A power distribution network distributed photovoltaic optimization configuration method and terminal

Abstract

The application discloses a power distribution network distributed photovoltaic optimization configuration method and a terminal, a combination weighting method based on analytic hierarchy process and grey correlation analysis is used to determine the combination weight of a first group of indexes including distributed photovoltaic configuration capacity, total cost and multiple safety margins, a target function is established based on the combination weight to maximize the distributed photovoltaic configuration capacity, minimize the total cost and maximize the multiple safety margins, and corresponding constraint conditions are established, all adjustable measures are traversed, for the traversed adjustable measures, the adjustable measures are implemented based on the obtained power distribution network frame parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and a distributed photovoltaic optimization configuration model is solved to obtain a distributed photovoltaic configuration result, and the optimal distributed photovoltaic configuration result is screened from all the distributed photovoltaic configuration results, so that the safe and stable operation of the power distribution network is effectively ensured, and the orderly development of the distributed photovoltaic industry has reference significance.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaic (PV) configuration, and more particularly to a method and terminal for optimizing distributed PV configuration in a power distribution network. Background Technology

[0002] With the continuous advancement of the "dual carbon" target and the county-wide photovoltaic development strategy, the dense integration of distributed photovoltaic (PV) power has led to a year-on-year increase in the penetration rate of distribution networks. The large-scale integration of distributed PV not only alters the flow of power in distribution network lines but also introduces intermittent and fluctuating power output, posing risks such as node voltage exceeding limits and line current overload. While some experts and scholars have explored multi-objective optimization methods for distributed PV, their safety indicators in constructing objective functions often only consider a single safety margin, such as overvoltage or overcurrent margin, which cannot guarantee the safe and stable operation of the distribution network. Furthermore, existing multi-objective function weighting methods for distribution networks have shortcomings, namely, a strong subjective element. Experts often make judgments and scores based on the operating conditions of the distribution network and relevant experience, failing to provide reasonable guidance for the optimal configuration of distributed PV. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and terminal for optimizing the configuration of distributed photovoltaic power in a distribution network, which can effectively ensure the safe and stable operation of the distribution network.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A method for optimizing the configuration of distributed photovoltaic power in a distribution network includes the following steps:

[0006] The combined weights of the first set of indicators are determined using a combined weighting method based on hierarchical analysis and grey relational analysis. The first set of indicators includes distributed photovoltaic configuration capacity, total cost, and multiple safety margins.

[0007] Based on the combined weights of the first set of indicators, an objective function is established to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins, and constraints corresponding to the objective function are established.

[0008] A distributed photovoltaic optimal configuration model is generated based on the objective function and the constraints.

[0009] The system acquires distribution network parameters, typical daily load data, and distributed photovoltaic power generation efficiency data, and iterates through all adjustable measures, which are determined from inverter power angle adjustment and demand response.

[0010] For the adjustable measures that have been traversed, the adjustable measures are implemented based on the distribution network parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimal configuration model is solved to obtain the distributed photovoltaic configuration result corresponding to the adjustable measures that have been traversed.

[0011] The optimal distributed photovoltaic (PV) configuration result is selected from all distributed PV configuration results.

[0012] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0013] A distributed photovoltaic (PV) optimization configuration terminal for a distribution network includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0014] The combined weights of the first set of indicators are determined using a combined weighting method based on hierarchical analysis and grey relational analysis. The first set of indicators includes distributed photovoltaic configuration capacity, total cost, and multiple safety margins.

[0015] Based on the combined weights of the first set of indicators, an objective function is established to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins, and constraints corresponding to the objective function are established.

[0016] A distributed photovoltaic optimal configuration model is generated based on the objective function and the constraints.

[0017] The system acquires distribution network parameters, typical daily load data, and distributed photovoltaic power generation efficiency data, and iterates through all adjustable measures, which are determined from inverter power angle adjustment and demand response.

[0018] For the adjustable measures that have been traversed, the adjustable measures are implemented based on the distribution network parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimal configuration model is solved to obtain the distributed photovoltaic configuration result corresponding to the adjustable measures that have been traversed.

[0019] The optimal distributed photovoltaic (PV) configuration result is selected from all distributed PV configuration results.

[0020] The beneficial effects of this invention are as follows: A combined weighting method based on hierarchical analysis and grey relational analysis is used to determine the combined weights of a first set of indicators, including distributed photovoltaic (PV) configuration capacity, total cost, and multiple safety margins. Based on these combined weights, an objective function is established to maximize distributed PV configuration capacity, minimize total cost, and maximize multiple safety margins, along with corresponding constraints. All adjustable measures are traversed, determined from inverter power angle adjustment and demand response. For each traversed adjustable measure, it is implemented based on the acquired distribution network parameters, typical daily load data, and distributed PV power generation efficiency data. Finally, the optimal configuration model for distributed PV is solved. The distributed photovoltaic (PV) configuration results corresponding to the traversed adjustable measures are obtained. The optimal distributed PV configuration result is selected from all the distributed PV configuration results. The distributed PV optimization configuration model considers multiple safety margins and source-load synergy, making it more comprehensive. Moreover, the weighting method based on a combination of hierarchical analysis and grey relational analysis is used for index weighting to avoid strong subjectivity. First, the distributed PV configuration results obtained by implementing different adjustable measures are obtained, and then the optimal distributed PV configuration result is selected from them. This allows for reasonable and comprehensive optimization of distributed PV configuration, thereby effectively ensuring the safe and stable operation of the distribution network and providing a reference for the orderly development of the distributed PV industry. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of a distributed photovoltaic optimization configuration method for a power distribution network according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of a distributed photovoltaic optimization configuration terminal for a distribution network according to an embodiment of the present invention;

[0023] Figure 3 (a) is a power flow analysis diagram of the passive distribution network before the distributed photovoltaic grid is connected in the distribution network distributed photovoltaic optimization configuration method of the present invention;

[0024] Figure 3 (b) is a power flow analysis diagram of the passive distribution network after distributed photovoltaic access in the distribution network distributed photovoltaic optimization configuration method of the present invention;

[0025] Figure 4 This is a flowchart illustrating the process of selecting the optimal distributed photovoltaic configuration result in the distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0026] Figure 5 This is an IEEE 33-node topology diagram in the distributed photovoltaic optimization configuration method for distribution networks according to an embodiment of the present invention.

