Probabilistic load flow calculation method and device for DC power distribution network, and storage medium

Abstract

The embodiment of the invention provides a probabilistic load flow calculation method and device for a direct-current power distribution network and a storage medium. The method comprises the following steps: constructing a combined generation power probability model of the DC power distribution network; wherein the combined generation power probability model is obtained by performing combined modeling processing based on the generation power of each new energy power station in the DC power distribution network; the DC power distribution network comprises at least one wind power station and / or at least one photovoltaic power station; performing linear modeling on the DC power distribution network, determining a parameter sensitivity matrix of the DC power distribution network, and determining a probabilistic load flow calculation result of the DC power distribution network according to the combined generation power probability model and the parameter sensitivity matrix; wherein the parameter sensitivity matrix represents the influence degree of the tiny change of the DC power distribution network on the power distribution network parameters; the probabilistic load flow calculation result comprises load flow probability distribution of the direct-current power distribution network. The method is used for achieving the effect of reducing the operation risk of the direct-current power distribution network.

Description

Technical Field

[0001] This application relates to the field of power distribution technology, and in particular to a probabilistic power flow calculation method, device and storage medium for DC power distribution networks. Background Technology

[0002] With the rapid development of power electronics technology and the continuous transformation of the energy structure, new DC power distribution systems have gained widespread application and attention due to their advantages in power supply reliability, economy, and flexibility. Compared with AC power distribution systems, DC power distribution networks significantly reduce line losses and voltage drops. Furthermore, the connection of DC loads does not require a complex AC-DC conversion process, improving system transmission efficiency. In terms of power quality, since there are no issues such as three-phase asymmetry and frequency offset, DC power distribution networks can provide a more stable and high-quality power supply, ensuring the reliable operation of the power system.

[0003] Therefore, there is an urgent need for a probabilistic power flow calculation method for DC distribution networks to improve the accuracy of the calculation results and thus reduce the operational risks of DC distribution networks. Summary of the Invention

[0004] This application provides a method, device, and storage medium for calculating probabilistic power flow in DC distribution networks, in order to improve and reduce the operational risks of DC distribution networks.

[0005] In a first aspect, embodiments of this application provide a probabilistic power flow calculation method for a DC distribution network, including:

[0006] A joint power generation probability model for a DC distribution network is constructed; wherein, the joint power generation probability model is obtained by jointly modeling the power generation of each new energy power station in the DC distribution network; the DC distribution network includes at least one wind power station and / or at least one photovoltaic power station;

[0007] The DC distribution network is linearized and modeled to determine the parameter sensitivity matrix of the DC distribution network. Based on the joint generation power probability model and the parameter sensitivity matrix, the probabilistic power flow calculation results of the DC distribution network are determined. The parameter sensitivity matrix characterizes the degree of influence of small changes in the DC distribution network on the distribution network parameters. The probabilistic power flow calculation results include the power flow probability distribution of the DC distribution network.

[0008] In one possible implementation, the parameter sensitivity matrix includes a node voltage sensitivity matrix and a branch transmission power sensitivity matrix; linearizing the DC distribution network to determine its parameter sensitivity matrix includes: obtaining the injected power of each distribution node in the DC distribution network, and performing linearization modeling on the DC distribution network based on the injected power of each distribution node to determine the node voltage sensitivity matrix; obtaining the transmission power of each distribution branch in the DC distribution network, and performing linearization modeling on the DC distribution network based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0009] In one possible implementation, the distribution node includes a power control node and a voltage droop node; the injected power of the distribution node includes the injected power of the power control node and the injected power of the voltage droop node; the DC distribution network is linearized and modeled based on the injected power of each distribution node to determine the node voltage sensitivity matrix, including: performing Taylor series expansion on the injected power of each power control node to determine the injected power change matrix of the power control node; performing Taylor series expansion on the injected power of each voltage droop node to determine the injected power change matrix of the voltage droop node; and determining the node voltage sensitivity matrix based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node.

[0010] In one possible implementation, the DC distribution network is linearized and modeled based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix. This includes: performing Taylor series expansion on the transmission power of each distribution branch to determine the transmission power change matrix of the distribution branch; and determining the branch transmission power sensitivity matrix based on the transmission power change matrix of the distribution branch and the node voltage sensitivity matrix.

[0011] In one possible implementation, constructing a joint power generation probability model for a DC distribution network includes: obtaining the power generation probability model of each new energy power station in the DC distribution network; and performing joint processing on the power generation probability models of each new energy power station based on a Gaussian mixture algorithm to obtain the joint power generation probability model.

[0012] In one possible implementation, the method further includes: performing risk warning processing on the DC distribution network based on the probabilistic power flow calculation results and preset parameter warning thresholds.

[0013] Secondly, embodiments of this application provide a probabilistic power flow calculation device for a DC distribution network, comprising:

[0014] A construction module is used to construct a joint power generation probability model of a DC distribution network; wherein, the joint power generation probability model is obtained by joint modeling based on the power generation of each new energy power station in the DC distribution network; the DC distribution network includes at least one wind power station and / or at least one photovoltaic power station;

[0015] The calculation module is used to perform linearization modeling on the DC distribution network, determine the parameter sensitivity matrix of the DC distribution network, and determine the probabilistic power flow calculation results of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix; wherein, the parameter sensitivity matrix characterizes the degree of influence of small changes in the DC distribution network on the distribution network parameters; the probabilistic power flow calculation results include the power flow probability distribution of the DC distribution network.

