AHP-CRITIC-VIKOR-based power distribution network distributed photovoltaic bearing capacity evaluation and improvement method

Abstract

The invention relates to an AHP-CRITIC-VIKOR-based power distribution network distributed photovoltaic bearing capacity evaluation and improvement method, and the method comprises the steps: constructing a power distribution network distributed photovoltaic bearing capacity evaluation index system which comprises a power grid operation safety index and a power grid loss index; obtaining a standardized evaluation matrix; determining subjective weight and objective weight of each index according to the standardized evaluation matrix; obtaining a combined weight according to the subjective weight and the objective weight; according to the combined weight, a VIKOR multi-criterion decision-making method is adopted to evaluate the distributed photovoltaic bearing capacity of the power distribution network; and if the distributed photovoltaic bearing capacity of the power distribution network does not meet the requirement, solving the distributed photovoltaic bearing capacity improvement model, and outputting the distributed photovoltaic maximum accessible capacity and the optimal access scheme corresponding to the improvement measure. According to the invention, the bearing capacity of the system can be effectively enhanced.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaic grid connection carrying capacity technology, and in particular to a method for assessing and improving the carrying capacity of distributed photovoltaic grids based on AHP-CRITIC-VIKOR. Background Technology

[0002] Distributed photovoltaic (PV) power generation has been widely adopted in my country due to its convenience and clean, renewable energy characteristics, with county-level grid connection rates gradually increasing. However, large-scale integration of distributed PV disrupts the existing power flow distribution of the distribution network, potentially causing problems such as voltage exceeding limits, increased harmonic content, and power backflow, which are detrimental to the absorption of new energy sources. Therefore, it is necessary to analyze the carrying capacity of PV in the distribution network. With the increasing penetration rate of PV, issues such as node voltage exceeding limits and line loss overload may occur, restricting the grid connection capacity of distribution networks containing distributed PV. Therefore, studying the carrying capacity of distributed PV in distribution networks with uncertainties has both theoretical and practical guiding significance.

[0003] In evaluating the load-bearing capacity of distributed photovoltaic (PV) systems, existing research methods mostly rely on two-stage robust optimization models and the Monte Carlo method to simulate and analyze the absorption capacity of large-scale grid integration. While the results are relatively reliable, they also have several shortcomings, including low computational efficiency, relatively complex models, and a lack of consideration for harmonic constraints. Based on the evaluation of distributed PV load-bearing capacity, existing methods have improved the model by increasing the power factor of PV inverters and configuring static synchronous compensators (SSCs) to enhance the grid integration capacity. However, these methods are insufficient for further improvements using single technologies. Different operating conditions in actual distribution networks require different combinations of technologies to better improve the load-bearing capacity of distributed PV systems, which is crucial for the safe grid integration of distributed PV. Therefore, it is necessary to design reasonable methods for load performance evaluation and improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing and improving the carrying capacity of distributed photovoltaic power distribution networks based on AHP-CRITIC-VIKOR. By combining the reactive power regulation function of photovoltaic inverters, additional reactive power compensation equipment, and tap changer operation of on-load tap-changing transformers, a multi-level regulation strategy is formed. Furthermore, a distributed photovoltaic carrying capacity improvement model is constructed using chance-constrained programming to effectively enhance the system's carrying capacity.

[0005] To achieve the above objectives, the present invention provides the following solution: A method for assessing and improving the distributed photovoltaic carrying capacity of distribution networks based on AHP-CRITIC-VIKOR includes: A distributed photovoltaic carrying capacity evaluation index system for distribution networks is constructed, which includes power grid operation safety index and power grid loss index. The indicators in the evaluation index system are normalized to obtain a standardized evaluation matrix; Based on the standardized evaluation matrix, the subjective weights of each indicator are determined by the Analytic Hierarchy Process (AHP), and the objective weights of each indicator are determined by the improved CRITIC method. The improved CRITIC method determines the objective weights of each indicator by calculating the standard deviation of each indicator and the relationship coefficient between the indicators. Based on the subjective weight and the objective weight, obtain the combined weight; Based on the combined weights, the VIKOR multi-criteria decision-making method is used to evaluate the distributed photovoltaic carrying capacity of the distribution network; If the distributed photovoltaic carrying capacity of the distribution network does not meet the requirements, then with the goal of maximizing the capacity of distributed photovoltaic access in the distribution network, a distributed photovoltaic carrying capacity improvement model is constructed and solved to output the optimal access scheme with the maximum accessible capacity of distributed photovoltaic and corresponding improvement measures.

[0006] Optionally, the power grid operation safety indicators include the voltage deviation index and the total harmonic distortion index, and the power grid loss indicators include the average loss degree and the degree of loss variation.

[0007] Optionally, the objective weights of each indicator may be determined using the improved CRITIC method, including: ; ; ; ; in, β j This is an objective weight value. For information content, It is the variance of the j-th indicator. The total number of samples, Let i be the value of the i-th indicator in the k-th sample. It is the value of the j-th indicator on the k-th sample. It is the average value of the i-th indicator. It is the value of the j-th indicator. It is the average value of the j-th indicator. It is the correlation coefficient between the i-th and j-th indicators. z ij This represents the normalized index value.

[0008] Optionally, the VIKOR multi-criteria decision-making method can be used to assess the distributed photovoltaic carrying capacity of the distribution network, including: Based on the standardized evaluation matrix, the maximum and minimum values ​​of each indicator are obtained, and the set of maximum values ​​is recorded as the best value among all candidate values ​​of each indicator, and the set of minimum values ​​is recorded as the worst value among all candidate values ​​of each indicator. Based on the set of maximum values, the set of minimum values, the combined weights, and the standardized evaluation matrix, calculate the group benefit value and the individual regret value; Based on the group benefit value and the individual regret value, obtain the maximum group benefit value and the minimum individual regret value; Calculate the compromise decision value based on the group benefit value, individual regret value, maximum group benefit value, and minimum individual regret value; The optimal access scheme is determined based on the compromise decision value and in combination with preset conditions, thereby realizing the assessment of the distributed photovoltaic carrying capacity of the distribution network.

[0009] Optionally, determining the optimal access scheme based on the compromise decision value and preset conditions includes: Arrange the options in ascending order according to the compromise decision value to obtain the option ranking sequence; If a certain solution meets two preset conditions, the priority order of the solutions is determined according to the compromise decision value from smallest to largest; If only the second precondition is not met, then the solutions ranked first and second are listed as compromise solutions; If the first preset condition is not met, all solutions are considered compromise solutions, and sensitivity analysis is performed on the decision coefficients to obtain the optimal access solution. The first preset condition is... Q (2) - Q (1) ≥1 / ( m -1), Q (1) and Q (2) Let m represent the first and second ranked solutions, respectively, and m be the total number of solutions. The second preset condition is that the solution is the optimal solution in the ranking based on the compromise decision value, and the ranking of the group benefit value and the individual regret value is also optimal.

