Method for evaluating suitability of reactive equipment of high-proportion distributed photovoltaic power distribution network

Abstract

The invention discloses a high-proportion distributed photovoltaic power distribution network reactive power equipment suitability evaluation method, which comprises the following steps of: constructing evaluation indexes for accessing reactive power compensation equipment in three typical scenes of voltage support, fluctuation stabilization and sag relief so as to measure voltage regulation effects in the scenes; the method comprises the following steps: constructing a total voltage regulation cost index according to three voltage regulation devices of an on-load voltage regulation transformer (OLTC), a capacitor bank (CBs) and a static var compensator (SVC) and the voltage regulation cost of a photovoltaic inverter; and calculating the weight of each index by adopting a method of combining a Delphi subjective weighting method and a CRITIC objective weighting method, and finally performing weighted summation to obtain the final adaptability of each reactive power resource scheme, thereby constructing a reactive power equipment adaptability evaluation framework of the high-proportion distributed photovoltaic access power distribution network.

Description

Technical Field

[0001] This invention relates to the field of comprehensive evaluation of reactive power resources, and in particular to a method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power in distribution networks continues to increase, the randomness and intermittency of its output pose a severe challenge to grid voltage regulation. Drastic fluctuations in PV output can easily lead to problems such as voltage exceeding limits and increased fluctuation amplitude. Furthermore, voltage dips caused by grid faults or impact loads will exhibit more complex evolutionary characteristics due to the integration of PV power, seriously threatening the safe and stable operation of the distribution network.

[0003] To address the aforementioned voltage issues, various reactive power regulation resources have been deployed in power systems, encompassing both traditional voltage regulating equipment and distributed resources. The former includes on-load tap changers (OLTCs) and capacitor banks (CBs), which have slower response times but lower costs, as well as static var compensators (SVCs), which offer rapid response but require higher initial investment. The latter is represented by numerous photovoltaic inverters, which possess rapid reactive power regulation capabilities, but their output is limited by their own operating conditions and environmental factors. These reactive power resources differ significantly in terms of regulation accuracy, response delay, operating losses, and total lifecycle costs. Therefore, selecting the appropriate resource combination based on the specific voltage problem scenario is crucial for the operation and control of the distribution network.

[0004] However, existing technologies still have significant shortcomings in reactive power resource selection and allocation decisions. Most current decisions rely on the field experience of operators or simplified single criteria, lacking a quantitative evaluation method that balances technical adaptability and economic rationality. Existing evaluation methods either focus solely on single technical indicators such as equipment response speed or overemphasize investment costs, failing to achieve a comprehensive balance between technical effectiveness and economic efficiency. Furthermore, existing solutions lack differentiated evaluation standards for different application scenarios such as voltage support, fluctuation mitigation, and sag reduction, resulting in a lack of targeted reactive power resource allocation and an inability to fully leverage the technical advantages of various resources. For distributed reactive power resources like photovoltaic inverters, the quantitative methods for their voltage regulation potential and economic value are not yet perfected, leading to an underutilization of their role in distribution network voltage regulation.

[0005] Therefore, establishing a reactive power equipment adaptability evaluation framework that can be adapted to different voltage scenarios and comprehensively consider technical performance and economic costs is an important prerequisite for solving the voltage regulation problem of distribution networks under high-proportion distributed photovoltaic access and realizing the optimal allocation of reactive power resources. It is of key significance for improving the reliability and economy of distribution network operation. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a method for evaluating the adaptability of reactive power equipment in high-proportion distributed photovoltaic power distribution networks.

[0007] The technical solution of this invention is: to provide a method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network, characterized by including the following steps:

[0008] S1: Construct evaluation indicators for the access reactive power compensation equipment under three typical scenarios: voltage support, fluctuation smoothing, and sag mitigation, in order to measure the voltage regulation effect under these scenarios.

[0009] S2: Calculate the voltage regulation costs of three types of voltage regulation equipment: on-load tap changer (OLTC), capacitor bank (CBs), and static var compensator (SVC), as well as photovoltaic inverter, and construct a total voltage regulation cost index accordingly.