[0027] Figure 6This is a typical daily distributed photovoltaic power generation efficiency diagram in the distributed photovoltaic optimization configuration method for distribution networks according to an embodiment of the present invention;

[0028] Figure 7 (a) is a schematic diagram of residential load data in the distributed photovoltaic optimization configuration method for distribution networks according to an embodiment of the present invention;

[0029] Figure 7 (b) is a schematic diagram of commercial load data in the distribution network distributed photovoltaic optimization configuration method of the present invention;

[0030] Figure 7 (c) is a schematic diagram of industrial load data in the distributed photovoltaic optimization configuration method for distribution networks according to an embodiment of the present invention;

[0031] Figure 8 This is a result diagram of the distributed photovoltaic optimization configuration scheme in Scenario 4 of the distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0032] Figure 9 This is a diagram showing the average voltage of distribution network nodes considering different adjustable measures in the distribution network distributed photovoltaic optimization configuration method of the present invention.

[0033] Figure 10 This is a power angle mean diagram of distributed photovoltaic inverters considering different adjustable measures in the distributed photovoltaic optimization configuration method for distribution networks according to an embodiment of the present invention. Detailed Implementation

[0034] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0035] Please refer to Figure 1 A method for optimizing the configuration of distributed photovoltaic power in a distribution network, comprising the following steps:

[0036] The combined weights of the first set of indicators are determined using a combined weighting method based on hierarchical analysis and grey relational analysis. The first set of indicators includes distributed photovoltaic configuration capacity, total cost, and multiple safety margins.

[0037] Based on the combined weights of the first set of indicators, an objective function is established to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins, and constraints corresponding to the objective function are established.

[0038] A distributed photovoltaic optimal configuration model is generated based on the objective function and the constraints.

[0039] The system acquires distribution network parameters, typical daily load data, and distributed photovoltaic power generation efficiency data, and iterates through all adjustable measures, which are determined from inverter power angle adjustment and demand response.

[0040] For the adjustable measures that have been traversed, the adjustable measures are implemented based on the distribution network parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimal configuration model is solved to obtain the distributed photovoltaic configuration result corresponding to the adjustable measures that have been traversed.

[0041] The optimal distributed photovoltaic (PV) configuration result is selected from all distributed PV configuration results.

[0042] As can be seen from the above description, the beneficial effects of this invention are as follows: A combined weighting method based on hierarchical analysis and grey relational analysis is used to determine the combined weights of a first set of indicators, including distributed photovoltaic (PV) configuration capacity, total cost, and multiple safety margins. Based on these combined weights, an objective function is established with the distributed PV configuration capacity maximized, total cost minimized, and multiple safety margins maximized. Corresponding constraints are also established. All adjustable measures are iterated through, determined from inverter power angle adjustment and demand response. For each iterated adjustable measure, the measures are implemented based on the acquired distribution network parameters, typical daily load data, and distributed PV power generation efficiency data. Finally, the distributed PV optimization solution is obtained. The configuration model yields distributed photovoltaic (PV) configuration results corresponding to the traversed adjustable measures. The optimal PV configuration result is then selected from all available results. This optimized PV configuration model considers multiple safety margins and source-load synergy, making it more comprehensive. Furthermore, the weighting of indicators employs a combined weighting method based on hierarchical analysis and grey relational analysis to avoid strong subjectivity. By first obtaining the PV configuration results obtained using different adjustable measures, and then selecting the optimal configuration, a reasonable and comprehensive optimization of distributed PV configurations can be achieved, effectively ensuring the safe and stable operation of the distribution network and providing valuable reference for the orderly development of the distributed PV industry.

[0043] Furthermore, the determination of the combined weights of the first set of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis includes:

[0044] The 1-9 scale method is used to quantitatively compare the indicators in the first group of indicators and construct a judgment matrix;

[0045] The eigenvectors of the judgment matrix are obtained using the square root method;

[0046] Calculate the consistency index of the judgment matrix, and perform a consistency test on the judgment matrix based on the consistency index to obtain the test result;

[0047] If the test result is passed, the weighting result of each indicator in the first group of indicators is obtained to obtain the indicator weight matrix of the expert group.

[0048] The reference sequence of weights is determined based on the indicator weight matrix of the expert group.

[0049] Calculate the relative distance between the indicator weight matrix of the expert group and the reference sequence;

[0050] The combined weights of the first set of indicators are calculated based on the relative distance.

[0051] As described above, choosing a reasonable weighting method is crucial for solving multi-indicator, multi-objective optimization problems. Traditional single subjective or objective weighting methods often have certain limitations and cannot ensure the consistency of subjectivity and objectivity when calculating weights. By combining weighting methods based on hierarchical analysis and grey relational analysis, the subjective nature of weights is ensured by relying on the experience and priority information of multiple experts. By optimizing the closeness of the correlation between indicators and system characteristics, the numerical calculation process of weights becomes objective and accurate. This achieves the complementary advantages of subjective and objective weighting methods, making the combined weights of the first set of indicators more reasonable and reliable.

[0052] Furthermore, the method of determining the combined weights of the first set of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis also includes:

[0053] The combined weights of the second set of indicators, including overvoltage margin, overcurrent margin, and reverse power transmission margin, are determined using a combined weighting method based on hierarchical analysis and grey relational analysis.

[0054] The objective function established based on the combined weights of the first set of indicators, with the goal of maximizing the distributed photovoltaic configuration capacity, minimizing the total cost, and maximizing the multiple safety margins, includes:

[0055] Determine the capacity of the distributed photovoltaic configuration;

[0056] The total cost is determined based on the investment cost, operation and maintenance cost, interaction cost between the distribution network and the upstream power grid, and demand response cost generated in the distributed photovoltaic configuration.

