Method for evaluating photovoltaic consumption capability of low-voltage power distribution network containing distributed photovoltaic

Abstract

The invention belongs to the technical field of electric power, and discloses a distributed photovoltaic-contained low-voltage power distribution network photovoltaic consumption capability evaluation method, which comprises the following steps of: firstly, selecting voltage deviation, a power factor, a low-carbon environmental protection degree and economic applicability as evaluation indexes; then, subjective empowerment and principal component analysis method empowerment are adopted to complete empowerment of the index data; secondly, constructing a multi-target evaluation model for the photovoltaic consumption capability of the low-voltage distribution network by taking the maximum photovoltaic power generation capacity and the minimum active power loss of the low-voltage distribution network as target functions, and solving the model by adopting a second-order cone relaxation technology; and finally, based on different schemes of different photovoltaic access modes and access positions, calculating the photovoltaic consumption capability with the optimal comprehensive index under different access schemes through the evaluation model and the evaluation index. According to the method, the scheme with the optimal comprehensive index can be selected according to the evaluation results of different schemes, and the influence of high-proportion photovoltaic access on the low-voltage side distribution network is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and in particular relates to a method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics. Background Art

[0002] With the rapid development of clean energy such as wind power and photovoltaics, especially the access of large-scale distributed photovoltaics to low-voltage distribution networks, the access of distributed photovoltaics with high penetration rates in distribution networks has caused the distribution networks to gradually transform from the original passive unidirectional distribution networks to active networks with bidirectional power flow, thereby increasing the risks of voltage over-limit and power flow reverse transmission in distribution networks.

[0003] With the increase in the penetration rate of distributed photovoltaics, a large number of distributed photovoltaics are connected to the low-voltage distribution network. Due to user demand limitations and the uncertainty of light intensity, the output of photovoltaic units cannot be fully and effectively absorbed, so that part of the photovoltaic power is fed back to the main grid, and the remaining power flows into the main grid to form a reverse current. In addition, the higher the penetration rate of photovoltaic power generation in the low-voltage distribution network, the more obvious problems such as voltage exceeding the limit, three-phase imbalance and voltage fluctuation will be, which increases the difficulty of operation and control of the distribution network system.

[0004] The main reasons for the problems of voltage over-limit, photovoltaic power flow reverse transmission, and voltage fluctuation in the low-voltage distribution network are the failure to properly evaluate the photovoltaic absorption capacity of the low-voltage distribution network under the high proportion of new energy access, and the failure to effectively improve the absorption capacity of the distribution network when the amount of distributed photovoltaic access in the distribution network exceeds its absorption capacity. Although relevant research has been conducted in the prior art, such as patent application CN117154825A, the evaluation in the prior art is not comprehensive and objective, and it is difficult to achieve real-time and accuracy. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics. By analyzing the main factors affecting the internal operating parameters of the distribution network and the power quality of the distribution network when photovoltaics are connected to the distribution network, evaluation indicators are selected to evaluate them from three aspects: power grid operation data, environmental benefits and economic benefits. An evaluation model for the photovoltaic absorption capacity of the low-voltage distribution network is constructed. Based on different schemes for different photovoltaic access methods and access locations, the photovoltaic absorption capacity with the best comprehensive indicators under different access scenarios is calculated.

[0006] The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics described in the present invention comprises the following steps:

[0007] Step 1: Based on the voltage over-limit and power flow reverse problems caused by the low-voltage distribution network connected to a high proportion of distributed photovoltaics, four sets of data from different angles, namely, the voltage deviation rate of the low-voltage distribution system, the power factor after photovoltaic grid connection, low-carbon environmental protection and economic benefits, are used as evaluation target values;

[0008] Step 2: Based on the operating data of the distributed photovoltaic low-voltage distribution system at different times of the day and under two different weather conditions, the evaluation data of the four evaluation indicators of the system, namely, voltage deviation rate, power factor after photovoltaic grid connection, low-carbon environmental protection and economic benefit, are collected and calculated in real time;

[0009] Step 3: Based on the indicator evaluation data calculated in step 2, the evaluation value weights of the evaluation data of the four indicators are calculated by combining subjective weighting and principal component analysis to obtain the final indicator weight values ​​of the four indicators;

[0010] Step 4: For the distributed photovoltaic low-voltage distribution system, a photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constructed with the maximum photovoltaic power generation and the minimum active network loss of the low-voltage side distribution network as the objective function, and the second-order cone relaxation technology is used to solve the model;

[0011] Step 5: Based on the different locations of the distribution nodes where the distributed photovoltaic grid is connected and the different scales of distributed photovoltaic access, the evaluation model is used to calculate the photovoltaic consumption with the best comprehensive indicators under different access schemes.

