Method for analyzing influence of distributed photovoltaic on line thermal stability based on active influence degree

Abstract

According to the method for analyzing the influence of the distributed photovoltaic on the line thermal stability based on the active influence degree, aiming at the problem that the analysis difficulty of the influence of the line thermal stability is high due to numerous schemes for accessing the distributed photovoltaic to a power distribution network and high uncertainty, the analysis difficulty of the influence of the distributed photovoltaic on the line thermal stability is improved based on the node active influence degree on the basis of measuring the line thermal stability degree by a reverse load rate. A distributed photovoltaic access sequence and a thermal stability analysis scene are determined, the node active influence degree effectively reflects the influence degree of node injection active change on flowing active power of all lines of the power distribution network, distributed photovoltaic access is performed in sequence from the node with the maximum influence degree, and the main trend of flowing power and load rate change of all the lines can be reflected. Therefore, the influence degree of distributed photovoltaic on the thermal stability of the circuit can be clarified, and the thermal stability analysis scene on the basis ensures the orderliness and scientificity of influence analysis.

Description

Technical Field

[0001] This invention belongs to the field of distributed photovoltaics, and in particular relates to a method for analyzing the impact of distributed photovoltaics on the thermal stability of power lines based on active power influence. Background Technology

[0002] With the successive proposals of the strategic goals of "carbon peaking and carbon neutrality" and "new power system", distributed photovoltaic (PV) power has experienced explosive growth in distribution networks. This not only poses new challenges to the carrying capacity and reliable power supply of distribution networks, but also increases the complexity of distribution network structure and operational uncertainty. On the one hand, the high proportion of distributed PV power means that when the distribution network's absorption capacity is insufficient, a large amount of unabsorbed PV power will generate backflow, causing power flow reversal and even leading to the line approaching its thermal stability limit, reducing the line's thermal stability level and threatening the safe and stable operation of the distribution network. On the other hand, due to the multiple uncertainties in the access location, number of access points, and access capacity of distributed PV power, the difficulty of assessing the thermal stability level of distribution network lines has increased. Summary of the Invention

[0003] To address the problems existing in the prior art, the purpose of this invention is to provide a scientific, effective, and highly applicable analytical method that can quantitatively describe the impact of distributed photovoltaic (PV) access on the thermal stability of distribution network lines. The aim is to further measure the maximum capacity of distributed PV that can be accessed in a regional distribution network within the limits of thermal stability, providing a theoretical reference for the development of high-proportion PV in new power systems.

[0004] The technical solution adopted to achieve the purpose of this invention is a method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence, characterized by comprising the following steps:

[0005] Step 1: Establish indicators for measuring the thermal stability of the line.

[0006] The reverse load factor is used as an indicator to measure the impact of distributed photovoltaic (PV) power on the thermal stability of different lines in the distribution network. The reverse load factor is:

[0007]

[0008] In the formula, λ is the line reverse load rate, and P D P represents the active power of distributed photovoltaic power connected to the line. L For the active load on the line, S l This represents the maximum allowable capacity of the line.

[0009] Step 2, Generation of Active Power Influence Vector of Distribution Network Nodes

[0010] 1) Generation of voltage amplitude sensitivity matrix at distribution network nodes.

[0011] ① Extract the Jacobi matrix from the power flow equations when the power flow of the distribution network converges.

[0012] The Jacobi matrix is ​​given by the power flow equations in polar coordinate form using the NR method, which can be expressed as:

[0013] F=J·ΔX (2)

[0014] In the formula, F is the error vector in polar coordinates of the NR method, J is the Jacobi matrix in polar coordinates of the NR method, and ΔX is the correction vector in polar coordinates of the NR method.

[0015] The specific expression for the power flow equations in polar coordinate form using the NR method is as follows:

[0016]

[0017] In the formula, ΔP is the column vector of changes in node injected active power, ΔQ is the column vector of changes in node injected reactive power, Δδ is the column vector of changes in node phase angle, ΔU is the column vector of changes in node voltage, and J is the block matrix in the Jacobi matrix. Pδ The block matrix J reflects the impact of node voltage phase angle changes on the injected active power changes at each node of the network. PU The block matrix J reflects the impact of node voltage amplitude changes on the injected active power changes at each PQ node. Qδ This reflects the impact of node voltage phase angle changes on the reactive power injected into each PQ node. The block matrix J... QU This reflects the impact of changes in the voltage amplitude of PQ nodes on the changes in the injected reactive power at each PQ node.