[0057] The overvoltage margin, the overcurrent margin, and the reverse power supply margin are determined, and the multiple safety margins are determined based on the combined weights of the second set of indicators, the overvoltage margin, the overcurrent margin, and the reverse power supply margin.

[0058] Based on the combined weights of the first set of indicators, an objective function is established to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins.

[0059] As described above, multiple safety margins are determined based on the combined weights of the second set of indicators, overvoltage margin, overcurrent margin, and reverse power transmission margin, ensuring the safe and stable operation of the distribution network after the optimized configuration of distributed photovoltaics. An objective function is established based on the combined weights of the first set of indicators to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins, so as to ensure the economic efficiency and safety reliability of the distributed photovoltaic configuration results.

[0060] Further, determining the overvoltage margin, the overcurrent margin, and the reverse power supply margin, and determining the multiple safety margins based on the combined weights of the second set of indicators, the overvoltage margin, the overcurrent margin, and the reverse power supply margin includes:

[0061]

[0062] In the formula, D represents multiple safety margins, λ1 represents the combined weight of overvoltage margins, and U out U represents the overvoltage margin. out,min U represents the minimum overvoltage margin. out,max λ represents the maximum value of the overvoltage margin, λ2 represents the combined weight of the overcurrent margin, and I out I represents the overcurrent margin. out,min I represents the minimum value of the overcurrent margin. out,max λ3 represents the maximum value of the overcurrent margin, λ3 represents the combined weight of the reverse current margin, and P represents the maximum value of the overcurrent margin. back,out P represents the reverse power margin. back,out,min P represents the minimum value of the reverse power margin. back,out,max U represents the maximum value of the reverse power margin. max This indicates the upper limit of the node voltage. I represents the voltage value at node i at time t. max Indicates the upper limit of the line current. P represents the current flowing through line j at time t. back,max This indicates the upper limit of the reverse power supply. This represents the active power that the distribution network feeds back to the upper-level grid at time t.

[0063] As can be seen from the above description, multiple safety margins take into account various safety margins, effectively ensuring the safety and stability of the distribution network.

[0064] Furthermore, the objective function established based on the combined weights of the first set of indicators to maximize the distributed photovoltaic configuration capacity, minimize the total cost, and maximize the multiple safety margins includes:

[0065]

[0066] In the formula, F represents the objective function, τ1 represents the combined weight of the distributed photovoltaic configuration capacity, and S represents the total distributed photovoltaic configuration capacity. min S represents the minimum total capacity of distributed photovoltaic (PV) systems. max Let τ2 represent the maximum total capacity of distributed photovoltaic (PV) configurations, τ2 represent the combination weight of the total cost, and C represent the total cost. min C represents the minimum total cost. max τ3 represents the maximum total cost, τ3 represents the combined weight of multiple safety margins, and D represents multiple safety margins. min D represents the minimum value of multiple safety margins. max This represents the maximum value of the multiple safety margins.

[0067] As described above, the established objective function can comprehensively consider the economy and security of distributed photovoltaic optimal configuration, thereby effectively improving the optimal configuration effect of distributed photovoltaic.

[0068] Furthermore, the constraints corresponding to the objective function include:

[0069] Establish distributed photovoltaic (PV) configuration capacity constraints, distributed PV output constraints, distributed PV inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sale constraints, and demand response constraints corresponding to the objective function.

[0070] As described above, by establishing distributed photovoltaic (PV) configuration capacity constraints, distributed PV output constraints, distributed PV inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sale constraints, and demand response constraints corresponding to the objective function, and by comprehensively considering safety constraints such as node voltage safety, line current carrying capacity, and non-exceeding limits for reverse power transmission, the safety of distributed PV configuration optimization in the distribution network is ensured.

[0071] Furthermore, the step of selecting the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results includes:

[0072] The optimal distributed photovoltaic (PV) configuration result is selected from all distributed PV configuration results using the approximation of ideal solution sorting method.

[0073] As described above, the approximation of ideal solution ranking method can make full use of the information in the original data and accurately reflect the differences between various distributed photovoltaic configuration schemes, making the selection of the optimal distributed photovoltaic configuration result more objective and accurate.

[0074] Furthermore, before solving the distributed photovoltaic optimal configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, the method further includes:

[0075] The node voltage constraint, line current constraint, and power flow balance constraint in the distributed photovoltaic optimization configuration model are linearized to obtain the processed distributed photovoltaic optimization configuration model.

[0076] The process of solving the distributed photovoltaic optimal configuration model to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures includes:

[0077] Solve the processed distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

[0078] As described above, since the node voltage constraints, line current constraints, and power flow balance constraints in the model are nonlinear constraints, linearization is performed on these constraints to facilitate solving the distributed photovoltaic optimal configuration model. This process is beneficial for solving the distributed photovoltaic optimal configuration model more efficiently.

[0079] Furthermore, all the adjustable measures include a first adjustable measure, a second adjustable measure, a third adjustable measure, and a fourth adjustable measure;

[0080] Also includes:

[0081] A first adjustable measure is determined without considering the inverter power angle adjustment and the demand response;

[0082] A second adjustable measure, including the inverter power angle adjustment, is determined;

[0083] Determine a third adjustable measure, including the aforementioned demand response;

[0084] A fourth adjustable measure is determined, including the inverter power angle adjustment and the demand response.

[0085] As described above, identifying different adjustable measures, including inverter power angle adjustment and the fourth adjustable measure of demand response, can further analyze the synergistic effect of source and load-side adjustable measures on the improvement of distributed photovoltaic optimization configuration scheme, thereby achieving a more effective distributed photovoltaic optimization configuration.

[0086] Please refer to Figure 2Another embodiment of the present invention provides a distributed photovoltaic optimization configuration terminal for a distribution network, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements each step in the above-described distributed photovoltaic optimization configuration method for a distribution network.