[0010] Optionally, before constructing the distributed photovoltaic carrying capacity enhancement model, the following steps are taken: determine the distributed photovoltaic carrying capacity enhancement measures for the distribution network, including reactive power regulation of distributed photovoltaic inverters, regulation of additional reactive power compensation devices, and adjustment of tap changers of on-load tap-changing transformers.

[0011] Optionally, the distributed photovoltaic carrying capacity enhancement model can be constructed by taking the maximum distributed photovoltaic capacity connected to the distribution network as the objective.

[0012] Optionally, the constraints of the distributed photovoltaic carrying capacity enhancement model include: power flow equation constraints, distributed photovoltaic installation capacity constraints, distributed photovoltaic output constraints, harmonic constraints, line current carrying capacity constraints, transformer reverse load rate constraints, node voltage opportunity constraints, and branch power opportunity constraints.

[0013] Optionally, solving the distributed photovoltaic carrying capacity improvement model includes: using a nonlinear back-learning whale optimization algorithm to solve the distributed photovoltaic carrying capacity improvement model, wherein the nonlinear back-learning whale optimization algorithm changes the values ​​of the control parameters in a nonlinear incremental manner and uses a back-learning strategy for position updates; The control parameter a is: ; In the formula: t is the number of iterations; t max This represents the maximum number of iterations.

[0014] Optionally, the distributed photovoltaic carrying capacity improvement model is solved using a nonlinear back-learning whale optimization algorithm, including: S1. Input the distribution network topology, line parameters and load data, set the algorithm population size and maximum number of iterations, initialize the position of individual whales and calculate the initial fitness value, where each individual whale represents a distributed photovoltaic access scheme; S2. Set a dynamic fitness threshold, using the current population fitness average as the threshold F; S3. Update the whale position according to the standard whale optimization algorithm rules, obtain the updated individual whale, and adjust the control parameter a in a non-linear manner; S4. For each updated whale individual, the improved Newton-Raphson method is used to calculate the fundamental power flow, the three-point estimation method is used to calculate the probabilistic power flow and check the constraints, and the fitness value corresponding to each access scheme is calculated. A penalty term is introduced for individuals that do not meet the constraints. S5. Generate a reverse solution for the updated whale individuals that are below the threshold F and evaluate their fitness. When the fitness of the reverse solution is better than that of the original solution, replace the original solution with the reverse solution, return to S2 to iterate to the maximum number of iterations, and output the optimal solution.

[0015] The beneficial effects of this invention are as follows: This invention selects photovoltaic carrying capacity assessment indicators from multiple dimensions such as power grid operation safety and power grid losses, and constructs a scientific and comprehensive indicator system. The proposed AHP-CRITIC-VIKOR comprehensive assessment method fully integrates subjective and objective factors, making the weighting process more reasonable, and ultimately obtaining the priority ranking of each photovoltaic access scheme. Experimental results show that this method can achieve a scientific and effective assessment of the distributed photovoltaic carrying capacity of the distribution network.

[0016] This invention effectively enhances the load-bearing capacity of the distribution network for distributed photovoltaic (PV) power generation by coordinating various measures, including reactive power regulation of distributed PV inverters, additional reactive power compensation devices, and tap changer adjustment of on-load tap-changing transformers. Compared to single measures, the combined strategy produces a more significant improvement. Experimental results show that, compared to the baseline model without any enhancement measures, the proposed model increases the PV load-bearing capacity by 19.2%, fully demonstrating the effectiveness and superiority of the proposed method. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for assessing and improving the distributed photovoltaic carrying capacity of a distribution network based on AHP-CRITIC-VIKOR, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the IEEE 33-node system according to an embodiment of the present invention; Figure 3 This is a graph showing the time variation of distributed photovoltaic power output and load according to an embodiment of the present invention; Figure 4 This is a graph showing the voltage deviation index values ​​at different times for different schemes in this embodiment of the invention. Figure 5 This is a graph showing the total harmonic distortion index (THD) values ​​of voltage at different times for different schemes in this embodiment of the invention. Figure 6 This is a graph showing the average line loss rate at different times for different schemes in this embodiment of the invention. Figure 7 This is a graph showing the degree of loss improvement at different times for different schemes in embodiments of the present invention. Figure 8 This is a graph showing the time variation of distributed photovoltaic power output and load according to an embodiment of the present invention; Figure 9 This is a diagram showing the effect of the reactive power compensation device capacity on the load-bearing capacity according to an embodiment of the present invention. Figure 10 This diagram illustrates the impact of the reactive power compensation device connection point on the load-bearing capacity according to an embodiment of the present invention. Detailed Implementation

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

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown in the figure, this embodiment proposes a method for assessing and improving the distributed photovoltaic carrying capacity of distribution networks based on AHP-CRITIC-VIKOR, including: Construct an evaluation index system for the distributed photovoltaic carrying capacity of the distribution network. The evaluation index system includes power grid operation safety indicators and power grid loss indicators. The indicators in the evaluation indicator system are normalized to obtain a standardized evaluation matrix; Based on the standardized evaluation matrix, the subjective weights of each indicator are determined by the Analytic Hierarchy Process (AHP), and the objective weights of each indicator are determined by the improved CRITIC method. The improved CRITIC method determines the objective weights of each indicator by calculating the standard deviation of each indicator and the relationship coefficient between the indicators. Obtain the combined weight based on subjective and objective weights; Based on the combined weights, the VIKOR multi-criteria decision-making method is used to evaluate the distributed photovoltaic carrying capacity of the distribution network; If the distributed photovoltaic carrying capacity of the distribution network does not meet the requirements, then with the goal of maximizing the capacity of distributed photovoltaic to be connected to the distribution network, a distributed photovoltaic carrying capacity improvement model is constructed and solved to output the optimal connection scheme with the maximum connectable capacity of distributed photovoltaic and corresponding improvement measures.

[0022] Specifically, this embodiment proposes an evaluation index system for distributed photovoltaic (PV) grid access carrying capacity (AHP-CRITIC-VIKOR) method: A PV access capacity calculation constraint model is constructed, and a whale optimization algorithm based on NLF-BFWL is used to solve for the PV access capacity constrained by node voltage, node voltage total harmonic distortion rate, line load current, and transformer reverse load rate. An evaluation index system for distributed PV carrying capacity based on grid operation safety and grid losses is constructed to assess the grid carrying capacity after distributed PV access. The subjective and objective weights of the distributed PV carrying capacity evaluation index system are calculated using AHP and the improved CRITIC method, respectively, and the weights are normalized. Finally, the VIKOR ranking method is used to rank the distributed PV access schemes to ensure the scientific validity and effectiveness of the weights and decision results. To address the issue of limited distribution network absorption capacity caused by the increasing penetration rate of distributed photovoltaic (PV) power, a method for enhancing the carrying capacity by integrating multiple regulation measures is proposed. This method combines the reactive power regulation function of PV inverters, additional reactive power compensation equipment, and the tap changer operation of on-load tap-changing transformers to form a multi-level regulation strategy. Furthermore, a distributed PV carrying capacity enhancement model is constructed using chance-constrained programming to effectively enhance the system's carrying capacity.

