Receiving end weak network-oriented photovoltaic power station super capacitor energy storage configuration method and system

Abstract

The invention discloses a photovoltaic power station super capacitor energy storage configuration method and system facing a receiving end weak network. According to the method, a hybrid super capacitor model containing a power grid forming type converter and a power grid following type converter is constructed, and the supporting capacity is measured through the generalized short circuit ratio, the small interference margin, the transient voltage margin, the frequency lowest point margin, the frequency change rate improvement and other indexes; establishing a dynamic energy coupling constraint based on multi-fault time domain integration; two-stage five-dimensional target NSGA-II optimization is adopted, improved AHP, CRITIC and relative entropy TOPSIS are combined for scheme decision making, and capacity configuration meeting the voltage and frequency dual support requirement is output. According to the invention, rapid voltage recovery and frequency suppression of the photovoltaic power station in the weak network scene of the receiving end are realized, and the investment cost and the energy safety margin are both considered.

Description

Technical Field

[0001] This invention belongs to the field of power system stability control and energy storage optimization configuration technology, and relates to a method and system for improving the active support capability of photovoltaic power plants based on supercapacitor energy storage. Background Technology

[0002] Receiving-end grids are typically located far from large synchronous power sources, resulting in limited short-circuit capacity. As the installed capacity of new energy sources such as photovoltaics increases rapidly, the system exhibits problems such as declining short-circuit ratios, slow voltage response, and insufficient inertia. Voltage dips during faults are difficult to recover from in a timely manner, and the rate of frequency change easily exceeds limits. Traditional configuration schemes based primarily on battery energy storage or single peak-shaving indicators cannot simultaneously meet the multiple constraints of small disturbances, transients, and frequency stability. Furthermore, the lack of dynamic coupling modeling between the millisecond-level high-power output and energy consumption of supercapacitors leads to a mismatch between capacity and actual support requirements; multi-indicator decisions also lack objective and consistent weighting criteria, easily causing scheme deviations. Therefore, there is an urgent need for a comprehensive configuration method that unifies generalized short-circuit ratios, transient criteria, virtual inertia, and dynamic energy constraints, enabling photovoltaic power plants to possess verifiable active support capabilities in weak receiving-end grids. Summary of the Invention

[0003] The technical problem to be solved by this invention is: in the process of configuring supercapacitor energy storage for photovoltaic power plants facing weak grids at the receiving end, how to ensure stability under small disturbances, transient voltage support, frequency support and energy safety margin, while also taking into account economy and control feasibility.

[0004] The principle of this invention is as follows: a hybrid supercapacitor model containing GFM and GFL is constructed, the generalized short-circuit ratio is used to evaluate static indicators, the multi-threshold transient criterion and virtual inertia model are used to evaluate dynamic indicators, energy constraints based on time-domain integration are introduced, and the optimal solution is output through a two-stage five-dimensional objective NSGA-II optimization and improved AHP+CRITIC combined with relative entropy TOPSIS decision-making process.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids includes the following steps:

[0007] Step 1. Collect parameter data from the receiving-end power grid, photovoltaic power plant, and supercapacitor energy storage system, including the equivalent short-circuit capacity of the receiving-end power grid. Rated active power of photovoltaic power station And the topology and control parameters of the supercapacitor energy storage system, wherein the control parameters include the number of supercapacitor modules connected in series and parallel, and the filter inductance of the DC / DC converter. With capacitor Parameters, inner loop current and outer loop voltage PI control coefficients , ;

[0008] Step 2. Based on the parameter data of the receiving-end power grid, photovoltaic power station and supercapacitor energy storage system, establish a hybrid supercapacitor energy storage equivalent model that includes both grid-shaping converter unit GFM and grid-following converter unit GFL. In the small-signal model, the grid-shaping converter unit GFM is equivalent to a controlled voltage source, and the grid-following converter unit GFL is equivalent to a controlled current source.

