A method, device and medium for configuring power grid power capacity taking into account power quality

Abstract

The present invention discloses a method, device and medium for configuring the capacity of a power grid that takes into account the quality of electric energy. The method includes: establishing a weight distribution of first-level indicators regarding the power quality problem based on the characteristic consistency of the power indicators in the power grid operation data; combining the weight distribution of the first-level indicators with a single-factor evaluation matrix to calculate the preliminary installed capacity; simulating and correcting the preliminary installed capacity, and when the change value of the preliminary installed capacity is less than a preset value, obtaining the final installed capacity, and configuring the capacity based on the final installed capacity. The present invention proposes a method, device and medium for configuring the capacity of a power grid that takes into account the quality of electric energy. Through a perfect power quality evaluation scheme, it takes into account the scenarios of various distributed new energy sources accessing the distribution network, ensures that the practicality and adaptability of the final installed capacity at each access node to be selected in the distribution network are optimal, and can solve the problem of difficulty in optimizing the capacity configuration of distributed power sources in the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network, and in particular to a method, device and medium for configuring power capacity of a power grid taking power quality into consideration. Background Art

[0002] In response to the "dual carbon" goal, my country has accelerated the construction of major projects such as large-scale wind power and photovoltaic bases, made every effort to increase the supply of clean electricity, and continuously achieved new breakthroughs in the development of renewable energy. According to statistics, by the end of 2023, my country's renewable energy power generation installed capacity will account for more than half of the total installed capacity, historically exceeding thermal power. However, a large number of power electronic devices are used when distributed power sources are connected to the distribution network, which also makes the power quality problem increasingly serious due to the high proportion of power electronic equipment penetration in the current new power system distribution network. At present, it is mainly through the planning stage of the new power system distribution network to fully evaluate the power quality of distributed new energy access to the distribution network, so as to timely adjust the access point and access capacity of the distributed power source of the distribution network in the planning and design stage, which can not only avoid readjustment after the project is completed, but also minimize the risk of failure.

[0003] However, power quality assessment is an extremely complex task. Current research has not yet comprehensively defined power quality assessment methods, let alone in-depth analysis of their interrelationships. Moreover, current research on power quality assessment is mainly focused on the power system operation stage, while there is relatively little research on assessment in the power system planning stage. In addition, since no actual operating data is generated during the early planning of the distribution network, the measured data of relevant power quality indicators, such as the total harmonic distortion rate of voltage and current after the connection of new energy to the grid, voltage deviation, voltage fluctuation, and voltage sag, cannot be obtained in advance. Therefore, it is difficult to assist in the site selection and sizing of distributed power sources in the distribution network while ensuring that the planning target of distributed power source installed capacity is achieved. Summary of the invention

[0004] The present invention provides a method, device and medium for configuring the capacity of a power grid taking into account the power quality, so as to solve the problem that it is difficult to optimize the capacity configuration of a distributed power source in a distribution network.

[0005] In order to solve the above problems, the present invention provides a method for configuring power grid power capacity taking into account power quality, comprising:

[0006] Obtain grid operation data from the distribution network;

[0007] According to the characteristic consistency of the power indicators in the power grid operation data, a primary indicator weight distribution for power quality issues is established;

[0008] The primary indicator weight distribution is combined with a single factor evaluation matrix to calculate the preliminary installed capacity of each access node to be selected in the distribution network; wherein the single factor evaluation matrix is ​​established according to voltage evaluation indicators of different dimensions;

[0009] The preliminary installed capacity is simulated and corrected, and when the change value of the preliminary installed capacity is less than a preset value, a final installed capacity is obtained, and the capacity of each access node to be selected in the distribution network is configured according to the final installed capacity.

[0010] The present invention establishes a primary indicator weight distribution according to the characteristic consistency of the electric energy indicator, and can use technical means to perform quantitative weight analysis and make reasonable estimates on multiple existing power quality problems at the same time, so that the obtained primary indicator weight distribution can reasonably determine the weight of the electric energy indicator, so as to more comprehensively evaluate the power quality status; since the single-factor judgment matrix is ​​established according to voltage evaluation indicators of different dimensions, it can truly reflect the operation of the distribution network; through the combination of the primary indicator weight distribution and the single-factor judgment matrix, in order to determine the initial installed capacity of each candidate access node, it can consider the scenarios of multiple distributed new energy sources accessing the distribution network, so that the solution is more in line with the actual application of the project, and can also fully demonstrate the typical power quality problems of the distribution network, providing data support for the determination of the final installed capacity; and finally, by controlling the change value to be less than the preset value, the obtained final installed capacity can make the distribution network have good adaptability and stability when applied, so that the capacity configuration is optimized.

[0011] Compared with the prior art, the present invention takes into account various scenarios of distributed renewable energy access to the distribution network through a complete power quality assessment scheme, thereby improving the applicability of the scheme; and through simulation and correction, it can ensure that the practicality and adaptability of the final installed capacity at each candidate access node in the distribution network are optimal, which can solve the problem of difficulty in achieving optimal capacity configuration of distributed power sources in the distribution network.

[0012] As a preferred solution, according to the characteristic consistency of the power indicators in the power grid operation data, a primary indicator weight distribution for power quality issues is established, specifically:

[0013] Establishing a consistency evaluation matrix for power quality issues based on voltage deviations and voltage fluctuations in the power grid operation data;

[0014] Using the first preset formula, the hierarchical analysis importance weight distribution is calculated according to the consistency evaluation matrix;

[0015] The hierarchical analysis importance weight distribution is combined with the preset expert scoring method weight distribution to calculate the first-level indicator weight distribution.

[0016] This preferred solution obtains the first-level indicator weight distribution by combining the hierarchical analysis importance of power quality problems with the expert scoring method. It can simultaneously use technical means to conduct quantitative weight analysis and make reasonable estimates of multiple existing power quality problems, so that the first-level indicator weight distribution obtained can reasonably determine the weight of each indicator, and can more comprehensively evaluate the power quality status, providing strong data support for the optimized operation of the power grid system and the access protection of distributed power sources.

[0017] As a preferred solution, a consistency evaluation matrix for power quality issues is established based on the voltage deviation and voltage fluctuation in the power grid operation data, specifically:

[0018] The load conditions of the distribution network users in the power grid operation data are combined with a preset importance scale table to establish an initial evaluation matrix; wherein the importance scale table is established according to the importance of different indicators in the distribution network;

[0019] Calculating a consistency index according to the maximum characteristic root and the matrix order of the initial evaluation matrix;

[0020] The parameters of the initial evaluation matrix are modified according to the numerical value of the consistency index to obtain the consistency evaluation matrix for the power quality problem.

[0021] The initial evaluation matrix established by this preferred solution can reflect the comprehensive performance of different equipment in the distribution network. By correcting the parameters of the initial evaluation matrix according to the numerical value of the consistency index, the obtained consistency evaluation matrix can more accurately evaluate the consistency of the performance, safety and reliability of the power grid system, so as to identify and quantify the correlation and interaction between different indicators, thereby ensuring that the overall performance of the power grid system is stable and consistent.

[0022] As a preferred solution, the weight distribution of the first-level indicators is combined with a single factor evaluation matrix to calculate the preliminary installed capacity of each access node to be selected in the distribution network, specifically:

[0023] According to the first-level indicator weight distribution and the single factor evaluation matrix, a comprehensive evaluation result of the power quality of each distributed power source to be selected for access is calculated;

[0024] According to the severity of power quality problems of distributed power sources at different nodes before access in the comprehensive evaluation results, an installed capacity allocation matrix is ​​established;

[0025] By combining the installed capacity allocation matrix with the current installed capacity of the distributed power source, the preliminary installed capacity of each access node to be selected in the distribution network is calculated.

[0026] The comprehensive evaluation results of this preferred solution are established based on the weight distribution of the first-level indicators that can reflect the power quality status and the single-factor evaluation matrix that can reflect the voltage evaluation index. Therefore, it can objectively and accurately reflect the quality of the power quality of each distributed power source to be connected to the node. Therefore, on this basis, the corresponding installed capacity allocation method can be adopted to obtain a comprehensive preliminary installed capacity for application.

[0027] As a preferred solution, according to the severity of the power quality problems of distributed power sources at different nodes before access in the comprehensive evaluation results, an installed capacity allocation matrix is ​​established, specifically:

[0028] Obtaining the severity of power quality problems before the distributed power sources at different nodes are connected from the comprehensive evaluation results, and determining the pros and cons of each candidate access node according to the severity of the power quality problems;

[0029] According to the advantages and disadvantages, the installed capacity allocation matrix is ​​established by increasing the installed capacity at excellent nodes.

[0030] By analyzing the pros and cons of the nodes, this preferred solution can more accurately determine which nodes can withstand higher access capacity, which helps to avoid waste of resources and allocate resources to the most needed and effective nodes, so as to reduce overload and bottlenecks in the power grid system and improve overall operating efficiency.

[0031] As a preferred solution, the single factor evaluation matrix is ​​established based on voltage evaluation indicators of different dimensions, specifically:

[0032] According to the distortion degree of each harmonic voltage, a first single factor judgment vector on the harmonic voltage compatibility level is established;

[0033] According to the severity of voltage fluctuation and the severity of voltage deviation of each distributed power source to be selected for access, a second single factor judgment vector for voltage fluctuation is established, and a third single factor judgment vector for voltage deviation is established;

[0034] According to the voltage sag evaluation results of each distributed power source to be selected for access, a fourth single factor evaluation vector for voltage sag is established;

[0035] The single factor evaluation matrix is ​​formed by the first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector.