[0018] ② Generate the node voltage amplitude sensitivity matrix.

[0019] The voltage sensitivity matrix can be obtained by inverting the Jacobi matrix in the polar coordinate form of the power flow equations of the NR method when the power flow of the distribution network converges, that is:

[0020]

[0021] In the formula, S is the voltage sensitivity matrix, S UP Let S be the node voltage amplitude sensitivity matrix to be obtained, and let S be the block matrix. δP The node voltage amplitude sensitivity matrix S reflects the impact of changes in injected active power on the phase angle changes of voltage at each node in the network. UP This reflects the impact of changes in the injected active power at PQ nodes on the voltage amplitude changes at each node in the network. The block matrix S δQ This reflects the impact of changes in reactive power injected by PQ nodes on the voltage phase angle changes of each node in the network. The block matrix S UQThis reflects the impact of changes in reactive power injected at PQ nodes on the voltage amplitude changes at each PQ node.

[0022] 2) Generation of the sensitivity matrix of active power to node voltage in distribution network lines.

[0023] The active power flowing through the distribution network line ij and the voltage amplitude U at its starting and ending nodes i U j The relevant analytical expression is:

[0024] P ij =(G ij cosδ ij +B ij sinδ ij )U i U j -G ij (U i ) 2 (5)

[0025] In the formula, i is the starting node number of the line, j is the ending node number of the line, and G ij B is the real part of the element at position ij in the nodal admittance matrix. ij It is the imaginary part of the elements at position ij in the nodal admittance matrix, δ ij It is the phase angle difference between node i and node j.

[0026] In the above formula, by taking the partial derivative of the active power of the line with respect to the voltages at the beginning and end nodes of the line, we can obtain the sensitivity element of the active power of the line with respect to the node voltage, and its expression is as follows:

[0027]

[0028] Let the matrix composed of these two elements be denoted as This matrix is ​​the sensitivity matrix of active power of a line to node voltage. The rows of the matrix represent the voltage amplitude of each node, and the number of rows is the number of nodes. The columns of the matrix represent the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

[0029] 3) Generation of active power sensitivity matrix for distribution network lines.

[0030] The active power sensitivity matrix of a distribution network line is a matrix composed of active power sensitivity elements injected into nodes. This matrix can be obtained from the node voltage magnitude sensitivity matrix and the line active power sensitivity matrix to node voltage. It should be noted that since the node voltage magnitude sensitivity elements only consider nodes P and Q, the line active power sensitivity elements to node voltage also only consider the voltage magnitude changes at nodes P and Q. Therefore, the line active power sensitivity elements obtained from these two matrices also only consider the active power injection changes at nodes P and Q.

[0031]

[0032] In the formula, It is the active power sensitivity matrix of the line. The meaning of the rows of this matrix is ​​the active power injected by each PQ node, and the number of rows is the number of PQ nodes. The meaning of the columns of the matrix is ​​the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

[0033] 4) Generation of active power influence vectors at distribution network nodes.

[0034] This vector can be obtained by summing the elements of the line active power sensitivity matrix column by column, that is:

[0035]

[0036] In the formula, A is the active power influence vector of a node, a is an element in the active power influence vector of a node, the number of which is the number of PQ nodes in the distribution network, B is the line set, i is the node number, and j is the line number;

[0037] Step 3: Determine the sequence of distributed photovoltaic (PV) grid connection.

[0038] The access sequence is determined based on the active power influence of the nodes. In the active power influence vector A, the difference in the value of each element reflects the different active power influence of each PQ node on the line. In accordance with the principle of highlighting the main factors, distributed photovoltaics are connected in sequence starting from the PQ node with the largest active power influence, and the degree of influence of distributed photovoltaic access on the thermal stability of the distribution network line is analyzed.