[0009] Step 3. For the preset multiple fault conditions, perform time-domain simulation of the joint active power response of GFM and GFL. Under the condition that all parameter settings are met simultaneously, calculate the annualized investment cost. Minimize and small disturbance stability margin Maximize the capacity of the GFM. With GFL capacity of Optimal solution set;

[0010] Step 4. For The supporting capabilities of all alternative solutions in the optimal solution set are evaluated, and the final configuration result is selected.

[0011] In the aforementioned method for configuring supercapacitor energy storage in a photovoltaic power plant with a weak receiving-end grid, step 2 involves calculating the generalized short-circuit ratio using the following formula. :

[0012]

[0013] in The equivalent contribution coefficient of GFM to the short-circuit ratio;

[0014] Based on the hybrid model, the critical generalized short-circuit ratio is calculated using the following formula. :

[0015]

[0016] in, For the equivalent reactance of the receiving-end power grid, For the equivalent reactance of a photovoltaic power station, The equivalent reactance for energy storage in a supercapacitor;

[0017] This yields the stability margin under small disturbances.

[0018]

[0019] Multiple voltage thresholds are determined using a multi-binary table criterion. With duration threshold Combining transient voltage evaluations yields the improvement in transient voltage stability margin. :

[0020]

[0021] Based on power grid inertia Photovoltaic virtual inertia coefficient With GFM virtual inertia coefficient Calculate the total inertia of the system :

[0022]

[0023] This refers to the rated active power of the grid-forming converter.

[0024] Calculate the minimum frequency margin after configuring a supercapacitor energy storage system. :

[0025]

[0026] Calculate the improvement in frequency change rate before and after configuring the supercapacitor energy storage system. :

[0027]

[0028] in, The lowest frequency after the fault. For frequency security threshold, and These represent the frequency change rates before and after configuring the supercapacitor energy storage system, respectively.

[0029] The aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids includes, as described above, a multi-binary meter criterion that includes at least the following: , , Three sets of voltage-time thresholds are used to determine whether the degree and duration of transient voltage drops meet safety requirements. pu represents the per-unit value.

[0030] In the aforementioned method for configuring supercapacitor energy storage in a photovoltaic power plant with a weak receiving-end grid, step 3 includes the following parameter setting conditions:

[0031] Small disturbance stability margin During the fault, the voltage should not be lower than the threshold. , as well as ;

[0032] in , Energy efficiency coefficient For the rated support duration, This represents the upper limit of the permissible rate of change of frequency.

[0033] In the aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids, step 3 involves using a multi-objective optimization algorithm based on NSGA-II to solve for the GFM capacity. With GFL capacity The Pareto optimal solution set.

[0034] The aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids incorporates dynamic energy coupling constraints as constraints in the NSGA-II-based multi-objective optimization algorithm to calculate the energy demand for each fault scenario. :

[0035]

[0036] in, For the first The initial time of the fault For the first System recovery time after a fault The supercapacitor stores energy and outputs power.

[0037] The maximum energy demand across all scenarios forms the dynamic energy coupling constraint:

[0038]

[0039] The annualized investment cost is calculated using the following formula:

[0040]

[0041] in, Cost per unit power Cost per unit of energy Rated energy capacity, The discount rate is... The design lifespan is defined as the number of years.

[0042] In the aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids, step 4 involves calculating the five-dimensional indicators of each alternative scheme through transient simulation and constructing an evaluation matrix containing these five-dimensional indicators, including the annualized investment cost. Small disturbance stability margin transient voltage stability margin improvement Frequency minimum margin And the improvement in the rate of change of frequency ;

[0043] The combined weights are obtained by linear weighting using improved AHP and CRITIC:

[0044]

[0045] in, The first result obtained by improving AHP calculation Subjective weighting of each indicator; The first one calculated by CRITIC The objective weight of each indicator; The preference coefficient for subjective and objective weighting;

[0046] Determine the positive ideal solution based on the weighted normalization matrix. and negative ideal solution :

[0047]

[0048]

[0049] Using the improved TOPSIS algorithm based on relative entropy distance, the nth... The relative entropy distance between each alternative solution and the positive and negative ideal solutions and The calculation formula is as follows:

[0050]

[0051]

[0052] To weighted normalize matrix elements, , The positive and negative ideal solutions are respectively in the th... The values ​​for each indicator The number of indicators;

[0053] Based on the relative entropy distance, calculate the first... Relative closeness of the alternatives :

[0054]

[0055] Choose relative closeness Biggest alternative As the final capacity configuration result, the corresponding GFM capacity will be... GFL capacity Total energy of supercapacitor module And control parameters are sent to the photovoltaic power station.