[0010] S3: The weights of each indicator are calculated by combining the Delphi subjective weighting method and the CRITIC objective weighting method. Finally, the weighted sum is used to obtain the final adaptability of each reactive power resource scheme, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic grid access.

[0011] Furthermore, in S1, in order to measure the voltage regulation effect of the reactive power compensation equipment under the three typical scenarios of voltage support, fluctuation suppression and sag mitigation, corresponding evaluation indicators are constructed.

[0012] (1) Calculate the scoring function for the voltage support scenario, which is determined by the voltage deviation at noon after loading reactive power resources. The scoring function is as follows:

[0013]

[0014] Total voltage deviation

[0015] U i Let U be the voltage magnitude at node i. i,ref For the reference voltage amplitude, ΔU dev,max C is the absolute value of the maximum acceptable total voltage deviation. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores.

[0016] (2) The scoring function for smoothing the fluctuation scenario is determined by the total relative voltage rise of all nodes at noon after the SVC is installed. The specific scoring function is as follows:

[0017]

[0018] Relative voltage rise at node j

[0019] Total relative voltage rise

[0020] U a,j For the voltage amplitude after loading SVC at node j, U b,j The voltage magnitude before the SVC is loaded at node j. ΔV dev,min and ΔV dev,max C represents the minimum and maximum values ​​of the relative voltage rise among all the schemes to be evaluated. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on voltage fluctuations.

[0021] (3) Calculate the scoring function for the temporary descent mitigation scenario:

[0022] Based on the AC power flow equations, the nonlinear power flow equations are linearized at the steady-state solution, and the reactive power resource suitability for sag mitigation scenarios is analyzed and obtained, as follows:

[0023]

[0024] In the formula, ΔP and ΔQ are the active and reactive power change matrices injected at the node, respectively; Δγ and ΔU are the change matrices of the phase angle magnitude of the node voltage, respectively; and J is the Jacobian matrix. Taking the inverse, we get:

[0025]

[0026] S QU The sensitivity factor represents the change in voltage amplitude caused by reactive power. The voltage regulation effect in sag mitigation scenarios is determined based on the sensitivity value; the specific function is as follows:

[0027]

[0028] In the formula, S i S is the sensitivity value of node i. max S min These are the lowest effective sensitivity and the highest saturation sensitivity, respectively, C max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on the magnitude of the sensitivity value.

[0029] Furthermore, in S2, to measure the voltage regulation costs of the three voltage regulation devices in S2—on-load tap-changing transformer (OLTC), capacitor banks (CBs), and static var compensators (SVCs)—as well as the photovoltaic inverter, a total voltage regulation cost index is constructed based on this. The total system voltage regulation cost consists of two parts: operating costs and equipment depreciation costs.

[0030] (1) Calculate the switching cost required for each tap change of the on-load tap-changing transformer:

[0031]

[0032] In the formula, Ω oltc Indicates a branch with OLTC, C oltc K represents the cost coefficient associated with OLTC. t,ij This represents the change in the number of tap steps of the OLTC connected to branch ij at time t compared to time t-1.

[0033] (2) Calculate the switching cost of the capacitor bank:

[0034]

[0035] Where C cbs It is the cost of each capacitor bank switching. This represents the change in the number of CB units connected to node i after the switch at time t compared to time t-1.

[0036] (3) Calculate the cost of the static var compensator, including operation and maintenance costs and its own power consumption:

[0037]

[0038] Where C svc,f It is the fixed operating and maintenance cost rate of SVC, C svc,q Q is the operating cost rate per unit of reactive power output. svc,t It is the reactive power output of SVC at time t.

[0039] (4) Calculate the depreciation cost of the reactive power compensation capacitor bank:

[0040]

[0041] In the formula: f CB For the total construction cost of reactive power compensation capacitors, λ CB c is the capital recovery factor for switching capacitors. CB This represents the unit capacity construction cost coefficient for switching capacitors. Let β be the rated capacity of the i-th group of capacitors, β be the discount rate for reducing resource investment, and assume its useful life is L. CB Year.