[0016] In one possible implementation, the parameter sensitivity matrix includes a node voltage sensitivity matrix and a branch transmission power sensitivity matrix; the calculation module is specifically used to obtain the injected power of each distribution node in the DC distribution network, and perform linearization modeling processing on the DC distribution network based on the injected power of each distribution node to determine the node voltage sensitivity matrix; obtain the transmission power of each distribution branch in the DC distribution network, and perform linearization modeling processing on the DC distribution network based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0017] In one possible implementation, the distribution node includes a power control node and a voltage droop node; the injected power of the distribution node includes the injected power of the power control node and the injected power of the voltage droop node; the calculation module is further specifically used to perform Taylor series expansion processing on the injected power of each of the power control nodes to determine the injected power change matrix of the power control node; perform Taylor series expansion processing on the injected power of each of the voltage droop nodes to determine the injected power change matrix of the voltage droop node; and determine the node voltage sensitivity matrix based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node.

[0018] In one possible implementation, the calculation module is further specifically used to perform Taylor series expansion processing on the transmission power of each of the distribution branches to determine the transmission power change matrix of the distribution branch; and to determine the branch transmission power sensitivity matrix based on the transmission power change matrix of the distribution branch and the node voltage sensitivity matrix.

[0019] In one possible implementation, the construction module is specifically used to obtain the power generation probability model of each new energy power station in the DC distribution network; and to perform joint processing on the power generation probability models of each new energy power station based on the Gaussian mixture algorithm to obtain the joint power generation probability model.

[0020] In one possible implementation, the calculation module is further configured to perform risk warning processing on the DC distribution network based on the probabilistic power flow calculation results and preset parameter warning thresholds.

[0021] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0022] The memory stores computer-executed instructions;

[0023] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0025] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0026] The probabilistic power flow calculation method, device, and storage medium for DC distribution networks provided in this application embodiment construct a joint generation power probability model of the DC distribution network, perform linearization modeling on the DC distribution network, determine the parameter sensitivity matrix of the DC distribution network, and determine the probabilistic power flow calculation results of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix. In the process of linearization modeling of the DC distribution network, by constructing a joint generation power probability model, the generation power of each new energy power station in the DC distribution network can be combined, thereby improving the comprehensiveness and accuracy of the probabilistic power flow calculation results based on the joint generation power probability model, and further reducing the operational risk of the DC distribution network. Furthermore, in the process of determining the probabilistic power flow calculation results of the DC distribution network, based on the parameter sensitivity matrix of the DC distribution network, complex nonlinear power flow equations can be converted into linear relationships, thereby improving the efficiency and accuracy of the probabilistic power flow calculation, and further reducing the operational risk of the DC distribution network. In summary, the probabilistic power flow calculation method for DC distribution networks provided in this application embodiment can reduce the operational risk of DC distribution networks. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0028] Figure 1 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 1 ;

[0029] Figure 2 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 2 ;

[0030] Figure 3 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 3 ;

[0031] Figure 4 A schematic diagram of voltage distribution results for an example of a DC distribution network provided in this application embodiment;

[0032] Figure 5 A schematic diagram of sample distribution data for an example of a DC distribution network provided in the embodiments of this application;

[0033] Figure 6 A schematic diagram of Gaussian mixture fitting results for an example of a DC distribution network provided in this application embodiment;

[0034] Figure 7A schematic diagram of the probability density curve of bus 13 in an example of a DC distribution network provided in this application embodiment;

[0035] Figure 8 A schematic diagram of the probability distribution curve of bus 13 in an example of a DC distribution network provided in this application embodiment;

[0036] Figure 9 A schematic diagram of the probability density curve of bus 14 in an example of a DC distribution network provided in this application embodiment;

[0037] Figure 10 A schematic diagram of the probability distribution curve of bus 14 in an example of a DC distribution network provided in this application embodiment;

[0038] Figure 11 A schematic diagram of the probabilistic power flow calculation device for the DC distribution network provided in this application;

[0039] Figure 12 A schematic diagram of the structure of the electronic device provided in this application.

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] In existing technologies, probabilistic power flow calculations are performed using analytical and Monte Carlo methods. Analytical methods typically treat output variables as linear functions of input variables and then derive the probability distribution of the output variables based on the semi-invariant method. Monte Carlo methods utilize sampling principles to obtain probabilistic analysis results based on random experiments with a large number of samples. However, existing probabilistic power flow calculation methods usually focus on the AC side, neglecting the impact of the VSC control strategy on the algorithm in voltage source converter stations. Existing analytical probabilistic power flow methods often simplify the joint probability distribution of multiple renewable energy outputs, failing to accurately obtain the system operating status. Furthermore, Monte Carlo probabilistic power flow calculations suffer from low efficiency.

[0043] The probabilistic power flow calculation method for DC distribution networks provided in this application constructs a joint generation power probability model of the DC distribution network, performs linearization modeling on the DC distribution network, determines the parameter sensitivity matrix of the DC distribution network, and determines the probabilistic power flow calculation result of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix. In the process of probabilistic power flow calculation of the DC distribution network, by constructing the joint generation power probability model of the DC distribution network, a joint probability model of the new energy power generation system can be constructed, thereby improving the comprehensiveness and accuracy of the probabilistic power flow calculation result based on the joint generation power probability model of the DC distribution network, and further reducing the operational risk of the DC distribution network. Furthermore, in the process of determining the probabilistic power flow calculation result of the DC distribution network, based on the parameter sensitivity matrix of the DC distribution network, the complex nonlinear power flow equations can be converted into linear relationships, thereby improving the efficiency of probabilistic power flow calculation. In summary, the probabilistic power flow calculation method for DC distribution networks provided in this application can reduce the operational risk of DC distribution networks.

[0044] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0045] Figure 1 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0046] Step S101: Construct a joint generation power probability model for the DC distribution network.

[0047] Specifically, a joint generation power probability model for DC distribution networks can be constructed.