[0056] The aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids includes a control parameter that includes a virtual inertia coefficient. droop control coefficient And reactive voltage sensitivity coefficient.

[0057] The aforementioned method for configuring supercapacitor energy storage in photovoltaic power plants with weak receiving-end grids, in the improved AHP, the improved method is to construct the judgment matrix using a three-scale method (0, 1, 2);

[0058] The improved TOPSIS algorithm refers to the TOPSIS algorithm that uses relative entropy distance instead of traditional Euclidean distance.

[0059] A computer device includes: a memory and a processor for storing a computer program on the memory and running on the processor, wherein the processor executes the computer program to implement the above-described supercapacitor energy storage configuration method for photovoltaic power plants with weak receiving-end grids.

[0060] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described supercapacitor energy storage configuration method for photovoltaic power plants with weak receiving-end grids.

[0061] The technical effects achieved by this invention are as follows: The method of this invention explicitly incorporates the contribution of GFM to the short-circuit ratio into the index system, thereby improving the static adaptability of photovoltaic power plants in weak receiving-end grids; through multi-threshold transient criteria and dynamic energy constraints, it ensures that voltage and frequency support requirements are met simultaneously under multiple fault scenarios while leaving an energy safety margin; the two-stage multi-objective optimization, combined with the fusion of subjective and objective weights and relative entropy TOPSIS decision-making, achieves objective, traceable and efficient scheme selection. Attached Figure Description

[0062] Figure 1 is a flowchart of the supercapacitor energy storage configuration method for photovoltaic power plants oriented towards weak grids at the receiving end in Embodiment 1 of the present invention. Detailed Implementation

[0063] To more clearly illustrate the technical solutions and advantages of the present invention, the accompanying drawings used in the embodiments will be briefly described below.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides a method for configuring supercapacitor energy storage in a photovoltaic power station with a weak receiving-end grid, including the following steps:

[0066] Step 1. Collect parameter data from the receiving-end power grid, photovoltaic power plant, and supercapacitor energy storage system, including the equivalent short-circuit capacity of the receiving-end power grid. Rated active power of photovoltaic power station And the topology and control parameters of the supercapacitor energy storage system, wherein the control parameters include the number of supercapacitor modules connected in series and parallel, and the filter inductance of the DC / DC converter. With capacitor Parameters, inner loop current and outer loop voltage PI control coefficients , ;

[0067] Step 2. Based on the parameter data of the receiving-end power grid, photovoltaic power station and supercapacitor energy storage system, establish a hybrid supercapacitor energy storage equivalent model that includes both grid-shaping converter unit GFM and grid-following converter unit GFL. In the small-signal model, the grid-shaping converter unit GFM is equivalent to a controlled voltage source, and the grid-following converter unit GFL is equivalent to a controlled current source.

[0068] Calculate the generalized short-circuit ratio using the following formula :

[0069]

[0070] in The equivalent contribution coefficient of GFM to the short-circuit ratio;

[0071] Based on the hybrid model, the critical generalized short-circuit ratio is calculated using the following formula. :

[0072]

[0073] in, For the equivalent reactance of the receiving-end power grid, For the equivalent reactance of a photovoltaic power station, The equivalent reactance for energy storage in a supercapacitor;

[0074] This yields the stability margin under small disturbances.

[0075]

[0076] Multiple voltage thresholds are determined using a multi-binary table criterion. With duration threshold Combining transient voltage evaluations yields the improvement in transient voltage stability margin. :

[0077]

[0078] The multi-binary table criterion includes at least the following: , , Three sets of voltage-time thresholds are used to determine whether the degree and duration of transient voltage drops meet safety requirements. pu represents the per-unit value, i.e., the normalized value relative to the reference value.