[0087] The above-described method and terminal for optimizing the configuration of distributed photovoltaic power in a distribution network are applicable to various scenarios involving the optimization configuration of distributed photovoltaic power. The following detailed embodiments illustrate this method:

[0088] Please refer to Figure 1 , Figures 3-10 Embodiment 1 of the present invention is as follows:

[0089] A method for optimizing the configuration of distributed photovoltaic power in a distribution network includes the following steps:

[0090] Under the overarching policy of "connecting all available distributed photovoltaic (PV) power," the large-scale development and extensive integration of distributed PV into distribution networks has transformed the distribution network from a "passive" to an "active" system, impacting voltage distribution and power flow. The dense integration of distributed PV may cause the output of some nodes to exceed load demand, leading to power backflow into the distribution network and resulting in reverse power flow. When the total output of distributed PV in the distribution network exceeds the total load demand, power backflow from the distribution network to the higher-level grid occurs through balancing nodes. The power flow direction of the distribution network before and after distributed PV integration is as follows: Figure 3 As shown.

[0091] Assuming the distribution network has n nodes, after distributed photovoltaic (PV) installation, the voltage U at node m is... m for:

[0092]

[0093] In the formula, U0 represents the slack line, i.e., the voltage at the connection point between the distribution network and the upstream power grid, r represents the resistance per unit length of the line, and l i P represents the length of the line between node i and node i-1. L,j P represents the active power of the load at node j. DG,j Let Q represent the active power output of the distributed photovoltaic system at node j, x represent the reactance per unit length of the line, and Q represent the reactive power output of the distributed photovoltaic system at node j. L,j Q represents the reactive power of the load at node j. DG,j U represents the distributed photovoltaic reactive power output of node j. i-1 This represents the voltage at node i-1.

[0094] As shown in the above formula, the node voltage increases with the increase of distributed photovoltaic (PV) output, and the voltage rise is more significant the longer the line length from the slack node. When the distributed PV capacity connected to the distribution network is too large, it may lead to node voltage exceeding limits and line current overload, which is detrimental to the safe and stable operation of the distribution network. At the same time, if the power fed back to the upper-level grid through the slack node is not limited, the transformer power may exceed the allowable value, thereby endangering the safety of the upper-level grid. Therefore, when optimizing the distributed PV configuration of the distribution network, it is necessary to comprehensively consider safety constraints such as node voltage safety, line current carrying capacity, and preventing the back-feeding power from exceeding limits.

[0095] S1. Determine the combined weights of the first set of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis. The first set of indicators includes distributed photovoltaic configuration capacity, total cost, and multiple safety margins, specifically including S11-S17:

[0096] S11. Use the 1-9 scaling method to quantitatively compare the indicators in the first group of indicators and construct a judgment matrix, as follows:

[0097]

[0098] In the formula, A represents the judgment matrix, A=(a ij ) n×n a ij Indicator a i and indicator a j Comparison of relative importance, when i≠j, a ij =1 / a ji , where n represents the order of the judgment matrix.

[0099] S12. Obtain the eigenvectors of the judgment matrix using the root method. According to relevant matrix theory, the eigenvector values ​​are the weight values ​​of each index in the matrix, specifically:

[0100]

[0101] W = [w1′, w2′, ..., w n ′];

[0102] In the formula, M i Let A represent the product of the elements in the i-th row of the matrix. ij Let w represent the judgment matrix. i w represents the nth root of the product of the elements in the i-th row of the matrix. i ′ represents the normalized value of the nth root of the product of the elements in the i-th row of the judgment matrix, and W represents the eigenvector matrix of the judgment matrix.

[0103] S13. Calculate the consistency index of the judgment matrix, and perform a consistency test on the judgment matrix based on the consistency index to obtain the test result, so as to ensure the scientific nature of the judgment matrix.

[0104] Specifically, the calculation of the consistency index of the judgment matrix is ​​as follows:

[0105]

[0106] In the formula, C R λ represents the consistency index of the judgment matrix. max R represents the largest eigenvalue of the judgment matrix. i The random consistency index of the judgment matrix is ​​related to the order n.

[0107] The consistency check of the judgment matrix based on the consistency index yields the following results:

[0108] If the consistency index is less than a preset value, the consistency of the judgment matrix is ​​considered to meet the acceptable range, and the test result is determined to be passed. If the consistency index is equal to or greater than the preset value, the consistency of the judgment matrix is ​​considered to not meet the acceptable range, and the test result is determined to be failed. In an optional implementation, the preset value is 0.1.

[0109] S14. If the test result is passed, the weighting result of each indicator in the first group of indicators is obtained to obtain the indicator weight matrix of the expert group.

[0110] Specifically, if the test result is passed, the weighting results of each indicator in the first group of indicators, determined by multiple experts, are obtained to obtain the indicator weight matrix of the expert group, specifically:

[0111]

[0112] In the formula, B represents the indicator weight matrix of the expert group, b ij Let represent the weighting coefficient of the j-th expert for the i-th indicator, and m represent the number of experts.

[0113] S15. Determine the reference sequence of weights based on the indicator weight matrix of the expert group, specifically as follows:

[0114] D = (d1, d2, ..., d i ,...,d n ) T ;

[0115] In the formula, D represents the reference sequence of the weights, and d iThis represents the maximum value of the element in the i-th row of the indicator weight matrix of the expert group. In other words, it represents the maximum value of each indicator weight in the indicator weight matrix of the expert group, which is the reference sequence of weights.

[0116] S16. Calculate the relative distance between the indicator weight matrix of the expert group and the reference sequence, specifically as follows:

[0117]

[0118] In the formula, L i This represents the relative distance between the indicator weight matrix of the expert group corresponding to the i-th indicator and the reference sequence. The importance of each indicator weight can be reflected by the relative distance between the indicator weight matrix of the expert group corresponding to each indicator and the reference sequence; the smaller the relative distance, the more important the indicator.

[0119] S17. Calculate the combined weight of the first group of indicators based on the relative distance, specifically as follows:

[0120]

[0121] In the formula, δ i δ represents the combined weight of the i-th indicator. i ' represents the normalized combined weight of the i-th indicator.