[0042] (5) Calculate the depreciation cost of the static var compensator:

[0043]

[0044] f SVC =λ SVC c SVC QSVC

[0045]

[0046] In the formula: f SVC λ represents the investment cost of a single static var compensator. CB c is the capital recovery factor for the static var compensator. SVC Q is the unit capacity construction cost coefficient for static var compensators. SVC Let β be the rated capacity of the static var compensator, β be the discount rate for investment in loss reduction resources, and assume its service life is L. SVC Year.

[0047] (6) Calculate the equipment losses caused by the photovoltaic inverter's participation in system voltage regulation:

[0048]

[0049] In the formula, Q pv,t Let C be the reactive power of the photovoltaic inverter at time t. p Δt is the compensation price for the unit reactive power provided by the photovoltaic inverter, and Δt is the unit duration for which the photovoltaic inverter participates in voltage regulation.

[0050] The total cost is:

[0051] f all =f switch_oltc +f swich_cbs +f run_svc +f CB_loss +f SVC_loss +f pv

[0052] Cost rating of the pressure regulating device based on total cost:

[0053]

[0054] f all,max f all,min C represents the highest and lowest total cost among all options. max and C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores, depending on the total cost.

[0055] Furthermore, in S3, the weights of each indicator are calculated by combining the Delphi subjective weighting method and the CRITIC objective weighting method. Finally, the weighted sum is used to obtain the final adaptability of each reactive power resource scheme, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic access to the distribution network.

[0056] (1) Subjective empowerment in the Delphi method:

[0057]

[0058] Let the subjective weight of the j-th indicator be . Let be the average score of the j-th indicator in the k-th round, and let the subjective weight vector be .

[0059] (1) Objective weighting using the CRITIC method:

[0060] Calculate CRITIC weights

[0061]

[0062] Let C be the objective weight of the j-th indicator. j Let j be the objective information content of the j-th indicator, and let the objective weight vector be...

[0063] (3) Linearly weight the subjective weights and objective weights to obtain the combined weights of each indicator:

[0064] W j =α j W D +(1-α j W C

[0065] Where j = 1, 2, ..., n, α j ∈[0,1] represents subjective and objective preference factors, and the weight vector of each indicator is W=(w1,w2,…,w n ).

[0066] (4) The weighted summation yields the comprehensive score for each reactive power resource scheme. The final score for the i-th scheme is shown below:

[0067]

[0068] S ij The i-th option is determined by its score on the j-th indicator.

[0069] Compared with the prior art, the advantages of the present invention are:

[0070] Against the backdrop of high-proportion distributed photovoltaic power grid integration, an evaluation index system has been established to comprehensively assess the technical and economic adaptability of various reactive resources under different scenarios. This system is beneficial for achieving refined and optimal utilization of reactive resources and is of great significance for improving the operational reliability and economy of high-proportion renewable energy power grids. Attached Figure Description

[0071] Figure 1 This is a flowchart of the evaluation method for this patent.

[0072] Figure 2 A summary chart of the rating indicators established for this patent.

[0073] Figure 3 This is a flowchart for calculating the reactive power adaptability of this patent. Detailed Implementation

[0074] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0075] Combination Figure 1 This invention proposes a method for evaluating the adaptability of reactive power equipment in high-proportion distributed photovoltaic power distribution networks, the specific steps of which are as follows:

[0076] S1: Construct evaluation indicators for the access reactive power compensation equipment under three typical scenarios: voltage support, fluctuation smoothing, and sag mitigation, in order to measure the voltage regulation effect under these scenarios.

[0077] S2: Calculate the voltage regulation costs of three types of voltage regulation equipment: on-load tap changer (OLTC), capacitor bank (CBs), and static var compensator (SVC), as well as photovoltaic inverter, and construct a total voltage regulation cost index accordingly.

[0078] S3: The weights of each indicator are calculated by combining the Delphi subjective weighting method and the CRITIC objective weighting method. Finally, the weighted sum is used to obtain the final adaptability of each reactive power resource scheme, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic grid access.

[0079] The specific steps for S1 are as follows:

[0080] We will construct evaluation indicators for the voltage regulation effect of reactive power compensation equipment under three typical scenarios: voltage support, fluctuation suppression, and sag mitigation.