[0023] Furthermore, power grid operation safety indicators include voltage deviation index and total harmonic distortion index, while power grid loss indicators include average loss degree and loss variation degree.

[0024] Specifically, the evaluation indicators for distributed photovoltaic (PV) systems include: Since numerous factors influence the carrying capacity of distributed PV systems connected to the distribution network, this embodiment constructs evaluation indicators for distributed PV carrying capacity based on the distribution network's distributed PV connection method. These indicators are grid operation safety and grid losses. Regarding grid operation safety, this embodiment considers the issue of safe and stable operation of the distribution network, introducing two indicators: voltage deviation and total harmonic distortion (THD). Regarding grid losses, this embodiment considers the economic efficiency of grid losses, introducing two indicators: average loss rate and grid loss variation rate.

[0025] (1) Power grid operation safety indicators: The voltage deviation index is the absolute value of the difference between the standard voltage of each node in the distribution network when the distributed photovoltaic power generation is in operation at a certain moment. Dividing it by the percentage value of the rated voltage of the system gives the percentage of the voltage deviation of the distributed photovoltaic power generation node from the standard voltage value. The sum of the percentage values ​​of all nodes in the distribution network is the voltage deviation index at that moment.

[0026] In the formula: U represents the actual calculated voltage of the node in the distributed photovoltaic power grid system; U N This indicates the nominal voltage of the power distribution network system.

[0027] Voltage total harmonic distortion index λ THDU Defined as: Under a specific operating condition after distributed photovoltaic access, the square root of the sum of the squares of the ratios of the effective values ​​of harmonic voltages to the effective values ​​of fundamental voltages in the voltage waveforms of each node in the system is taken and expressed as a percentage; the sum of the values ​​of this index at all nodes is the overall voltage harmonic distortion index of the system.

[0028] In the formula: h Represented as harmonic order; U h Represented as the first h Subharmonic voltage; U 1 This represents the effective value of the fundamental voltage.

[0029] (2) Power grid loss indicators: Average loss C LR,avg The average loss is the percentage of the total network loss to the total number of lines at a certain moment after photovoltaic power is connected.

[0030] In the formula: L oSSsum Total network loss of the system; L Total number of lines.

[0031] degree of loss change C LR,cha This refers to the percentage of total network loss relative to the total number of lines at a given moment after photovoltaic (PV) grid connection.

[0032] In the formula: Lossbefore is the loss value of the system before it is connected to distributed photovoltaic power; Lossafter is the loss value of the system after it is connected to distributed photovoltaic power.

[0033] The above indicators include both cost indicators and benefit indicators. Indicators such as voltage deviation index, total harmonic distortion index, and average loss are cost indicators, and it is desirable for their expected values ​​to be as low as possible. Indicators of loss variation are benefit indicators, and it is desirable for their expected values ​​to be as high as possible.

[0034] Furthermore, the weighting of the relevant evaluation indicators for distributed photovoltaic systems includes: Because a single weighting method may introduce errors, a combination of subjective judgment and objective data is used to assign weights to each indicator. Since differences in units and numerical ranges can affect the evaluation results and comparability of the indicators, normalization processing of the indicators is necessary. If a system contains m objects and n indicators, an initial evaluation matrix X for the objects to be evaluated can be established based on its original data for subsequent weight calculation and ranking.

[0035] In the formula: x ij Let be the initial value of the j-th evaluation index for the i-th evaluated object in the initial evaluation matrix X mentioned above.

[0036] For cost-type indicators, transformation is performed according to equation (6); for benefit-type indicators, transformation is performed according to equation (7); the standardized evaluation matrix Z. To perform value synthesis and final ranking, the original data matrix is ​​transformed into. Wherein.

[0037] In the formula: z ij This represents the normalized index value.

[0038] (1) Determining subjective weights using the analytic hierarchy process: The steps for implementing subjective weighting using the Analytic Hierarchy Process (AHP) include: 1) Construct the judgment matrix: Based on expert evaluation opinions, the relative importance of each evaluation indicator was compared one by one, and a judgment matrix was constructed based on the 1-9 scale method. Then, the weights were calculated using the analytic hierarchy process (AHP).

[0039] 2) Consistency check: After obtaining the largest eigenvalue, the CI value is calculated using formula (8), and the consistency coefficient CR is calculated using formula (9). When CR < 0.1, the matrix passes the consistency test; when CR > 0.1, the elements of the matrix need to be adjusted, and the consistency test is performed again until the consistency condition is met.

[0040] In the formula, λ max represents the largest eigenvalue of the judgment matrix; n represents the order of the judgment matrix; RI represents the random consistency index.

[0041] 3) Calculate subjective weights: Calculate the largest eigenvalue λ of the judgment matrix that passes the consistency test. maxThe corresponding feature vector is obtained, and the feature vector is normalized to obtain the weight vector corresponding to each evaluation index; the specific calculation method of the final weight value is given by equation (10).

[0042] In the formula, α j This is a subjective weighting value; t j These are the eigenvalues.

[0043] (2) Improved CRITIC method for determining objective weights: The standard deviation in the traditional CRITIC weighting method describes the relative magnitudes of evaluation indicators. To eliminate the influence of differences in indicator means, this embodiment uses the coefficient of variation to describe the degree of variation in indicator values ​​and employs the correlation coefficient to measure the conflict and correlation of indicator values. An improved CRITIC objective weighting method is obtained through this approach. The specific steps are as follows.

[0044] First, based on the standardized evaluation matrix, the standard deviation of each evaluation indicator is calculated, as shown in Equation (11); then, the relationship coefficient between any two indicators is calculated according to Equation (12), which serves as one of the objective weighting bases.

[0045] In the formula: It is the first j Standard deviation of each indicator; It is the value of the j-th indicator. It is the first i The average of the indicators yes The value after normalization; It is the first i With the j Correlation coefficients of the indicators; The closer it is to 1, the better. i With the j The better the positive correlation between the indicators.

[0046] Based on the standard deviation of each indicator and the correlation coefficient between the indicators, the information content of the j-th indicator can be calculated. The information content reflects how much information the j-th indicator contains. The smaller the value, the more information the j-th indicator provides, meaning the more important the j-th indicator is. By comparing the information content of the j-th indicator with the sum of the information content of all indicators, the objective weight of the j-th indicator can be calculated according to equation (14):

[0047] In the formula: βj This is an objective weight value. The total number of samples, It is the correlation coefficient between the i-th and j-th indicators. Let i be the value of the i-th indicator in the k-th sample. It is the value of the j-th indicator on the k-th sample.