[0122] Executing S1 also includes:

[0123] The combined weights of the second set of indicators, including overvoltage margin, overcurrent margin, and reverse power transmission margin, are determined using a combined weighting method based on hierarchical analysis and grey relational analysis.

[0124] The specific process of determining the combined weights of the second set of indicators is similar to that of determining the combined weights of the first set of indicators, as described in S11-S17, and will not be repeated here.

[0125] This invention, based on a combined weighting method of hierarchical analysis and grey relational analysis, ensures the subjectivity of weights by relying on the experience and priority information of multiple experts, while making the numerical calculation process of weights objective and accurate by optimizing the closeness of the correlation between indicators and system characteristics. This achieves the complementary advantages of subjective and objective weighting methods.

[0126] S2. Based on the combined weights of the first set of indicators, establish an objective function with the maximum distributed photovoltaic configuration capacity, the minimum total cost, and the maximum multiple safety margins, and establish the constraints corresponding to the objective function, specifically including S21-S25:

[0127] S21. Determine the distributed photovoltaic configuration capacity, specifically as follows:

[0128]

[0129] In the formula, S represents the total capacity of the distributed photovoltaic system, and N represents the number of nodes in the distribution network. pv,i This represents the configured capacity of the distributed photovoltaic system at node i.

[0130] S22. The total cost is determined based on the investment cost, operation and maintenance cost, interaction cost between the distribution network and the upstream power grid, and demand response cost generated in the distributed photovoltaic configuration, specifically as follows:

[0131] C = C inv +C ope +C grid +C IDR ;

[0132]

[0133] In the formula, C represents the total cost, C inv C represents the investment cost incurred in a distributed photovoltaic (PV) configuration. ope C represents the operating and maintenance costs. grid C represents the interaction cost between the distribution network and the upstream power grid. IDR Let r represent the demand response cost, y represent the project discount rate for distributed photovoltaic (PV) configuration, and c represent the operating life of the distributed PV system. inv This represents the cost per unit capacity of distributed photovoltaic (PV) systems, where T represents the optimization timescale, which is 96 hours. c represents the power generation of the distributed photovoltaic system at node i at time t. ope This represents the operation and maintenance cost coefficient of distributed photovoltaic power generation. This indicates the unit price of electricity purchased in the distribution network. The electricity purchase price adopts the time-of-use pricing. c represents the electricity purchased by the distribution network at time t. sell This indicates the unit price of electricity sold in the distribution network. c represents the electricity sold by the distribution network at time t, where the electricity sold by the distribution network to the upper-level grid is the reverse-feed electricity. IDR This represents the compensation cost coefficient for demand response load adjustments. This indicates the increase in demand response load. This indicates the amount of reduction in demand response load.

[0134] S23. Determine the overvoltage margin, the overcurrent margin, and the reverse power supply margin, and determine the multiple safety margins based on the combined weights of the second set of indicators, the overvoltage margin, the overcurrent margin, and the reverse power supply margin, to ensure the safe and stable operation of the distribution network after the optimized configuration of distributed photovoltaic power, including:

[0135]

[0136] In the formula, D represents multiple safety margins, λ1 represents the combined weight of overvoltage margins, and U out U represents the overvoltage margin. out,min U represents the minimum overvoltage margin. out,max λ represents the maximum value of the overvoltage margin, λ2 represents the combined weight of the overcurrent margin, and I out I represents the overcurrent margin. out,min I represents the minimum value of the overcurrent margin. out,max λ3 represents the maximum value of the overcurrent margin, λ3 represents the combined weight of the reverse current margin, and P represents the maximum value of the overcurrent margin. back,out P represents the reverse power margin. back,out,min P represents the minimum value of the reverse power margin. back,out,max U represents the maximum value of the reverse power margin. max This indicates the upper limit of the node voltage. I represents the voltage value at node i at time t. max Indicates the upper limit of the line current. P represents the current flowing through line j at time t. back,max This indicates the upper limit of the reverse power supply. This represents the active power that the distribution network feeds back to the upper-level grid at time t.

[0137] S24. Based on the combined weights of the first set of indicators, establish an objective function with the following objectives: maximizing the distributed photovoltaic configuration capacity, minimizing the total cost, and maximizing the multiple safety margins.

[0138]

[0139] In the formula, F represents the objective function, τ1 represents the combined weight of distributed photovoltaic configuration capacity, and S min S represents the minimum total capacity of distributed photovoltaic (PV) systems. max τ2 represents the maximum total capacity of distributed photovoltaic power generation, and C represents the combined weight of the total cost. min C represents the minimum total cost. max τ3 represents the maximum total cost, τ3 represents the combined weight of multiple safety margins, and D represents the maximum total cost. min D represents the minimum value of multiple safety margins. max This represents the maximum value of the multiple safety margins.

[0140] S25. Establish distributed photovoltaic configuration capacity constraints, distributed photovoltaic power output constraints, distributed photovoltaic inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sale constraints, and demand response constraints corresponding to the objective function.

[0141] The distributed photovoltaic (PV) configuration capacity constraint, which means that the distributed PV configured at each node must meet the capacity threshold constraint, specifically includes:

[0142] S pv,min ≤S pv,i ≤S pv,max ;

[0143] In the formula, S pv,min S represents the lower limit of the distributed photovoltaic configuration capacity. pv,max This indicates the upper limit of the distributed photovoltaic configuration capacity.

[0144] The output of distributed photovoltaic (PV) power includes active power and reactive power output. The specific constraints on distributed PV power output are as follows:

[0145]

[0146] In the formula, This indicates the power generation efficiency of distributed photovoltaic systems. This represents the reactive power output of the distributed photovoltaic system at node i at time t. The inverter power angle at time t represents the distributed photovoltaic power at node i.

[0147] When adjusting the power angle of the distributed photovoltaic inverter, the adjustable range constraint should be met. Specifically, the distributed photovoltaic inverter power angle constraint is as follows:

[0148]

[0149] In the formula, θ pv,max θ represents the upper limit of the power angle of a distributed photovoltaic inverter. pv,min This represents the lower limit of the power angle of a distributed photovoltaic inverter.