[0036] This preferred solution considers four different dimensions, namely harmonic voltage, voltage fluctuation, voltage deviation and voltage sag, to establish respective single-factor evaluation vectors. This multi-faceted evaluation of the voltage of the power grid in a numerical way enables the finally established single-factor evaluation matrix to truly reflect the operation status of the distribution network.

[0037] As a preferred solution, according to the degree of distortion of each harmonic voltage, a first single factor evaluation vector for the harmonic voltage compatibility level is established, specifically:

[0038] Obtain the voltage effective values ​​of the fundamental wave and each frequency harmonic of different access nodes to be selected, and obtain the voltage effective value of the fundamental wave and the voltage effective value of the harmonic wave respectively;

[0039] According to the said fundamental voltage effective value and harmonic voltage effective value, by calculating the severity of node harmonic distortion, a secondary factor evaluation matrix is ​​obtained;

[0040] Each row of data in the secondary factor evaluation matrix is ​​normalized to obtain the first single factor evaluation vector for the compatibility level of each harmonic voltage.

[0041] After normalization, this preferred solution obtains the first single factor judgment vector. This vector, as a parameter that describes the relative size relationship between voltage harmonics and fundamental waves in the power grid system, can reflect the severity of harmonic distortion in the power grid system. When the harmonic voltage compatibility level is high, it means that the harmonic distortion in the power grid system is more serious, which may have a negative impact on electrical equipment. Therefore, the first single factor judgment vector can be used to know at the first time whether the harmonic voltage has an adverse effect.

[0042] As a preferred solution, according to the severity of voltage fluctuation and voltage deviation of each distributed power supply to be selected for access, a second single factor evaluation vector for voltage fluctuation is established, and a third single factor evaluation vector for voltage deviation is established, specifically:

[0043] According to the voltage fluctuation and deviation classification table, the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the power grid operation data are scored and evaluated to obtain a second single factor evaluation vector for voltage fluctuation and a third single factor evaluation vector for voltage deviation;

[0044] Among them, the voltage fluctuation and deviation level classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be connected node based on the absolute average value of the voltage fluctuation and voltage deviation within a day in the preset historical power grid operation data.

[0045] This preferred solution obtains the second single factor evaluation vector and the third single factor evaluation vector according to the voltage fluctuation and deviation grade classification table, which can quickly and accurately know the severity of voltage fluctuation and voltage deviation. The parameter acquisition method is fast and effective.

[0046] The present invention also provides a power grid power capacity configuration device taking into account power quality, comprising a data module, a weight module, a capacity module and an installed capacity module;

[0047] Wherein, the data module is used to obtain power grid operation data from the distribution network;

[0048] The weight module is used to establish a primary indicator weight distribution for power quality issues according to the characteristic consistency of the power indicators in the power grid operation data;

[0049] The capacity module is used to combine the weight distribution of the first-level indicators with a single factor evaluation matrix to calculate the preliminary installed capacity of each access node to be selected in the distribution network; wherein the single factor evaluation matrix is ​​established according to voltage evaluation indicators of different dimensions;

[0050] The installation module is used to simulate and correct the preliminary installed capacity. When the change value of the preliminary installed capacity is less than a preset value, the final installed capacity is obtained, and the capacity of each access node to be selected in the distribution network is configured according to the final installed capacity.

[0051] As a preferred solution, the weight module includes a consistency unit, an importance unit and an index unit;

[0052] The consistency unit is used to establish a consistency evaluation matrix for power quality issues based on the voltage deviation and voltage fluctuation in the power grid operation data;

[0053] The importance unit is used to calculate the hierarchical analysis importance weight distribution according to the consistency evaluation matrix using a first preset formula;

[0054] The indicator unit is used to combine the hierarchical analysis importance weight distribution with the preset expert scoring method weight distribution to calculate the first-level indicator weight distribution.

[0055] As a preferred solution, the consistency unit includes a first subunit, a second subunit and a third subunit;

[0056] The first subunit is used to combine the load conditions of the distribution network users in the power grid operation data with a preset importance scale table to establish an initial evaluation matrix; wherein the importance scale table is established according to the importance of different indicators in the distribution network;

[0057] The second subunit is used to calculate the consistency index according to the maximum eigenvalue and matrix order of the initial evaluation matrix;

[0058] The third subunit is used to perform parameter correction on the initial evaluation matrix according to the numerical value of the consistency index to obtain the consistency evaluation matrix for the power quality problem.

[0059] As a preferred solution, the capacity module includes an evaluation unit, a matrix unit and a capacity unit;

[0060] The evaluation unit is used to calculate the comprehensive evaluation result of the power quality of each distributed power source to be selected as an access node according to the first-level indicator weight distribution and the single factor evaluation matrix;

[0061] The matrix unit is used to establish an installed capacity allocation matrix according to the severity of power quality problems of distributed power sources at different nodes before access in the comprehensive evaluation results;

[0062] The capacity unit is used to calculate the preliminary installed capacity of each access node to be selected in the distribution network by combining the installed capacity allocation matrix with the current installed capacity of the distributed power source.

[0063] As a preferred solution, the matrix unit includes a fourth subunit and a fifth subunit;

[0064] The fourth subunit is used to obtain the severity of power quality problems before the distributed power sources of different nodes are connected from the comprehensive evaluation results, and determine the pros and cons of each candidate access node according to the severity of the power quality problems;

[0065] The fifth subunit is used to establish the installed capacity allocation matrix by increasing the installed capacity at excellent nodes according to the superior and inferior conditions.

[0066] As a preferred solution, the capacity module includes a first vector unit, a second vector unit, a third vector unit and a combination unit;

[0067] The first vector unit is used to establish a first single factor evaluation vector for the harmonic voltage compatibility level according to the degree of distortion of each harmonic voltage;

[0068] The second vector unit is used to establish a second single factor evaluation vector for voltage fluctuation and a third single factor evaluation vector for voltage deviation according to the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access.

[0069] The third vector unit is used to establish a fourth single factor evaluation vector for voltage sag according to the voltage sag evaluation results of the candidate access nodes of each distributed power source;

[0070] The combination unit is used to form the single factor evaluation matrix by using the first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector.

[0071] As a preferred solution, the first vector unit includes a valid value subunit, a matrix subunit and a vector subunit;

[0072] The effective value subunit is used to obtain the voltage effective values ​​of the fundamental wave and each frequency harmonic of different access nodes to be selected, and obtain the effective value of the fundamental wave voltage and the effective value of the harmonic voltage respectively;

[0073] The matrix subunit is used to obtain a secondary factor evaluation matrix by calculating the severity of node harmonic distortion according to the effective value of the fundamental voltage and the effective value of the harmonic voltage;

[0074] The vector subunit is used to perform normalization processing on each row of data in the secondary factor evaluation matrix to obtain the first single factor evaluation vector regarding the compatibility level of each harmonic voltage.

[0075] As a preferred solution, the second vector unit is specifically:

[0076] According to the voltage fluctuation and deviation classification table, the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the power grid operation data are scored and evaluated to obtain a second single factor evaluation vector for voltage fluctuation and a third single factor evaluation vector for voltage deviation;

[0077] Among them, the voltage fluctuation and deviation level classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be connected node based on the absolute average value of the voltage fluctuation and voltage deviation within a day in the preset historical power grid operation data.

[0078] The present invention also provides a storage medium having a computer program stored thereon, the computer program being called and executed by a computer to implement the above-mentioned method for configuring power supply capacity of a power grid taking into account power quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flow chart of a method for configuring power grid power capacity taking into account power quality provided by an embodiment of the present invention;

[0080] Figure 2It is a flow chart of site selection and capacity optimization of distributed power sources in a distribution network provided by an embodiment of the present invention;

[0081] Figure 3 It is a structural schematic diagram of a power grid power capacity configuration device taking power quality into consideration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0083] In the description of the present application, it should be understood that the terms "first", "second" ... "fifth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second" ... "fifth" may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, "several" means two or more.

[0084] A grid power capacity configuration method taking power quality into account provided in an embodiment of the present invention is mainly used to reasonably evaluate the power quality issues of distributed renewable energy access to the distribution network during the distribution network planning stage, and to assist in completing the site selection and capacity determination of distributed power sources in the distribution network while ensuring that the distributed power source installed capacity planning target is achieved, so as to achieve the optimization of power quality.

[0085] Embodiment 1:

[0086] See also Figure 1 The embodiment of the present invention provides a method for configuring the capacity of a power grid taking into account the power quality, including S1 to S4, and the specific implementation steps are as follows:

[0087] S1. Obtain grid operation data from the distribution network.

[0088] Step S1 of the embodiment of the present invention is specifically:

[0089] Obtain grid operation data from the distribution network;

[0090] The grid operation data includes model data such as the type, quantity, and location of the selected access nodes of the distributed power sources planned in the distribution network, and the total installed capacity of different types of distributed power sources, and clarifies the main power quality assessment indicators;

[0091] In addition, the power quality assessment indicators considered in the first embodiment include: the compatibility level (percentage of fundamental component) of each harmonic voltage specified in GB / Z 17625.14-2017 Electromagnetic Compatibility - Limits - Harmonics, interharmonics, voltage fluctuations and unbalanced emission limit assessment of disturbance devices connected to low-voltage power systems, voltage fluctuations, voltage deviations specified in GB / T 12325-2008 Power Quality - Supply Voltage Deviation, and voltage sags specified in GB / T 39270-2020 Voltage Sag Indicator and Severity Assessment Method, that is, the compatibility level of harmonic voltages, voltage fluctuations, voltage deviations and voltage sags; these four indicators are used as the factor set U={U1, U2, U3, U4}.

[0092] S2. According to the characteristic consistency of power indicators in the power grid operation data, establish the weight distribution of the first-level indicators regarding power quality issues.