[0039] Step 4: Determine the thermal stability analysis scenario

[0040] When distributed photovoltaic (PV) power is connected to the distribution network, the impact of the connection capacity and the number of connection locations on thermal stability analysis must be considered separately. Therefore, two analysis scenarios are designed:

[0041] Scenario 1 is a comparison scenario where the distributed photovoltaic (PV) grid connection capacity is the same, but the degree of dispersion is different, i.e., the number of grid connection locations is different. In this scenario, the degree of dispersion of distributed PV is gradually increased according to the distributed PV grid connection sequence determined in step 2.

[0042] Scenario 2: Distributed photovoltaic (PV) decentralization, i.e., a comparison scenario where the grid connection locations are the same but the grid connection capacities differ. The difference in grid connection capacity is measured by the distributed PV penetration rate, which is defined as:

[0043]

[0044] In the formula, P is the distributed photovoltaic penetration rate, P PV For the total capacity of the distributed photovoltaic system connected to the grid, P load_PQ The active power values ​​of all PQ node loads in the distribution network;

[0045] To enhance the analytical dimensions of scenarios, for scenario one, we need to increase the degree of distributed photovoltaic (PV) decentralization under various distributed PV grid connection capacities; for scenario two, we need to increase the distributed PV grid connection capacity under various distributed PV decentralization capacities.

[0046] This invention presents a method for analyzing the impact of distributed photovoltaic (PV) power on line thermal stability based on active power influence. Addressing the challenge of analyzing the impact of numerous and uncertain distributed PV integration schemes on distribution networks, which makes line thermal stability analysis difficult, this method uses the reverse load rate to measure line thermal stability. Based on the active power influence of nodes, it determines the sequence of distributed PV integration and the thermal stability analysis scenario. The active power influence of nodes effectively reflects the degree of impact of changes in injected active power on the active power flowing through all lines in the distribution network. Starting from the node with the greatest influence, distributed PV is sequentially integrated, reflecting the main trends in the changes in flowing power and load rate of each line. This clarifies the degree of impact of distributed PV on line thermal stability. The thermal stability analysis scenario based on this method ensures the orderliness and scientific nature of the impact analysis. Attached Figure Description

[0047] Figure 1 The process for solving the node sequence of distributed photovoltaic power grid access based on active power influence;

[0048] Figure 2 This is a diagram of the power distribution network structure.

[0049] Figure 3 This is a voltage distribution diagram of the distribution network nodes in Scenario 1.

[0050] Figure 4 This is a distribution diagram of the load rate of the power distribution network line when a single node is connected in Scenario 2.

[0051] Figure 5 This is a distribution diagram of the load rate of the power distribution network when all accessible nodes are connected in Scenario 2. Detailed Implementation

[0052] The present invention will be further described in detail below with reference to specific embodiments. The following examples are used to illustrate the present invention, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0053] This invention provides a method for analyzing the impact of distributed photovoltaic (PV) power on line thermal stability based on active power influence. The method includes: first, establishing a line thermal stability measurement index; second, generating an active power influence vector for distribution network nodes; then, connecting distributed PV power to each node in descending order of active power influence in the vector; and finally, analyzing the impact of distributed PV power on line thermal stability under two scenarios: a fixed distributed PV capacity but varying degrees of dispersion, and a fixed degree of dispersion but varying capacity. The specific steps are as follows:

[0054] Step 1: Establish indicators for measuring the thermal stability of the line.

[0055] The impact of distributed photovoltaic (PV) power on the thermal stability of different power distribution lines is measured based on the reverse load rate. A higher reverse load rate corresponds to lower thermal stability. The thermal stability limit is defined as a reverse load rate of 80%. The reverse load rate is:

[0056]

[0057] In the formula, λ is the line reverse load rate, and P D P represents the active power of distributed photovoltaic power connected to the line. L For the active load on the line, S l This represents the maximum allowable capacity of the line.

[0058] Step 2, Generation of Active Power Influence Vector of Distribution Network Nodes

[0059] First, generate the voltage amplitude sensitivity matrix of the distribution network nodes.