[0150] To ensure the safe and stable operation of the distribution network, node voltages must be kept within a safe range and must not exceed the limits. Specifically, the node voltage constraints are as follows:

[0151]

[0152] In the formula, U max U represents the upper limit of the node voltage. min This indicates the lower limit of the node voltage.

[0153] The current flowing through the line must not exceed the maximum current-carrying capacity of the line. Specifically, this line current constraint is as follows:

[0154]

[0155] In the formula, I max This indicates the maximum line capacity.

[0156] The power flow balance constraints include active and reactive power balance constraints, node voltage balance constraints, and second-order cone constraints.

[0157] The active and reactive power balance constraint is specifically as follows:

[0158]

[0159] In the formula, Ω represents the active load of node i at time t. up,i Let i represent the set of paths that start at node i. Ω represents the active power transmitted by line j at time t. down,i Let r represent the set of lines ending at node i. j This represents the resistance of line j. This represents the reactive load of node i at time t. x represents the reactive power transmitted by line j at time t. j This represents the reactance of line j.

[0160] Since for the equilibrium node:

[0161]

[0162] In the formula, This represents the reactive power that the distribution network feeds back to the upper-level grid at time t;

[0163] The node voltage balance constraint is specifically as follows:

[0164]

[0165] In the formula, This represents the voltage at the end node i2 of line j at time t. This represents the voltage at the starting node i1 of line j at time t.

[0166] The second-order cone constraint is specifically as follows:

[0167]

[0168] Due to transformer capacity and reverse load rate constraints, there are maximum limits on the power purchase and sale capacity of the distribution network, and power purchase and sale cannot be carried out simultaneously. Specifically, these distribution network power purchase and sale constraints are as follows:

[0169]

[0170] In the formula, This represents a binary indicator variable representing the electricity purchased by the distribution network at time t. This represents a binary indicator variable representing the electricity sales of the distribution network at time t. and P cannot be 1 at the same time to ensure that the purchase and sale of electricity in the distribution network cannot occur simultaneously. buy,max P represents the maximum power purchased by the distribution network. back,max This represents the maximum power sold by the distribution network, where the power sold is the reverse power, which is constrained by the reverse load rate of the transformer.

[0171] Demand response load adjustments, including load increases and decreases, should all fall within a certain adjustment range and maintain balance within each typical day. These demand response constraints are specifically:

[0172]

[0173] In the formula, A binary indicator variable representing the increase in demand response load at time t. A binary indicator variable representing the decrease in demand response load at time t. and P cannot be 1 at the same time to ensure that increases and decreases in demand response load cannot occur simultaneously. lu,max P represents the maximum increase in demand response load. ld,max This indicates the maximum reduction in demand response load.

[0174] S3. Generate a distributed photovoltaic optimal configuration model based on the objective function and the constraints.

[0175] The distributed photovoltaic (PV) optimization configuration model considers multiple safety margins and comprehensively takes into account the coordinated cooperation between the power angle adjustment of the distributed PV inverter on the source side and the demand response on the load side, so as to improve the optimization configuration effect of distributed PV.

[0176] S4. Obtain distribution network parameters, typical daily load data, and distributed photovoltaic power generation efficiency data, and iterate through all adjustable measures. These adjustable measures are determined from inverter power angle adjustment and demand response. All adjustable measures include a first adjustable measure, a second adjustable measure, a third adjustable measure, and a fourth adjustable measure, such as... Figure 4 As shown;

[0177] For the adjustable measures that have been traversed, the adjustable measures are implemented based on the distribution network parameters, the typical daily load data, and the distributed photovoltaic power generation efficiency data. The distributed photovoltaic optimal configuration model is then solved to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, such as... Figure 4 As shown.

[0178] The constructed distributed photovoltaic optimal configuration model considering multiple safety margins has nonlinear constraints and cannot be directly solved using the CPLEX solver; therefore, linearization of the model is required. For the square terms of voltage and current, quadratic terms are introduced as variables. and Alternative and Therefore, before solving the distributed photovoltaic optimal configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, the method further includes:

[0179] The node voltage constraints, line current constraints, and power flow balance constraints in the distributed photovoltaic optimization configuration model are linearized to obtain the processed distributed photovoltaic optimization configuration model.

[0180] Specifically, the node voltage constraints after linearization are as follows:

[0181]

[0182] The line current constraint after linearization is as follows:

[0183]

[0184] The power flow balance constraints after linearization are as follows:

[0185]

[0186]

[0187] The process of solving the distributed photovoltaic optimal configuration model to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures includes:

[0188] Solve the processed distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

[0189] Specifically, programming is performed in Python, combining the distribution network parameters, the typical daily load data, the distributed photovoltaic power generation efficiency data, and the traversed adjustable measures, and calling the CPLEX solver to solve the processed distributed photovoltaic optimization configuration model, thereby obtaining the distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

[0190] In one alternative implementation, before S4, the following is also included:

[0191] A first adjustable measure is determined without considering the inverter power angle adjustment and the demand response;

[0192] A second adjustable measure, including the inverter power angle adjustment, is determined;

[0193] Determine a third adjustable measure, including the aforementioned demand response;

[0194] A fourth adjustable measure is determined, including the inverter power angle adjustment and the demand response.

[0195] S5. Select the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results.

[0196] In one alternative implementation, an approximation-ideal-solution ranking method is used to select the optimal distributed photovoltaic (PV) configuration from all the possible configurations, such as... Figure 4 As shown.

[0197] Specifically, positive and negative ideal solutions are determined based on the index type; the distance and proximity between each distributed photovoltaic configuration result and the positive and negative ideal solutions are calculated; all distributed photovoltaic configuration results are sorted in descending order of proximity to obtain all sorted distributed photovoltaic configuration results; the first distributed photovoltaic configuration result is selected from all sorted distributed photovoltaic configuration results as the optimal distributed photovoltaic configuration result.