[0048] As the penetration rate of new energy sources such as wind and solar power in DC distribution networks gradually increases, the randomness and complexity of the system also rise. The intermittency and unpredictability of these energy sources lead to significant fluctuations in grid operating voltage and drive the shift of power flow from traditional unidirectional transmission to bidirectional transmission. This shift results in more complex grid operation modes and control strategies, increasing the challenges for applications such as intermittent energy consumption management and power flow analysis.

[0049] Therefore, it is necessary to construct a joint generation power probability model for the DC distribution network. This joint generation power probability model is obtained by jointly modeling the generation power of each renewable energy power station in the DC distribution network. The DC distribution network includes at least one wind power station and / or at least one photovoltaic power station.

[0050] Specifically, this application does not limit the process of constructing the joint power generation probability model of the DC distribution network. Optionally, the power generation probability models of each new energy power station in the DC distribution network can be obtained first; then, the power generation probability models of each new energy power station can be jointly processed based on the Gaussian mixture algorithm to obtain the joint power generation probability model.

[0051] Step S102: Perform linearization modeling on the DC distribution network, determine the parameter sensitivity matrix of the DC distribution network, and determine the probabilistic power flow calculation results of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix.

[0052] Specifically, linearization modeling can be performed on the DC distribution network to determine the parameter sensitivity matrix of the DC distribution network.

[0053] The parameter sensitivity matrix characterizes the impact of minute changes in a DC distribution network on its parameters. Specifically, in a DC distribution network, the parameter sensitivity matrix reflects the rate of change of network parameters, such as node voltages and branch power, when minute changes occur in parameters like line resistance, inductance, and power supply voltage. For example, when the resistance of a line in a DC distribution network changes, the parameter sensitivity matrix can quantify the impact of this change on the voltages of each node and the power of each branch.

[0054] In probabilistic power flow calculations, traditional iterative algorithms require multiple iterations to converge to an exact solution. However, based on the parameter sensitivity matrix, a linear approximation of the system state variables with respect to parameter changes can be provided. Using this relationship, even with small parameter variations, the change in system state variables can be quickly calculated directly through the sensitivity matrix, avoiding large-scale iterative calculations and significantly reducing computation time. Furthermore, the sensitivity matrix transforms complex nonlinear power flow equations into linear relationships, making the calculation process simpler and more direct. For DC distribution networks containing multiple generators and complex network structures, this simplification can significantly reduce computational complexity and improve computational efficiency and accuracy.

[0055] Specifically, this application does not limit the process of linearizing the DC distribution network and determining the parameter sensitivity matrix of the DC distribution network. Optionally, the parameter sensitivity matrix may include the node voltage sensitivity matrix and the branch transmission power sensitivity matrix. The injected power of each distribution node in the DC distribution network can be obtained, and the DC distribution network can be linearized based on the injected power of each distribution node to determine the node voltage sensitivity matrix. The transmission power of each distribution branch in the DC distribution network can be obtained, and the DC distribution network can be linearized based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0056] Specifically, after linearizing the DC distribution network and determining its parameter sensitivity matrix, the probabilistic power flow calculation results can be determined based on the joint generation power probability model and the parameter sensitivity matrix. These results include the power flow probability distribution of the DC distribution network. Specifically, the type of power flow probability distribution included in the probabilistic power flow calculation results corresponds to the type of the parameter sensitivity matrix described above. For example, if the parameter sensitivity matrix includes a node voltage sensitivity matrix and a branch transmission power sensitivity matrix, then the type of power flow probability distribution included in the probabilistic power flow calculation results is the power flow probability distribution of node voltages and the power flow probability distribution of branch transmission power.

[0057] The probabilistic power flow calculation method for DC distribution networks provided in this application constructs a joint generation power probability model of the DC distribution network, performs linearization modeling on the DC distribution network, determines the parameter sensitivity matrix of the DC distribution network, and determines the probabilistic power flow calculation result of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix. In the process of linearization modeling of the DC distribution network, by constructing a joint generation power probability model, the generation power of each new energy power station in the DC distribution network can be combined, thereby improving the comprehensiveness and accuracy of the probabilistic power flow calculation result based on the joint generation power probability model, and further reducing the operational risk of the DC distribution network. Furthermore, in determining the probabilistic power flow calculation result of the DC distribution network, based on the parameter sensitivity matrix of the DC distribution network, complex nonlinear power flow equations can be converted into linear relationships, thereby improving the efficiency and accuracy of the probabilistic power flow calculation, and further reducing the operational risk of the DC distribution network. In summary, the probabilistic power flow calculation method for DC distribution networks provided in this application can reduce the operational risk of DC distribution networks.

[0058] Figure 2 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, this paper details the linearization modeling process for DC distribution networks and the determination of the parameter sensitivity matrix of the DC distribution network. The method includes:

[0059] Step S201: Obtain the injected power of each distribution node in the DC distribution network, and perform linearization modeling processing on the DC distribution network based on the injected power of each distribution node to determine the node voltage sensitivity matrix.

[0060] The parameter sensitivity matrix includes the node voltage sensitivity matrix and the branch transmission power sensitivity matrix. Specifically, the node voltage sensitivity matrix characterizes the degree of impact of small changes in the DC distribution network on the node voltages of the distribution network. The branch transmission power sensitivity matrix characterizes the degree of impact of small changes in the DC distribution network on the branch transmission power of the distribution network.

[0061] Specifically, the injected power of each distribution node in the DC distribution network can be obtained, and the DC distribution network can be linearized and modeled based on the injected power of each distribution node to determine the node voltage sensitivity matrix. Here, the injected power of a distribution node refers to the amount of power exchange between each node and an external system or device in the DC distribution network.