[0012] Furthermore, in step 1, the voltage deviation rate is calculated as follows:

[0013]

[0014] Where V d0 represents the voltage deviation rate, T represents the scheduling period, N represents the total number of system distribution nodes, V t,n represents the node voltage of the nth node at time t, V 0 The safe voltage reference value set for the system, V max and V min They are the maximum and minimum voltage values ​​that produce voltage deviation respectively;

[0015] Calculate the maximum voltage deviation rate to complete the evaluation calculation under extreme conditions, that is:

[0016]

[0017] Where V dmax is the maximum voltage deviation rate;

[0018] The power factor is calculated as follows:

[0019]

[0020] Where P f is the power factor of the low-voltage distribution network line; P is the active power of the distribution network node; Q is the reactive power of the distribution network node;

[0021] The calculation formula of low carbon environmental protection is as follows:

[0022]

[0023] In the formula, E nvir The low carbon and environmental protection degree of photovoltaic access; E pv is the total amount of photovoltaic power generation in a year; E total is the total electricity consumption of the power grid system in one year, in kW·h;

[0024] The economic benefit, i.e. the return on investment of photovoltaic equipment, is as follows:

[0025]

[0026] In the formula, R eturn is the return on investment of photovoltaic equipment; M pv is the annual income generated by photovoltaic power generation; M loss The loss caused by network loss; M total is the total investment in photovoltaic equipment.

[0027] Furthermore, in step 2, considering different time periods within 24 hours in a day under cloudy and sunny weather conditions, the system voltage and power data collected in real time are calculated to obtain the index evaluation data. The specific calculation method is as follows:

[0028]

[0029] Where V d0 ' is the voltage deviation rate considering weather and time period, V d0kt is the voltage deviation rate V d0 The node voltage deviation rate at time t in the scheduling period T under weather state k, where k includes two weather conditions, namely sunny and cloudy. Considering that different weather conditions have a greater impact on photovoltaic power output, α k The weather coefficient represented can be dynamically adjusted based on the cloudy and sunny conditions, where α k ∈[0,1].

[0030] Since the power factor of the photovoltaic power generation system can be continuously adjusted within the range of 0.95 leading or lagging, 0.95 is taken as the basis for the index calculation:

[0031]

[0032] Where P f' is the power factor of the distribution network considering weather and time period, P fkt is the power factor of the first node of the system at time t under weather condition k;

[0033] The low-carbon environmental protection and economic benefit indicators need to be calculated on an annual basis, namely:

[0034]

[0035] In the formula, E nvir 'To consider the low carbon and environmental protection degree under different conditions; E pvkt and E 负荷kt They are the system photovoltaic output capacity and load consumption in time period t under weather condition k;

[0036]

[0037] In the formula, R eturn ' is the economic rate of return under different conditions; E pvkt and E losskt are the system photovoltaic output and network loss at time t under weather condition k, in kW·h; E pvtotal is the total installed capacity of photovoltaic power, in kW; M1, M2 and M3 are the photovoltaic power price, government subsidy power price and investment cost per watt of photovoltaic equipment in the area where the low-voltage distribution network is located.

