction is output according to a solving result. According to the method, the flexibility of a reactive power compensation mode on multiple time scales can be realized, the accuracy of reactive power coordination decision is improved, and the DPV cluster is enabled to operate safely and stably.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic grid-connected stable control, and particularly to a multi-time-scale reactive power coordination auxiliary decision-making control method and system for a DPV cluster. Background Art

[0002] After a high-penetration DPV cluster is connected to the grid on a large scale, if the traditional centralized reactive power regulation method is adopted, the defects of slow response speed and insufficient multi-time-scale coordination will lead to an increased risk of distribution network voltage over-limit due to the randomness of DPV output and the volatility of reactive power.

[0003] In addition, the output of the DPV cluster is strongly spatio-temporally different due to factors such as light and temperature. The traditional voltage control method based on a fixed sensitivity matrix is prone to cause frequent operation or even oscillation of reactive power equipment. When DPV grid connection is superimposed with multiple load fluctuations, the voltage problem presents a multi-time coupling characteristic. If a reactive power-voltage coupling quantitative analysis framework is not established, it is difficult to meet the control accuracy requirements. Existing in-situ control strategies ignore the reactive power resource coordination potential of multiple DPV units in the cluster, and the adjustment margin is limited. If the existing adjustment method continues to be used, the risk of distribution network voltage over-limit will continue to increase, ultimately leading to a decline in power supply reliability, an increase in operating costs, and a decrease in the new energy consumption rate. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-time-scale reactive power coordination auxiliary decision-making control method and system for a DPV cluster, aiming to solve the problem of low control reliability when traditional DPV grid connection is superimposed with multiple load fluctuations.

[0005] In a first aspect, the present invention provides a multi-time-scale reactive power coordination auxiliary decision-making control method for a DPV cluster, and the method includes: Obtain the daily load prediction curve of the target DPV cluster, solve the active power loss and voltage deviation in the reactive power coordination control area according to the daily load prediction curve, and establish a day-ahead reactive power optimization objective function according to the solution results; Obtain the minimum value weight factor according to the day-ahead reactive power optimization objective function, construct an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solve the optimal switching action combination of slow-acting reactive power equipment for the next 24 hours on a day-ahead basis with a first preset time scale; Obtain the active power at each moment on a day-ahead basis corresponding to the optimal switching action combination, and calculate the photovoltaic output fluctuation threshold according to the active power; Solve for the maximum number of PV inverters in the DPV cluster in the free state during the day according to the PV output fluctuation threshold, construct an objective function for the reactive power adjustable amount of the DPV cluster, and take the reactive power adjustable amount of the DPV cluster as the goal, and construct constraint conditions according to the maximum number, solve the objective function, and output a control instruction according to the solution result.

[0006] In a second aspect, the present invention provides a multi-time scale reactive power coordination auxiliary decision control system for a DPV cluster, and the system includes: A first objective function construction module, configured to obtain a daily load prediction curve of a target DPV cluster, solve the active power loss and voltage deviation in the reactive power coordination control area according to the daily load prediction curve, and establish a day-ahead reactive power optimization objective function according to the solution result; A harmonic model solving module, configured to obtain a minimum value weight factor according to the day-ahead reactive power optimization objective function, construct an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solve the optimal switching action combination of the slow-motion reactive power equipment for the next 24 hours of the day according to a first preset time scale; A fluctuation threshold calculation module, configured to obtain the active power of each moment of the day-ahead corresponding to the optimal switching action combination, and calculate the PV output fluctuation threshold according to the active power; A second objective function construction module, configured to solve for the maximum number of PV inverters in the DPV cluster in the free state during the day according to the PV output fluctuation threshold, construct an objective function for the reactive power adjustable amount of the DPV cluster, and take the reactive power adjustable amount of the DPV cluster as the goal, and construct constraint conditions according to the maximum number, solve the objective function, and output a control instruction according to the solution result.

[0007] In a third aspect, the present invention provides a storage medium, and the storage medium stores one or more programs, and when the program is executed by a processor, the above-mentioned multi-time scale reactive power coordination auxiliary decision control method for a DPV cluster is implemented.