[0062] Optionally, the distribution node includes a power control node and a voltage droop node. The injected power of the distribution node includes the injected power of the power control node and the injected power of the voltage droop node.

[0063] Specifically, since voltage source converters (VSCs) employ various control strategies, such as master-slave control and droop control, linearization modeling must consider not only the impact of node injected power on power flow distribution but also the effects of VSC control modes and their control parameters. Therefore, different calculations are required for different types of distribution nodes to improve the accuracy of determining the node voltage sensitivity matrix.

[0064] Optionally, in a DC distribution network, different types of nodes perform specific functions to maintain the stable operation of the system. Power control nodes, voltage droop nodes, and constant voltage nodes are three common node types, which are explained in detail below:

[0065] A power-controlled node, or P-control node, is a node whose injected active power P is known, and whose voltage amplitude needs to be calculated. These nodes typically represent load nodes or distributed generation systems with fixed power output. Loads absorb active power from the grid, the amount of which is determined by the user's electricity demand; while some distributed generation systems, such as small-scale photovoltaic systems operating in maximum power point tracking (MPPT) mode, inject maximum power into the grid, and their output power is relatively fixed.

[0066] A voltage droop node is a node where a specific droop relationship exists between its voltage amplitude and injected active power (i.e., the voltage decreases as power increases). This droop relationship is typically achieved through droop control strategies, a distributed control method based on local information that eliminates the need for complex communication between nodes. A droop node can automatically adjust its voltage based on its own power output to achieve reasonable power distribution and stable system operation.

[0067] A constant voltage node, or slack bus node, is a node whose voltage amplitude is known, and the injected active power needs to be calculated. In DC distribution networks, constant voltage nodes are typically provided by power sources with sufficient capacity and regulation capabilities, such as large energy storage devices or converter stations connected to the AC grid. These power sources maintain a constant node voltage, providing voltage support for the system.

[0068] For a DC distribution network with n distribution nodes, there are m P-control nodes, numbered Np = [1, 2, ..., m]; nm-1 voltage droop nodes, numbered Nv = [m+1, m+2, ..., n-1]; and one slack bus node, numbered n, where n > m.

[0069] Since there is typically only one constant voltage node, and the voltage is constant, the calculation of the node voltage sensitivity matrix for a DC distribution network only considers power control nodes and voltage droop nodes. Furthermore, the injected power of power control nodes and voltage droop nodes are considered only when obtaining the node voltage sensitivity matrix for a DC distribution network.

[0070] Specifically, the formula for the injected power of the power control node is as follows:

[0071]

[0072] Among them, P i The injected power for the P-control node, i.e., the injected active power, Y ijLet V be the admittance of branch ij, that is, the admittance of the branch with nodes i and j as endpoints. j Let N be the voltage at node j. p N represents the number of P-control nodes. p =m.

[0073] Specifically, the formula for the injected work at the voltage droop node is as follows:

[0074]

[0075] Among them, P p,0 The injected power, i.e., the injected active power, Y, is the power injected into the voltage droop node. pj V is the admittance of branch pj, that is, the admittance of the branch with nodes p and j as endpoints. p V is the voltage at node p. j Let V be the voltage at node j. p,0 k represents the voltage value of node p under no-load conditions. p N is the droop factor, reflecting the droop characteristic relationship between the injected power and the node voltage at a voltage droop node. v N represents the number of Voltage droop nodes. v =nm-1.

[0076] Optionally, the DC distribution network is linearized based on the injected power at each distribution node to determine the node voltage sensitivity matrix, including:

[0077] The injected power of each power control node is expanded using Taylor series to determine the matrix of changes in injected power of the power control node.

[0078] The injected power of each voltage droop node is subjected to Taylor series expansion to determine the matrix of injected power variation of the voltage droop node.

[0079] The node voltage sensitivity matrix is ​​determined based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node.

[0080] Specifically, the formula for the Taylor series expansion of the injected power at each power control node is shown below:

[0081]

[0082] Wherein, ΔP m The change in node injection power for the m-th P-control node is calculated using formula (1); ΔV n-1 N represents the voltage change at the (n-1)th distribution node; m,n-1Let be the elements of the Jacobian matrix in formula (3). The elements of the Jacobian matrix are shown below:

[0083] N mj =Y mj V m ,m≠j……(4)

[0084]

[0085] Where, N m,j Let Y be the element of the Jacobian matrix in the m-th row and j-th column. Formula (4) represents the element value when the row and column are different, and formula (5) represents the element value when the row and column are the same. mj Y is the admittance of branch mj, that is, the admittance of the branch with nodes m and j as endpoints. mm Let Y be the self-admittance of node m, where Y is the self-admittance of node m. mm V can be obtained by adding the admittances of all branches with node m as the endpoint. j Let V be the voltage at node j. m Let be the voltage at node m.

[0086] Specifically, the formula for the Taylor series expansion of the injected power at each voltage droop node is shown below:

[0087]

[0088] Wherein, ΔP m+1,0 The change in node injection power for the (m+1)th voltage droop node is calculated using formula (2); N n-1,n-1 Let be the elements of the Jacobian matrix in formula (6). The elements of the Jacobian matrix are shown below:

[0089] N pj =Y pj V p ,p≠j…… (7)

[0090]

[0091] Where, N p,j Let Y be the element of the Jacobian matrix in row p and column j. Formula (7) represents the element value when the row and column are different, and formula (8) represents the element value when the row and column are the same. pj Y is the admittance of branch pj, that is, the admittance of the branch with nodes p and j as endpoints. pp Let Y be the self-admittance of node p, where Y is the self-admittance of node p. pp V can be obtained by adding the admittances of all branches with node p as an endpoint. j Let V be the voltage at node j. pLet k be the voltage at node m. p This is the droop coefficient.