[0060] The first step is to extract the Jacobi matrix from the power flow equations when the power flow of the distribution network converges. The Jacobi matrix is ​​given by the power flow equations in polar coordinate form using the NR method:

[0061] F=J·ΔX (2)

[0062] In the formula, F is the error vector in polar coordinates of the NR method, J is the Jacobi matrix in polar coordinates of the NR method, ΔX is the correction vector in polar coordinates of the NR method, and the specific expression of the power flow equation in polar coordinates of the NR method is:

[0063]

[0064] In the formula, ΔP is the column vector of changes in node injected active power, ΔQ is the column vector of changes in node injected reactive power, Δδ is the column vector of changes in node phase angle, ΔU is the column vector of changes in node voltage, and J is the block matrix in the Jacobi matrix. Pδ The block matrix J reflects the impact of node voltage phase angle changes on the injected active power changes at each node of the network. PUThe block matrix J reflects the impact of node voltage amplitude changes on the injected active power changes at each PQ node. Qδ This reflects the impact of node voltage phase angle changes on the reactive power injected into each PQ node. The block matrix J... QU This reflects the impact of changes in the voltage amplitude of PQ nodes on the changes in the injected reactive power at each PQ node.

[0065] The second step is to generate a node voltage magnitude sensitivity matrix based on the extracted Jacobi matrix. The voltage sensitivity matrix can be obtained by inverting the extracted Jacobi matrix, i.e.:

[0066]

[0067] In the formula, S is the voltage sensitivity matrix, S UP Let S be the node voltage amplitude sensitivity matrix to be obtained, and let S be the block matrix. δP The node voltage amplitude sensitivity matrix S reflects the impact of changes in injected active power on the phase angle changes of voltage at each node in the network. UP This reflects the impact of changes in the injected active power at PQ nodes on the voltage amplitude changes at each node in the network. The block matrix S δQ This reflects the impact of changes in reactive power injected by PQ nodes on the voltage phase angle changes of each node in the network. The block matrix S UQ This reflects the impact of changes in reactive power injected at PQ nodes on the voltage amplitude changes at each PQ node.

[0068] Next, the sensitivity matrix of active power to node voltage of the distribution network line is generated.

[0069] The active power flowing through the distribution network line ij and the voltage amplitude U at its starting and ending nodes i U j The relevant analytical expression is:

[0070] P ij =(G ij cosδ ij +B ij sinδ ij )U i U j -G ij (U i ) 2 (5)

[0071] In the formula, i is the starting node number of the line, j is the ending node number of the line, and G ij B is the real part of the element at position ij in the nodal admittance matrix. ij It is the imaginary part of the elements at position ij in the nodal admittance matrix, δ ij It is the phase angle difference between node i and node j.

[0072] In the above formula, by taking the partial derivative of the active power of the line with respect to the voltages at the beginning and end nodes of the line, we can obtain the sensitivity element of the active power of the line with respect to the node voltage, and its expression is as follows:

[0073]

[0074] Let the matrix composed of these two elements be denoted as This matrix is ​​the sensitivity matrix of active power of a line to node voltage. The rows of the matrix represent the voltage amplitude of each node, and the number of rows is the number of nodes. The columns of the matrix represent the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

[0075] Then, the active power sensitivity matrix of the distribution network lines is generated.

[0076] The active power sensitivity matrix of a distribution network line is a matrix composed of active power sensitivity elements injected into nodes. This matrix can be obtained from the node voltage magnitude sensitivity matrix and the line active power sensitivity matrix to node voltage. It should be noted that since the node voltage magnitude sensitivity elements only consider nodes P and Q, the line active power sensitivity elements to node voltage also only consider the voltage magnitude changes at nodes P and Q. Therefore, the line active power sensitivity elements obtained from both matrices only consider the active power injection changes at nodes P and Q.

[0077]

[0078] In the formula, It is the active power sensitivity matrix of the line. The meaning of the rows of this matrix is ​​the active power injected by each PQ node, and the number of rows is the number of PQ nodes. The meaning of the columns of the matrix is ​​the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

[0079] Finally, the active power influence vector of the distribution network nodes is generated.