[0048] (3) Calculate the portfolio weights: This embodiment uses the principle of least discriminative information to determine the combined weights, aiming to optimize the overall weight of each indicator. ω j As close as possible to subjective weighting α j and β j This avoids obvious bias towards any source of weight. The corresponding objective function is shown in equation (15):

[0049] Solve the optimization model to obtain the combined weight values. ω j .

[0050] Furthermore, the VIKOR multi-criteria decision-making method is used to assess the distributed photovoltaic carrying capacity of the distribution network, including: Based on the standardized evaluation matrix, the maximum and minimum values ​​of each indicator are obtained, and the set of maximum values ​​is recorded as the best value among all the candidate values ​​of each indicator, and the set of minimum values ​​is recorded as the worst value among all the candidate values ​​of each indicator. Calculate the group benefit value and individual regret value based on the maximum value set, minimum value set, combined weight, and standardized evaluation matrix; Based on the group benefit value and the individual regret value, obtain the maximum group benefit value and the minimum individual regret value; Calculate the compromise decision value based on the group benefit value, the individual regret value, the maximum group benefit value, and the minimum individual regret value; The optimal access scheme is determined based on the compromise decision value and in combination with preset conditions, thereby realizing the assessment of the distributed photovoltaic carrying capacity of the distribution network.

[0051] Furthermore, determining the optimal access scheme based on the compromise decision value and preset conditions includes: Arrange the options in ascending order based on their compromise decision values ​​to obtain the ranking sequence of the options; If a certain solution meets two preset conditions, the priority of the solutions is determined according to the compromise decision value from smallest to largest. If only the second precondition is not met, then the solutions ranked first and second are listed as compromise solutions; If the first preset condition is not met, all solutions are considered compromise solutions, and sensitivity analysis is performed on the decision coefficients to obtain the optimal access solution. The first preset condition is... Q (2) - Q (1) ≥1 / ( m -1), Q (1) and Q (2) Let m represent the first and second ranked solutions, respectively, and m be the total number of solutions. The second preset condition is that the solution is the optimal solution in the ranking based on the compromise decision value, and the ranking of the group benefit value and the individual regret value is also optimal.

[0052] Specifically, the comprehensive assessment of distributed photovoltaic carrying capacity based on AHP-CRITIC-VIKOR includes: After obtaining the combined weights of each indicator, the VIKOR multi-criteria decision method is used to comprehensively evaluate and rank the distributed photovoltaic absorption capacity of each access scheme. The specific method is as follows: First, positive and negative ideal solutions are selected according to the evaluation indicators. Then, the weighted distance between the evaluation indicator values ​​of each access scheme and the positive and negative ideal solutions is calculated. The maximization of maximum group utility and the maximum individual regret are taken into account. The scheme is ranked and the most satisfactory feasible solution for the group is selected.

[0053] (1) Determine the positive and negative ideal solutions: Based on each column of the standardized evaluation matrix Z, the maximum and minimum values ​​of each evaluation indicator are determined. The set of maximum values ​​is denoted as the best value among all candidate values ​​of each evaluation indicator, and the set of minimum values ​​is denoted as the worst value among all candidate values ​​of each evaluation indicator. In the VIKOR multi-attribute decision, the above positive ideal solution is the best value among all candidate values ​​of each evaluation indicator, and the above negative ideal solution is the worst value among all candidate values ​​of each evaluation indicator.

[0054] (2) Calculate the group benefit value, individual regret value, and compromise decision value: Calculate the group benefit value according to equations (19) and (20). S i Individual Regret Value R i .

[0055] In the VIKOR method, the maximum group benefit value (S) of each scheme is determined by equations (21) and (22), respectively. i ) and minimum individual regret value ( R iThis is used to quantify the overall performance of the plan in terms of both overall benefits and deviations from individual indicators.

[0056] Calculate the compromise decision value Q i :

[0057] In the formula: ε For decision coefficients, ε ∈[0,1].

[0058] (3) Determine the ranking of decision-making options and the compromise decision-making options: Assuming the various schemes are divided into Q i Sort the values ​​in ascending order to obtain the sequence. Q (1) , Q (2) ,..., Q (m) , where m is the total number of schemes. If a scheme satisfies both of the following conditions, then it can be directly determined based on... Q i The priority of the solutions is determined by the values ​​from smallest to largest. Q i The smaller the value, the better the corresponding solution: Condition 1 (Acceptable dominance threshold): Satisfied Q (2) - Q (1) ≥1 / ( m -1), where Q (1 and Q (2) These represent the first and second ranked solutions, respectively. Condition 2 (Acceptable decision reliability): Based on The optimal solution in value sorting is... value or The order of values ​​must also be optimal.

[0059] If only condition 2 is not met, then both solution 1 and solution 2 should be listed as compromise solutions, i.e., the optimal access solution. If condition 1 is not met, then all solutions are considered compromise solutions. This means that under the current evaluation system, the overall performance of multiple access solutions is very similar. In this case, the carrying capacity assessment result is "multiple solutions have high and similar carrying capacities." Further consideration is needed regarding whether the assessment decision results fluctuate drastically with changes in subjective parameters, i.e., the impact on the decision coefficients. εPerform sensitivity analysis and obtain the optimal access solution based on the results.

[0060] Furthermore, the construction of a distributed photovoltaic carrying capacity enhancement model includes: increasing the distributed photovoltaic capacity of the distribution network by adjusting the reactive power of the distributed photovoltaic inverter, adjusting the reactive power compensation device of the distributed photovoltaic, and adjusting the tap changer of the on-load tap-changing transformer. Based on the opportunity-constrained programming theory, an optimization model for distributed photovoltaic capacity enhancement is constructed, taking into account operating constraints such as node voltage, total harmonic distortion rate of node voltage, line current carrying capacity, and transformer reverse load rate, and constructing a model for the maximum capacity of distributed photovoltaic.

[0061] 1. Measures to enhance the carrying capacity of distributed photovoltaic power include: (1) Reactive power regulation constraints of distributed photovoltaic inverters: Distributed photovoltaic (PV) inverters can regulate node voltage levels by supplying or absorbing reactive power to the grid, thereby enhancing the distribution network's capacity to accommodate PV. The corresponding reactive power regulation relationship can be expressed by the following formula:

[0062] In the formula: max , min These are the maximum and minimum allowable values ​​for the inverter power factor angle, respectively.

[0063] (2) Reactive power compensation device: Reactive power compensation devices can dynamically absorb or release reactive power, reducing voltage fluctuations caused by photovoltaic power generation fluctuations to maintain voltage within a specified safe range. Simultaneously, reactive power regulation can improve the power factor and reduce line losses. The constraints on its reactive power compensation are:

[0064] In the formula: Q C,max , Q C,min These are the upper and lower limits for reactive power compensation by the reactive power compensation device; Q C This represents the actual reactive power.