[0093] In step S2 of the embodiment of the present invention, S2 includes S2.1 to S2.4; wherein S2.1 is the process of constructing the initial evaluation matrix R0, S2.2 is the process of constructing the consistency index CR, S2.3 is the process of constructing the consistency evaluation matrix R, and S2.4 is the process of constructing the first-level index weight distribution A, specifically:

[0094] S2.1. Combine the distribution network user load conditions in the power grid operation data with the preset importance scale and planning design scheme to establish an initial evaluation matrix R0;

[0095] Among them, the importance scale is established according to the importance of different indicators in the distribution network. The load conditions of distribution network users include the load operation conditions of equipment such as frequency conversion equipment and arc furnaces in metallurgical plants.

[0096] Table 1 Importance scale

[0097]

[0098]

[0099] To apply the embodiment of the present invention, please refer to Table 1. This embodiment provides an importance scale table. Table 1 is used to compare the importance of indicators i and j at each layer in the distribution network.

[0100] S2.2. According to the maximum characteristic root and matrix order of the initial evaluation matrix R0, the consistency index CR is calculated; wherein the consistency index is:

[0101]

[0102] Among them, for the parameter CI:

[0103]

[0104] Among them, λ max and n are the maximum eigenvalue and matrix order of the initial evaluation matrix R0 respectively; in this embodiment, n=4; the value of RI is determined by the matrix order of the initial evaluation matrix R0, please refer to Table 2.

[0105] Table 2 Value table of consistency index RI

[0106]

[0107] To apply the embodiment of the present invention, please refer to Table 2. This embodiment provides a value table of the consistency index RI. Table 2 shows that when the matrix order n of the initial evaluation matrix R0 takes different values, the corresponding parameter RI will also have different values.

[0108] S2.3. According to the numerical value of the consistency index CR, the parameters of the initial evaluation matrix R0 are modified to obtain the consistency evaluation matrix R for the power quality problem, which is specifically:

[0109] Determine the value of the consistency index CR:

[0110] Case 1: When CR<0.1, it means that the initial evaluation matrix R0 passes the consistency test, and the initial evaluation matrix R0 is directly used as the consistency evaluation matrix R.

[0111] Case 2: When CR ≥ 0.1, it means that the initial evaluation matrix R0 fails the consistency test. Then, the consistency evaluation matrix R is obtained by reconstructing the evaluation matrix with a larger consistency index CR.

[0112] Among them, the consistency evaluation matrix is:

[0113]

[0114] The initial evaluation matrix established by S2.1 to S2.3 of this embodiment can reflect the comprehensive performance of different equipment in the distribution network. By correcting the parameters of the initial evaluation matrix according to the numerical value of the consistency index, the obtained consistency evaluation matrix can more accurately evaluate the consistency of the performance, safety and reliability of the power grid system, so as to identify and quantify the correlation and interaction between different indicators, thereby ensuring that the overall performance of the power grid system is stable and consistent.

[0115] S2.4. Use the first preset formula to calculate the hierarchical analysis importance weight distribution A according to the consistency evaluation matrix R 01 Among them, A 01 =[a 01 ,a 02 ,a 03 ,a04 ];

[0116] Assign the importance weights of the hierarchical analysis to A 01 Combined with the preset expert scoring method weight distribution A 02 , calculate the first-level index weight distribution A; where A 02 =[a 05 ,a 06 ,a 07 ,a 08 ], expert scoring method weight distribution A 02 The statistical data is obtained by first conducting multiple rounds of anonymous consultation and feedback from representative experts in the industry on the four indicators in the factor set U, and then normalizing the statistical data;

[0117] Among them, the hierarchical analysis importance weight distribution A 01 Parameter a in 0i for:

[0118]

[0119] Expert scoring method weight distribution A 02 Parameter a in 0j satisfy:

[0120]

[0121] The weight distribution A of the first-level indicators is:

[0122]

[0123] Among them, r ij is the i-th row and j-th column element in the consistency evaluation matrix R, a 0i The parameter 4 in the expression is because there are 4 evaluation indicators at this level; assuming that the four parameters in the first-level indicator weight distribution A are a1, a2, a3 and a4, then a i satisfy:

[0124] In this embodiment S2.4, by combining the hierarchical analysis importance of power quality issues with the expert scoring method, the first-level indicator weight distribution can be obtained. It can use technical means to conduct quantitative weight analysis on multiple existing power quality problems and make reasonable estimates, so that the obtained first-level indicator weight distribution can reasonably determine the weight of each indicator, and can more comprehensively evaluate the power quality status, providing strong data support for the optimized operation of the power grid system and the access protection of distributed power sources.

[0125] S3. Combine the weight distribution of the first-level indicators with the single-factor evaluation matrix to calculate the preliminary installed capacity of each candidate access node in the distribution network; wherein the single-factor evaluation matrix is ​​established based on voltage evaluation indicators of different dimensions.

[0126] In step S3 of the embodiment of the present invention, S3 includes S3.1 to S3.7; wherein S3.1 is a process of constructing a secondary factor evaluation matrix B1, S3.2 is a process of constructing a first single factor evaluation vector X1, S3.3 is a process of constructing a second single factor evaluation vector X2 and a third single factor evaluation vector X3, S3.4 is a process of constructing a fourth single factor evaluation vector X4 and a single factor evaluation matrix X, S3.5 is a process of constructing a comprehensive evaluation result Y0, S3.6 is a process of constructing an installed capacity allocation matrix F0, and S3.7 is a process of constructing a preliminary installed capacity P i The process is as follows:

[0127] S3.1. By performing fast Fourier transform (FFT) on the voltage of the candidate access node of the distributed power source obtained by simulation, the voltage effective values ​​of the fundamental wave and each frequency harmonic of different candidate access nodes are obtained, and then the fundamental wave voltage effective value RMSU is obtained respectively. 0j The harmonic voltage effective value RMSU ij ; Wherein, i represents the harmonic frequency is i+1th, and j represents the jth access node to be selected;

[0128] According to the fundamental voltage effective value RMSU 0j The harmonic voltage effective value RMSU ij , by calculating the severity of node harmonic distortion, the secondary factor evaluation parameter b is obtained ij ;

[0129] Judging parameter b according to secondary factors ij , by calculating each candidate access node and the harmonic level of different frequencies (percentage of fundamental component), the secondary factor evaluation matrix B1 is obtained;

[0130] Among them, the secondary factor evaluation matrix is:

[0131]

[0132] The secondary factor evaluation parameters are:

[0133]

[0134] Where, h represents the harmonic frequency of h+1 harmonic, n is the number of nodes to be connected to the distributed generation in the distribution network; k ib) The compatibility level limits (percentage of fundamental component) of harmonic voltages of different frequencies specified in IEC 61000-2-2 Electromagnetic Compatibility (EMC), i.e. the compatibility level of low-frequency conducted disturbances and signals in public low-voltage power supply systems; b) ij The size of represents the severity of the i+1th harmonic distortion of the jth distributed generation candidate access node.

[0135] S3.2. Normalize each row of data in the secondary factor evaluation matrix B1 to obtain the first single factor evaluation vector X1 for the compatibility level of each harmonic voltage, that is, X1 = [x 11 x 12 ... x 1n ];

[0136] Specifically: taking the 3rd, 5th and 7th harmonics, which are the most common in engineering practice and the most concerned in the distribution network, as an example, first normalize each row in the secondary factor evaluation matrix B1 to obtain a first matrix of size 3×h; then add the first matrix by column to obtain a second matrix of 1×h; finally, normalize the second matrix to obtain the first single factor evaluation vector X1 of the harmonic voltage compatibility level of different candidate access nodes.

[0137] It should be noted that the harmonic order range considered in S3.1 to S3.2 of this embodiment is h+1 harmonics, that is, 2-h+1, a total of h frequencies. The harmonic frequency range considered can be determined based on planning requirements.

[0138] After normalization processing, S3.2 of this embodiment obtains the first single factor judgment vector. This vector, as a parameter that describes the relative size relationship between voltage harmonics and fundamental waves in the power grid system, can reflect the severity of harmonic distortion in the power grid system. When the harmonic voltage compatibility level is high, it means that the harmonic distortion in the power grid system is more serious, which may have a negative impact on electrical equipment. Therefore, the first single factor judgment vector can be used to know at the first time whether the harmonic voltage has an adverse effect.

[0139] S3.3. According to the voltage fluctuation and deviation classification table, the severity of voltage fluctuation and voltage deviation of each distributed power source to be selected for access in the grid operation data is evaluated by scores, and the nodes with higher severity are scored as high scores to focus on the nodes, and the second single factor evaluation vector X2 for voltage fluctuation and the third single factor evaluation vector X3 for voltage deviation are obtained, that is, X2 = [x 21 x 22 ... x 2n ],X3=[x 31 x 32 ... x3n ];

[0140] Among them, the voltage fluctuation and deviation classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be connected to the node based on the absolute average value of the voltage fluctuation and voltage deviation within a day in the preset historical power grid operation data.

[0141] Table 3 Classification of voltage fluctuation and deviation

[0142]

[0143] To apply the embodiment of the present invention, please refer to Table 3. This embodiment provides a voltage fluctuation and deviation classification table. Table 3 shows the classification method of voltage fluctuation and voltage deviation. The standard requirements in the table are divided into 4 levels, namely excellent, good, qualified and unqualified.