[0080] The active power impact vector at each node comprehensively reflects the impact of changes in active power injected at node PQ on the active power flow of all lines. This vector can be obtained by summing the elements of the line active power sensitivity matrix column by column, i.e.:

[0081]

[0082] In the formula, A is the active power influence vector of a node, a is an element in the active power influence vector of a node, the number of which is the number of PQ nodes in the distribution network, B is the line set, i is the node number, and j is the line number;

[0083] Step 3: Determine the sequence of distributed photovoltaic (PV) grid connection.

[0084] The connection sequence is determined based on the active power influence of the nodes. In the active power influence vector A, the difference in the value of each element reflects the different active power influence of each PQ node on the line. Adhering to the principle of highlighting the main factors, distributed photovoltaic power is connected sequentially from the PQ node with the largest active power influence to analyze the degree of impact of distributed photovoltaic power connection on the thermal stability of the distribution network.

[0085] The solution process described in steps 2 and 3 is as follows: Figure 1 ;

[0086] Step 4: Determine the thermal stability analysis scenario

[0087] When distributed photovoltaic (PV) power is connected to the distribution network, the impact of the connection capacity and the number of connection locations on thermal stability analysis must be considered separately. Therefore, two analysis scenarios are designed:

[0088] Scenario 1 is a comparison scenario where the distributed photovoltaic (PV) grid connection capacity is the same, but the degree of dispersion is different, i.e., the number of grid connection locations is different. In this scenario, the degree of dispersion of distributed PV is gradually increased according to the distributed PV grid connection sequence determined in step 2.

[0089] Scenario 2: Distributed photovoltaic (PV) decentralization, i.e., a comparison scenario where the grid connection locations are the same but the grid connection capacities differ. The difference in grid connection capacity is measured by the distributed PV penetration rate, which is defined as:

[0090]

[0091] In the formula, P is the distributed photovoltaic penetration rate, P PV For the total capacity of the distributed photovoltaic system connected to the grid, P load_PQ The active power values ​​of all PQ node loads in the distribution network;

[0092] It should be noted that, to enhance the analytical scope of scenarios, for Scenario 1, the degree of distributed photovoltaic (PV) decentralization needs to be increased under various distributed PV grid connection capacities; for Scenario 2, the distributed PV grid connection capacity needs to be increased under various degrees of distributed PV decentralization.

[0093] In steps 2 to 4, which involve generating the nodal active power influence vector, determining the distributed photovoltaic grid connection sequence, and defining the thermal stability analysis scenario, the IEEE 33-node distribution system is used as the implementation object. The corresponding simulation analysis of this method is performed, and the structure of the example system is as follows: Figure 2 As shown;

[0094] Step 5: Perform corresponding simulation analysis on specific examples using the analysis method of this invention.

[0095] In the IEEE 33-node example system, based on the two analysis scenarios proposed in step 4, and using the reverse load rate as a metric, distributed photovoltaic (PV) systems are connected according to the connection sequence proposed in this invention to analyze the impact of distributed PV connection on line thermal stability.

[0096] The line load rate distribution of distributed photovoltaic systems under different degrees of dispersion, as described in Scenario 1, is as follows: Figure 3 As shown in the diagram, the total capacity of distributed photovoltaic (PV) grid connection is half of the active power load of the example system. When distributed PV is connected at a single point, the reverse load rate of the line is the highest and the thermal stability is the lowest. As distributed PV progresses from single-point connection to full grid connection, the reverse load rate of the line gradually decreases and the thermal stability gradually increases.

[0097] The line load rate distribution of distributed photovoltaic systems described in Scenario 2 under single-point access at the node with the greatest active power impact is as follows: Figure 4 As shown, with the increase in distributed photovoltaic (PV) grid connection capacity, the line load rate gradually decreases. Starting from a distributed PV penetration rate of 40%, the lines begin to exhibit reverse load rates, which gradually increase with further increases in distributed PV grid connection capacity. The thermal stability gradually decreases, but only a few lines show significant changes in thermal stability.