[0081] (1) Calculate the scoring function for the voltage support scenario, which is determined by the voltage deviation at noon after loading reactive power resources. The scoring function is as follows:

[0082]

[0083] Total voltage deviation

[0084] U i Let U be the voltage magnitude at node i. i,ref For the reference voltage amplitude, ΔU dev,max C is the absolute value of the maximum acceptable total voltage deviation. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores.

[0085] (2) The scoring function for smoothing the fluctuation scenario is determined by the total relative voltage rise of all nodes at noon after the SVC is installed. The specific scoring function is as follows:

[0086]

[0087] Relative voltage rise at node j

[0088] Total relative voltage rise

[0089] U a,j For the voltage amplitude after loading SVC at node j, U b,j The voltage magnitude before the SVC is loaded at node j. ΔV dev,min and ΔV dev,max C represents the minimum and maximum values ​​of the relative voltage rise among all the schemes to be evaluated. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on voltage fluctuations.

[0090] (3) Calculate the scoring function for the temporary descent mitigation scenario:

[0091] Based on the AC power flow equations, the nonlinear power flow equations are linearized at the steady-state solution, and the reactive power resource suitability for sag mitigation scenarios is analyzed and obtained, as follows:

[0092]

[0093] In the formula, ΔP and ΔQ are the active and reactive power change matrices injected at the node, respectively; Δθ and ΔU are the change matrices of the phase angle magnitude of the node voltage, respectively; and J is the Jacobian matrix. Taking the inverse, we get:

[0094]

[0095] S QU Each value in the equation corresponds to a set of differential relations. The larger the value, the greater the U. i Based on Q j The more sensitive the change, that is, the more significant the increase in voltage at node i will be due to an increase in reactive power injection at node j. Therefore, the sensitivity factor S QU This refers to the change in voltage amplitude caused by reactive power.

[0096] The effectiveness of voltage regulation in mitigation scenarios is determined based on the sensitivity value. The specific function is as follows:

[0097]

[0098] In the formula, S i S is the sensitivity value of node i. min This represents the minimum effective sensitivity; any point with sensitivity below this value can be considered to contribute negligibly to transient relief. max This represents the highest saturation sensitivity. Points with sensitivity exceeding this value can be considered to have achieved a very satisfactory effect. (C) max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on the magnitude of the sensitivity value.

[0099] The specific steps for S2 are as follows:

[0100] The voltage regulation costs of three types of voltage regulation equipment—on-load tap-changing transformers (OLTC), capacitor banks (CBs), and static var compensators (SVC)—as well as photovoltaic inverters are calculated, and a total voltage regulation cost index is constructed based on this.

[0101] The total cost of system voltage regulation consists of two parts: operating cost and equipment depreciation cost.

[0102] (1) Calculate the switching cost required for each tap change of the on-load tap-changing transformer:

[0103]

[0104] In the formula, Ω oltc Indicates a branch with OLTC, C oltc k represents the cost coefficient associated with OLTC. t,ij This represents the change in the number of tap steps of the OLTC connected to branch ij at time t compared to time t-1.

[0105] (2) Calculate the switching cost of the capacitor bank:

[0106]

[0107] Where C cbs It is the cost of each capacitor bank switching. This represents the change in the number of CB units connected to node i after the switch at time t compared to time t-1.

[0108] (3) Calculate the cost of the static var compensator, including operation and maintenance costs and its own power consumption:

[0109]

[0110] Where C svc,f It is the fixed operating and maintenance cost rate of SVC, C svc,q Q is the operating cost rate per unit of reactive power output. svc,t It is the reactive power output of SVC at time t.

[0111] (4) Calculate the depreciation cost of the reactive power compensation capacitor bank:

[0112]

[0113] In the formula: f CB For the total construction cost of reactive power compensation capacitors, λ CB c is the capital recovery factor for switching capacitors. CB This represents the unit capacity construction cost coefficient for switching capacitors. Let β be the rated capacity of the i-th group of capacitors, β be the discount rate for reducing resource investment, and assume its useful life is L. CB Year.