[0144] Due to the randomness, intermittency and volatility of distributed power sources, different distributed power sources have different characteristics. GB / Z 17625.14-2017 Electromagnetic compatibility - Limits - Evaluation of emission limits of harmonics, interharmonics, voltage fluctuations and imbalances of disturbance devices connected to low-voltage power systems stipulates that the voltage fluctuation of the distribution network should be limited to 3% of the nominal supply voltage, and GB / T 12325-2008 Power quality - Supply voltage deviation stipulates that the supply voltage deviation of 20kV and below is ±7% of the nominal voltage;

[0145] Therefore, a 10kV distribution network object is used as an example to apply the process of obtaining the second single factor evaluation vector and the third single factor evaluation vector. Wind turbines, photovoltaics and energy storage are connected to the distribution network. Other distributed power sources can refer to similar analysis, specifically:

[0146] The assessment levels of voltage fluctuation and voltage deviation are divided into four levels according to the standard requirements of GB / Z 17625.14-2017 Electromagnetic compatibility - Limits - Evaluation of emission limits of harmonics, interharmonics, voltage fluctuations and unbalance of disturbance devices connected to low-voltage power systems and GB / T12325-2008 Power quality - Supply voltage deviation, namely excellent, good, qualified and unqualified, and the corresponding assessment results are 0.1, 0.3, 0.6 and 1 respectively;

[0147] From Table 3, we can get the initial second single factor evaluation vector and the third single factor evaluation vector, in which the elements are the evaluation results of the voltage fluctuation and voltage deviation of each distributed power supply to be selected for access node. After normalization operation, we can get the final second single factor evaluation vector X2 and the third single factor evaluation vector X3.

[0148] In this embodiment S3.3, since wind turbines and photovoltaics are greatly affected by external environments such as wind speed and light intensity, the rationality of the classification in the voltage fluctuation and deviation classification table can be ensured by taking the average value of the absolute value of the data deviation as the basis for classification;

[0149] Furthermore, by obtaining the second single factor evaluation vector and the third single factor evaluation vector according to the voltage fluctuation and deviation classification table, the severity of the voltage fluctuation and the severity of the voltage deviation can be quickly and accurately known. This parameter acquisition method is fast and effective.

[0150] S3.4. Evaluate the influence of voltage sag on each candidate access node of each distributed power source according to the voltage sag severity classification table to obtain an initial single factor evaluation vector; wherein the elements of the initial single factor evaluation vector are the evaluation results of the voltage sag on each candidate access node of each distributed power source;

[0151] The initial single factor evaluation vector is normalized to obtain the fourth single factor evaluation vector X4 for voltage sag, that is, X4 = [x 41 x 42 ... x 4n ];

[0152] The first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector constitute a single factor evaluation matrix X;

[0153] Among them, the single factor evaluation matrix is:

[0154]

[0155] Among them, X i represents the i-th secondary evaluation vector, and n is the number of nodes to be connected to the distributed generation.

[0156] Table 4 Classification of voltage sag severity

[0157]

[0158]

[0159] To apply the embodiment of the present invention, please refer to Table 4. This embodiment provides a voltage sag severity classification table. Table 4 shows the comparison relationship between the residual voltage duration and severity caused by the voltage sag.

[0160] For example, in the GB / T 39270-2020 voltage sag index and severity assessment method standard, voltage sag is defined as a phenomenon in which the RMS value of the power frequency voltage at a certain point in the power system suddenly drops to 0.1pu~0.9pu and returns to normal after a short duration of 10ms~1min;

[0161] Therefore, the voltage sag evaluation standard of the candidate access node is defined as shown in Table 4. The evaluation level is divided into 4 levels, namely poor, bad and very poor, and the corresponding scores are 0.1, 0.3, 0.6 and 1 respectively. If there is no voltage sag, it is excellent.

[0162] In this embodiment S3.4, four different dimensions, namely harmonic voltage, voltage fluctuation, voltage deviation and voltage sag, are considered respectively to establish respective single factor evaluation vectors. This multi-faceted evaluation of the voltage of the power grid is performed in a numerical manner, so that the single factor evaluation matrix finally established can truly reflect the operation of the distribution network.

[0163] Moreover, in the present embodiments S3.1 to S3.4, the line parameters and user load types in the distribution network are obtained as detailed as possible through the planning scheme, and the converter and harmonic sources are modeled, so that a relatively complete distribution network model can be obtained, that is, a single factor evaluation matrix X, which can simulate a relatively realistic distribution network operation condition.

[0164] S3.5. According to the primary indicator weight distribution A and the single factor evaluation matrix X, the comprehensive evaluation result Y0 of the power quality of each distributed power source to be selected for access is calculated;

[0165] Among them, the comprehensive evaluation results are:

[0166] Y0=A×X=[y1 y2 … y n ]

[0167] S3.6. Obtain the severity of power quality problems before the distributed power sources at different nodes are connected from the comprehensive evaluation result Y0, and determine the pros and cons of each candidate connection node according to the severity of the power quality problems;

[0168] According to the good and bad conditions, the installed capacity allocation matrix F0 is established by increasing the installed capacity at the good nodes and reducing the capacity at the bad nodes;

[0169] Among them, the installed capacity allocation matrix is:

[0170]

[0171] By analyzing the quality of the nodes, S3.6 of this embodiment can more accurately determine which nodes can withstand higher access capacity, which helps to avoid waste of resources and allocate resources to the most needed and effective nodes, so as to reduce overload and bottlenecks in the power grid system and improve overall operating efficiency.

[0172] S3.7, by combining the installed capacity allocation matrix F0 with the current installed capacity P of the distributed power source, the preliminary installed capacity P of each candidate access node in the distribution network is calculated. i ;

[0173] Among them, the initial installed capacity is:

[0174]

[0175] The comprehensive evaluation results of S3.5 to S3.7 of this embodiment are established based on the weight distribution of the first-level indicators that can reflect the power quality status and the single-factor evaluation matrix that can reflect the voltage evaluation index. Therefore, they can objectively and accurately reflect the quality of the power quality of each distributed power source to be selected for access. Therefore, based on this, the corresponding installed capacity allocation method can be adopted to obtain a comprehensive preliminary installed capacity for application.

[0176] S4. Simulate and correct the preliminary installed capacity. When the change value of the preliminary installed capacity is less than the preset value, obtain the final installed capacity, and configure the capacity of each access node to be selected in the distribution network according to the final installed capacity.

[0177] In step S4 of the embodiment of the present invention, S4 includes S4.1 to S4.2; wherein S4.1 is to construct the final installed capacity P l S4.2 is the process of actual capacity configuration, which is as follows:

[0178] S4.1. The initial installed capacity P i When the distributed power source is connected, the initial installed capacity P is obtained by repeatedly calculating the single factor evaluation matrix X to obtain the installed capacity. i Make corrections until the initial installed capacity P i When the change in installed capacity before and after the cycle is less than 1%, the final installed capacity P is obtained. l .

[0179] S4.2, according to the final installed capacity P l Configure the capacity of each candidate access node in the distribution network.

[0180] To apply the embodiment of the present invention, a new power system distribution network park with a high power electronic device penetration rate in a certain planning stage in Guangdong Province is taken as an example to apply a power grid power supply capacity configuration method taking into account power quality provided by the embodiment, specifically:

[0181] 1) Preset conditions: The distribution network park plans to build distributed photovoltaic, small and medium-sized wind turbines and other distributed new energy sources, and the distribution network voltage level is 10kV; assuming that the planned distributed photovoltaic installed capacity in the distribution network park is 2MW, other distributed power sources can refer to this example to implement a similar site selection and capacity determination process. 2) Specific implementation method:

[0183] First, through the importance scale table, according to the consistency index CR and the consistency evaluation matrix R, we can get the consistency evaluation matrix R for the four power quality issues of harmonic voltage, voltage deviation, voltage fluctuation and voltage sag. In this example, it is assumed that the consistency evaluation matrix R is:

[0184]

[0185] According to the consistency evaluation matrix R, the hierarchical analysis importance weight distribution A can be calculated 01 =[0.4824,0.2718,0.1576,0.0882], assuming that the weight distribution A of harmonic voltage, voltage deviation, voltage fluctuation and voltage sag is obtained according to the expert scoring method 02 =[0.5,0.25,0.15,0.1]; and then the importance weights of A are assigned by hierarchical analysis 01 and voltage sag weight distribution A 02 The first-level indicator weight distribution can be calculated as A=[0.4912, 0.2609, 0.1538, 0.0941].

[0186] Secondly, to simplify the expression, it is assumed that the harmonic order range considered in the example is 3rd, 5th and 7th harmonics, and there are 5 access nodes to be selected; the first single factor evaluation vector X1=A1×B1 can be obtained, with a size of 1×5, where the size of A1 is 1×3 and the size of B1 is 3×5; it is assumed that after calculation, X1=[0.288, 0.354, 0.2355, 0.0865, 0.036] can be obtained.

[0187] Thirdly, according to the simulation results, the voltage deviation and voltage fluctuation problems of the selected access nodes are analyzed, and the voltage fluctuation severity and voltage deviation severity of each distributed power supply selected access node are obtained according to the voltage fluctuation and deviation level classification table for score evaluation. It is assumed that the normalized second single factor evaluation vector X2 and the third single factor evaluation vector X3 can be obtained, which are X2 = [0.0667, 0.4, 0.0667, 0.0667, 0.4] and X3 = [0.25, 0.4167, 0.04167, 0.25, 0.04167] respectively;

[0188] According to the simulation results, the voltage sag problem of the selected access node is analyzed, and the voltage sag evaluation index is obtained according to the voltage sag severity classification table. It is assumed that the normalized fourth single factor X4 can be obtained as X4=[0.1, 0.6, 0.1, 0.1, 0.1].