[0098] The line load rate distribution of distributed photovoltaic systems under all accessible nodes as described in Scenario 2 is as follows: Figure 5 As shown, unlike the single-point access case, the load rate of all lines changes significantly. As the capacity of distributed photovoltaic access increases, the thermal stability of all lines decreases significantly.

[0099] The embodiments of the present invention are not exhaustive and do not constitute a limitation on the scope of protection of the claims. Those skilled in the art, upon learning from the embodiments of the present invention, can conceive of other substantially equivalent alternatives without inventive effort, all of which are within the scope of protection of the present invention.

Claims

1. A method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence, characterized in that, It includes the following steps: Step 1: Establish indicators for measuring the thermal stability of the line. The reverse load rate of the line is used as an indicator to measure the degree of impact of distributed photovoltaic power on the thermal stability of different lines in the distribution network; Step 2, Generation of Active Power Influence Vector of Distribution Network Nodes 1) Generation of voltage amplitude sensitivity matrix at distribution network nodes. ① Extract the Jacobi matrix from the power flow equations when the power flow of the distribution network converges. ② Generate the node voltage amplitude sensitivity matrix. 2) Generation of the sensitivity matrix of active power to node voltage in distribution network lines. 3) Generation of active power sensitivity matrix for distribution network lines. 4) Generation of active power influence vectors at distribution network nodes. Step 3: Determine the sequence of distributed photovoltaic (PV) grid connection. Distributed photovoltaic systems are connected sequentially according to the descending order of the active power impact of each PQ node in the distribution network. Step 4: Determine the thermal stability analysis scenario Scenario 1 compares the access scenarios of distributed photovoltaic systems with the same access capacity but different degrees of dispersion. Scenario 2 compares the access scenarios of distributed photovoltaic systems with the same degree of dispersion but different access capacities.

2. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, The line reverse load rate in step 1 is: In the formula, λ is the line reverse load rate, and P D P represents the active power of distributed photovoltaic power connected to the line. L For the active load on the line, S l This represents the maximum allowable capacity of the line.

3. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, Step 2, 1), ① extracting the Jacobi matrix from the power flow equations at the convergence of the power flow in the distribution network is specifically as follows: The Jacobi matrix is ​​given by the power flow equations in polar coordinate form using the NR method, which can be expressed as: F=J·ΔX (2) In the formula, F is the error vector in polar coordinates of the NR method, J is the Jacobi matrix in polar coordinates of the NR method, and ΔX is the correction vector in polar coordinates of the NR method. The specific expression for the power flow equations in polar coordinate form using the NR method is as follows: In the formula, ΔP is the column vector of changes in node injected active power, ΔQ is the column vector of changes in node injected reactive power, Δδ is the column vector of changes in node phase angle, ΔU is the column vector of changes in node voltage, and J is the block matrix in the Jacobi matrix. Pδ The block matrix J reflects the impact of node voltage phase angle changes on the injected active power changes at each node of the network. PU The block matrix J reflects the impact of node voltage amplitude changes on the injected active power changes at each PQ node. Qδ This reflects the impact of node voltage phase angle changes on the reactive power injected into each PQ node. The block matrix J... QU This reflects the impact of changes in the voltage amplitude of the PQ nodes on the changes in the injected reactive power at each PQ node.

4. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, Step 2, 1), step ②, generating the node voltage amplitude sensitivity matrix, specifically involves: The voltage sensitivity matrix can be obtained by inverting the Jacobi matrix in the polar coordinate form of the power flow equations of the NR method when the power flow of the distribution network converges, that is: In the formula, S is the voltage sensitivity matrix, S UP Let S be the node voltage amplitude sensitivity matrix to be obtained, and let S be the block matrix. δP The node voltage amplitude sensitivity matrix S reflects the impact of changes in injected active power on the phase angle changes of voltage at each node in the network. UP This reflects the impact of changes in the injected active power at PQ nodes on the voltage amplitude changes at each node in the network. The block matrix S δQ This reflects the impact of changes in reactive power injected by PQ nodes on the voltage phase angle changes of each node in the network. The block matrix S UQ This reflects the impact of changes in the injected reactive power at PQ nodes on the voltage amplitude changes at each PQ node.

5. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, The generation of the sensitivity matrix of active power to node voltage in step 2) of the distribution network line is as follows: The active power flowing through the distribution network line ij and the voltage amplitude U at its starting and ending nodes i U j The relevant analytical expression is: P ij =(G ij cosδ ij +B ij sinδ ij )U i U j -G ij (U i ) 2 (5) In the formula, i is the starting node number of the line, j is the ending node number of the line, and G ij B is the real part of the element at position ij in the nodal admittance matrix. ij It is the imaginary part of the elements at position ij in the nodal admittance matrix, δ ij It is the phase angle difference between node i and node j. In the above formula, by taking the partial derivative of the active power of the line with respect to the voltages at the beginning and end nodes of the line, we can obtain the sensitivity element of the active power of the line with respect to the node voltage, and its expression is as follows: Let the matrix composed of these two elements be denoted as This matrix is ​​the sensitivity matrix of active power of the line to the node voltage. The rows of the matrix represent the voltage amplitude of each node, and the number of rows is the number of nodes. The columns of the matrix represent the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

6. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, The generation of the active power sensitivity matrix of the distribution network lines in step 2, step 3) is specifically as follows: The active power sensitivity matrix of a distribution network line is a matrix composed of active power sensitivity elements injected into nodes. This matrix can be obtained from the node voltage magnitude sensitivity matrix and the line active power sensitivity matrix to node voltage. It should be noted that since the node voltage magnitude sensitivity elements only consider nodes P and Q, the line active power sensitivity elements to node voltage also only consider the voltage magnitude changes at nodes P and Q. Therefore, the line active power sensitivity elements obtained from these two matrices also only consider the active power injection changes at nodes P and Q. In the formula, This is the active power sensitivity matrix of the line. The rows of this matrix represent the active power injected by each PQ node, and the number of rows is the number of PQ nodes. The columns of the matrix represent the active power flowing from the starting node to the ending node of each line, and the number of columns is the number of lines.

7. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, The generation of the active power influence vector of the distribution network node in step 2, 4) is as follows: The active power influence vector at a node can be obtained by summing the elements of the line active power sensitivity matrix column by column, i.e.: In the formula, A is the active power influence vector of a node, a is an element in the active power influence vector of a node, the number of which is the number of PQ nodes in the distribution network, B is the line set, i is the node number, and j is the line number.

8. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, Step 3, determining the sequence of distributed photovoltaic (PV) grid connection, specifically involves: In the active power influence vector A, the difference in the value of each element reflects the different active power influence of each PQ node on the line. Based on the principle of highlighting the main factors, starting from the PQ node with the largest active power influence, distributed photovoltaic power is connected in sequence to analyze the degree of influence of distributed photovoltaic power connection on the thermal stability of the distribution network line.

9. The method for analyzing the impact of distributed photovoltaic power generation on line thermal stability based on active power influence as described in claim 1, characterized in that, Step 4, determining the thermal stability analysis scenario, specifically involves: When distributed photovoltaic (PV) power is connected to the distribution network, the impact of the connection capacity and the number of connection locations on thermal stability analysis must be considered separately. Therefore, two analysis scenarios are designed: Scenario 1 is a comparison scenario where the distributed photovoltaic (PV) grid connection capacity is the same, but the degree of dispersion is different, i.e., the number of grid connection locations is different. In this scenario, the degree of dispersion of distributed PV is gradually increased according to the distributed PV grid connection order determined in step 2. Scenario 2: Distributed photovoltaic (PV) decentralization, i.e., a comparison scenario where the grid connection locations are the same but the grid connection capacities differ. The difference in grid connection capacity is measured by the distributed PV penetration rate, which is defined as: In the formula, P is the distributed photovoltaic penetration rate, P PV For the total capacity of the distributed photovoltaic system connected to the grid, P load_PQ The active power values ​​of all PQ node loads in the distribution network; To enhance the analytical dimensions of scenarios, for scenario one, we need to increase the degree of distributed photovoltaic (PV) decentralization under various distributed PV grid connection capacities; for scenario two, we need to increase the distributed PV grid connection capacity under various distributed PV decentralization capacities.

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