[0114] (5) Calculate the depreciation cost of the static var compensator:

[0115]

[0116] f SVC =λ SVC c SVC Q SVC

[0117]

[0118] In the formula: f SVC λ represents the investment cost of a single static var compensator. CB c is the capital recovery factor for the static var compensator. SVC Q is the unit capacity construction cost coefficient for static var compensators. SVC Let β be the rated capacity of the static var compensator, β be the discount rate for investment in loss reduction resources, and assume its service life is L. SVC Year.

[0119] (6) Calculate the equipment losses caused by the photovoltaic inverter's participation in system voltage regulation:

[0120]

[0121] In the formula, Q pv,t Let C be the reactive power of the photovoltaic inverter at time t. p Δt is the compensation price for the unit reactive power provided by the photovoltaic inverter, and Δt is the unit duration for which the photovoltaic inverter participates in voltage regulation.

[0122] The total cost is:

[0123] f all =f switch_oltc +f swich_cbs +f run_svc +f CB_loss +f SVC_loss +f pv

[0124] Cost rating of the pressure regulating device based on total cost:

[0125]

[0126] f all,max f all,min C represents the highest and lowest total cost among all options. max and C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores, depending on the total cost.

[0127] Combination Figure 2 You can intuitively view the various indicators that have been established.

[0128] The specific steps for S3 are as follows:

[0129] Combination Figure 3 This invention uses a combination of the Delphi subjective weighting method and the CRITIC objective weighting method to calculate the weights of each indicator, and finally calculates the final adaptability of each reactive power resource scheme by weighted summation, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic access to the distribution network.

[0130] (1) Subjective empowerment in the Delphi method:

[0131] The evaluation system includes n indicators, and m experts are invited. In the k-th round, the i-th expert scores s for the j-th indicator. ijk ((i=1,2,...,m); (j=1,2,...,n); (k=1,2,...,t), where t is the total number of rounds).

[0132] Calculate the average score of the j-th indicator in the k-th round.

[0133]

[0134] Calculate the standard deviation σ of the j-th indicator in the k-th round. jk :

[0135]

[0136] Calculate the coefficient of variation (CV) jk :

[0137]

[0138] The coefficient of variation is usually less than 0.1 or 0.15 as a convergence criterion.

[0139] Calculate Delphi weights

[0140]

[0141] Subjective weight vector is

[0142] (1) Objective weighting using the CRITIC method:

[0143] Suppose there are m solutions to be evaluated and n evaluation indicators. The original data matrix is ​​X = (x ij ) m×n , where x ij This represents the value of the i-th scheme on the j-th index.

[0144] Data standardization:

[0145] For efficiency-type indicators:

[0146]

[0147] For cost-related indicators:

[0148]

[0149] Standardized data x' ij All fall within the interval [0,1]. If max i (x ij ) = min i (x ij If all values ​​of the indicator are the same and have no distinguishing effect, they can be directly assigned a weight of 0 or excluded.

[0150] Calculate the standard deviation of the j-th indicator.

[0151]

[0152] in, It is the average value of the j-th indicator after standardization.

[0153] Calculate the conflict magnitude R between the j-th indicator and all other indicators. j :

[0154]

[0155] Pearson correlation coefficient r jk The calculation formula is as follows:

[0156]

[0157] Calculate the objective information content C of the j-th indicator. j :

[0158] C j =Sj ×R j

[0159] Calculate CRITIC weights

[0160]

[0161] The objective weight vector is

[0162] (3) Linearly weight the subjective weights and objective weights to obtain the combined weights of each indicator:

[0163] W j =α j W D +(1-α j W C

[0164] Where j = 1, 2, ..., n, α j ∈[0,1] represents subjective and objective preference factors. Appropriate subjective and objective weight preferences can be selected based on the actual situation. The weight vector for each indicator is W=(w1,w2,…,w…). n ).

[0165] (4) The weighted summation yields the comprehensive score for each reactive power resource scheme. The final score for the i-th scheme is shown below:

[0166]

[0167] S ij The i-th option is determined by its score on the j-th indicator.

[0168] Example:

[0169] Three planning schemes were proposed. In each scheme, CB and SVC are connected at designated node locations. Nodes 8, 16, and 15 in the system are equipped with photovoltaic inverters. The compensation capacity of a single CB group is 0.05 MVar, and the maximum compensation capacity of a single photovoltaic inverter is 0.5 MVar. The details of the three schemes are shown in Table 1, and their reactive power resource suitability and total cost scores are shown in Table 2.