[0079] Based on power grid inertia Photovoltaic virtual inertia coefficient With GFM virtual inertia coefficient Calculate the total inertia of the system :

[0080]

[0081] This refers to the rated active power of the grid-forming converter.

[0082] Calculate the minimum frequency margin after configuring a supercapacitor energy storage system. :

[0083]

[0084] Calculate the improvement in frequency change rate before and after configuring the supercapacitor energy storage system. :

[0085]

[0086] in, The lowest frequency after the fault. For frequency security threshold, and These represent the frequency change rates before and after configuring the supercapacitor energy storage system, respectively.

[0087] Step 3. For the preset multiple fault conditions, perform time-domain simulation of the joint active power response of GFM and GFL. Under the condition that all parameter settings are met simultaneously, calculate the annualized investment cost. Minimize and small disturbance stability margin With the goal of maximizing, a multi-objective optimization algorithm based on NSGA-II is used to solve the GFM capacity. With GFL capacity The Pareto optimal solution set.

[0088] The parameter setting conditions include:

[0089] Small disturbance stability margin During the fault, the voltage should not be lower than the threshold. , as well as ;

[0090] in , Energy efficiency coefficient For the rated support duration, The upper limit of the allowable rate of change;

[0091] In the NSGA-II-based multi-objective optimization algorithm, dynamic energy coupling constraints are used as constraints to calculate the energy demand for each fault scenario. :

[0092]

[0093] in, For the first The initial time of the fault For the first System recovery time after a fault The supercapacitor stores energy and outputs power.

[0094] The maximum energy demand across all scenarios forms the dynamic energy coupling constraint:

[0095]

[0096] Dynamic energy coupling constraints ensure that the capacity configuration of the supercapacitor energy storage system can meet the maximum energy demand calculated under all preset fault scenarios.

[0097] The dynamic energy coupling constraint satisfies:

[0098]

[0099] in , .

[0100] The annualized investment cost Calculate using the following formula:

[0101]

[0102] in, Cost per unit power Cost per unit of energy Rated energy capacity, The discount rate is... The design lifespan is defined as the number of years.

[0103] Step 4. For The supporting capabilities of all alternative solutions in the optimal solution set are evaluated, and the final configuration result is selected.

[0104] In step 4, the five-dimensional indicators of each alternative scheme are calculated through transient simulation, and an evaluation matrix containing these indicators is constructed. This allows for the selection of the optimal energy storage configuration scheme that meets the "voltage-frequency dual support" requirements of the receiving-end weak-grid photovoltaic power station, based on factors such as economic efficiency, static stability, transient voltage support, and frequency support. The five-dimensional indicators include the annualized investment cost. Small disturbance stability margin transient voltage stability margin improvement Frequency minimum margin And the improvement in the rate of change of frequency .

[0105] The improved AHP and CRITIC (Criteria Importance Through Intercriteria Correlation, an objective weighting method based on indicator correlation) are used to obtain the comprehensive weight through linear weighting:

[0106]

[0107] in, The first value obtained by improving AHP (third scaling method) Subjective weighting of each indicator; The first value calculated using CRITIC (based on index correlation) The objective weight of each indicator; The preference coefficient, which measures subjective and objective factors, has a range of values. When prioritizing expert experience, it is advisable to... When focusing on fluctuations in objective data, one can take... Generally speaking, it is acceptable. To take into account both subjective and objective information.

[0108] The comprehensive weight Used to calculate the weighted distance between the elements of the weighted normalized matrix and the positive and negative ideal solutions. , .