[0065] (3) Transformer tap changer: Adjusting the tap position can reduce the transformer's output voltage, thereby offsetting the voltage rise effect on the grid caused by distributed photovoltaic power generation. The adjustment constraint is as follows:

[0066] In the formula: U ex This refers to the voltage at the transformer's output side.U hi This refers to the voltage on the high-voltage side of the transformer. σ The step size by which the number of turns ratio changes when the tap is adjusted to one gear; k This refers to the transformer tap position; k max , k min These are the maximum and minimum adjustable positions of the tap changer of the on-load tap-changing transformer.

[0067] 2. Objective function for distributed photovoltaic (PV) grid connection capacity: Distributed photovoltaic capacity connected to the distribution network f The objective is to maximize the value of a product or service. The specific mathematical expression is as follows:

[0068] In the formula: N This represents the total number of nodes in the distribution network. S PV,i For nodes i The total capacity of distributed photovoltaic power connected to the site.

[0069] 3. Constraints: (1) Power flow equality constraint:

[0070] In the formula: P PV,i Q PV,i P represents the active power and reactive power output of the photovoltaic device at node i, respectively. L,i Q L,i These represent the active power and reactive power consumed by the load at node i, respectively. , The voltages at nodes i and j are respectively; G ij B ij θ represents the conductance and susceptance of the line between nodes i and j, respectively; ij Let be the power amplitude between nodes i and j.

[0071] (2) Distributed photovoltaic installation capacity constraints: There is an upper limit to the distributed photovoltaic capacity that each node can access.

[0072] In the formula: k i P represents the number of distributed photovoltaic systems at node i; PV,i,0 Q PV,i,0 P represents the output of a single distributed photovoltaic system at node i; PVmax,i Q PVmax,i This represents the total distributed power output and reactive power output at node i.

[0073] (3) Constraints on distributed photovoltaic output: Considering the inherent capacity limitations of distributed photovoltaic inverters, their active and reactive power outputs should meet the following constraints:

[0074] (4) Harmonic constraints: At a specific voltage level, the total harmonic distortion (THD) of the voltage at each node during system operation must strictly comply with the limits specified for that voltage level.

[0075] In the formula: THD U Indicates the total harmonic distortion rate of the voltage; δ This represents the upper limit of the total harmonic distortion rate (THD) of a specific voltage level.

[0076] (5) Line current carrying capacity constraints: To avoid line overload, it is necessary to ensure that the line's current carrying capacity does not exceed its permissible limit:

[0077] In the formula: I l This indicates the actual current magnitude on line l; I l,max This represents the line. l The maximum allowable current.

[0078] (6) Transformer reverse load rate constraint: To ensure the safe, efficient, and reliable operation of transformers in the distribution network, their reverse load rate should be limited to the following range:

[0079] In the formula: S e Indicates the actual operating limits of the transformer; λ max This indicates the maximum permissible value of the transformer's reverse load rate.

[0080] (7) Nodal voltage factor constraints: When distributed photovoltaic systems are connected to the distribution network, it should be ensured that the grid voltage does not exceed the predetermined operating range under certain probability levels.

[0081] In the formula: P r To constrain the probability of the constraint being satisfied; U i,max ,U i,min They are nodes i Maximum and minimum permissible values ​​for voltage amplitude; β U To meet the confidence level of the node voltage constraint.

[0082] (8) Branch power opportunity constraints: When distributed photovoltaic power is connected to the distribution network, it should be ensured that the power of the branch in the grid will not exceed its capacity under a certain probability level.

[0083] In the formula: S k,max , S k,min Branch roads k Maximum and minimum permissible power values; β S To satisfy the confidence level of the branch power constraint.

[0084] With the goal of maximizing the capacity of distributed photovoltaic (PV) grid connections, a distributed PV carrying capacity enhancement model is constructed, including: (37); in, f To connect distributed photovoltaic capacity to the distribution network, N The total number of distribution network nodes. S PV,i For nodes i The total capacity of distributed photovoltaic power connected to the site.

[0085] Furthermore, the solution to the distributed photovoltaic carrying capacity enhancement model includes: using a nonlinear inverse learning whale optimization algorithm to solve the distributed photovoltaic carrying capacity enhancement model. In this algorithm, the nonlinear inverse learning whale optimization algorithm changes the values ​​of the control parameters in a nonlinear incremental manner and uses an inverse learning strategy for position updates.

[0086] Specifically, addressing the issues of traditional WOA's tendency to get trapped in local search extrema, its poor ability to solve complex optimization problems, and its slow convergence speed, the proposed Nonlinear Backward Learning Whale Optimization Algorithm (NOWOA) utilizes a nonlinear regulator to adaptively adjust important control parameters in the algorithm, and then leverages backward learning capabilities to effectively improve the algorithm's global search and convergence capabilities.

[0087] (1) Nonlinear control mechanism: In the improved WOA (WHALE ALGORITHM) algorithm, 'a' is a control parameter that determines the speed at which the whale converges towards the current optimal point when updating its position. During the iteration process, the value of 'a' usually decreases linearly from 2 to 0. In order to improve the search capability of the algorithm, this paper adopts a non-linear increment method to change the value of 'a' and the variation law of the sine function to achieve adaptive adjustment of 'a'.

[0088] In the formula: t is the number of iterations; t max This represents the maximum number of iterations.

[0089] The value of parameter a is determined by a sine function with the iteration number t as the independent variable. This method can expand the search space of the algorithm in the early and later iterations, while ensuring a certain search volume in the middle process, thus guaranteeing the algorithm's performance.

[0090] (2) Reverse learning strategy: Since the initial population and random search are the main methods for controlling population diversity in traditional WOA, the Whale Optimization algorithm has a weak ability to maintain population diversity. To address this issue, reverse learning is introduced to enhance the search performance of the Whale Optimization algorithm and improve population diversity.

[0091] In each iteration, the inverse solution for the j-th dimension of the i-th whale population is shown in the following equation:

[0092] In the formula: Indicates the upper limit of the search space; This indicates the lower bound of the search space.

[0093] After each position update, the reverse solution corresponding to the solution at each new position is calculated. The fitness values ​​of the two solutions are then calculated. If the fitness value of the reverse solution is better than that of the original solution, the reverse solution replaces the original solution. This strategy can enrich the population diversity and avoid premature algorithm convergence.

[0094] Furthermore, the distributed photovoltaic carrying capacity improvement model is solved using a nonlinear back-learning whale optimization algorithm, including: S1. Input the distribution network topology, line parameters and load data, set the algorithm population size and maximum number of iterations, initialize the position of individual whales and calculate the initial fitness value, where each individual whale represents a distributed photovoltaic access scheme; S2. Set a dynamic fitness threshold, using the current population fitness average as the threshold F; S3. Update the whale position according to the standard whale optimization algorithm rules, obtain the updated individual whale, and adjust the control parameter a in a non-linear manner; S4. For each updated whale individual, the improved Newton-Raphson method is used to calculate the fundamental power flow, the three-point estimation method is used to calculate the probabilistic power flow and check the constraints, and the fitness value corresponding to each access scheme is calculated. A penalty term is introduced for individuals that do not meet the constraints. S5. Generate a reverse solution for the updated whale individuals that are below the threshold F and evaluate their fitness. When the fitness of the reverse solution is better than that of the original solution, replace the original solution with the reverse solution, return to S2 to iterate to the maximum number of iterations, and output the optimal solution.