[0092] Specifically, in determining the node voltage sensitivity matrix based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node, firstly, the results of Taylor series expansion of the injected power of each power control node and the results of Taylor series expansion of the injected power of each voltage droop node need to be merged; then, the merged result is inverted to obtain the node voltage sensitivity matrix.

[0093] Specifically, the results of Taylor series expansion of the injected power of each power control node and the results of Taylor series expansion of the injected power of each voltage droop node are combined, that is, the result of combining formula (3) and formula (6) is shown below:

[0094] [ΔP n-1,1 ] = [N n-1,n-1 ][ΔV n-1,1 ... (9)

[0095] Among them, [ΔP n-1,1 [N] Injects the power change matrix into the nodes after merging. n-1,n-1 [ΔV] represents the Jacobian matrix after merging. n-1,1 [ ] is the voltage change matrix of the distribution nodes after merging, i.e., the node voltage deviation matrix.

[0096] Specifically, the formula for inverting the result of the merging process is shown below:

[0097] [ΔV n-1,1 ] = [N n-1,n-1 ] -1 [ΔP n-1,1 ... (10)

[0098] Among them, [N n-1,n-1 ] -1 This is the node voltage sensitivity matrix. Specifically, the node voltage sensitivity matrix can be calculated using formulas (4), (5), (7), and (8). Among them, the node injection power change matrix after merging is [ΔP]. n-1,1 It can be calculated based on the joint generation power probability model of DC distribution network.

[0099] Optionally, the node voltage deviation matrix is ​​a probability distribution in the power flow probability distribution of the DC distribution network. It is calculated according to formula (10) after the node voltage sensitivity matrix and the node injected power change matrix after merging are calculated.

[0100] In determining the node voltage sensitivity matrix, different control modes of the voltage source converter station (VSC) are considered, which can improve the accuracy of the node voltage sensitivity matrix determination, thereby improving the accuracy of the probabilistic power flow calculation results and further reducing the operational risks of the DC distribution network.

[0101] Step S202: Obtain the transmission power of each distribution branch in the DC distribution network, and perform linearization modeling on the DC distribution network based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0102] Specifically, the transmission power of each distribution branch in the DC distribution network can be obtained, and the DC distribution network can be linearized and modeled based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0103] For example, in a DC distribution network containing b distribution branches, where branch l ij That is, the transmission power of the branch with nodes i and j as endpoints can be expressed by the following formula:

[0104] P ij =Y ij V i (V i -V j )……(11)

[0105] Among them, P ij For branch road l ij The transmission power, Y ij For branch road l ij Admittance, V j Let V be the voltage at node j. i Let be the voltage at node i.

[0106] Optionally, based on the transmission power and node voltage sensitivity matrices of each distribution branch, a linearization model of the DC distribution network is performed to determine the branch transmission power sensitivity matrix, including:

[0107] The transmission power of each distribution branch is processed by Taylor series expansion to determine the matrix of transmission power variation of the distribution branch.

[0108] The power transmission sensitivity matrix of a branch is determined based on the power transmission variation matrix and the node voltage sensitivity matrix of the distribution branch.

[0109] Specifically, the formula for Taylor series expansion of the transmission power of each distribution branch is shown below:

[0110]

[0111] Wherein, ΔPli The power change of the i-th distribution branch is calculated using formula (11); ΔV n-1 T represents the voltage change at the (n-1)th distribution node. lb,n-1 Let be the elements of the Jacobian matrix in formula (12). The elements of the Jacobian matrix are shown below:

[0112] T lb,i =Y ij (2V i -V j )……(13)

[0113] T lb,j =-Y ij V i ……(14)

[0114] T lb,k =0……(15)

[0115] Among them, T lb,i Let T represent the sensitivity of the i-th branch to the voltage change at node i, where Yij is the admittance of branch ij, Vi is the voltage at node i, and Vj is the voltage at node j. lb,j T represents the sensitivity of the i-th branch to voltage changes at node j, with a negative sign, reflecting the power flow direction. lb,k This indicates that the voltage change of the i-th branch is independent of other nodes k that are not directly connected, therefore its sensitivity is zero.

[0116] Specifically, in determining the branch transmission power sensitivity matrix based on the transmission power change matrix and the node voltage sensitivity matrix of the distribution branch, formula (10) can be substituted into formula (12) to obtain the following formula:

[0117] [ΔP lb ] = [T][N] -1 [ΔP]……(16)

[0118] Among them, [ΔP lb [ ] is the power transmission change matrix of the distribution branch, [T] is the Jacobian matrix of formula (12), and [N] is the power transmission change matrix of the distribution branch. -1 Let be the node voltage sensitivity matrix, and [ΔP] be the node injection power change matrix after merging.

[0119] Among them, [ΔP lb ] = [S][ΔP], where [S] is the branch power sensitivity matrix. [ΔP lb [ ] represents the branch transmission power deviation matrix. Specifically, the branch power sensitivity matrix can be calculated by combining formulas (13), (14), (15) and the node voltage sensitivity matrix.

[0120] Among them, the branch transmission power deviation matrix is ​​a type of probability distribution in the power flow probability distribution of DC distribution network. It is calculated by substituting the calculated branch power sensitivity matrix and the node injection power change matrix after merging into the above formula.

[0121] In determining the branch transmission power sensitivity matrix, combining it with the node voltage sensitivity matrix allows for consideration of the impact of different control modes of the voltage source converter station (VSC), improving the accuracy of the branch transmission power sensitivity matrix determination. This, in turn, enhances the accuracy of probabilistic power flow calculations and further reduces the operational risks of the DC distribution network.