[0008] In a fourth aspect, the present invention provides an electronic device, and the electronic device includes a memory and a processor, wherein: The memory is used to store a computer program; The processor is configured to implement the above-mentioned multi-time scale reactive power coordination auxiliary decision control method for a DPV cluster when executing the computer program stored on the memory.

[0009] Compared with the prior art, the present invention has the following advantages: The present invention performs calculation and optimization processing on the multi-time scale reactive power optimization means of the DPV cluster based on the daily load prediction curve, which is a beneficial supplement to the current reactive power coordination control decision system of the DPV cluster, and adopts entropy- The weighted harmonic model solves the optimal switching action combination of slow - acting reactive power equipment for the next 24 hours on a daily basis with the first preset duration as the time scale, reducing the calculation complexity of the weighted parameters of the same - type objective function. Then, it solves the number of times of grid - connected voltage violation of the DPV cluster under the second preset duration, and uses the photovoltaic fluctuation threshold algorithm based on Poisson distribution to dynamically track the probability of voltage violation under the second preset duration. Compared with the static threshold setting method, it provides a solution to the problem of lagging reactive power compensation, effectively ensures the reactive power compensation ability of the system at different operating points and under different working conditions, realizes the flexibility of reactive power compensation methods on multiple time scales, improves the accuracy of reactive power coordination decision - making, and enables the safe and stable operation of the DPV cluster. Brief Description of the Drawings

[0010] Figure 1 It is a flowchart of the multi - time - scale reactive power coordination auxiliary decision - making control method for the DPV cluster proposed in an embodiment of the present invention; Figure 2 It is one of the morphological diagrams of the daily load prediction curve of the DVP cluster exemplified in an embodiment of the present invention; Figure 3 It is the diagram of the best switching combination for the next day exemplified in an embodiment of the present invention; Figure 4 It is a reference diagram for the calculation logic of the number of times of grid - connected voltage violation of the DPV cluster exemplified in an embodiment of the present invention; Figure 5 It is a schematic diagram of the photovoltaic output fluctuation threshold at each moment exemplified in an embodiment of the present invention; Figure 6 It is a schematic structural diagram of the multi - time - scale reactive power coordination auxiliary decision - making control system for the DPV cluster proposed in an embodiment of the present invention.

[0011] The following specific embodiments will further illustrate the present invention in conjunction with the above - mentioned drawings. Specific Embodiments

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art in the field to which the present invention belongs. The words such as "including" used herein mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects.

[0013] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a DPV cluster multi-time scale reactive power coordination auxiliary decision-making control method, which includes steps S101 to S104, where: Step S101: Obtain the daily load prediction curve of the target DPV cluster, solve the active power network loss and voltage deviation within the reactive power coordination control area according to the daily load prediction curve, and establish a day-ahead reactive power optimization objective function based on the solution results; It should be noted that, as Figure 2 shown, one form of the DVP cluster daily load prediction curve is shown. Based on this daily load prediction curve, the load distribution and start-up plan for each hour can be determined, and then the active power network loss within the reactive power coordination control area can be solved. Then, based on the solution results, a day-ahead reactive power optimization objective function is established: ; Wherein, is the day-ahead reactive power optimization objective function, is the active power network loss weight factor, is the voltage deviation weight factor, is to minimize the active power network loss, is to minimize the voltage deviation, is the line conductance between the i side and the j side of the a-th line, is the phase difference between the i side and the j side of the a-th line, and are the voltages between the i side and the j side of the a-th line respectively, is the rated voltage of the i-th node, m represents the total number of lines, n is the total number of nodes, is the actual voltage of the i-th node.

[0014] Step S102: Obtain the minimum value weight factor according to the day-ahead reactive power optimization objective function, construct an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solve the optimal switching action combination of the slow-motion reactive power equipment for the next 24 hours of the day with the first preset time scale; It should be pointed out that after constructing the day-ahead reactive power optimization objective function, the active power network loss weight factor and the voltage deviation weight factor are continuously assigned values, and then the minimum objective function value is calculated. The active power network loss weight factor and the voltage deviation weight factor corresponding to the minimum objective function value are the minimum value weight factors, that is, the target active power network loss weight factor and the target voltage deviation weight factor. Exemplarily, with an assignment interval of 0.05, positive integer assignments are made to the active power network loss weight factor and the voltage deviation weight factor, and then multiple objective function values are obtained. Then, the minimum objective function value is screened out, and then the minimum value weight factor is obtained.