[0038] Furthermore, step 3 is specifically as follows:

[0039] First, subjective weighting is performed by hierarchical analysis, and the subjective weighting of indicators is performed based on the experience of decision makers in the power grid dispatching center and the importance of indicators. Then, the original data set {z i}, where i = 1, 2, 3…, n, z i is an m×1 vector, that is, m features are defined for the indicator, and there are:

[0040]

[0041] Get the original data matrix of the index data, and then use nonlinear mapping to set the mapping function as By mapping In order to realize the mapping of original data from input space to high-dimensional space, all data satisfy the centralization condition, that is,

[0042]

[0043] Then construct the covariance matrix C of the feature space:

[0044]

[0045] Solve the covariance matrix C for the eigenvector v with eigenvalue λ≥0:

[0046] Cv=λv

[0047] And according to the eigenvalues, they are arranged in descending order to obtain the eigenvectors v1, v2, v3, ...v n Then, the unit orthogonalization of the eigenvectors is completed by the Schmidt orthogonal method, and z1′, z2′, z3′…z′ are obtained. n ;

[0048] Extract the m eigenvectors of the original data of the indicator and obtain the transformation matrix:

[0049]

[0050] Finally, the index data is transformed into the principal component components extracted by principal component analysis.

[0051]

[0052] The combined weighting method is used to calculate the subjective and objective comprehensive weight value:

[0053]

[0054] In the formula, j is the number of selected indicators; m is the number of weighting methods; r jm represents the weight of the jth indicator in the mth weighting method, is the importance of the mth weighting method, that is, the proportion of subjective and objective importance; Q j is the combined weight of the jth indicator.

[0055] Furthermore, step 4 is specifically as follows:

[0056] Assume that the total number of distribution nodes in the distribution network is M, and the number of distributed photovoltaic access nodes is M i , the objective functions are established with the maximum photovoltaic output and the minimum active network loss on the low-voltage side distribution network:

[0057]

[0058] In the formula, F1 indicates that the photovoltaic output of the low-voltage distribution network is the largest; F2 indicates that the active network loss is the smallest; P pv,n (t) represents the active power output of the nth photovoltaic component in the t period; T represents the scheduling period; N represents the number of photovoltaic components in the low-voltage side of the system; e(i) represents the active power output of the nth photovoltaic component in the t period; i The set of all nodes connected to the node; M represents the total number of nodes; I ij (t) represents the current between branches ij; R ijRepresents the resistance between branches ij;

[0059] Since the low-voltage distribution network system has the largest photovoltaic absorption capacity when the difference between the photovoltaic unit power generation and the system network loss is the largest, the objective function of the photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constructed:

[0060] F=max(σ1f1-σ2f2)

[0061] In the formula, σ1 and σ2 represent the weight coefficients of photovoltaic power generation and network loss, respectively, σ1+σ2=1, σ1≤1, σ2≤1; f1 and f2 represent the normalized values ​​of the two objective functions of photovoltaic power generation and network loss, respectively;

[0062] The photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constrained by three aspects: node voltage, PV reactive output and photovoltaic storage power:

[0063] Node voltage constraints:

[0064]

[0065] In the formula, Respectively represent the upper and lower limits of the safe operation range of the distribution network, V i is the node voltage of distribution network node i at time t;

[0066] PV reactive power output constraints:

[0067]

[0068] Where P i,PV and Q i,PV Respectively represent the active and reactive output of photovoltaic equipment; Table 1 shows the maximum active output of the ith photovoltaic device, S i,PV Indicates the rated capacity of the PV inverter;

[0069] PV power constraints:

[0070]

[0071] In the formula, They represent the upper and lower limits of the charging power of the photovoltaic storage element respectively; They represent the upper and lower limits of the discharge power of the photovoltaic element respectively; E i,c (t), E i,f (t) represents the charge and discharge state of the photoelectric storage element at time t, and the working state of the energy storage device can only be one of the charging state, discharging state and non-working state, that is:

[0072] E i,c (t)+E i,f (t)≤1,Ei,c (t), E i,f (t) = 1 or 0.