[0189] From this, according to X1, X2, X3 and X4, we can calculate for each factor U in the factor set U i The single factor evaluation matrix X is:

[0190]

[0191] The comprehensive evaluation result Y0 of the power quality of each distributed generation candidate access node can be expressed as:

[0192] Y0=A×X=[0.2067 0.3988 0.1489 0.1077 0.1379]

[0193] According to the comprehensive evaluation result Y0, the installed capacity allocation matrix F0 is obtained as follows:

[0194] F0=[F1 F2 … F n ]=[4.8379 2.5075 6.7159 9.2851 7.2516]

[0195] Then according to the initial installed capacity P i The expression of , calculates the initial installed capacity P of each candidate access node i , respectively 0.3162MW, 0.1639MW, 0.439MW, 0.6069MW and 0.474MW.

[0196] Finally, the distributed photovoltaic with the current capacity is connected to the corresponding node, and the simulation is performed again to correct the result, and this operation is repeated; when the installed capacity change value is less than 1%, the cycle ends, and the output result is the final installed capacity P l , and according to the final installed capacity P lThe capacity of each candidate access node in the distribution network is actually configured.

[0197] To apply the embodiments of the present invention, please refer to Figure 2 , Figure 2 : is a flow chart of the optimization of site selection and capacity determination of distributed power sources in a distribution network provided by an embodiment of the present invention, which shows the general process of installing capacity in this embodiment, specifically:

[0198] 1. Start.

[0199] 2. Obtain the types, quantity, and locations of candidate access nodes of planned distributed power sources in the distribution network, as well as the installed capacity of different types of distributed power sources: Obtain grid operation data from the distribution network, where the grid operation data includes model data such as the types, quantity, and locations of candidate access nodes of planned distributed power sources in the distribution network, and the total installed capacity of different types of distributed power sources, and clarify the main power quality assessment indicators.

[0200] 3. Clarify the power quality evaluation indicators, and use the hierarchical analysis importance combined with the expert scoring method to obtain the first-level indicator weight distribution A: Combine the distribution network user load conditions in the power grid operation data with the preset importance scale and planning and design scheme to establish the initial evaluation matrix R0, and calculate the consistency evaluation matrix R based on the initial evaluation matrix R0; Use the hierarchical analysis importance combined with the expert scoring method, and combine the consistency evaluation matrix R to calculate the first-level indicator weight distribution A.

[0201] 4. Calculate and evaluate the compatibility level of each harmonic voltage, voltage fluctuation, voltage deviation, voltage sag and other power quality issues specified in the national standard of each distributed power source to be connected to the node, so as to obtain the secondary judgment vector: according to the degree of distortion of each harmonic voltage, establish the first single factor judgment vector on the harmonic voltage compatibility level; according to the severity of voltage fluctuation and voltage deviation of each distributed power source to be connected to the node, establish the second single factor judgment vector and the third single factor judgment vector; according to the voltage sag evaluation results of each distributed power source to be connected to the node, establish the fourth single factor judgment vector; the first single factor judgment vector, the second single factor judgment vector, the third single factor judgment vector and the fourth single factor judgment vector constitute the single factor judgment matrix X.

[0202] 5. According to the comprehensive evaluation results of the power quality of each distributed power supply candidate access node, the distributed power supply installed capacity of each node is reasonably allocated with the help of the allocation matrix F: the comprehensive evaluation result Y0 is calculated according to the first-level indicator weight distribution A and the single factor evaluation matrix X, and then the preliminary installed capacity P is calculated using the installed capacity allocation matrix F0. i .

[0203] 6. Determine whether the change in installed capacity before and after exceeds the specified range: Set the initial installed capacity Pi When the distributed power source is connected, the initial installed capacity P is obtained by repeatedly calculating the single factor evaluation matrix X to obtain the installed capacity. i Make corrections until the initial installed capacity P i When the change in installed capacity before and after the cycle is less than 1%, the final installed capacity P is obtained. l .

[0204] 7. End.

[0205] In general, the embodiments of the present invention have the following beneficial effects:

[0206] This embodiment establishes a first-level indicator weight distribution according to the characteristic consistency of the electric energy indicator, and can use technical means to perform quantitative weight analysis and make reasonable estimates on multiple existing power quality problems at the same time, so that the obtained first-level indicator weight distribution can reasonably determine the weight of the electric energy indicator, so as to more comprehensively evaluate the power quality status; since the single-factor evaluation matrix is ​​established according to voltage evaluation indicators of different dimensions, it can truly reflect the operation of the distribution network; through the first-level indicator weight distribution combined with the single-factor evaluation matrix, in order to determine the preliminary installed capacity of each candidate access node, it can consider the scenarios of multiple distributed new energy sources accessing the distribution network, so that the solution is more in line with the actual application of the project, and can also fully demonstrate the typical power quality problems of the distribution network, providing data support for the determination of the final installed capacity; and finally, by controlling the change value to be less than the preset value, the obtained final installed capacity can make the distribution network have good adaptability and stability when applied, so that the capacity configuration is optimized.

[0207] In addition, by fully considering the typical power quality problems of the new power system distribution network, such as harmonic voltage, voltage deviation, voltage fluctuation and voltage sag, which may be caused by the access of distributed power sources to the distribution network, the weight of each power quality problem is given by using an improved fuzzy comprehensive evaluation method combined with the expert scoring method, which can provide suggestions for the optimal capacity of accessing distributed renewable energy to each candidate access point in the planning stage of the new power system distribution network.

[0208] Embodiment 2:

[0209] See also Figure 3 , an embodiment of the present invention provides a power grid power capacity configuration device taking into account power quality, including a data module 10, a weight module 20, a capacity module 30 and an installed module 40;

[0210] Wherein, the data module 10 is used to obtain power grid operation data from the distribution network;

[0211] A weight module 20, for establishing a primary indicator weight distribution for power quality issues according to the characteristic consistency of power indicators in the power grid operation data;

[0212] The capacity module 30 is used to combine the weight distribution of the primary indicators with the single factor evaluation matrix to calculate the preliminary installed capacity of each access node to be selected in the distribution network; wherein the single factor evaluation matrix is ​​established according to the voltage evaluation indicators of different dimensions;

[0213] The installation module 40 is used to simulate and correct the preliminary installed capacity. When the change value of the preliminary installed capacity is less than the preset value, the final installed capacity is obtained, and the capacity of each access node to be selected in the distribution network is configured according to the final installed capacity.

[0214] In one embodiment, the data module 10 is specifically:

[0215] Obtain grid operation data from the distribution network;

[0216] The grid operation data includes model data such as the type, quantity, and location of the selected access nodes of the distributed power sources planned in the distribution network, and the total installed capacity of different types of distributed power sources, and clarifies the main power quality assessment indicators;

[0217] In addition, the power quality assessment indicators considered in the second embodiment include: the compatibility level (percentage of fundamental component) of each harmonic voltage specified in GB / Z 17625.14-2017 Electromagnetic Compatibility - Limits - Harmonics, interharmonics, voltage fluctuations and unbalanced emission limits for the connection of disturbance devices to low-voltage power systems, voltage fluctuations, voltage deviations specified in GB / T12325-2008 Power Quality - Supply Voltage Deviation, and voltage sags specified in GB / T 39270-2020 Voltage Sag Indicator and Severity Assessment Method, that is, the compatibility level of harmonic voltages, voltage fluctuations, voltage deviations and voltage sags; these four indicators are used as the factor set U={U1, U2, U3, U4}.

[0218] In one embodiment, the weight module 20 includes a first subunit, a second subunit, a third subunit, an importance unit, and an index unit;

[0219] Among them, the first subunit is the process of constructing the initial evaluation matrix R0, the second subunit is the process of constructing the consistency index CR, the third subunit is the process of constructing the consistency evaluation matrix R, the importance unit and the index unit are the processes of constructing the first-level index weight distribution A, specifically:

[0220] The first subunit is used to establish an initial evaluation matrix R0 by combining the distribution network user load conditions in the power grid operation data with a preset importance scale table and a planning and design scheme;

[0221] Among them, the importance scale is established according to the importance of different indicators in the distribution network. The load conditions of distribution network users include the load operation conditions of equipment such as frequency conversion equipment and arc furnaces in metallurgical plants.

[0222] Table 1 Importance scale

[0223]

[0224] To apply the embodiment of the present invention, please refer to Table 1. This embodiment provides an importance scale table. Table 1 is used to compare the importance of indicators i and j at each layer in the distribution network.

[0225] The second subunit is used to calculate the consistency index CR according to the maximum characteristic root and matrix order of the initial evaluation matrix R0;

[0226] Among them, the consistency index is:

[0227]

[0228] Among them, for the parameter CI:

[0229]

[0230] Among them, λ max and n are the maximum eigenvalue and matrix order of the initial evaluation matrix R0 respectively; in this embodiment, n=4; the value of RI is determined by the matrix order of the initial evaluation matrix R0, please refer to Table 2.

[0231] Table 2 Value table of consistency index RI

[0232]

[0233] To apply the embodiment of the present invention, please refer to Table 2. This embodiment provides a value table of the consistency index RI. Table 2 shows that when the matrix order n of the initial evaluation matrix R0 takes different values, the corresponding parameter RI will also have different values.

[0234] The third subunit is used to modify the parameters of the initial evaluation matrix R0 according to the value of the consistency index CR, and obtain the consistency evaluation matrix R about the power quality problem, which is specifically:

[0235] Determine the value of the consistency index CR:

[0236] Case 1: When CR<0.1, it means that the initial evaluation matrix R0 passes the consistency test, and the initial evaluation matrix R0 is directly used as the consistency evaluation matrix R.

[0237] Case 2: When CR ≥ 0.1, it means that the initial evaluation matrix R0 fails the consistency test. Then, the consistency evaluation matrix R is obtained by reconstructing the evaluation matrix with a larger consistency index CR.