[0015] In addition, it should be noted that when the traditional DPV cluster multi-time scale reactive power coordinated control method solves the optimal switching action combination of slow-action reactive power equipment for the 24 hours of the day-ahead, it only separately considers the entropy weighting method and the AHP weighting method, resulting in problems of "over-adjustment" and "partial adjustment". Based on this, the present invention introduces an entropy-AHP weighted harmonic model, reasonably utilizes the AHP weighted harmonic factor to give a quantitative result for the dynamic data of state variables such as photovoltaic power output prediction, load demand, and reactive power equipment, and at the same time uses the entropy weighted harmonic factor to evaluate the impact of the historical single-day switching action times on reactive power coordinated control, upgrades and improves the hierarchical control structure algorithm of the traditional photovoltaic cluster multi-time scale reactive power coordinated control method, and has the robustness of the traditional method and the self-adaptability of the entropy-AHP weighted harmonic algorithm in solving the optimal switching action combination of slow-action reactive power equipment for the 24 hours of the day-ahead.

[0016] Specifically, an entropy-AHP weighted harmonic model is constructed according to the following formula: ; Wherein, represents the optimal switching action combination of slow-action reactive power equipment at time t, is the target active power loss weight factor, is the target voltage deviation weight factor, is the AHP weighted harmonic factor part of the entropy-AHP weighted harmonic model at time t, is the entropy weighted harmonic factor part of the entropy-AHP weighted harmonic model at time t; The AHP weighted harmonic factor part and the entropy weighted harmonic factor part are calculated according to the following formula: ; Wherein, are the first, second, kth, and s1th AHP weighted harmonic factor parameters at time t respectively, s1 is the total number of AHP weighted harmonic factor parameters, and the AHP weighted harmonic factor parameters include but are not limited to weight factors such as photovoltaic power output prediction, load demand, and reactive power equipment status, etc., is the set of AHP weighted harmonic factor parameters at time t, , are the first and lth entropy weighted harmonic factor parameters under the jth photovoltaic unit selected by the user respectively, is the kth entropy weighted harmonic factor parameter under the jth photovoltaic unit, and the entropy weighted harmonic factor parameters include but are not limited to reactive power interaction intensity, equipment operation efficiency, photovoltaic penetration rate, etc., p is the total number of photovoltaic units, and s2 is the total number of entropy weighted harmonic factor parameters, The rated apparent power of the reactive power device that adopts the k-th entropy-weighted harmonic factor parameter for the j-th photovoltaic unit, and q is the number of actions at each moment of the device switching history on a single day.

[0017] Exemplarily, as Figure 3 shown, the first preset duration is 1 hour, and a daily optimal switching combination diagram is obtained. In addition, the purpose of setting the first preset duration is to reduce the calculation complexity of the weighted parameters of the same type of objective function. In some embodiments, the first preset duration can also be other durations, but the first preset duration must be greater than the second preset duration.

[0018] In summary, the present invention uses an entropy-weighted harmonic model to dynamically allocate weight factors, combined with the daily reactive power optimization objective function, to achieve accurate solution of the action combinations of slow-action devices (such as capacitors). For example: by calculating the subjective weight (AHP method), the reactive power output characteristic quantities of the DPV cluster are assigned on the voltage deviation and scale, and then the entropy weight method is used to group the objective weight vector groups of the DPV cluster dataset. The active power loss weight factor and the voltage deviation weight factor are used as the weight factors of the entropy-weighted harmonic model. The actual calculation results improve the decision-making accuracy by about 12% compared with the traditional fixed weight method.