[0073] Furthermore, step 5 is specifically as follows:

[0074] The solution to the second-order cone programming problem model is as follows:

[0075] minx i {c T x|Ax=b,x i ∈K,i=1,2,…,N}

[0076]

[0077] In the formula, x represents an N-dimensional real variable; A represents an M×N real constant; b and c are N-dimensional real constants; K represents a second-order cone; R N represents the set of real numbers;

[0078] Perform a second-order cone deformation on the set model:

[0079]

[0080] According to the objective function of minimizing active network loss, we get:

[0081]

[0082] Where: P i (t) = P i,PV (t)-P i,c (t)-P i,load (t); Q i (t) = Q i,PV (t)-Q i,c (t)-Q i,load (t), P ij (t) and Q ij (t) respectively represent the active power and reactive power of the line between node i and node j at time t, P j (t), Q j (t) and P i (t), Q i (t) represents the active power and reactive power flowing into the system at node j and node i at time t respectively;

[0083] Continue to convert it into a standard second-order cone equivalently:

[0084]

[0085] Furthermore, step 6 is specifically as follows:

[0086] According to the different locations of the low-voltage distribution network throughout the year, multiple evaluation schemes are set under the two access modes of decentralized access or centralized access. The results of each scheme are solved based on the constructed low-voltage distribution network photovoltaic absorption capacity evaluation model, and the photovoltaic absorption capacity when the comprehensive indicators are optimal under different photovoltaic access conditions is evaluated based on the evaluation indicators.

[0087] The beneficial effects described in the present invention are as follows: the present invention fully considers the impact of high-proportion access of distributed photovoltaics to low-voltage distribution networks, and by comprehensively considering grid operation data, environmental benefits and economic benefits, selects four main influencing factors with greater impact, namely, voltage deviation rate, power factor after photovoltaic grid connection, low-carbon environmental protection, and economic benefits, as evaluation indicators for calculation; the weights of indicators are divided by combining subjective weighting with principal component analysis, and by comprehensively simulating the actual operation status of the grid, it is possible to refer to the influence of objective factors and make subjective adjustments based on actual conditions, implement dynamic evaluation based on real-time data, and enable the evaluation strategy to be dynamically adjusted based on different needs; the second-order cone relaxation technology is applied to solve nonlinear problems, and a multi-objective optimization strategy is adopted to significantly improve the accuracy and adaptability of the evaluation of photovoltaic absorption capacity, effectively overcome the limitations of the existing technology in solving accuracy and processing complex models, and realize scientific and accurate evaluation of the photovoltaic absorption capacity of the low-voltage distribution network, effectively reducing the impact of high-proportion photovoltaic access on the low-voltage side distribution network. Through this comprehensive evaluation, the present invention can provide more accurate decision support for grid operators and policy makers, optimize the access strategy of photovoltaic power generation, improve the efficiency and economy of grid operation, and promote a low-carbon and environmentally friendly energy development strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 is a flow chart of the method of the present invention;

[0089] Figure 2 It is a flow chart for calculating the weights of the evaluation values ​​of the four indicators by combining subjective weighting and principal component analysis;

[0090] Figure 3 It is a flow chart for solving the photovoltaic absorption capacity evaluation model of low-voltage distribution network using the second-order cone relaxation technology. DETAILED DESCRIPTION

[0091] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0092] like Figure 1As shown, the photovoltaic absorption capacity evaluation method of a low-voltage distribution network containing distributed photovoltaics described in the present invention, after the low-voltage distribution network is connected to a high proportion of distributed photovoltaics, four groups of data from different angles, namely, the voltage deviation rate of the low-voltage distribution system, the power factor after photovoltaic grid connection, the low-carbon environmental protection degree and the economic benefit degree, are selected as evaluation target values; the voltage and power operation data of the distributed photovoltaic low-voltage distribution system in a certain place are selected based on different time periods of 24 hours a day and two different weather conditions of cloudy and sunny.

[0093] The voltage deviation rate calculation method based on real-time operation data is:

[0094]

[0095] Where V d0 ' is the voltage deviation rate considering weather and time period, V d0kt is the voltage deviation rate V d0 The node voltage deviation rate at time t in the scheduling period T under weather state k, where k includes two weather conditions, namely sunny and cloudy. Considering that different weather conditions have a greater impact on photovoltaic power output, α k The weather coefficient represented can be dynamically adjusted based on the cloudy and sunny conditions, where α k ∈[0,1].