[0095] Specifically, the solution steps based on the NOWOA model are as follows: The distributed photovoltaic access capacity model is solved based on the nonlinear backward learning whale optimization algorithm (NOWOA), specifically implemented according to the following steps: 1) Input basic parameters such as distribution network topology, line parameters and load data, and set the population size, maximum number of iterations and other relevant parameters of the NOWOA algorithm. Randomly initialize the position of individual whales and calculate their corresponding initial fitness values. 2) Set a dynamic fitness threshold, using the average fitness of the current population as the threshold F, and perform reverse learning on individuals whose fitness is lower than the threshold F; 3) Update the whale position according to the standard whale optimization algorithm rules, and use equation (37) to nonlinearly adjust the control parameter a; 4) For each updated whale individual and its corresponding distributed photovoltaic access scheme, perform the following operations in sequence: First, use the improved Newton-Raphson method to calculate the fundamental power flow (where the Jacobian matrix J is stored and manipulated using a compressed column storage sparse format) to improve computational efficiency; then, use the three-point estimation method (3PEM) to calculate the probabilistic power flow, assess the uncertainty of the system state, and verify whether it meets the set constraints; finally, calculate the fitness value corresponding to each access scheme, and introduce a penalty term to reduce the fitness of individuals that do not meet the constraints. 5) After each round of position update, generate a reverse solution for each new solution and evaluate its fitness value; 6) When the fitness of the reverse solution is better than that of the original solution, replace the current solution with the reverse solution to improve search efficiency; 7) Iteratively execute steps 2) to 6) until the termination condition of the maximum number of iterations is met, and output the optimized distributed photovoltaic capacity configuration scheme.

[0096] Parameter settings for this embodiment: This embodiment was simulated and verified on the IEEE 33-bus distribution system, the structure of which is as follows: Figure 2 As shown, it consists of 4 feeders and 33 nodes, with a rated voltage of 12.66kV. Figure 2 The dashed lines represent the interconnections, with node 1 serving as the balancing node and the remaining nodes as PQ nodes. The connected distributed photovoltaic system is also modeled as a PQ node, with its power factor set to 0.98 (inductive load).

[0097] The NOWOA algorithm and system constraint parameters used in the simulation analysis of this embodiment are set as follows: population size is 30, maximum number of iterations is 50, and spiral shape constant is 1. System operating constraints are set as follows: allowable node voltage deviation range is 0.93~1.07 pu, voltage deviation rate ε does not exceed 7%, and the total harmonic distortion rate limit δ is 4%; the long-term allowable maximum current carrying capacity of the line is 0.6 kA, and the branch power limit is 6 MW; the main transformer operating capacity limit is 14.2 MVA, and its reverse load rate limit is 80%; the maximum allowable capacity of distributed photovoltaic power at each node is 1 MW. The adjustable range of the distributed photovoltaic power factor is -0.95~0.95 pu; reactive power compensation devices are configured at nodes 17 and 30; the on-load tap changer adjustment range is 0.95~1.05 pu, the adjustable tap position is ±4 levels, and the step amount is 1.25%.

[0098] The present invention will now be described in further detail with reference to specific access schemes.

[0099] This embodiment defines five distributed photovoltaic (PV) access schemes. Schemes 1, 2, and 3 correspond to the configuration scenarios of the beginning, middle, and end of a centralized PV feeder, respectively; Schemes 4 and 5 correspond to the configuration modes of PV uniformly distributed access to the same feeder and different feeders, respectively. By comparing the calculation results of the distributed PV access capacity under each scheme, the distributed PV access capacity under different access schemes is obtained as shown in Table 1: Table 1

[0100] Based on the calculation results of the distributed photovoltaic (PV) access capacity corresponding to each access scheme, a comprehensive evaluation of the distributed PV carrying capacity of different schemes is then conducted. Firstly, based on... Figure 2 The per-unit curves showing the output and load changes of distributed photovoltaic power over the 24 time periods throughout the day are used to obtain the evaluation index results of five grid connection schemes for each time period through simulation calculations, such as... Figure 3 As shown.

[0101] according to Figures 4-5The results show that Schemes 1 and 5 exhibit larger voltage deviation indices during the midday period when distributed photovoltaic (PV) output is higher. This is mainly because both schemes concentrate distributed PV at the feeder head, where the voltage level is already high, and PV access further exacerbates the rise in node voltage. Simultaneously, Schemes 1 and 5 have lower total harmonic distortion (THD) indices, while the other schemes have relatively higher indices. This phenomenon can be attributed to the fact that the fundamental current mainly flows along the line to the load, while harmonic currents originate from nonlinear equipment and are injected into the system; the closer the PV access point is to the feeder head, the smaller the proportion of harmonic current in the fundamental current, and the lower the degree of distortion, thus reducing the THD. Overall, the THD typically reaches its maximum value at midday, indicating that as the PV capacity increases, harmonic distortion becomes more significant.

[0102] according to Figures 6-7 The average line loss rate did not differ significantly among the various schemes. However, during periods of high photovoltaic output, Schemes 1 and 5 exhibited slightly higher line loss rates than the others, and their degree of loss improvement was also relatively low. This phenomenon may be related to the location of the photovoltaic grid connection: in both of the above schemes, the photovoltaic grid is located at the beginning of the feeder. When the photovoltaic power generation exceeds the local load demand, the surplus power needs to be transmitted back to the main grid, leading to an increase in line current and thus causing additional line losses.

[0103] Under clear weather conditions, the output of distributed photovoltaic (PV) systems shows a trend of first increasing and then decreasing throughout the 24-hour period. Since PV output reaches a relatively high level at 13:00 while load demand is relatively low, grid connection of PV systems at this time is more likely to cause problems such as grid voltage exceeding limits, harmonic content exceeding standards, and capacity overload. Therefore, the evaluation index data at this time is selected for the assessment of the carrying capacity of distributed PV systems.

[0104] The combined weights of each evaluation indicator were determined based on the AHP and modified CRITIC methods. ω j =[0.4053,0.4321,0.1238,0.0388], and then the VIKOR method is used to comprehensively evaluate the carrying capacity of different distributed photovoltaic access schemes. To analyze the impact of decision preferences on the ranking results, a sensitivity analysis is further conducted to examine the impact of different decision coefficients ε on the final decision. ε is set in steps of 0.1 to obtain the compromise decision value Q under different ε values. i The result is as follows Figure 8 As shown according to Figure 8 The analysis results show that, under different ε values, each scheme is determined by the compromise decision value Q. i To perform ascending sorting, in the VIKOR method, Q... iThe smaller the value, the higher the ranking. When ε is in the range of [0, 0.5], the ranking trend of each scheme remains relatively stable; when ε is in the range of [0.6, 0.9], the rankings of Scheme 2 and Scheme 4 rise; when ε is 1, the ranking of Scheme 5 falls. In summary, to ensure the stability of the decision results, it is recommended to set the value of ε between 0 and 0.5.