[0019] Step S103: Obtain the active power at each moment of the day corresponding to the optimal switching action combination, and calculate the photovoltaic output fluctuation threshold according to the active power. It should be noted that for the multi-time scale reactive power coordinated control of the traditional DPV cluster, the method of using a fixed intra-day photovoltaic output fluctuation threshold is likely to lead to the rigidity of the static threshold, and then cause the problem of reactive power compensation lag. However, the present invention uses the Poisson distribution to solve the probability of the grid-connected voltage over-limit times of the DPV cluster , fully considering the number of times of the voltage over-limit probability under the average photovoltaic output of the minute-level control target, and giving a reasonable margin range to the intra-day photovoltaic output fluctuation threshold. Make the value more in line with the actual threshold distribution, reduce the risk of voltage over-limit, and reduce the impact of new energy fluctuations on the power grid and the risk of cascading disconnection. Specifically, in this step, first, taking the second preset duration as the time scale, obtain the target grid-connected voltage over-limit times of the DPV cluster; then use the Poisson distribution to solve the probability value that the grid-connected voltage over-limit times of the DPV cluster is the target grid-connected voltage over-limit times, and judge whether the probability value is greater than or equal to the preset threshold; if the probability value is greater than the preset threshold, substitute the probability value into the photovoltaic output fluctuation threshold function to solve the photovoltaic output fluctuation threshold.

[0020] In some embodiments, the grid-connected voltage over-limit times are calculated according to the following formula: ; where is the first preset duration, is the second preset duration, k is the number of times the target grid-connected voltage exceeds the limit, is the active power at time is the active power at time t, is the standard active power difference set by the system, is the correction parameter, is the average power of the day-ahead active power, is the floor function; The probability value is calculated according to the following formula: ; where, is the probability value that the number of times the grid-connected voltage of the DPV cluster exceeds the limit is the target grid-connected voltage exceeding the limit, is the average number of operations per unit time, is the average PV output value, is the standard deviation of the average PV output; The PV output fluctuation threshold is calculated according to the following formula: ; where, is the PV output fluctuation threshold at time t, , are the extreme values of the PV output power curve at time t and time respectively, is the reference illumination intensity for PV grid connection at time is the standard illumination intensity for PV grid connection, is the rated power of PV grid connection, is the PV fluctuation coefficient, is the probability value greater than the preset threshold.

[0021] Exemplarily, in this embodiment, the first preset duration is 15 min, the preset threshold is 5%, taking 15 minutes as the time scale, the number of times the grid-connected voltage of the DPV cluster exceeds the limit is obtained through the FIX floor function; then the probability function of the number of times the grid-connected voltage of the DPV cluster exceeds the limit is established using the Poisson distribution; then judge value, when , it is regarded as low risk; when , substitute into the PV output fluctuation threshold function; in the PV output fluctuation threshold function, it is also necessary to take 15 minutes as the scale and substitute value into function for solution. As shown in Figure 4 , a calculation logic reference diagram of the number of times the grid-connected voltage of a DPV cluster exceeds the limit is shown. As shown in Figure 5As shown, the photovoltaic output fluctuation thresholds at various moments are presented.

[0022] In summary, through the above photovoltaic fluctuation threshold algorithm, the 15-minute-level voltage crossing probability is dynamically tracked. Compared with the static threshold setting method, it provides a solution to the problem of lagging reactive power compensation. For example, taking a 1MW photovoltaic inverter as an example, the number of grid-connected voltage crossings of the DPV cluster with Poisson distribution , then the voltage fluctuation range can be narrowed from ±15% to ±6%, reducing the risk of reactive overcompensation by 17%.

[0023] Step S104: Solve the maximum number of photovoltaic inverters in the DPV cluster in the free state during the day according to the photovoltaic output fluctuation threshold, construct an objective function for the reactive power adjustable amount of the DPV cluster, and take the reactive power adjustable amount of the DPV cluster as the goal. Then, construct constraint conditions according to the maximum number, solve the objective function, and output a control instruction according to the solution result.