[0096] According to real-time operation data, the power factor of the photovoltaic power generation system can be continuously adjusted within the range of leading or lagging 0.95, so 0.95 is taken as the benchmark for indicator calculation. The power factor calculation method after photovoltaic grid connection is:

[0097]

[0098] Where P f 'To consider the power factor of the distribution network under weather and time period, D fkt is the power factor of the first node of the system at time t under weather condition k;

[0099] According to the real-time operation data, the low-carbon environmental protection and economic benefit indicators need to be calculated on an annual basis, namely:

[0100]

[0101] In the formula, E nvir 'To consider the low carbon and environmental protection degree under different conditions; E pvkt and E 负荷kt They are the system photovoltaic output capacity and load consumption in time period t under weather condition k;

[0102]

[0103] In the formula, R eturn' is the economic rate of return under different conditions; E pvkt and E losskt are the system photovoltaic output and network loss at time t under weather condition k, in kW·h; E pvtotal is the total installed capacity of photovoltaic power, in kW; M1, M2 and M3 are the photovoltaic power price, government subsidy power price and investment cost per watt of photovoltaic equipment in the area where the low-voltage distribution network is located.

[0104] like Figure 2 As shown in the figure, the voltage deviation rate, power factor after photovoltaic grid connection, low-carbon environmental protection and economic benefit calculated based on the real-time operation data of the distributed photovoltaic low-voltage distribution system are firstly weighted by the hierarchical analysis method based on the experience and importance of the decision makers of the power grid dispatching center; then the objective weight is calculated by the principal component analysis method, in which the principal component analysis method constructs the original data set {z i}, where i = 1, 2, 3…, n, z i is an m×1 vector, that is, m features are defined for the indicator, and there are:

[0105]

[0106] Get the original data matrix of the index data, and then use nonlinear mapping to set the mapping function as By mapping In order to realize the mapping of original data from input space to high-dimensional space, all data satisfy the centralization condition, that is,

[0107]

[0108] Then construct the covariance matrix C of the feature space:

[0109]

[0110] Solve the covariance matrix C for the eigenvector v with eigenvalue λ≥0:

[0111] Cv=λv

[0112] And according to the eigenvalues, they are arranged in descending order to obtain the eigenvectors v1, v2, v3, ...v n Then, the unit orthogonalization of the eigenvectors is completed by the Schmidt orthogonal method, and z1′, z2′, z3′…z′ are obtained. n ;

[0113] Extract the m eigenvectors of the original data of the indicator and obtain the transformation matrix:

[0114]

[0115] Finally, the index data is transformed into the principal component components extracted by principal component analysis.

[0116]

[0117] Finally, the combined weighting method is used to calculate the subjective and objective comprehensive weight value:

[0118]

[0119] In the formula, j is the number of selected indicators; m is the number of weighting methods; r jm represents the weight of the jth indicator in the mth weighting method, is the importance of the mth weighting method, that is, the proportion of subjective and objective importance; Q j is the combined weight of the jth indicator.

[0120] Then as Figure 3 As shown in the figure, a low-voltage distribution network photovoltaic absorption capacity evaluation model is constructed. Assume that the total number of distribution nodes in the distribution network is M, and the number of distributed photovoltaic access nodes is M. i , the objective functions are established with the maximum photovoltaic output and the minimum active network loss on the low-voltage side distribution network:

[0121]

[0122] In the formula, F1 indicates that the photovoltaic output of the low-voltage distribution network is the largest; F2 indicates that the active network loss is the smallest; P pv,n (t) represents the active power output of the nth photovoltaic component in the t period; T represents the scheduling period; N represents the number of photovoltaic components in the low-voltage side of the system; e(i) represents the active power output of the nth photovoltaic component in the t period; i The set of all nodes connected to the node; M represents the total number of nodes; I ij (t) represents the current between branches ij; R ij Represents the resistance between branches ij;

[0123] Since the low-voltage distribution network system has the largest photovoltaic absorption capacity when the difference between the photovoltaic unit power generation and the system network loss is the largest, the objective function of the photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constructed:

[0124] F=max(σ1f1-σ2f2)

[0125] In the formula, σ1 and σ2 represent the weight coefficients of photovoltaic power generation and network loss, respectively, σ1+σ2=1, σ1≤1, σ2≤1; f1 and f2 represent the normalized values ​​of the two objective functions of photovoltaic power generation and network loss, respectively;