[0109] First, determine the positive ideal solution based on the weighted normalization matrix. and negative ideal solution :

[0110]

[0111]

[0112] Using the improved TOPSIS algorithm based on relative entropy distance, the nth... The relative entropy distance between each alternative solution and the positive and negative ideal solutions and The calculation formula is as follows:

[0113]

[0114]

[0115] To weighted normalize matrix elements, , The positive and negative ideal solutions are respectively in the th... The values ​​for each indicator The number of indicators is 5 in this example, each corresponding to an annualized investment cost. Small disturbance stability margin transient voltage stability margin improvement Frequency minimum margin And the improvement in the rate of change of frequency Five-dimensional indicators.

[0116] in, The smaller the value, the smaller the difference between the probability distribution of the proposed solution and the ideal solution. The larger the value, the greater the difference between the proposed solution and the negative ideal solution.

[0117] Based on the relative entropy distance, calculate the first... Relative closeness of the alternatives :

[0118]

[0119] Choose relative closeness Biggest alternative This will be the final capacity configuration result, and the corresponding GFM capacity will be... GFL capacity Total energy of supercapacitor module And control parameters are sent to the photovoltaic power station.

[0120] The control parameters include the virtual inertia coefficient. droop control coefficient And reactive voltage sensitivity coefficient.

[0121] In the improved AHP (Analytic Hierarchy Process), the improved method uses a three-scale method (0, 1, 2) to construct the judgment matrix, which reduces subjectivity and simplifies the consistency test compared to the traditional nine-scale method.

[0122] The improved TOPSIS algorithm (Technique for Order Preference by Similarity to Ideal Solution) replaces the traditional Euclidean distance with relative entropy distance in the TOPSIS algorithm. Relative entropy distance refers to the Kullback-Leibler divergence. The improvement can more accurately measure the difference between the probability distribution of the proposed solution and the ideal solution.

[0123] The NSGA-II algorithm has a population size of 100, a simulated binary crossover probability of 0.9, a mutation probability of 0.1, and an iteration number of 200 to generate at least 50 Pareto candidate solutions.

[0124] Based on the above steps, the main parameter settings used for simulation and calculation in this embodiment are shown in Table 1 below:

[0125] Table 1 Main Parameter Settings

[0126]

[0127] Those skilled in the art can make equivalent substitutions to the above embodiments without departing from the spirit of the present invention, and all such substitutions fall within the protection scope of the present invention.

[0128] Example 2

[0129] A computer device includes: a memory and a processor for storing a computer program on the memory and running on the processor, wherein the processor executes the computer program to implement the above-described supercapacitor energy storage configuration method for photovoltaic power plants with weak receiving-end grids.

[0130] Example 3

[0131] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described supercapacitor energy storage configuration method for photovoltaic power plants with weak receiving-end grids.

[0132] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for configuring supercapacitor energy storage in a photovoltaic power plant for weak receiving-end grids, characterized in that, Includes the following steps: Step 1. Collect parameter data from the receiving-end power grid, photovoltaic power plant, and supercapacitor energy storage system, including the equivalent short-circuit capacity of the receiving-end power grid. Rated active power of photovoltaic power station And the topology and control parameters of the supercapacitor energy storage system; Step 2. Based on the parameter data of the receiving-end power grid, photovoltaic power station and supercapacitor energy storage system, establish a hybrid supercapacitor energy storage equivalent model that includes both grid-shaping converter unit GFM and grid-following converter unit GFL. In the small-signal model, the grid-shaping converter unit GFM is equivalent to a controlled voltage source, and the grid-following converter unit GFL is equivalent to a controlled current source. Step 3. For the preset multiple fault conditions, perform time-domain simulation of the joint active power response of GFM and GFL. Under the condition that all parameter settings are met simultaneously, calculate the annualized investment cost. Minimize and small disturbance stability margin To maximize the capacity of the GFM, solve for the capacity of the GFM. With GFL capacity of Optimal solution set; Step 4. For The supporting capabilities of all alternative solutions in the optimal solution set are evaluated, and the final configuration result is selected.

2. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 1, is characterized in that... In step 2, the generalized short-circuit ratio is calculated using the following formula. : ； in The equivalent contribution coefficient of GFM to the short-circuit ratio; Based on the hybrid model, the critical generalized short-circuit ratio is calculated using the following formula. : ； in, For the equivalent reactance of the receiving-end power grid, For the equivalent reactance of a photovoltaic power station, The equivalent reactance for energy storage in a supercapacitor; This yields the stability margin under small disturbances. ； Multiple voltage thresholds are determined using a multi-binary table criterion. With duration threshold Combining transient voltage evaluations yields the improvement in transient voltage stability margin. : ； Based on power grid inertia Photovoltaic virtual inertia coefficient With GFM virtual inertia coefficient Calculate the total inertia of the system : ； This refers to the rated active power of the grid-forming converter. Calculate the minimum frequency margin after configuring a supercapacitor energy storage system. : ； Calculate the improvement in frequency change rate before and after configuring the supercapacitor energy storage system. : ； in, The lowest frequency after the fault. For frequency security threshold, and These represent the frequency change rates before and after configuring the supercapacitor energy storage system.

3. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 2, is characterized in that... The multi-binary table criterion includes at least the following: , , Three sets of voltage-time thresholds are used to determine whether the degree and duration of transient voltage drops meet safety requirements. pu represents the per-unit value.

4. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 2, is characterized in that... In step 3, the parameter setting conditions include: Small disturbance stability margin During the fault, the voltage should not be lower than the threshold. , as well as ; in , Energy efficiency coefficient For the rated support duration, This represents the upper limit of the permissible rate of change of frequency.

5. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 1, is characterized in that... In step 3, the GFM capacity is solved using a multi-objective optimization algorithm based on NSGA-II. With GFL capacity The Pareto optimal solution set.

6. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 5, is characterized in that... In the NSGA-II-based multi-objective optimization algorithm, dynamic energy coupling constraints are used as constraints to calculate the energy demand for each fault scenario. : ； in, For the first The initial time of the fault For the first System recovery time after a fault The supercapacitor stores energy and outputs power. The maximum energy demand across all scenarios forms the dynamic energy coupling constraint: ； The annualized investment cost is calculated using the following formula: ； in, Cost per unit power Cost per unit of energy Rated energy capacity, The discount rate is... The design lifespan is defined as the number of years.

7. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 6, is characterized in that... In step 4, the five-dimensional indicators of each alternative scheme are calculated through transient simulation, and an evaluation matrix containing the five-dimensional indicators is constructed. The five-dimensional indicators include the annualized investment cost. Small disturbance stability margin Transient voltage stability margin improvement Frequency minimum margin And the improvement in the rate of change of frequency ; The combined weights are obtained by linear weighting using improved AHP and CRITIC: ； in, The first result obtained by improving AHP calculation Subjective weighting of each indicator; The first one calculated by CRITIC The objective weight of each indicator; The preference coefficient for subjective and objective weighting; Determine the positive ideal solution based on the weighted normalization matrix. and negative ideal solution : ； ； Using the improved TOPSIS algorithm based on relative entropy distance, the nth... The relative entropy distance between each alternative solution and the positive and negative ideal solutions and The calculation formula is as follows: ； ； To weighted normalize matrix elements, , The positive and negative ideal solutions are respectively in the th... The values ​​for each indicator The number of indicators; Based on the relative entropy distance, calculate the first... Relative closeness of the alternatives : ； Choose relative closeness Biggest alternative As the final capacity configuration result, the corresponding GFM capacity will be... GFL capacity Total energy of supercapacitor module And control parameters are sent to the photovoltaic power station.

8. The method for configuring supercapacitor energy storage in a photovoltaic power station for a weak receiving-end grid, as described in claim 7, is characterized in that... In the improved AHP, the improved method is to construct the judgment matrix using a three-scale method (0, 1, 2); The improved TOPSIS algorithm refers to the TOPSIS algorithm that uses relative entropy distance instead of traditional Euclidean distance.

9. A computer device, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the supercapacitor energy storage configuration method for photovoltaic power plants with weak receiving-end grids as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the supercapacitor energy storage configuration method for photovoltaic power plants oriented towards weak receiving-end grids as described in any one of claims 1-8.

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