[0024] It should be noted that in this step, the maximum number is calculated according to the following formula: ; Among them, is the maximum number of photovoltaic inverters in the DPV cluster in the free state during the day, is the total number of group-controlled photovoltaic inverters in the DPV cluster in the system, is the number of MPPTs of a single inverter, is the rated output active power of a single inverter; The reactive power adjustable amount of the DPV cluster is solved according to the following formula: ; Among them, is the maximum reactive power adjustable amount of the DPV cluster, is the reactive power adjustable amount of the DPV cluster, , , are the rated reactive powers of the a-th, b-th, and m-th types of photovoltaic inverters in the DPV cluster respectively, , , are the numbers of the a-th, b-th, and m-th types of photovoltaic inverters in the DPV cluster respectively; The control instruction is the maximum reactive power adjustable amount of the DPV cluster.

[0025] Since the maximum reactive power adjustable amount of the traditional multi-time scale reactive power coordination control method for DPV clusters focuses on balancing and adjusting local optimality, the overall balance is poor. The main reason is the insufficient tolerance for the differences in the types and rated reactive powers of photovoltaic inverters. In this step, the multi-objective unit optimization method for photovoltaic inverters is adopted, fully considering the differences in different types of photovoltaic units and inverter parameters, balancing the local adjustable amount and the regional balance compensation amount, and increasing the dynamic stability of the adjustable amount.

[0026] In summary, according to the above-mentioned multi-time scale reactive power coordination auxiliary decision-making control method for DPV clusters, by calculating and optimizing the multi-time scale reactive power optimization means for DPV clusters based on the daily load prediction curve, it is a beneficial supplement to the current reactive power coordination control decision-making system for DPV clusters. The entropy- weighted harmonic model uses the first preset time period as the time scale to solve the optimal switching action combination of slow-action reactive power equipment for the previous 24 hours, reducing the calculation complexity of the weighted parameters of the same type of objective function. Then, it solves the number of times of grid-connected voltage over-limit of the DPV cluster under the second preset time period, and uses the photovoltaic fluctuation threshold algorithm with Poisson distribution to dynamically track the voltage over-limit probability under the second preset time period. Compared with the static threshold setting method, it provides a solution to the problem of lagging reactive power compensation, effectively ensuring the reactive power compensation ability of the system at different working points and different operating conditions, realizing the flexibility of reactive power compensation methods on multiple time scales, improving the accuracy of reactive power coordination decision-making, and enabling the safe and stable operation of the DPV cluster.

[0027] As Figure 6 shown, an embodiment of the present invention provides a multi-time scale reactive power coordination auxiliary decision-making control system for a DPV cluster. The system includes: A first objective function construction module 10, configured to obtain the daily load prediction curve of the target DPV cluster, solve the active power loss and voltage deviation in the reactive power coordination control area according to the daily load prediction curve, and establish a day-ahead reactive power optimization objective function according to the solution results; A harmonic model solving module 20, configured to obtain the minimum value weight factor according to the day-ahead reactive power optimization objective function, and construct an entropy- weighted harmonic model according to the minimum value weight factor, and solve the optimal switching action combination of slow-action reactive power equipment for the previous 24 hours with the first preset time period as the time scale; A fluctuation threshold calculation module 30, configured to obtain the active power of each moment of the day-ahead corresponding to the optimal switching action combination, and calculate the photovoltaic output fluctuation threshold according to the active power; The second objective function construction module 40 is configured to solve for the maximum number of PV inverters in the DPV cluster in the free state during the day according to the PV output fluctuation threshold, construct an objective function regarding the reactive power adjustable amount of the DPV cluster, take the reactive power adjustable amount of the DPV cluster as the objective, and construct a constraint condition according to the maximum number to solve the objective function, and output a control instruction according to the solution result.

[0028] On the other hand, the present invention also proposes a storage medium, on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned multi-time scale reactive power coordination auxiliary decision-making control method for the DPV cluster is implemented.

[0029] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the above-mentioned multi-time scale reactive power coordination auxiliary decision-making control method for the DPV cluster.

[0030] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0031] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0032] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0033] Although the embodiments of the present invention have been described in detail above, it is obvious to those skilled in the art that various modifications and changes can be made to these embodiments. However, it should be understood that such modifications and changes are all within the scope and spirit of the present invention described in the claims. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various ways.