[0170] Table 1. Connection of voltage regulating devices for each scheme

[0171]

[0172] Table 2 Reactive resource adaptability and total cost score in multiple scenarios

[0173]

[0174] We propose to use the Delphi method to assign weights to the four indicators, resulting in the following weight vectors for each indicator at the subjective level:

[0175] W D =[0.25,0.25,0.25,0.25]

[0176] The CRITIC method was used to assign weights to the four indicators at the objective level, resulting in the following weight vectors for each indicator at the objective level:

[0177] W C =[0.180,0.192,0.191,0.436]

[0178] By assigning weights to the four indicators at both the subjective and objective levels, the final weights are obtained through a combined weighting method. Taking the subjective / objective preference factor as 0.7, the linearly weighted combined weight vector is as follows:

[0179] W = [0.229, 0.233, 0.232, 0.306]

[0180] The total reactive resource fit scores of the three schemes obtained based on the combined weights are shown in Table 3:

[0181] Table 3 Total Adaptability Score of Reactive Resources

[0182]

[0183] In the context of high-proportion distributed photovoltaic power grid integration, this invention establishes an evaluation framework that can comprehensively assess the technical and economic adaptability of various reactive resources in different scenarios. This framework is beneficial for achieving refined and optimized utilization of reactive resources and is of great significance for improving the operational reliability and economy of high-proportion renewable energy power grids.

Claims

1. A method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network, characterized in that: Includes the following steps: S1: Construct evaluation indicators for the access reactive power compensation equipment under three typical scenarios: voltage support, fluctuation smoothing, and sag mitigation, in order to measure the voltage regulation effect under these scenarios. S2: Calculate the voltage regulation costs of three types of voltage regulation equipment: on-load tap changer (OLTC), capacitor bank (CBs), and static var compensator (SVC), as well as photovoltaic inverter, and construct a total voltage regulation cost index accordingly. S3: The weights of each indicator are calculated by combining the Delphi subjective weighting method and the CRITIC objective weighting method. Finally, the weighted sum is used to obtain the final adaptability of each reactive power resource scheme, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic grid access.

2. The method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network according to claim 1, characterized in that it measures... S1 constructs corresponding evaluation indicators for the voltage regulation effect of reactive power compensation equipment under three typical scenarios: voltage support, fluctuation suppression, and sag mitigation. (1) Calculate the scoring function for the voltage support scenario, which is determined by the voltage deviation at noon after loading reactive power resources. The scoring function is as follows: Total voltage deviation U i Let U be the voltage magnitude at node i. i,ref For the reference voltage amplitude, ΔU dev,max C is the absolute value of the maximum acceptable total voltage deviation. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores. (2) The scoring function for smoothing the fluctuation scenario is determined by the total relative voltage rise of all nodes at noon after the SVC is installed. The specific scoring function is as follows: Relative voltage rise at node j Total relative voltage rise U a,j For the voltage amplitude after loading SVC at node j, U b,j The voltage magnitude before the SVC is loaded at node j. ΔV dev,min and ΔV dev,max C represents the minimum and maximum values ​​of the relative voltage rise among all the schemes to be evaluated. max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on voltage fluctuations. (3) Calculate the scoring function for the temporary descent mitigation scenario: Based on the AC power flow equations, the nonlinear power flow equations are linearized at the steady-state solution, and the reactive power resource suitability for sag mitigation scenarios is analyzed and obtained, as follows: In the formula, ΔP and ΔQ are the active and reactive power change matrices injected at the node, respectively; Δθ and ΔU are the change matrices of the phase angle magnitude of the node voltage, respectively; and J is the Jacobian matrix. Taking the inverse, we get: S QU The sensitivity factor represents the change in voltage amplitude caused by reactive power. The voltage regulation effect in sag mitigation scenarios is determined based on the sensitivity value; the specific function is as follows: In the formula, S i S is the sensitivity value of node i. max S min These are the lowest effective sensitivity and the highest saturation sensitivity, respectively, C max C min These are the maximum and minimum values ​​set by the evaluator based on the expected scoring range, depending on the magnitude of the sensitivity value.