[0238] Among them, the consistency evaluation matrix is:

[0239]

[0240] The initial evaluation matrix established by the first subunit, the second subunit and the third subunit of this embodiment can reflect the comprehensive performance of different equipment in the distribution network. The parameters of the initial evaluation matrix are corrected according to the numerical value of the consistency index, so that the obtained consistency evaluation matrix can more accurately evaluate the consistency of the performance, safety and reliability of the power grid system, so as to identify and quantify the correlation and interaction between different indicators, thereby ensuring that the overall performance of the power grid system is stable and consistent.

[0241] The importance unit is used to calculate the hierarchical analysis importance weight distribution A according to the consistency evaluation matrix R using the first preset formula 01 Among them, A 01 =[a 01 ,a 02 ,a 03 ,a 04 ];

[0242] Indicator unit, used to assign the importance weight of hierarchical analysis to A 01 Combined with the preset expert scoring method weight distribution A 02 , calculate the first-level index weight distribution A; where A 02 =[a 05 ,a 06 ,a 07 ,a 08 ], expert scoring method weight distribution A 02 The statistical data is obtained by first conducting multiple rounds of anonymous consultation and feedback from representative experts in the industry on the four indicators in the factor set U, and then normalizing the statistical data;

[0243] Among them, the hierarchical analysis importance weight distribution A 01 Parameter a in 0i for:

[0244]

[0245] Expert scoring method weight distribution A 02 Parameter a in 0j satisfy:

[0246]

[0247] The weight distribution A of the first-level indicators is:

[0248]

[0249] Among them, r ij is the i-th row and j-th column element in the consistency evaluation matrix R, a 0i The parameter 4 in the expression is because there are 4 evaluation indicators at this level; assuming that the four parameters in the first-level indicator weight distribution A are a1, a2, a3 and a4, then a i satisfy:

[0250] The importance unit and the indicator unit of this embodiment obtain the first-level indicator weight distribution by combining the hierarchical analysis importance of power quality problems with the expert scoring method, and can simultaneously use technical means to perform quantitative weight analysis on multiple existing power quality problems and make reasonable estimates, so that the obtained first-level indicator weight distribution can reasonably determine the weight of each indicator, and can more comprehensively evaluate the power quality status, providing strong data support for the optimized operation of the power grid system and the access protection of distributed power sources.

[0251] In one embodiment, the capacity module 30 includes a valid value subunit, a matrix subunit, a vector subunit, a second vector unit, a third vector unit, a combination unit, an evaluation unit, a fourth subunit, a fifth subunit, and a capacity unit;

[0252] Among them, the effective value subunit and the matrix subunit are the process of constructing the secondary factor evaluation matrix B1, the vector subunit is the process of constructing the first single factor evaluation vector X1, the second vector unit is the process of constructing the second single factor evaluation vector X2 and the third single factor evaluation vector X3, the third vector unit and the combination unit are the process of constructing the fourth single factor evaluation vector X4 and the single factor evaluation matrix X, the evaluation unit is the process of constructing the comprehensive evaluation result Y0, the fourth subunit and the fifth subunit are the process of constructing the installed capacity allocation matrix F0, and the capacity unit is the process of constructing the preliminary installed capacity P i The process is as follows:

[0253] The effective value subunit is used to obtain the voltage effective values ​​of the fundamental wave and each frequency harmonic of different candidate access nodes by performing fast Fourier transform (FFT) on the voltage of the candidate access node of the distributed power source obtained by simulation, and then obtain the effective value RMSU of the fundamental wave voltage respectively. 0j and harmonic voltage RMS U ij ; Wherein, i represents the harmonic frequency is i+1th, and j represents the jth access node to be selected;

[0254] Matrix subunit, used to calculate the effective value of the fundamental voltage RMSU 0j The harmonic voltage effective value RMSU ij , by calculating the severity of node harmonic distortion, the secondary factor evaluation parameter b is obtained ij ;

[0255] The matrix subunit is also used to judge the parameter b according to the secondary factors ij , by calculating each candidate access node and the harmonic level of different frequencies (percentage of fundamental component), the secondary factor evaluation matrix B1 is obtained;

[0256] Among them, the secondary factor evaluation matrix is:

[0257]

[0258] The secondary factor evaluation parameters are:

[0259]

[0260] Where, h represents the harmonic frequency of h+1 harmonic, n is the number of nodes to be connected to the distributed generation in the distribution network; k i b) The compatibility level limits (percentage of fundamental component) of harmonic voltages of different frequencies specified in IEC 61000-2-2 Electromagnetic Compatibility (EMC), i.e. the compatibility level of low-frequency conducted disturbances and signals in public low-voltage power supply systems; b) ij The size of represents the severity of the i+1th harmonic distortion of the jth distributed generation candidate access node.

[0261] The vector subunit is used to normalize each row of data in the secondary factor evaluation matrix B1 to obtain the first single factor evaluation vector X1 about the compatibility level of each harmonic voltage, that is, X1 = [x 11 x 12 ... x 1n ];

[0262] Specifically: taking the 3rd, 5th and 7th harmonics, which are the most common in engineering practice and the most concerned in the distribution network, as an example, first normalize each row in the secondary factor evaluation matrix B1 to obtain a first matrix of size 3×h; then add the first matrix by column to obtain a second matrix of 1×h; finally, normalize the second matrix to obtain the first single factor evaluation vector X1 of the harmonic voltage compatibility level of different candidate access nodes.

[0263] It should be noted that in the effective value subunit, matrix subunit and first vector subunit of this embodiment, the harmonic order range considered is h+1 harmonics, that is, 2-h+1, a total of h frequencies. The harmonic frequency range considered can be determined according to planning requirements.

[0264] In this embodiment, the first single factor evaluation vector is obtained after normalization processing of the quantum unit. This vector, as a parameter that describes the relative size relationship between the voltage harmonics and the fundamental wave in the power grid system, can reflect the severity of the harmonic distortion in the power grid system. When the harmonic voltage compatibility level is high, it means that the harmonic distortion in the power grid system is more serious, which may have a negative impact on electrical equipment. Therefore, the first single factor evaluation vector can be used to know at the first time whether the harmonic voltage has an adverse effect.

[0265] The second vector unit is used to evaluate the severity of voltage fluctuation and voltage deviation of each distributed power source to be selected for access in the grid operation data according to the voltage fluctuation and deviation classification table, and give a high score to the node with higher severity to focus on the node, thereby obtaining the second single factor evaluation vector X2 on voltage fluctuation and the third single factor evaluation vector X3 on voltage deviation, that is, X2=[x 21 x 22 ... x 2n ],X3=[x 31 x 32 ... x 3n ];

[0266] Among them, the voltage fluctuation and deviation classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be connected to the node based on the absolute average value of the voltage fluctuation and voltage deviation within a day in the preset historical power grid operation data.

[0267] Table 3 Classification of voltage fluctuation and deviation

[0268]

[0269] To apply the embodiment of the present invention, please refer to Table 3. This embodiment provides a voltage fluctuation and deviation classification table. Table 3 shows the classification method of voltage fluctuation and voltage deviation. The standard requirements in the table are divided into 4 levels, namely excellent, good, qualified and unqualified.

[0270] Due to the randomness, intermittency and volatility of distributed power sources, different distributed power sources have different characteristics. GB / Z 17625.14-2017 Electromagnetic compatibility - Limits - Evaluation of emission limits of harmonics, interharmonics, voltage fluctuations and imbalances of disturbance devices connected to low-voltage power systems stipulates that the voltage fluctuation of the distribution network should be limited to 3% of the nominal supply voltage, and GB / T 12325-2008 Power quality - Supply voltage deviation stipulates that the supply voltage deviation of 20kV and below is ±7% of the nominal voltage;

[0271] Therefore, a 10kV distribution network object is used as an example to apply the process of obtaining the second single factor evaluation vector and the third single factor evaluation vector. Wind turbines, photovoltaics and energy storage are connected to the distribution network. Other distributed power sources can refer to similar analysis, specifically:

[0272] The assessment levels of voltage fluctuation and voltage deviation are divided into four levels according to the standard requirements of GB / Z 17625.14-2017 Electromagnetic compatibility - Limits - Evaluation of emission limits of harmonics, interharmonics, voltage fluctuations and unbalance of disturbance devices connected to low-voltage power systems and GB / T12325-2008 Power quality - Supply voltage deviation, namely excellent, good, qualified and unqualified, and the corresponding assessment results are 0.1, 0.3, 0.6 and 1 respectively;

[0273] From Table 3, we can get the initial second single factor evaluation vector and the third single factor evaluation vector, in which the elements are the evaluation results of the voltage fluctuation and voltage deviation of each distributed power supply to be selected for access node. After normalization operation, we can get the final second single factor evaluation vector X2 and the third single factor evaluation vector X3.

[0274] In the second vector unit of this embodiment, since wind turbines and photovoltaics are greatly affected by external environments such as wind speed and light intensity, the average value of the absolute value of the data deviation is used as the basis for grading, so as to ensure the rationality of the grading in the grading table of voltage fluctuation and deviation;

[0275] Furthermore, by obtaining the second single factor evaluation vector and the third single factor evaluation vector according to the voltage fluctuation and deviation classification table, the severity of the voltage fluctuation and the severity of the voltage deviation can be quickly and accurately known. This parameter acquisition method is fast and effective.