Claims

1. A reactive power coordinated auxiliary decision-making control method for a DPV cluster with multiple time scales, characterized in that The method includes: Obtaining the daily load prediction curve of the target DPV cluster, solving the active power loss and voltage deviation within the reactive power coordination control area according to the daily load prediction curve, and establishing a day-ahead reactive power optimization objective function based on the solution results; Obtaining the minimum value weight factor according to the day-ahead reactive power optimization objective function, constructing an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solving the optimal switching action combination of the slow-action reactive power equipment for the day-ahead 24 hours with the first preset time scale; Obtaining the active power at each moment of the day-ahead corresponding to the optimal switching action combination, and calculating the photovoltaic output power fluctuation threshold according to the active power; Solving the maximum number of photovoltaic inverters of the DPV cluster in the free state during the day according to the photovoltaic output power fluctuation threshold, constructing an objective function regarding the reactive power adjustable amount of the DPV cluster, taking the reactive power adjustable amount of the DPV cluster as the objective, and constructing constraint conditions according to the maximum number, solving the objective function, and outputting a control instruction according to the solution result.

2. The DPV cluster multi-time scale reactive power coordination auxiliary decision-making control method according to claim 1, wherein The steps of obtaining the daily load prediction curve of the target DPV cluster, solving the active power loss and voltage deviation within the reactive power coordination control area according to the daily load prediction curve, and establishing a day-ahead reactive power optimization objective function include: Constructing a day-ahead reactive power optimization objective function according to the following formula: ； Among them, is the objective function of reactive power optimization at present, is the weight factor of active power loss, is the weight factor of voltage deviation, is to minimize the active power loss, is to minimize the voltage deviation, is the line conductance between the i - side and the j - side of the a - th line, is the phase difference between the i - side and the j - side of the a - th line, and are the voltages between the i - side and the j - side of the a - th line respectively, is the rated voltage of the i - th node, m represents the total number of lines, n is the total number of nodes, is the actual voltage of the i - th node.

3. The DPV cluster multi-time scale reactive power coordinated auxiliary decision-making control method according to claim 2, wherein The steps of obtaining the minimum value weight factor according to the day-ahead reactive power optimization objective function, constructing an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solving the optimal switching action combination of the slow-action reactive power equipment for the day-ahead 24 hours with the first preset time scale include: Assigning values and solving for the active power loss weight factor and the voltage deviation weight factor, and obtaining the target active power loss weight factor and the target voltage deviation weight factor according to the solution results; Constructing an entropy-AHP weighted harmonic model according to the following formula: ； Among them, represents the optimal switching action combination of the slow - motion reactive power device at time t, is the target active power loss weight factor, is the target voltage deviation weight factor, is the AHP weighted harmonic factor part of the entropy - AHP weighted harmonic model at time t, is the entropy weighted harmonic factor part of the entropy - AHP weighted harmonic model at time t; Calculating the AHP weighted harmonic factor part and the entropy weighted harmonic factor part according to the following formula: ； Among them, are the first, second, k-th, and s1-th AHP weighted harmonic factor parameters at time t, respectively, where s1 is the total number of AHP weighted harmonic factor parameters, is the set of AHP weighted harmonic factor parameters at time t, , are the first and l-th entropy weighted harmonic factor parameters under the j-th photovoltaic unit selected by the user, is the k-th entropy weighted harmonic factor parameter under the j-th photovoltaic unit, p is the total number of photovoltaic units, and s2 is the total number of entropy weighted harmonic factor parameters, is the rated apparent power of the reactive power equipment when the j-th photovoltaic unit adopts the k-th entropy weighted harmonic factor parameter, and q is the number of times the equipment switch operates at each moment in a single day of the historical record.

4. The DPV cluster multi-time scale reactive power coordination auxiliary decision-making control method according to claim 3, characterized in that The steps of obtaining the active power at each moment of the day-ahead corresponding to the optimal switching action combination, and calculating the photovoltaic output power fluctuation threshold according to the active power include: Taking the second preset time scale, obtaining the number of times the target grid-connected voltage of the DPV cluster exceeds the limit; Solving the probability value that the number of times the grid-connected voltage of the DPV cluster exceeds the limit is the number of times the target grid-connected voltage exceeds the limit by using the Poisson distribution, and judging whether the probability value is greater than or equal to the preset threshold; If the probability value is greater than the preset threshold, substituting the probability value into the photovoltaic output power fluctuation threshold function to solve the photovoltaic output power fluctuation threshold.