[0122] The process of linearizing and modeling a DC distribution network to determine its parameter sensitivity matrix, as provided in this application embodiment, involves obtaining the injected power of each distribution node in the DC distribution network and performing linearization and modeling based on the injected power of each distribution node to determine the node voltage sensitivity matrix. It also involves obtaining the transmission power of each distribution branch in the DC distribution network and performing linearization and modeling based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix. In determining the parameter sensitivity matrix of the DC distribution network, probabilistic power flow calculations are used to determine the node voltage sensitivity matrix and the branch transmission power sensitivity matrix based on different source data. This improves the comprehensiveness and accuracy of the probabilistic power flow calculation results, thereby reducing the operational risk of the DC distribution network.

[0123] Figure 3 A flowchart illustrating the probabilistic power flow calculation method for DC distribution networks provided in this application. Figure 3 ,like Figure 3 As shown, in this embodiment... Figure 1 or Figure 2 Based on the examples, a detailed explanation is provided on constructing a joint generation power probability model for a DC distribution network. This method includes:

[0124] Step S301: Obtain the power generation probability model of each new energy power station in the DC distribution network.

[0125] Specifically, probabilistic models of power generation for each renewable energy power station in the DC distribution network can be obtained. This DC distribution network includes at least one wind power station and / or at least one photovoltaic power station. Therefore, probabilistic models of power generation for each wind power station and each photovoltaic power station in the DC distribution network can be obtained.

[0126] Among them, the output characteristics of wind turbines and photovoltaic power generation systems are significantly random and uncertain due to the influence of meteorological conditions such as wind speed and sunlight intensity.

[0127] Specifically, the Weibull distribution can effectively describe the frequency and intensity of wind speed changes, so it is often used to probabilistically model wind speed. Its formula is shown below:

[0128]

[0129] Where v is the wind speed, and k and c are the model parameters of the probability model of the power generation of the wind power station.

[0130] Specifically, the Beta distribution can simulate the solar radiation variation curve well, so this distribution is used to probabilistically model light intensity, and its formula is shown below:

[0131]

[0132] Where a and b are the model parameters of the probability model for the power generation of the photovoltaic power station; r max Γ represents the maximum light intensity; Γ is the Gamma function.

[0133] The DC load of the DC distribution network approximately follows a normal distribution, and its probability density function is shown below:

[0134]

[0135] Where P is the DC load of the DC distribution network, μ p and σ p These represent the expected value and standard deviation of the active power of the DC load, respectively.

[0136] While the Weibull and Beta distributions provide reasonable statistical models for wind speed and solar intensity, these models typically assume independent and identically distributed distributions, failing to consider the wind speed correlations between different turbine clusters within a wind farm or the interactions between different photovoltaic panels. This oversight may lead to insufficient risk assessment of the overall system.

[0137] Therefore, Gaussian mixture models (GMMs) can be introduced to better simulate the complexity and correlation of new energy power generation characteristics. A Gaussian mixture model uses a superposition method of linear combinations of Gaussian distributions to simulate complex probability density functions. By adjusting the parameters of the Gaussian distributions and the coefficients of the linear combination, simulations with arbitrary precision can be achieved.

[0138] Step S302: Based on the Gaussian mixture algorithm, the power generation probability models of each new energy power station are jointly processed to obtain a joint power generation probability model.

[0139] Specifically, the Gaussian mixture algorithm can be used to jointly process the power generation probability models of each new energy power station obtained in step S301 to obtain a joint power generation probability model.

[0140] Specifically, firstly, the probability model for the prediction error variable X of new energy output can be performed using a linear combination of L D-dimensional Gaussian distributions. The formula for this process is shown below:

[0141]

[0142]

[0143] Where f(X) is the joint probability density function of the random variable X; μ l Let be a D-dimensional mean vector; ∑l be the covariance matrix; |∑l| be the determinant. π l These are the coefficients of the linear combination.

[0144] Specifically, secondly, the Gaussian mixture model, i.e., the joint probability density function of the random variable X, can be linearly transformed. After the Gaussian mixture model undergoes linear transformation, the output variable still follows a Gaussian mixture distribution, and its distribution parameters will change accordingly based on the transformation matrix. The output variable Y after linear transformation satisfies the following formula:

[0145] Y = AX + b……(23)

[0146] Where A and b are the coefficients of the linear transformation treatment.

[0147] The joint power generation probability model, i.e., the joint probability density function and distribution function of the output variable Y, are shown in Equation (24) and Equation (25), respectively:

[0148]

[0149] Where Φ represents the cumulative distribution function of the multivariate normal distribution.

[0150] Since the Gaussian mixture distribution still satisfies the Gaussian mixture distribution after linear transformation, when the new energy prediction error is modeled using the Gaussian mixture distribution, the changes in node voltage and branch power also satisfy the Gaussian mixture distribution after linear power flow transformation. Specifically:

[0151] Assume that the prediction error ΔP of new energy sources follows a multidimensional Gaussian distribution: ΔP ~ N n-1 (μ Δp ,∑Δp), through linear power flow transformation, the joint probability distribution of node voltage and branch power changes can be obtained as shown in the following equation: ΔV~N n-1 (N -1 μ Δp N -1 ∑Δp(N -1 ) T ), ΔP l ~Nn-1 (S -1 μ Δp ,S -1 ∑Δp(S -1 ) T ).

[0152] The process of constructing a joint power generation probability model for a DC distribution network provided in this application embodiment involves obtaining the power generation probability models of each renewable energy power station in the DC distribution network, and then performing joint processing on the power generation probability models of each renewable energy power station based on the Gaussian mixture algorithm to obtain a joint power generation probability model. The Gaussian mixture algorithm can combine the power generation probability models of each renewable energy power station, and the resulting joint power generation probability model can simulate the randomness and correlation of renewable energy output, thereby improving the accuracy of probabilistic power flow calculation results and further reducing the operational risks of the DC distribution network.