[0276] The third vector unit is used to evaluate the voltage sag impact degree of each distributed power source to be selected for access according to the voltage sag severity classification table to obtain an initial single factor evaluation vector; wherein the elements of the initial single factor evaluation vector are the evaluation results of the voltage sag of each distributed power source to be selected for access;

[0277] The third vector unit is also used to normalize the initial single factor evaluation vector to obtain a fourth single factor evaluation vector X4 for voltage sag, that is, X4 = [x 41 x 42 ... x 4n ];

[0278] A combining unit, used for forming a single factor evaluation matrix X by using the first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector;

[0279] Among them, the single factor evaluation matrix is:

[0280]

[0281] Among them, X i represents the i-th secondary evaluation vector, and n is the number of nodes to be connected to the distributed generation.

[0282] Table 4 Classification of voltage sag severity

[0283]

[0284]

[0285] To apply the embodiment of the present invention, please refer to Table 4. This embodiment provides a voltage sag severity classification table. Table 4 shows the comparison relationship between the residual voltage duration and severity caused by the voltage sag.

[0286] For example, in the GB / T 39270-2020 voltage sag index and severity assessment method standard, voltage sag is defined as a phenomenon in which the RMS value of the power frequency voltage at a certain point in the power system suddenly drops to 0.1pu~0.9pu and returns to normal after a short duration of 10ms~1min;

[0287] Therefore, the voltage sag evaluation standard of the candidate access node is defined as shown in Table 4. The evaluation level is divided into 4 levels, namely poor, bad and very poor, and the corresponding scores are 0.1, 0.3, 0.6 and 1 respectively. If there is no voltage sag, it is excellent.

[0288] The third vector unit and the combination unit of this embodiment consider four different dimensions, namely harmonic voltage, voltage fluctuation, voltage deviation and voltage sag, respectively, to establish respective single factor evaluation vectors. This multi-faceted evaluation of the voltage of the power grid can be performed in a numerical manner, so that the single factor evaluation matrix finally established can truly reflect the operation status of the distribution network.

[0289] Moreover, the effective value subunit, matrix subunit, vector subunit, second vector unit, third vector unit and combination unit of the present embodiment obtain the line parameters, user load types, etc. in the distribution network as detailed as possible through the planning scheme, and model the converter and harmonic sources, etc., and finally obtain a relatively complete distribution network model, that is, a single factor evaluation matrix X, which can simulate a relatively realistic distribution network operation condition.

[0290] An evaluation unit, used to calculate a comprehensive evaluation result Y0 of the power quality of each distributed power source to be selected for access node according to the primary indicator weight distribution A and the single factor evaluation matrix X;

[0291] Among them, the comprehensive evaluation results are:

[0292] Y0=A×X=[y1 y2 … y n ]

[0293] The fourth subunit is used to obtain the severity of power quality problems before the distributed power sources of different nodes are connected from the comprehensive evaluation result Y0, and determine the pros and cons of each candidate access node according to the severity of the power quality problem;

[0294] The fifth subunit is used to establish an installed capacity allocation matrix F0 by increasing the installed capacity at excellent nodes and reducing the capacity at inferior nodes according to the superior and inferior conditions;

[0295] Among them, the installed capacity allocation matrix is:

[0296]

[0297] By analyzing the quality of the nodes, S3.6 of this embodiment can more accurately determine which nodes can withstand higher access capacity, which helps to avoid waste of resources and allocate resources to the most needed and effective nodes, so as to reduce overload and bottlenecks in the power grid system and improve overall operating efficiency.

[0298] The capacity unit is used to calculate the initial installed capacity P of each candidate access node in the distribution network by combining the installed capacity allocation matrix F0 with the current installed capacity P of the distributed power source. i ;

[0299] Among them, the initial installed capacity is:

[0300]

[0301] In the evaluation unit, the fourth subunit, the fifth subunit and the capacity unit of the present embodiment, the comprehensive evaluation result is established based on the weight distribution of the first-level indicators that can reflect the power quality status and the single-factor evaluation matrix that can reflect the voltage evaluation index. Therefore, it can objectively and accurately reflect the quality of the power quality of each distributed power source to be selected for access. Therefore, on this basis, the corresponding installed capacity allocation method can be adopted to obtain a comprehensive preliminary installed capacity for application.

[0302] In one embodiment, the installation module 40 includes a simulation unit and an application unit; wherein the simulation unit is used to construct the final installed capacity P l The application unit is the process of actual capacity configuration, which is as follows:

[0303] The simulation unit is used to calculate the initial installed capacity P iWhen the distributed power source is connected, the initial installed capacity P is obtained by repeatedly calculating the single factor evaluation matrix X to obtain the installed capacity. i Make corrections until the initial installed capacity P i When the change in installed capacity before and after the cycle is less than 1%, the final installed capacity P is obtained. l .

[0304] Application unit, used to calculate the final installed capacity P l Configure the capacity of each candidate access node in the distribution network.

[0305] To apply the embodiment of the present invention, a new power system distribution network park with a high power electronic device penetration rate in a certain planning stage in Guangdong Province is taken as an example to illustrate the application of a power grid power capacity configuration device taking into account power quality provided by the embodiment, specifically:

[0306] 3) Pre-conditions: The distribution network park plans to build distributed photovoltaic, small and medium-sized wind turbines and other distributed new energy sources, and the distribution network voltage level is 10kV; assuming that the planned distributed photovoltaic installed capacity in the distribution network park is 2MW, other distributed power sources can refer to this example to implement a similar site selection and capacity determination process. 4) Specific implementation method:

[0308] First, through the importance scale table, according to the consistency index CR and the consistency evaluation matrix R, we can get the consistency evaluation matrix R for the four power quality issues of harmonic voltage, voltage deviation, voltage fluctuation and voltage sag. In this example, it is assumed that the consistency evaluation matrix R is:

[0309]

[0310] According to the consistency evaluation matrix R, the hierarchical analysis importance weight distribution A can be calculated 01 =[0.4824,0.2718,0.1576,0.0882], assuming that the weight distribution A of harmonic voltage, voltage deviation, voltage fluctuation and voltage sag is obtained according to the expert scoring method 02 =[0.5,0.25,0.15,0.1]; and then the importance weights of A are assigned by hierarchical analysis 01 and voltage sag weight distribution A 02 The first-level indicator weight distribution can be calculated as A=[0.4912, 0.2609, 0.1538, 0.0941].

[0311] Secondly, to simplify the expression, it is assumed that the harmonic order range considered in the example is 3rd, 5th and 7th harmonics, and there are 5 access nodes to be selected; the first single factor evaluation vector X1=A1×B1 can be obtained, with a size of 1×5, where the size of A1 is 1×3 and the size of B1 is 3×5; it is assumed that after calculation, X1=[0.288, 0.354, 0.2355, 0.0865, 0.036] can be obtained.

[0312] Thirdly, according to the simulation results, the voltage deviation and voltage fluctuation problems of the selected access nodes are analyzed, and the voltage fluctuation severity and voltage deviation severity of each distributed power supply selected access node are obtained according to the voltage fluctuation and deviation level classification table for score evaluation. It is assumed that the normalized second single factor evaluation vector X2 and the third single factor evaluation vector X3 can be obtained, which are X2 = [0.0667, 0.4, 0.0667, 0.0667, 0.4] and X3 = [0.25, 0.4167, 0.04167, 0.25, 0.04167] respectively;

[0313] According to the simulation results, the voltage sag problem of the selected access node is analyzed, and the voltage sag evaluation index is obtained according to the voltage sag severity classification table. It is assumed that the normalized fourth single factor X4 can be obtained as X4=[0.1, 0.6, 0.1, 0.1, 0.1].

[0314] From this, according to X1, X2, X3 and X4, we can calculate for each factor U in the factor set U i The single factor evaluation matrix X is:

[0315]

[0316] The comprehensive evaluation result Y0 of the power quality of each distributed generation candidate access node can be expressed as:

[0317] Y0=A×X=[0.2067 0.3988 0.1489 0.1077 0.1379]

[0318] According to the comprehensive evaluation result Y0, the installed capacity allocation matrix F0 is obtained as follows:

[0319] F0=[F1 F2 … F n ]=[4.8379 2.5075 6.7159 9.2851 7.2516]

[0320] Then according to the initial installed capacity P i The expression of , calculates the initial installed capacity P of each candidate access node i, respectively 0.3162MW, 0.1639MW, 0.439MW, 0.6069MW and 0.474MW.

[0321] Finally, the distributed photovoltaic with the current capacity is connected to the corresponding node, and the simulation is performed again to correct the result. This operation is repeated. When the installed capacity change value is less than 1%, the cycle ends and the output result is the final installed capacity P l , and according to the final installed capacity P l The capacity of each candidate access node in the distribution network is actually configured.

[0322] To apply the embodiments of the present invention, please refer to Figure 2 , Figure 2 : is a flow chart of the optimization of site selection and capacity determination of distributed power sources in a distribution network provided by an embodiment of the present invention, which shows the general process of installing capacity in this embodiment, specifically:

[0323] 1. Start.

[0324] 2. Obtain the types, quantity, and locations of candidate access nodes of planned distributed power sources in the distribution network, as well as the installed capacity of different types of distributed power sources: Obtain grid operation data from the distribution network, where the grid operation data includes model data such as the types, quantity, and locations of candidate access nodes of planned distributed power sources in the distribution network, and the total installed capacity of different types of distributed power sources, and clarify the main power quality assessment indicators.

[0325] 3. Clarify the power quality evaluation indicators, and use the hierarchical analysis importance combined with the expert scoring method to obtain the first-level indicator weight distribution A: Combine the distribution network user load conditions in the power grid operation data with the preset importance scale and planning and design scheme to establish the initial evaluation matrix R0, and calculate the consistency evaluation matrix R based on the initial evaluation matrix R0; Use the hierarchical analysis importance combined with the expert scoring method, and combine the consistency evaluation matrix R to calculate the first-level indicator weight distribution A.