5. The DPV cluster multi-time scale reactive power coordination auxiliary decision-making control method according to claim 4, wherein Calculating the number of times the grid-connected voltage exceeds the limit according to the following formula: ； Among them, is the first preset duration, is the second preset duration, k is the number of times the target grid-connected voltage exceeds the limit, is the active power at time is the active power at time t, is the standard active power difference set by the system, is the correction parameter, is the average power of the active power for the day ahead, is the floor function; Calculating the probability value according to the following formula: ； Among them, is the probability value that the number of grid-connected voltage violations of the DPV cluster is the target number of grid-connected voltage violations, is the average number of operations per unit time, is the average PV output value, is the standard deviation of the average PV output; Calculating the photovoltaic output power fluctuation threshold according to the following formula: ； Among them, is the PV output power fluctuation threshold at time t, , are the extreme values of the PV output power curve at time t and respectively, is the reference illumination intensity for PV grid connection at time is the standard illumination intensity for PV grid connection, is the rated power for PV grid connection, is the PV fluctuation coefficient, is the probability value greater than the preset threshold.

6. The DPV cluster multi-time scale reactive power coordinated auxiliary decision-making control method according to claim 4, characterized in that The steps of solving for the maximum number of PV inverters in the DPV cluster in the intraday free state according to the PV output fluctuation threshold, constructing an objective function for the reactive power adjustable amount of the DPV cluster, taking the reactive power adjustable amount of the DPV cluster as the objective, and constructing constraint conditions according to the maximum number, and solving the objective function and outputting a control instruction according to the solution result include: Calculating the maximum number according to the following formula: ； Among them, is the maximum number of DPV cluster photovoltaic inverters in the free state within a day, is the total number of group-controlled photovoltaic inverters in the DPV cluster in the system, is the number of MPPTs of a single inverter, is the rated active power output of a single inverter; Solving for the reactive power adjustable amount of the DPV cluster according to the following formula: ； Among them, is the maximum adjustable reactive power of the DPV cluster, is the adjustable reactive power of the DPV cluster, , , are the rated reactive powers of the a-th, b-th, and m-th type of PV inverters in the DPV cluster respectively, , , are the numbers of the a-th, b-th, and m-th type of PV inverters in the DPV cluster respectively; The control instruction is the maximum adjustable reactive power of the DPV cluster.

7. A DPV cluster multi-time scale reactive power coordination auxiliary decision-making control system, characterized in that The system includes: A first objective function construction module, configured to obtain the daily load prediction curve of the target DPV cluster, solve for the active power loss and voltage deviation in the reactive power coordination control area according to the daily load prediction curve, and establish a day-ahead reactive power optimization objective function according to the solution result; A harmonic model solution module, configured to obtain the minimum value weight factor according to the day-ahead reactive power optimization objective function, construct an entropy-AHP weighted harmonic model according to the minimum value weight factor, and solve for the optimal switching action combination of the slow-motion reactive power equipment in the day-ahead 24 hours with a first preset time scale; A fluctuation threshold calculation module, configured to obtain the active power at each moment of the day-ahead corresponding to the optimal switching action combination, and calculate the PV output fluctuation threshold according to the active power; A second objective function construction module, configured to solve for the maximum number of PV inverters in the DPV cluster in the intraday free state according to the PV output fluctuation threshold, construct an objective function for the reactive power adjustable amount of the DPV cluster, take the reactive power adjustable amount of the DPV cluster as the objective, and construct constraint conditions according to the maximum number, solve the objective function, and output a control instruction according to the solution result.

8. A storage medium, characterized in that, The storage medium stores one or more programs, which when executed by a processor implement the DPV cluster multi-time scale reactive power coordination auxiliary decision control method according to any one of claims 1-6.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store a computer program; When the processor executes the computer program stored on the memory, it implements the DPV cluster multi-time scale reactive power coordination auxiliary decision control method according to any one of claims 1-6.

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