[0126] The photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constrained by three aspects: node voltage, PV reactive output and photovoltaic storage power:

[0127] Node voltage constraints:

[0128]

[0129] In the formula, Respectively represent the upper and lower limits of the safe operation range of the distribution network, V i is the node voltage of distribution network node i at time t;

[0130] PV reactive power output constraints:

[0131]

[0132] Where P i,PV and Q i,PV Respectively represent the active and reactive output of photovoltaic equipment; Table 1 shows the maximum active output of the ith photovoltaic device, S i,PV Indicates the rated capacity of the PV inverter;

[0133] PV power constraints:

[0134]

[0135] In the formula, They represent the upper and lower limits of the charging power of the photovoltaic storage element respectively; They represent the upper and lower limits of the discharge power of the photovoltaic element respectively; E i,c (t), E i,f (t) represents the charge and discharge state of the photoelectric storage element at time t, and the working state of the energy storage device can only be one of the charging state, discharging state and non-working state, that is:

[0136] E i,c (t)+E i,f (t)≤1,E i,c (t), E i,f (t) = 1 or 0.

[0137] Based on the evaluation indicators established above and the constraints of the objective function of the low-voltage distribution network photovoltaic absorption capacity evaluation model, the objective function solution of the low-voltage distribution network photovoltaic absorption capacity evaluation model is obtained using the second-order cone programming problem model as follows:

[0138] minx i {c T x|Ax=b,x i ∈K,i=1,2,…,N}

[0139]

[0140] In the formula, x represents an N-dimensional real variable; A represents an M×N real constant; b and c are N-dimensional real constants; K represents a second-order cone; R N represents the set of real numbers;

[0141] Perform a second-order cone deformation on the set model:

[0142]

[0143] According to the objective function of minimizing active network loss, we get:

[0144]

[0145] Where: P i (t) = P i,PV (t)-P i,c (t)-P i,load (t); Q i (t) = Q i,PV (t)-Q i,c (t)-Q i,load (t), P ij (t) and Q ij (t) respectively represent the active power and reactive power of the line between node i and node j at time t, P j (t), Q j (t) and P i (t), Q i (t) represent the active power and reactive power flowing into the system at node j and node i at time t respectively.

[0146] Continue to convert it into a standard second-order cone equivalently:

[0147]

[0148] Finally, the global optimal approximate solution is obtained. For the different locations of the low-voltage distribution network throughout the year, multiple evaluation schemes are set under the two access modes of decentralized access or centralized access. The result of each scheme is solved based on the constructed low-voltage distribution network photovoltaic absorption capacity evaluation model, and the photovoltaic absorption capacity when the comprehensive indicators are optimal under different photovoltaic access conditions is evaluated in combination with the evaluation indicators.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to be a further limitation of the present invention. All equivalent changes made using the contents of the present specification and drawings are within the protection scope of the present invention.

Claims

1. A method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics, characterized in that: The following steps are involved: Step 1: Based on the voltage over-limit and power flow reverse problems caused by the low-voltage distribution network connected to a high proportion of distributed photovoltaics, four sets of data from different angles, namely, the voltage deviation rate of the low-voltage distribution system, the power factor after photovoltaic grid connection, low-carbon environmental protection and economic benefits, are used as evaluation target values; Step 2: Based on the operating data of the distributed photovoltaic low-voltage distribution system at different times of the day and under two different weather conditions, the voltage deviation rate of the system, the power factor after photovoltaic grid connection, the low-carbon environmental protection degree and the economic benefit evaluation data are collected and calculated in real time; Step 3: Based on the indicator evaluation data calculated in step 2, the evaluation value weights of the evaluation data of the four indicators are calculated by combining subjective weighting and principal component analysis to obtain the final indicator weight values ​​of the four indicators; Step 4: For the distributed photovoltaic low-voltage distribution system, a photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constructed with the maximum photovoltaic power generation and the minimum active network loss of the low-voltage side distribution network as the objective function, and the second-order cone relaxation technology is used to solve the model; Step 5: Based on the different locations of the distribution nodes where the distributed photovoltaic grid is connected and the different scales of distributed photovoltaic access, the evaluation model is used to calculate the photovoltaic consumption with the best comprehensive indicators under different access schemes.

2. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 1 is characterized in that: In step 1, the voltage deviation rate is calculated as follows: Where V d0 represents the voltage deviation rate, T represents the scheduling period, N represents the total number of system distribution nodes, V t,n represents the node voltage of the nth node at time t, V 0 The safe voltage reference value set for the system, V max and V min They are the maximum and minimum voltage values ​​that produce voltage deviation respectively; Calculate the maximum voltage deviation rate to complete the evaluation calculation under extreme conditions, that is: Where V dmax is the maximum voltage deviation rate; The power factor is calculated as follows: Where P f is the power factor of the low-voltage distribution network line; P is the active power of the distribution network node; Q is the reactive power of the distribution network node; The calculation formula of low carbon environmental protection degree is as follows: In the formula, E nvir The low carbon and environmental protection degree of photovoltaic access; E pv is the total amount of photovoltaic power generation in a year; E total is the total electricity consumption of the power grid system in one year, in kW·h; The economic benefit, i.e. the return on investment of photovoltaic equipment, is as follows: In the formula, R eturn is the return on investment of photovoltaic equipment; M pv is the annual income generated by photovoltaic power generation; M loss The loss caused by network loss; M total is the total investment in photovoltaic equipment.

3. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 2 is characterized in that: In step 2, considering the different time periods of 24 hours in a day under cloudy and sunny weather conditions, the system voltage and power data collected in real time are calculated to obtain the index evaluation data. The specific calculation method is as follows: Where V d0 ' is the voltage deviation rate considering weather and time period, V d0kt is the voltage deviation rate V d0 The node voltage deviation rate at time t in the scheduling period T under weather state k, where k includes two weather conditions, namely sunny and cloudy. Considering that different weather conditions have a greater impact on photovoltaic power output, α k The weather coefficient represented can be dynamically adjusted based on the cloudy and sunny conditions, where α k ∈[0,1]. Since the power factor of the photovoltaic power generation system can be continuously adjusted within the range of 0.95 leading or lagging, 0.95 is taken as the basis for the index calculation: Where P f ' is the power factor of the distribution network considering weather and time period, P fkt is the power factor of the first node of the system at time t under weather condition k; The low-carbon environmental protection and economic benefit indicators need to be calculated on an annual basis, namely: In the formula, E nvir 'To consider the low carbon and environmental protection degree under different conditions; E pvkt and E 负荷kt are the system photovoltaic output capacity and load consumption in time period t under weather condition k; In the formula, R eturn ' is the economic rate of return under different conditions; E pvkt and E losskt are the system photovoltaic output and network loss at time t under weather condition k, in kW·h; E pvtotal is the total installed capacity of photovoltaic power, in kW; M1, M2 and M3 are the photovoltaic power price, government subsidy power price and investment cost per watt of photovoltaic equipment in the area where the low-voltage distribution network is located.

4. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 3 is characterized in that: Step 3 is as follows: First, subjective weighting is performed by hierarchical analysis, and the subjective weighting of indicators is performed based on the experience of decision makers in the power grid dispatching center and the importance of indicators. Then, the original data set {z i }, where i = 1, 2, 3, ..., n, z i is an m×1 vector, that is, m features are defined for the indicator, and there are: Get the original data matrix of the index data, and then use nonlinear mapping to set the mapping function as By mapping In order to realize the mapping of original data from input space to high-dimensional space, all data satisfy the centralization condition, that is, Then construct the covariance matrix C of the feature space: Solve the covariance matrix C for the eigenvector v with eigenvalue λ≥0: Cv=λv And according to the eigenvalues, they are arranged in descending order to obtain the eigenvectors v1, v2, v3, ...v n Then, the unit orthogonalization of the eigenvectors is completed by the Schmidt orthogonal method, and z1′, z2′, z3′…z′ are obtained. n ; Extract the m eigenvectors of the original data of the indicator and obtain the transformation matrix: Finally, the index data is transformed into the principal component components extracted by principal component analysis. The combined weighting method is used to calculate the subjective and objective comprehensive weight value: In the formula, j is the number of selected indicators; m is the number of weighting methods; r jm represents the weight of the jth indicator in the mth weighting method, is the importance of the mth weighting method, that is, the proportion of subjective and objective importance; Q j is the combined weight of the jth indicator.

5. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 1 is characterized in that: Step 4 is as follows: Assume that the total number of distribution nodes in the distribution network is M, and the number of distributed photovoltaic access nodes is M i , the objective functions are established with the maximum photovoltaic output and the minimum active network loss on the low-voltage side distribution network: In the formula, F1 indicates that the photovoltaic output of the low-voltage distribution network is the largest; F2 indicates that the active network loss is the smallest; P pv,n (t) represents the active power output of the nth photovoltaic component in the t period; T represents the scheduling period; N represents the number of photovoltaic components in the low-voltage side of the system; e(i) represents the active power output of the nth photovoltaic component in the t period; i The set of all nodes connected to the node; M represents the total number of nodes; I ij (t) represents the current between branches ij; R ij Represents the resistance between branches ij; Since the low-voltage distribution network system has the largest photovoltaic absorption capacity when the difference between the photovoltaic unit power generation and the system network loss is the largest, the objective function of the photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constructed: F=max(σ1f1-σ2f2) In the formula, σ1 and σ2 represent the weight coefficients of photovoltaic power generation and network loss, respectively, σ1+σ2=1, σ1≤1, σ2≤1; f1 and f2 represent the normalized values ​​of the two objective functions of photovoltaic power generation and network loss, respectively; The photovoltaic absorption capacity evaluation model of the low-voltage distribution network is constrained by three aspects: node voltage, PV reactive output and photovoltaic storage power: Node voltage constraints: In the formula, Respectively represent the upper and lower limits of the safe operation range of the distribution network, V i is the node voltage of distribution network node i at time t; PV reactive power output constraints: Where P i,PV and Q i,PV Respectively represent the active and reactive output of photovoltaic equipment; Table 1 shows the maximum active output of the ith photovoltaic device, S i,PV Indicates the rated capacity of the PV inverter; PV power constraints: In the formula, They represent the upper and lower limits of the charging power of the photovoltaic storage element respectively; They represent the upper and lower limits of the discharge power of the photovoltaic element respectively; E i,c (t), E i,f (t) represents the charge and discharge state of the photoelectric storage element at time t, and the working state of the energy storage device can only be one of the charging state, discharging state and non-working state, that is: E i,c (t) + E i,f (t) ≤ 1, E i,c (t), E i,f (t) = 1 or 0.

6. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 1 is characterized in that: Step 5 is as follows: The solution to the second-order cone programming problem model is as follows: minx i {c T x|Ax=b,x i ∈K,i=1,2,…,N} In the formula, x represents an N-dimensional real variable; A represents an M×N real constant; b and c are N-dimensional real constants; K represents a second-order cone; R N represents the set of real numbers; Perform a second-order cone deformation on the set model: According to the objective function of minimizing active network loss, we get: Where: P i (t) = P i,PV (t)-P i,c (t)-P i,load (t); Q i (t) = Q i,PV (t)-Q i,c (t)-Q i,load (t), P ij (t) and Q ij (t) respectively represent the active power and reactive power of the line between node i and node j at time t, P j (t), Q j (t) and P i (t), Q i (t) represents the active power and reactive power flowing into the system at node j and node i at time t respectively; Continue to convert it into a standard second-order cone equivalently:

7. The method for evaluating the photovoltaic absorption capacity of a low-voltage distribution network containing distributed photovoltaics according to claim 1 is characterized in that: Step 6 is as follows: According to the different locations of the low-voltage distribution network throughout the year, multiple evaluation schemes are set under the two access modes of decentralized access or centralized access. The results of each scheme are solved based on the constructed low-voltage distribution network photovoltaic absorption capacity evaluation model, and the photovoltaic absorption capacity when the comprehensive indicators are optimal under different photovoltaic access conditions is evaluated based on the evaluation indicators.

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