[0326] 4. Calculate and evaluate the compatibility level of each harmonic voltage, voltage fluctuation, voltage deviation, voltage sag and other power quality issues specified in the national standard of each distributed power source to be connected to the node, so as to obtain the secondary judgment vector: according to the degree of distortion of each harmonic voltage, establish the first single factor judgment vector on the harmonic voltage compatibility level; according to the severity of voltage fluctuation and voltage deviation of each distributed power source to be connected to the node, establish the second single factor judgment vector and the third single factor judgment vector; according to the voltage sag evaluation results of each distributed power source to be connected to the node, establish the fourth single factor judgment vector; the first single factor judgment vector, the second single factor judgment vector, the third single factor judgment vector and the fourth single factor judgment vector constitute the single factor judgment matrix X.

[0327] 5. According to the comprehensive evaluation results of the power quality of each distributed power supply candidate access node, the distributed power supply installed capacity of each node is reasonably allocated with the help of the allocation matrix F: the comprehensive evaluation result Y0 is calculated according to the first-level indicator weight distribution A and the single factor evaluation matrix X, and then the preliminary installed capacity P is calculated using the installed capacity allocation matrix F0. i .

[0328] 6. Determine whether the change in installed capacity before and after exceeds the specified range: Set the initial installed capacity P i When the distributed power source is connected, the initial installed capacity P is obtained by repeatedly calculating the single factor evaluation matrix X to obtain the installed capacity. i Make corrections until the initial installed capacity P i When the change in installed capacity before and after the cycle is less than 1%, the final installed capacity P is obtained. l .

[0329] 7. End.

[0330] In general, the embodiments of the present invention have the following beneficial effects:

[0331] This embodiment establishes a first-level indicator weight distribution according to the characteristic consistency of the electric energy indicator, and can use technical means to perform quantitative weight analysis and make reasonable estimates on multiple existing power quality problems at the same time, so that the obtained first-level indicator weight distribution can reasonably determine the weight of the electric energy indicator, so as to more comprehensively evaluate the power quality status; since the single-factor evaluation matrix is ​​established according to voltage evaluation indicators of different dimensions, it can truly reflect the operation of the distribution network; through the first-level indicator weight distribution combined with the single-factor evaluation matrix, in order to determine the preliminary installed capacity of each candidate access node, it can consider the scenarios of multiple distributed new energy sources accessing the distribution network, so that the solution is more in line with the actual application of the project, and can also fully demonstrate the typical power quality problems of the distribution network, providing data support for the determination of the final installed capacity; and finally, by controlling the change value to be less than the preset value, the obtained final installed capacity can make the distribution network have good adaptability and stability when applied, so that the capacity configuration is optimized.

[0332] In addition, by fully considering the typical power quality problems of the new power system distribution network, such as harmonic voltage, voltage deviation, voltage fluctuation and voltage sag, which may be caused by the access of distributed power sources to the distribution network, the weight of each power quality problem is given by using an improved fuzzy comprehensive evaluation method combined with the expert scoring method, which can provide suggestions for the optimal capacity of accessing distributed renewable energy to each candidate access point in the planning stage of the new power system distribution network.

[0333] Embodiment three:

[0334] An embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for configuring power grid power capacity taking into account power quality;

[0335] Wherein, the method for configuring the capacity of a power grid taking into account the power quality, if implemented in the form of a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0336] The above are preferred embodiments of the present invention. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring power grid power capacity taking into account power quality, characterized in that: include: Obtain grid operation data from the distribution network; According to the characteristic consistency of the electric energy indicators in the power grid operation data, a first-level indicator weight distribution for power quality issues is established, specifically: the load conditions of the distribution network users in the power grid operation data are combined with a preset importance scale to establish an initial evaluation matrix; wherein the importance scale is established according to the importance of different indicators in the distribution network; the consistency index is calculated according to the maximum characteristic root and matrix order of the initial evaluation matrix; the parameters of the initial evaluation matrix are corrected according to the numerical value of the consistency index to obtain the consistency evaluation matrix for power quality issues; the hierarchical analysis importance weight distribution is calculated according to the consistency evaluation matrix using the first preset formula; the hierarchical analysis importance weight distribution is combined with the preset expert scoring method weight distribution to calculate the first-level indicator weight distribution; The primary indicator weight distribution is combined with the single factor evaluation matrix to calculate the preliminary installed capacity of each candidate access node in the distribution network, specifically: according to the primary indicator weight distribution and the single factor evaluation matrix, the comprehensive evaluation result of the power quality of each candidate access node of the distributed power source is calculated; the severity of the power quality problems before the distributed power sources of different nodes are connected is obtained from the comprehensive evaluation result, and the advantages and disadvantages of each candidate access node are determined according to the severity of the power quality problems; according to the advantages and disadvantages, the installed capacity allocation matrix is ​​established by increasing the installed capacity at the excellent nodes; the preliminary installed capacity of each candidate access node in the distribution network is calculated by combining the installed capacity allocation matrix with the current installed capacity of the distributed power source; Simulating and correcting the preliminary installed capacity, obtaining a final installed capacity when a change value of the preliminary installed capacity is less than a preset value, and configuring the capacity of each access node to be selected in the distribution network according to the final installed capacity; The single factor evaluation matrix is ​​established based on voltage evaluation indicators of different dimensions, specifically: The voltage effective values ​​of the fundamental wave and each frequency harmonic of different access nodes to be selected are obtained, and the fundamental wave voltage effective value and the harmonic voltage effective value are obtained respectively; according to the fundamental wave voltage effective value and the harmonic voltage effective value, a secondary factor evaluation matrix is ​​obtained by calculating the severity of node harmonic distortion; each row of data in the secondary factor evaluation matrix is ​​normalized to obtain a first single factor evaluation vector on the compatibility level of each harmonic voltage; According to the voltage fluctuation and deviation classification table, the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the power grid operation data are scored and evaluated to obtain a second single factor judgment vector for voltage fluctuation and a third single factor judgment vector for voltage deviation; wherein the voltage fluctuation and deviation classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the preset historical power grid operation data based on the average absolute value of the voltage fluctuation and voltage deviation within one day; According to the voltage sag evaluation results of each distributed power source to be selected for access, a fourth single factor evaluation vector for voltage sag is established; The single factor evaluation matrix is ​​formed by the first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector.

2. A power grid power capacity configuration device taking into account power quality, characterized in that: Includes data module, weight module, capacity module and installed capacity module; Wherein, the data module is used to obtain power grid operation data from the distribution network; The weight module is used to establish a primary indicator weight distribution for power quality issues based on the characteristic consistency of the power indicators in the power grid operation data, specifically: the distribution network user load conditions in the power grid operation data are combined with a preset importance scale to establish an initial evaluation matrix; wherein the importance scale is established based on the importance of different indicators in the distribution network; the consistency index is calculated based on the maximum characteristic root and matrix order of the initial evaluation matrix; the initial evaluation matrix is ​​parameterized based on the numerical value of the consistency index to obtain the consistency evaluation matrix for power quality issues; the hierarchical analysis importance weight distribution is calculated based on the consistency evaluation matrix using a first preset formula; the hierarchical analysis importance weight distribution is combined with the preset expert scoring method weight distribution to calculate the primary indicator weight distribution; The capacity module is used to combine the first-level indicator weight distribution with the single-factor evaluation matrix to calculate the preliminary installed capacity of each candidate access node in the distribution network, specifically: according to the first-level indicator weight distribution and the single-factor evaluation matrix, calculate the comprehensive evaluation result of the power quality of each candidate access node of the distributed power source; obtain the severity of the power quality problems of different nodes before the distributed power sources are connected from the comprehensive evaluation results, and determine the pros and cons of each candidate access node according to the severity of the power quality problems; according to the pros and cons, establish the installed capacity allocation matrix by increasing the installed capacity at the excellent nodes; calculate the preliminary installed capacity of each candidate access node in the distribution network by combining the installed capacity allocation matrix with the current installed capacity of the distributed power source; The installation module is used to simulate and correct the preliminary installed capacity, obtain the final installed capacity when the change value of the preliminary installed capacity is less than the preset value, and configure the capacity of each access node to be selected in the distribution network according to the final installed capacity; The single factor evaluation matrix is ​​established based on voltage evaluation indicators of different dimensions, specifically: The voltage effective values ​​of the fundamental wave and each frequency harmonic of different access nodes to be selected are obtained, and the fundamental wave voltage effective value and the harmonic voltage effective value are obtained respectively; according to the fundamental wave voltage effective value and the harmonic voltage effective value, a secondary factor evaluation matrix is ​​obtained by calculating the severity of node harmonic distortion; each row of data in the secondary factor evaluation matrix is ​​normalized to obtain a first single factor evaluation vector on the compatibility level of each harmonic voltage; According to the voltage fluctuation and deviation classification table, the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the power grid operation data are scored and evaluated to obtain a second single factor judgment vector for voltage fluctuation and a third single factor judgment vector for voltage deviation; wherein the voltage fluctuation and deviation classification table is obtained by classifying the voltage fluctuation severity and voltage deviation severity of each distributed power source to be selected for access in the preset historical power grid operation data based on the average absolute value of the voltage fluctuation and voltage deviation within one day; According to the voltage sag evaluation results of each distributed power source to be selected for access, a fourth single factor evaluation vector for voltage sag is established; The single factor evaluation matrix is ​​formed by the first single factor evaluation vector, the second single factor evaluation vector, the third single factor evaluation vector and the fourth single factor evaluation vector.

3. A storage medium, characterized in that: The storage medium stores a computer program, which is called and executed by a computer to implement a method for configuring power grid power capacity taking into account power quality as described in claim 1 above.

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