[0153] In one possible embodiment, the method further includes:

[0154] Risk warning processing is carried out on the DC distribution network based on the probabilistic power flow calculation results and preset parameter warning thresholds.

[0155] Specifically, based on the type of power flow probability distribution of the DC distribution network included in the probabilistic power flow calculation results, corresponding parameter warning thresholds can be preset to perform risk warning processing on the DC distribution network.

[0156] Optionally, based on the description in the above embodiments, the probabilistic power flow calculation results of the DC distribution network include: node voltage deviation matrix and branch transmission power deviation matrix. Therefore, the parameter warning thresholds of the node voltage deviation matrix and the parameter warning thresholds of the branch transmission power deviation matrix can be preset to perform risk warning processing on the DC distribution network.

[0157] After calculating the probabilistic power flow results of the DC distribution network, risk warning processing of the DC distribution network can be carried out simply and efficiently by setting preset parameter warning thresholds, thereby reducing the operational risks of the DC distribution network.

[0158] For example, a DC distribution network may contain 14 DC buses and 17 DC transmission lines, integrating two photovoltaic power generation systems and two wind power generation systems. Additionally, the system may include two VSC converters and nine DC loads. The photovoltaic power generation systems are all connected to the grid via bus 8, while the wind power generation systems are connected via buses 3 and 4 respectively. The AC power generation systems are supported by buses 1 and 2, with bus 1 operating in voltage control mode and bus 2 operating in droop control mode.

[0159] When the system is operating in steady state, the bus voltage distribution results are as follows: Figure 4 As shown, where, Figure 4 This is a schematic diagram showing the voltage distribution results of an example of a DC distribution network provided in an embodiment of this application. For example... Figure 4 As shown, the system operating voltage is between 0.953 and 1.003 pu, which is within the normal operating range. The actual operating voltages of AC grid connection points 1 and 2 are 1 pu and 0.982 pu, respectively.

[0160] When the prediction errors of the two wind power systems exhibit randomness, probabilistic power flow calculations are needed to perform probability distribution calculations and risk assessments of the bus voltage and branch power. It is known that the prediction errors of the two wind power systems are correlated, and their sample distribution data are as follows... Figure 5 As shown, where, Figure 5 A schematic diagram of sample distribution data for an example of a DC distribution network provided in this application embodiment.

[0161] Specifically, the Gaussian mixture model (GMM) is used to fit and model the prediction error sample data of the two wind power generation systems. For example, for this sample data, the model is built using three 2-dimensional Gaussian distributions, and the mean vectors of each Gaussian distribution are shown below. The Gaussian mixture fitting result is as follows: Figure 6 As shown, where, Figure 6 A schematic diagram of Gaussian mixture fitting results for an example of a DC distribution network provided in this application embodiment. (See diagram below.) Figure 5 and Figure 6 As shown, the fitting results of the Gaussian mixture distribution of GMM are basically consistent with the original sample data, indicating that the GMM model can perform probabilistic modeling of correlated wind power data well.

[0162] To investigate the impact of wind farm prediction errors on DC bus node voltage, bus 13 and bus 14 were selected as research objects. The probability distribution of bus voltage was calculated using probabilistic power flow calculation methods, and the calculation results are as follows: Figure 7 , Figure 8 , Figure 9 ,as well as Figure 10 As shown, where, Figure 7 A schematic diagram of the probability density curve of bus 13 in an example of a DC distribution network provided in this application embodiment. Figure 8 A schematic diagram of the probability distribution curve of bus 13 in an example of a DC distribution network provided in this application embodiment. Figure 9 A schematic diagram of the probability density curve of bus 14 in an example of a DC distribution network provided in this application embodiment. Figure 10 This is a schematic diagram of the probability distribution curve of bus 14 in an example of a DC distribution network provided in this application embodiment. Figure 6 Figure 8 , Figure 9 ,as well as Figure 10 It can be seen that when the prediction error of the wind power plant has random characteristics, the voltage of bus 13 fluctuates between 0.956 and 0.966, and the expected value of the voltage is 0.961; the voltage of bus 14 fluctuates between 0.95 and 0.962, and the expected value of the voltage is 0.9559, indicating that the system can maintain stable voltage operation.

[0163] Figure 11 A schematic diagram of the probabilistic power flow calculation device for a DC distribution network provided in this application is shown below. Figure 11 As shown, the probabilistic power flow calculation device 110 for DC distribution networks provided in this embodiment includes:

[0164] Module 111 is used to construct a joint power generation probability model of a DC distribution network; wherein, the joint power generation probability model is obtained by joint modeling based on the power generation of each new energy power station in the DC distribution network; the DC distribution network includes at least one wind power station and / or at least one photovoltaic power station;

[0165] The calculation module 112 is used to perform linearization modeling of the DC distribution network, determine the parameter sensitivity matrix of the DC distribution network, and determine the probabilistic power flow calculation results of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix. The parameter sensitivity matrix characterizes the degree of influence of small changes in the DC distribution network on the distribution network parameters. The probabilistic power flow calculation results include the power flow probability distribution of the DC distribution network.