[0105] Regarding the selection of the optimal solution: when the decision coefficient ε is in the range [0, 0.8], Solution 1 is the optimal solution, and Solution 5 is the second-best solution. At this time, Q... (2) -Q (1) >1 / 4, meeting the acceptable difference threshold condition; meanwhile, Scheme 1 has a group benefit value S i With individual regret value R i In the ranking, it always ranks first, meeting the decision reliability requirements; therefore, Scheme 1 can be considered the optimal access scheme. When ε=0.9, Scheme 1 is still the optimal scheme, and Scheme 5 is the second-best scheme, but at this time Q... (2) -Q (1) If ε < 1 / 4, the threshold condition is no longer met; when ε = 1, Scheme 1 is the optimal scheme, and Scheme 2 becomes the suboptimal scheme. At this point, Q also exists. (2) -Q (1) <1 / 4, which does not meet the acceptable threshold standard.

[0106] In summary, to balance decision stability, advantage threshold conditions, and decision reliability requirements, it is recommended that the decision coefficient ε be set to a range of 0 to 0.5. Within this range, based on the proposed AHP-CRITIC-VIKOR comprehensive evaluation method, Scheme 1 is determined to be the optimal grid connection scheme for distributed photovoltaic (PV) carrying capacity. Through comparison... Figures 4-7 The evaluation data for each scheme show that although Scheme 1's grid loss index is slightly higher than some schemes, its voltage deviation index is generally lower than other schemes. Furthermore, its total harmonic distortion rate is significantly lower than Schemes 2, 3, and 4, and its line loss improvement is also better than Scheme 5. Overall, Scheme 1 performs well across all evaluation indicators, further validating the effectiveness of the proposed evaluation index system and comprehensive evaluation method.

[0107] To evaluate the effect of various adjustment measures in the proposed model on improving the photovoltaic carrying capacity of the distribution network, nodes 2, 7, 12, 17, 26, and 31 were selected as the access points for distributed photovoltaic systems, evenly distributed across the branches at intervals of four nodes. Based on this setup, four comparative schemes were constructed, and simulation analysis was performed. The calculation results are shown in Table 2, which describes the distributed photovoltaic carrying capacity of the distribution network under different schemes. 1) Basic scenario, no adjustments are introduced; 2) Only enable the reactive power regulation function of the photovoltaic inverter; 3) Only additional reactive power compensation devices are configured; 4) Relying solely on the tap changer of the on-load tap-changing transformer for adjustment; 5) By comprehensively applying inverter reactive power regulation and reactive power compensation devices, as well as on-load tap changer taps, a multi-method combined regulation can be achieved. Table 2

[0108] As shown in Table 2, in Scheme 2, by introducing the reactive power regulation function of the distributed photovoltaic inverter, the reactive power output of the photovoltaic units at nodes 2, 7, 12, 17, 26, and 31 are -80 kvar, 30 kvar, 60 kvar, 90 kvar, 50 kvar, and 110 kvar, respectively. The total distributed photovoltaic carrying capacity of the system reaches 4.8937 MW, an increase of 5.1% compared to Scheme 1. Scheme 3 uses an additional reactive power compensation device for regulation, which provides 200 kvar and 500 kvar of reactive power at nodes 17 and 30, respectively, increasing the total distributed photovoltaic carrying capacity to 4.9747 MW, an increase of 6.9% compared to Scheme 1. Scheme 4 uses on-load tap changer adjustment, setting the tap to the lowest position. In this scenario, the total photovoltaic carrying capacity is 5.0376 MW, an increase of 8.2% compared to Scheme 1.

[0109] Scheme 5 comprehensively applies three methods: reactive power regulation of distributed photovoltaic (PV) inverters, additional reactive power compensation devices, and tap changer regulation of on-load tap-changing transformers. Specifically, the reactive power output of the PV inverters at each access node (2, 7, 12, 17, 26, 31) is -70 kvar, 40 kvar, 30 kvar, 50 kvar, 30 kvar, and 100 kvar, respectively; the reactive power compensation devices output 230 kvar and 400 kvar reactive power at nodes 17 and 30, respectively; and the on-load tap changer tap remains at its lowest setting. Under this configuration, the system's distributed PV carrying capacity reaches 5.5492 MW, a significant increase of 19.2% compared to Scheme 1. The results show that by synergistically optimizing multiple regulation methods and adapting to actual grid conditions, Scheme 5 can effectively further enhance the distribution network's carrying capacity for distributed PV.

[0110] To analyze the impact of reactive power compensation device capacity and connection location on photovoltaic carrying capacity, reactive power compensation devices were deployed at nodes 17 and 30, and their capacity was adjusted in increments of 0.05 Mvar to obtain the changes in photovoltaic carrying capacity under different reactive power compensation capacity values, such as... Figure 9 As shown.

[0111] according to Figure 9 The analysis results show that when the reactive power compensation device capacity is between 0.05 Mvar and 0.5 Mvar, the photovoltaic carrying capacity increases approximately linearly with the increase in capacity; as the capacity continues to increase, the increase in carrying capacity tends to level off; and when the capacity exceeds 0.8 Mvar, the carrying capacity basically stops increasing. The main reasons for this phenomenon are twofold: First, the photovoltaic carrying capacity is limited by physical constraints such as the current-carrying capacity of the distribution network lines. Even if the reactive power compensation capacity is further increased, it cannot exceed the upper limit of hardware conditions such as the rated current of the lines. Second, the reactive power compensation device mainly stabilizes the grid voltage through reactive power regulation. Once the voltage is within a reasonable range, further increasing the compensation capacity has limited effect on improving the voltage, thus gradually weakening its effect on improving the photovoltaic carrying capacity.

[0112] Furthermore, reactive power compensation devices with a fixed capacity of 0.5 Mvar were connected to nodes 7, 9, 17, 22, 25, and 30 respectively to study the impact of different connection points on photovoltaic carrying capacity. The corresponding results are shown in [Table showing results]. Figure 10 .

[0113] according to Figure 10 The analysis results show that when the reactive power compensation device is installed at node 17 or node 30, the photovoltaic carrying capacity is higher than when it is connected to node 7 or node 9. This is mainly because nodes 17 and 30 are closer to the end of the line. When the output of distributed photovoltaic is large, the system injects a large amount of active power into the grid, causing the voltage along the line to gradually decrease. If the local reactive power support is insufficient, it is easy to cause the voltage at the end to deviate from the lower limit. The reactive power compensation device can provide reactive power compensation at such nodes, effectively suppressing the voltage drop, thereby improving the photovoltaic carrying capacity. At the same time, the photovoltaic carrying capacity when the reactive power compensation device is installed at node 7 is better than that at nodes 22 or 25. This is because node 7 itself is a distributed photovoltaic access point, and excessive photovoltaic capacity may cause the voltage to rise beyond the limit. At this time, the reactive power compensation device can moderately reduce the voltage level by injecting inductive reactive power, reducing the risk of voltage exceeding the upper limit, thereby enhancing the system's carrying capacity for photovoltaic. In conclusion, by reasonably selecting the installation location of the reactive power compensation device and optimizing its configuration capacity, the distributed photovoltaic carrying capacity of the distribution network can be significantly improved.