[0198] The example uses the IEEE 33-node system, and the system topology diagram is as follows: Figure 5 As shown, there are 33 nodes and 32 lines, with node 1 being the balancing node. The system base capacity is 10 MVA, the base voltage is 12.66 kV, the node voltage safety range is [0.95, 1.05] pu, and the maximum allowable long-term current carrying capacity of the lines is 800 A. The node load types include three categories: residential load, commercial load, and industrial load. The load types corresponding to each node are shown in Table 1.

[0199] Table 1 Node Load Types

[0200] Load type node Residential load 4 / 7 / 10 / 13 / 16 / 19 / 22 / 25 / 28 Commercial load 2 / 5 / 8 / 11 / 14 / 17 / 20 / 23 / 26 / 29 / 32 Industrial load 3 / 6 / 9 / 12 / 15 / 18 / 21 / 24 / 27 / 30 / 33

[0201] The unit capacity investment cost of distributed photovoltaic (PV) is 8000 yuan / kW, and the unit power generation operation and maintenance cost is 0.15 yuan / kWh; the operating life is 15 years, and the project discount rate is 6%. Distributed PV can be installed on all nodes except the balancing node, with a capacity configuration range of [0, 800] kW. The compensation cost per unit load adjustment in demand response is 0.25 yuan / kWh, the unit price for electricity sold from the distribution network to the upstream grid is 0.5 yuan / kWh, and the time-of-use electricity price is shown in Table 2.

[0202] Table 2 Time-of-use Electricity Prices

[0203] Time period Electricity price (yuan / kWh) 01:00—07:00、23:00—24:00 0.31 08:00—11:00、15:00—18:00 0.45 12:00—14:00、19:00—22:00 0.82

[0204] Typical daily distributed photovoltaic power generation efficiency and load data are as follows: Figure 6 and Figure 7 As shown, the power reference values ​​for residential load, commercial load, and industrial load are 100kW, 80kW, and 80kW, respectively.

[0205] Set up the following four scenarios for optimized configuration of distributed photovoltaic systems:

[0206] Scenario 1: Distributed photovoltaic optimization configuration scheme considering only overvoltage margin; Scenario 2: Distributed photovoltaic optimization configuration scheme considering only overcurrent margin; Scenario 3: Distributed photovoltaic optimization configuration scheme considering only reverse power supply margin; Scenario 4: Distributed photovoltaic optimization configuration scheme considering multiple safety margins, i.e., the method described above in this invention.

[0207] The optimization results for the above four distributed photovoltaic optimization configuration scenarios are shown in Table 3.

[0208] Table 3 Optimization results for different scenarios

[0209]

[0210]

[0211] Table 3 shows that scenario 4 has the highest approximation among the four scenarios, indicating that it is the optimal configuration scheme for distributed photovoltaic (PV) power. Compared with the other three scenarios, scenario 4 maximizes multiple safety margins of the distribution network, including overvoltage margin, overcurrent margin, and reverse power transmission margin, while maintaining good economic efficiency. This ensures the safe and stable operation of the distribution network to the greatest extent possible, minimizing risks such as node overvoltage, line overcurrent, and transformer overload. Scenarios 1 to 3 all exhibit situations where one or more safety margins are zero, which is detrimental to the safe and stable operation of the distribution network. These results fully verify the effectiveness of the distributed PV power optimization configuration method for distribution networks described in this invention.

[0212] The specific configuration results for scenario 4 are as follows: Figure 8 As shown, the data format is node number / distributed photovoltaic configuration capacity (kW). To further analyze the synergistic effect of source-load dual-side adjustable measures on the improvement of the distributed photovoltaic optimal configuration scheme, a comparative analysis of the optimal configuration results considering different adjustable measures is conducted based on scenario 4, as shown in Table 4.

[0213] Table 4 shows the optimization results considering different adjustable measures.

[0214]

[0215]

[0216] Table 4 shows that among the different adjustable measures, the scheme considering inverter power angle adjustment and demand response coordination measures has the highest approximation, indicating that it is the optimal solution. Specifically, it has the lowest total cost, the largest multiple safety margins, and the lowest total voltage deviation and total network loss. The total network loss is reduced by 16.39% compared to the scheme without adjustable measures (i.e., without inverter power angle adjustment + demand response), and its improvement in overcurrent margin is the most significant, effectively avoiding the risk of distribution network line current exceeding limits. These results fully demonstrate that considering inverter power angle adjustment and demand response coordination measures can effectively improve the economy and safety of distributed photovoltaic optimal configuration schemes, enabling the distribution network to operate safely and economically.

[0217] The node voltages of the distribution network considering different adjustable measures are analyzed, and the mean node voltage curves of the distribution network under each condition are plotted, such as... Figure 9 As shown. By Figure 9 It can be seen that considering inverter power angle adjustment and demand response coordination measures can effectively improve the average voltage of each node in the distribution network, achieving the highest value among different adjustable measures. This indicates that considering inverter power angle adjustment and demand response coordination measures can reduce the voltage deviation of each node in the distribution network, thereby improving power quality, and therefore achieving the lowest total voltage deviation.

[0218] The power angle mean of distributed photovoltaic inverters considering different adjustable measures, such as Figure 10 As shown. By Figure 10 It can be seen that, compared with a single inverter power angle adjustment measure, considering the coordinated measures of inverter power angle adjustment and demand response can effectively reduce the average power angle of distributed photovoltaic inverters, that is, reduce the number of times the inverter power angle is adjusted, prevent the inverter from being damaged due to excessively frequent power angle adjustments, improve the safety of distributed photovoltaic operation, and thus ensure the safe and stable operation of the distribution network.

[0219] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0220] A distributed photovoltaic (PV) optimization configuration terminal for a distribution network includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps in the aforementioned distributed PV optimization configuration method for a distribution network.