[0166] In one possible embodiment, the parameter sensitivity matrix includes a node voltage sensitivity matrix and a branch transmission power sensitivity matrix; the calculation module 112 is specifically used to obtain the injected power of each distribution node in the DC distribution network, and perform linearization modeling processing on the DC distribution network based on the injected power of each distribution node to determine the node voltage sensitivity matrix; obtain the transmission power of each distribution branch in the DC distribution network, and perform linearization modeling processing on the DC distribution network based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

[0167] In one possible embodiment, the distribution node includes a power control node and a voltage droop node; the injected power of the distribution node includes the injected power of the power control node and the injected power of the voltage droop node; the calculation module 112 is further specifically used to perform Taylor series expansion processing on the injected power of each power control node to determine the injected power change matrix of the power control node; perform Taylor series expansion processing on the injected power of each voltage droop node to determine the injected power change matrix of the voltage droop node; and determine the node voltage sensitivity matrix based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node.

[0168] In one possible embodiment, the calculation module 112 is further specifically used to perform Taylor series expansion processing on the transmission power of each distribution branch to determine the transmission power change matrix of the distribution branch; and to determine the branch transmission power sensitivity matrix based on the transmission power change matrix of the distribution branch and the node voltage sensitivity matrix.

[0169] In one possible embodiment, the construction module 111 is specifically used to obtain the power generation probability model of each new energy power station in the DC distribution network; and to jointly process the power generation probability models of each new energy power station based on the Gaussian mixture algorithm to obtain a joint power generation probability model.

[0170] In one possible embodiment, the calculation module 112 is further configured to perform risk warning processing on the DC distribution network based on the probabilistic power flow calculation results of the DC distribution network and the preset parameter warning threshold.

[0171] The probabilistic power flow calculation device for DC distribution network provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0172] Figure 12 A schematic diagram of the structure of the electronic device provided in this application. Figure 12 As shown, the electronic device 120 provided in this embodiment includes at least one processor 121 and a memory 122. Optionally, the electronic device 120 further includes a communication component 123. The processor 121, the memory 122, and the communication component 123 are connected via a bus 124.

[0173] In a specific implementation, at least one processor 121 executes computer execution instructions stored in memory 122, causing at least one processor 121 to perform the above-described method.

[0174] The specific implementation process of processor 121 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0175] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0176] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0177] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0178] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0179] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0180] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0181] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0182] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0187] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A probabilistic power flow calculation method for a DC distribution network, characterized in that, include: A joint power generation probability model for a DC distribution network is constructed; wherein, the joint power generation probability model is obtained by performing joint probability modeling processing based on the power generation of each new energy power station in the DC distribution network; the DC distribution network includes at least one wind power station and / or at least one photovoltaic power station; Linearization modeling is performed on the DC distribution network to determine the parameter sensitivity matrix of the DC distribution network. Based on the joint generation power probability model and the parameter sensitivity matrix, the probabilistic power flow calculation results of the DC distribution network are determined. The parameter sensitivity matrix characterizes the degree of influence of small changes in the DC distribution network on the network parameters; the probabilistic power flow calculation results include the power flow probability distribution of the DC distribution network.

2. The method according to claim 1, characterized in that, The parameter sensitivity matrix includes the node voltage sensitivity matrix and the branch transmission power sensitivity matrix; The DC distribution network is linearized and modeled to determine the parameter sensitivity matrix of the DC distribution network, including: The injected power of each distribution node in the DC distribution network is obtained, and the DC distribution network is linearized and modeled based on the injected power of each distribution node to determine the node voltage sensitivity matrix. The transmission power of each distribution branch in the DC distribution network is obtained, and the DC distribution network is linearized and modeled based on the transmission power of each distribution branch and the node voltage sensitivity matrix to determine the branch transmission power sensitivity matrix.

3. The method according to claim 2, characterized in that, The power distribution node includes a power control node and a voltage droop node; the injected power of the power distribution node includes the injected power of the power control node and the injected power of the voltage droop node. The DC distribution network is linearized and modeled based on the injected power at each distribution node to determine the node voltage sensitivity matrix, including: The injected power of each power control node is subjected to Taylor series expansion to determine the injected power change matrix of the power control node; The injected power of each voltage droop node is subjected to Taylor series expansion to determine the injected power change matrix of the voltage droop node; The node voltage sensitivity matrix is ​​determined based on the injected power change matrix of the power control node and the injected power change matrix of the voltage droop node.

4. The method according to claim 2, characterized in that, Based on the transmission power of each distribution branch and the node voltage sensitivity matrix, the DC distribution network is linearized and modeled to determine the branch transmission power sensitivity matrix, including: The transmission power of each of the power distribution branches is subjected to Taylor series expansion to determine the transmission power variation matrix of the power distribution branches. The transmission power sensitivity matrix of the branch is determined based on the transmission power change matrix of the distribution branch and the node voltage sensitivity matrix.

5. The method according to claim 1, characterized in that, Constructing a joint generation power probability model for a DC distribution network, including: Obtain the power generation probability model of each new energy power station in the DC distribution network; The power generation probability models of each new energy power station are jointly processed using the Gaussian mixture algorithm to obtain the joint power generation probability model.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Risk warning processing is performed on the DC distribution network based on the probabilistic power flow calculation results and preset parameter warning thresholds.

7. A probabilistic power flow calculation device for a DC distribution network, characterized in that, include: A construction module is used to construct a joint power generation probability model of a DC distribution network; wherein, the joint power generation probability model is obtained by joint modeling based on the power generation of each new energy power station in the DC distribution network; the DC distribution network includes at least one wind power station and / or at least one photovoltaic power station; The calculation module is used to perform linearization modeling on the DC distribution network, determine the parameter sensitivity matrix of the DC distribution network, and determine the probabilistic power flow calculation results of the DC distribution network based on the joint generation power probability model and the parameter sensitivity matrix; wherein, the parameter sensitivity matrix characterizes the degree of influence of small changes in the DC distribution network on the distribution network parameters; the probabilistic power flow calculation results include the power flow probability distribution of the DC distribution network.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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