3. The method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network according to claim 1, characterized in that it measures... S2 calculates the voltage regulation costs of three types of voltage regulation equipment: on-load tap changer (OLTC), capacitor bank (CBs), and static var compensator (SVC), as well as the voltage regulation costs of photovoltaic inverters, and constructs a total voltage regulation cost index based on this. The total cost of system voltage regulation consists of two parts: operating cost and equipment depreciation cost. (1) Calculate the switching cost required for each tap change of the on-load tap-changing transformer: In the formula, Ω oltc Indicates a branch with OLTC, C oltc K represents the cost coefficient associated with OLTC. t,ij This represents the change in the number of tap steps of the OLTC connected to branch ij at time t compared to time t-1. (2) Calculate the switching cost of the capacitor bank: Where C cbs It is the cost of each capacitor bank switching. This represents the change in the number of CB units connected to node i after the switch at time t compared to time t-1. (3) Calculate the cost of the static var compensator, including operation and maintenance costs and its own power consumption: Where C svc,f It is the fixed operating and maintenance cost rate of SVC, C svc,q Q is the operating cost rate per unit of reactive power output. svc,t It is the reactive power output of SVC at time t. (4) Calculate the depreciation cost of the reactive power compensation capacitor bank: In the formula: f CB For the total construction cost of reactive power compensation capacitors, λ CB c is the capital recovery factor for switching capacitors. CB This represents the unit capacity construction cost coefficient for switching capacitors. Let be the rated capacity of the u-th capacitor group, β be the discount rate for reducing resource investment, and assume its service life is L. CB Year. (5) Calculate the depreciation cost of the static var compensator: f SVC =λ SVC c SVC Q SVC In the formula: f SVC λ represents the investment cost of a single static var compensator. CB c is the capital recovery factor for the static var compensator. SVC Q is the unit capacity construction cost coefficient for static var compensators. SVC Let β be the rated capacity of the static var compensator, β be the discount rate for investment in loss reduction resources, and assume its service life is L. SVC Year. (6) Calculate the equipment losses caused by the photovoltaic inverter's participation in system voltage regulation: In the formula, Q pv,t Let C be the reactive power of the photovoltaic inverter at time t. p Δt is the compensation price for the unit reactive power provided by the photovoltaic inverter, and Δt is the unit duration for which the photovoltaic inverter participates in voltage regulation. The total cost is: f all =f switch_oltc +f swich_cbs +f run_svc +f CB_loss +f SVC_loss +f pv Cost rating of the pressure regulating device based on total cost: f all,max f all,min C represents the highest and lowest total cost among all options. max and C min These are the maximum and minimum values ​​set by the evaluator based on the expected range of scores, depending on the total cost.

4. The method for evaluating the adaptability of reactive power equipment in a high-proportion distributed photovoltaic distribution network according to claim 1, characterized in that: The method in S3 combines the Delphi subjective weighting method and the CRITIC objective weighting method to calculate the weight of each indicator, and finally calculates the final adaptability of each reactive power resource scheme by weighted summation, thereby constructing a reactive power equipment adaptability evaluation framework for high-proportion distributed photovoltaic grid access. (1) Subjective empowerment in the Delphi method: Let the subjective weight of the j-th indicator be . Let be the average score of the j-th indicator in the k-th round, and let the subjective weight vector be . (2) Objective weighting using the CRITIC method: Calculate CRITIC weights Let C be the objective weight of the j-th indicator. j Let j be the objective information content of the j-th indicator, and let the objective weight vector be... (3) Linearly weight the subjective weights and objective weights to obtain the combined weights of each indicator: W j =a j W D +(1-a j )W C Where j = 1, 2, ..., n, α j ∈[0,1] represents subjective and objective preference factors, and the weight vector of each indicator is W=(w1,w2,…,w n ). (4) The weighted summation yields the comprehensive score for each reactive power resource scheme. The final score for the i-th scheme is shown below: S ij The i-th option is determined by its score on the j-th indicator.

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