[0114] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for assessing and improving the distributed photovoltaic carrying capacity of distribution networks based on AHP-CRITIC-VIKOR, characterized in that, include: A distributed photovoltaic carrying capacity evaluation index system for distribution networks is constructed, which includes power grid operation safety index and power grid loss index. The indicators in the evaluation index system are normalized to obtain a standardized evaluation matrix; Based on the standardized evaluation matrix, the subjective weights of each indicator are determined by the Analytic Hierarchy Process (AHP), and the objective weights of each indicator are determined by the improved CRITIC method. The improved CRITIC method determines the objective weights of each indicator by calculating the standard deviation of each indicator and the relationship coefficient between the indicators. Based on the subjective weight and the objective weight, obtain the combined weight; Based on the combined weights, the VIKOR multi-criteria decision-making method is used to evaluate the distributed photovoltaic carrying capacity of the distribution network; If the distributed photovoltaic carrying capacity of the distribution network does not meet the requirements, then with the goal of maximizing the capacity of distributed photovoltaic access in the distribution network, a distributed photovoltaic carrying capacity improvement model is constructed and solved to output the optimal access scheme with the maximum accessible capacity of distributed photovoltaic and corresponding improvement measures.

2. The method according to claim 1, characterized in that, The power grid operation safety indicators include the voltage deviation index and the total harmonic distortion index, and the power grid loss indicators include the average loss degree and the degree of loss variation.

3. The method according to claim 1, characterized in that, The objective weights of each indicator are determined using the improved CRITIC method, including: ； ； ； ； in, β j This is an objective weight value. For information content, It is the variance of the j-th indicator. The total number of samples, Let i be the value of the i-th indicator in the k-th sample. It is the value of the j-th indicator on the k-th sample. It is the average value of the i-th indicator. It is the value of the j-th indicator. It is the average value of the j-th indicator. It is the correlation coefficient between the i-th and j-th indicators. z ij This represents the normalized index value.

4. The method according to claim 1, characterized in that, The VIKOR multi-criteria decision-making method is used to assess the distributed photovoltaic carrying capacity of the distribution network, including: Based on the standardized evaluation matrix, the maximum and minimum values ​​of each indicator are obtained, and the set of maximum values ​​is recorded as the best value among all candidate values ​​of each indicator, and the set of minimum values ​​is recorded as the worst value among all candidate values ​​of each indicator. Based on the set of maximum values, the set of minimum values, the combined weights, and the standardized evaluation matrix, calculate the group benefit value and the individual regret value; Based on the group benefit value and the individual regret value, obtain the maximum group benefit value and the minimum individual regret value; Calculate the compromise decision value based on the group benefit value, individual regret value, maximum group benefit value, and minimum individual regret value; The optimal access scheme is determined based on the compromise decision value and in combination with preset conditions, thereby realizing the assessment of the distributed photovoltaic carrying capacity of the distribution network.

5. The method according to claim 4, characterized in that, Determining the optimal access scheme based on the compromise decision value and preset conditions includes: Arrange the options in ascending order according to the compromise decision value to obtain the option ranking sequence; If a certain solution meets two preset conditions, the priority order of the solutions is determined according to the compromise decision value from smallest to largest; If only the second preset condition is not met, then the first and second ranked solutions are listed as compromise solutions, i.e., the optimal access solutions; If the first preset condition is not met, all solutions are considered compromise solutions, and sensitivity analysis is performed on the decision coefficients to obtain the optimal access solution. The first preset condition is... Q (2) - Q (1) ≥1 / ( m -1), Q (1) and Q (2) Let m represent the first and second ranked solutions, respectively, and m be the total number of solutions. The second preset condition is that the solution is the optimal solution in the ranking based on the compromise decision value, and the ranking of the group benefit value and the individual regret value is also optimal.

6. The method according to claim 1, characterized in that, Before constructing the distributed photovoltaic carrying capacity enhancement model, the following steps are taken: determine the measures to enhance the distributed photovoltaic carrying capacity of the distribution network, including reactive power regulation of distributed photovoltaic inverters, regulation of additional reactive power compensation devices, and adjustment of tap changers of on-load tap-changing transformers.

7. The method according to claim 1, characterized in that, The distributed photovoltaic carrying capacity enhancement model includes: constructing a distributed photovoltaic carrying capacity enhancement model with the goal of maximizing the capacity of distributed photovoltaic grid access.

8. The method according to claim 1, characterized in that, The constraints of the distributed photovoltaic carrying capacity enhancement model include: power flow equation constraints, distributed photovoltaic installation capacity constraints, distributed photovoltaic output constraints, harmonic constraints, line current carrying capacity constraints, transformer reverse load rate constraints, node voltage opportunity constraints, and branch power opportunity constraints.

9. The method according to claim 1, characterized in that, Solving the distributed photovoltaic carrying capacity improvement model includes: using a nonlinear back-learning whale optimization algorithm to solve the distributed photovoltaic carrying capacity improvement model, wherein the nonlinear back-learning whale optimization algorithm changes the values ​​of the control parameters in a nonlinear incremental manner and uses a back-learning strategy for position updates; The control parameter a is: ； In the formula: t is the number of iterations; t max This represents the maximum number of iterations.

10. The method according to claim 9, characterized in that, The solution to the distributed photovoltaic carrying capacity improvement model is performed using a nonlinear back-learning whale optimization algorithm, including: S1. Input the distribution network topology, line parameters and load data, set the algorithm population size and maximum number of iterations, initialize the position of individual whales and calculate the initial fitness value, where each individual whale represents a distributed photovoltaic access scheme; S2. Set a dynamic fitness threshold, using the current population fitness average as the threshold F; S3. Update the whale position according to the standard whale optimization algorithm rules, obtain the updated individual whale, and adjust the control parameter a in a non-linear manner; S4. For each updated whale individual, the improved Newton-Raphson method is used to calculate the fundamental power flow, the three-point estimation method is used to calculate the probabilistic power flow and check the constraints, and the fitness value corresponding to each access scheme is calculated. A penalty term is introduced for individuals that do not meet the constraints. S5. Generate a reverse solution for the updated whale individuals that are below the threshold F and evaluate their fitness. When the fitness of the reverse solution is better than that of the original solution, replace the original solution with the reverse solution, return to S2 to iterate to the maximum number of iterations, and output the optimal solution.

Images