[0221] In summary, this invention provides a method and terminal for optimizing the configuration of distributed photovoltaic (PV) power in a distribution network. It uses a combined weighting method based on hierarchical analysis and grey relational analysis to determine the combined weights of a first set of indicators, including distributed PV configuration capacity, total cost, and multiple safety margins. Based on these combined weights, an objective function is established to maximize distributed PV configuration capacity, minimize total cost, and maximize multiple safety margins, with corresponding constraints. All adjustable measures are traversed, determined from inverter power angle adjustment and demand response. For each traversed adjustable measure, it is implemented based on the acquired distribution network parameters, typical daily load data, and distributed PV power generation efficiency data. The distributed PV optimization configuration model is then solved to obtain the distributed PV configuration results corresponding to the traversed adjustable measures. Finally, the results are analyzed from all distributed PV configuration results. The optimal distributed photovoltaic (PV) configuration result is selected through screening. The PV optimization configuration model considers multiple safety margins and source-load synergy, making it more comprehensive. Furthermore, the weighting method for indicators adopts a combination of hierarchical analysis and grey relational analysis to avoid strong subjectivity. First, the PV configuration results obtained by implementing different adjustable measures are obtained, and then the optimal PV configuration result is selected. This allows for reasonable and comprehensive optimization of PV configuration, effectively ensuring the safe and stable operation of the distribution network and providing a reference for the orderly development of the PV industry. In addition, different adjustable measures are identified, including inverter power angle adjustment and a fourth adjustable measure for demand response. This allows for further analysis of the synergistic effect of source-load-side adjustable measures on the PV optimization configuration scheme, thereby achieving a more effective PV optimization configuration.

[0222] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A power distribution network distributed photovoltaic optimization configuration method, characterized in that, The method comprises the steps of: determining the combined weight of the first group of indexes by using a combined weighting method based on analytic hierarchy process and grey correlation analysis, the first group of indexes including distributed photovoltaic configuration capacity, total cost and multiple safety margins; establishing a target function based on the combined weight of the first group of indexes with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins, and establishing a constraint condition corresponding to the target function; generating a distributed photovoltaic optimization configuration model according to the target function and the constraint condition; obtaining power grid network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures determined from inverter power angle adjustment and demand response; for the traversed adjustable measure, implementing the traversed adjustable measure based on the power grid network parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, and solving the distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measure; screening the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results; the method of determining the combined weight of the first group of indexes by using the combined weighting method based on analytic hierarchy process and grey correlation analysis further comprises: determining the combined weight of the second group of indexes by using the combined weighting method based on analytic hierarchy process and grey correlation analysis, the second group of indexes including overvoltage margin, overcurrent margin and reverse power flow margin; the method of establishing a target function based on the combined weight of the first group of indexes with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins comprises: determining the distributed photovoltaic configuration capacity; determining the total cost according to investment cost, operation and maintenance cost, power grid and upper grid interaction cost and demand response cost generated in distributed photovoltaic configuration; determining the overvoltage margin, the overcurrent margin and the reverse power flow margin, and determining the multiple safety margins based on the combined weight of the second group of indexes, the overvoltage margin, the overcurrent margin and the reverse power flow margin; establishing a target function based on the combined weight of the first group of indexes with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins; the method of determining the overvoltage margin, the overcurrent margin and the reverse power flow margin, and determining the multiple safety margins based on the combined weight of the second group of indexes, the overvoltage margin, the overcurrent margin and the reverse power flow margin comprises: ； ； wherein D represents a multiple safety margin, a combined weight representing an overvoltage margin, an overvoltage margin, a minimum value of an overvoltage margin, a maximum value of an overvoltage margin, a combined weight representing an overcurrent margin, an overcurrent margin, a minimum value of an overcurrent margin, a maximum value of an overcurrent margin, a combined weight representing a reverse power flow margin, a reverse power flow margin, a minimum value of a reverse power flow margin, a maximum value of a reverse power flow margin, an upper limit value of a node voltage, a voltage value of node i at time t, an upper limit value of a line current, a current value of line j at time t, an upper limit value of a reverse power flow, an active power of a power distribution network to a higher-level power grid at time t; the method of establishing a target function based on the combined weight of the first group of indexes with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins comprises: ； In the formula, F represents a target function, represents a combination weight of distributed photovoltaic configuration capacity, and S represents a total capacity of distributed photovoltaic configuration, represents a minimum total capacity of distributed photovoltaic configuration, represents a maximum total capacity of distributed photovoltaic configuration, represents a combination weight of total cost, and C represents total cost, represents a minimum value of total cost, represents a maximum value of total cost, represents a combination weight of multiple safety margins, and D represents multiple safety margins, represents a minimum value of multiple safety margins, represents a maximum value of multiple safety margins.

2. The method of claim 1, wherein, the method of determining the combined weight of the first group of indexes by using the combined weighting method based on analytic hierarchy process and grey correlation analysis comprises: quantitative comparison between each index in the first group of indexes is made by using 1-9 scale method to construct a judgment matrix; the characteristic vector of the judgment matrix is calculated according to square root method; The consistency index of the judgment matrix is calculated, and consistency checking is performed on the judgment matrix based on the consistency index, to obtain a checking result; If the checking result is passed, the weighting result of each index in the first group of indexes is obtained, to obtain an index weight matrix of the expert group; A reference sequence of weights is determined according to the index weight matrix of the expert group; The relative distance between the index weight matrix of the expert group and the reference sequence is calculated; The combined weight of the first group of indexes is calculated according to the relative distance.

3. The method of claim 1, wherein, The establishment of the constraint condition corresponding to the target function includes: The establishment of the constraint condition corresponding to the target function includes:

4. The method of claim 1, wherein, The establishment of the constraint condition corresponding to the target function includes: The establishment of the constraint condition corresponding to the target function includes:

5. The method of claim 3, wherein, The establishment of the constraint condition corresponding to the target function includes: The establishment of the constraint condition corresponding to the target function includes: The establishment of the constraint condition corresponding to the target function includes: The establishment of the constraint condition corresponding to the target function includes:

6. The method of claim 1, wherein, The establishment of the constraint condition corresponding to the target function includes: The processor executes the computer program to realize each step in the power distribution network distributed photovoltaic optimization configuration method of any one of claims 1 to 6. ​ ​ ​ ​ 7. A distributed photovoltaic optimization configuration terminal of a power distribution network, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, ​

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