Network loss sensitivity data processing method and device

Abstract

The invention discloses a network loss sensitivity data processing method and device, and the method comprises the steps: obtaining the operation data of a to-be-detected power grid system, and constructing a graph database model of the to-be-detected power grid system based on the operation data; calculating an admittance matrix and an N-dimensional vector of the graph database model; calculating the network loss sensitivity based on the admittance matrix and the N-dimensional vector; and according to the network loss sensitivity, scheduling optimization of generator operation is carried out on the to-be-tested power grid system. According to the method, the graph database model is constructed for the to-be-tested power grid system, the topological relation and the data dependency relation among the components in the system can be better reflected, the admittance matrix and the N-dimensional vector are calculated based on the graph database model, the accuracy of data calculation can be improved, the calculation accuracy of the network loss sensitivity is improved, and the calculation accuracy of the network loss sensitivity is improved. Therefore, the scheduling optimization of the operation of the generator is more accurate, and the operation efficiency of the to-be-tested power grid system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to a network loss sensitivity data processing method and device. Background Art

[0002] Grid losses usually exist in power grid systems. In-depth analysis of grid losses can not only provide a reference for system grid loss management, but also an important dispatching basis for optimizing power planning. Since grid loss calculation is one of the more complex calculation parts in power system flow analysis, there is currently no technology that can analyze the sensitivity of grid loss in power grid systems.

[0003] Therefore, a network loss sensitivity data processing strategy is urgently needed to solve the problem that the network loss sensitivity of the power grid system cannot be analyzed. Summary of the invention

[0004] The embodiments of the present invention provide a network loss sensitivity data processing method and device to solve the problem that the network loss sensitivity of the power grid system cannot be analyzed.

[0005] In order to solve the above problem, an embodiment of the present invention provides a network loss sensitivity data processing method, comprising:

[0006] Acquire operation data of the power grid system to be tested, and construct a graph database model of the power grid system to be tested based on the operation data;

[0007] Calculating the admittance matrix and N-dimensional vector of the graph database model;

[0008] Calculating network loss sensitivity based on the admittance matrix and the N-dimensional vector;

[0009] According to the network loss sensitivity, the dispatching optimization of the generator operation is performed on the power grid system to be tested.

[0010] As an improvement of the above solution, the step of constructing a graph database model of the power grid system to be tested based on the operation data includes:

[0011] Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model;

[0012] Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses;

[0013] According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

[0014] As an improvement of the above solution, the calculation of the admittance matrix and the N-dimensional vector of the graph database model includes:

[0015] The bus active power and bus reactive power of each node in the graph database model are input into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions:

[0016]

[0017] Where B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation;

[0018] The bus voltage amplitude, line reactance and bus phase angle of each node in the graph database model are input into the N-dimensional vector calculation formula, and the N-dimensional vector is obtained through graph parallel power flow calculation; wherein, the N-dimensional vector calculation formula satisfies the following conditions:

[0019]

[0020] Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

[0021] As an improvement of the above solution, the network loss sensitivity is calculated based on the admittance matrix and the N-dimensional vector, including:

[0022] Substitute the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain the initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is:

[0023]

[0024] Where, [LF] is the initial network loss sensitivity matrix;

[0025] The initial network loss sensitivity matrix is ​​verified, and after the verification is passed, a final network loss sensitivity matrix is ​​determined, and the final network loss sensitivity matrix is ​​determined as the network loss sensitivity.

[0026] As an improvement of the above solution, the verification of the initial network loss sensitivity matrix includes:

[0027] In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node;

[0028] Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix;

[0029] According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified;

[0030] If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0031] If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0032] Otherwise, the verification is judged as failed.

[0033] Accordingly, an embodiment of the present invention further provides a network loss sensitivity data processing device, comprising: a data acquisition module, a first calculation module, a second calculation module and an optimization module;

[0034] The data acquisition module is used to acquire the operating data of the power grid system to be tested, and to construct a graph database model of the power grid system to be tested based on the operating data;

[0035] The first calculation module is used to calculate the admittance matrix and N-dimensional vector of the graph database model;

[0036] The second calculation module is used to calculate the network loss sensitivity based on the admittance matrix and the N-dimensional vector;

[0037] The optimization module is used to optimize the scheduling of generator operation for the power grid system to be tested according to the network loss sensitivity.

[0038] As an improvement of the above solution, the step of constructing a graph database model of the power grid system to be tested based on the operation data includes:

[0039] Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model;

[0040] Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses;

[0041] According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

[0042] As an improvement of the above solution, the first calculation module includes: a first calculation unit and a second calculation unit;

[0043] The first calculation unit is used to input the bus active power and bus reactive power of each node in the graph database model into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions:

[0044]

[0045] Where B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation;

[0046] The second calculation unit is used to input the bus voltage amplitude of each node, the line reactance of the bus line, and the bus phase angle in the graph database model into the N-dimensional vector calculation formula, and obtain the N-dimensional vector through the graph parallel power flow calculation; wherein the N-dimensional vector calculation formula satisfies the following conditions:

[0047]

[0048] Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

[0049] As an improvement of the above solution, the second calculation module includes: a data calculation unit and a verification unit;

[0050] The data calculation unit is used to bring the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain an initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is:

[0051]

[0052] Where, [LF] is the initial network loss sensitivity matrix;

[0053] The verification unit is used to calculate and verify the initial network loss sensitivity matrix, and after the verification is passed, determine the final network loss sensitivity matrix, and determine the final network loss sensitivity matrix as the network loss sensitivity.

[0054] As an improvement of the above solution, the calculation and verification of the initial network loss sensitivity matrix includes:

[0055] In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node;

[0056] Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix;

[0057] According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified;

[0058] If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0059] If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0060] Otherwise, the verification is judged as failed.

[0061] As can be seen from the above, the present invention has the following beneficial effects:

[0062] The present invention provides a method for processing network loss sensitivity data, which obtains the operating data of a power grid system to be tested, constructs a graph database model of the power grid system to be tested based on the operating data; calculates the admittance matrix and N-dimensional vector of the graph database model; calculates the network loss sensitivity based on the admittance matrix and N-dimensional vector; and optimizes the dispatching of generator operation for the power grid system to be tested according to the network loss sensitivity. The present invention can better reflect the topological relationship and data dependency relationship between components in the system by constructing a graph database model for the power grid system to be tested, and can improve the accuracy of data calculation by calculating the admittance matrix and N-dimensional vector based on the graph database model, thereby improving the calculation accuracy of network loss sensitivity, thereby making the dispatching optimization of generator operation more accurate, which is beneficial to improving the operating efficiency of the power grid system to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a method for processing network loss sensitivity data provided by an embodiment of the present invention;

[0064] Figure 2 It is a structural schematic diagram of a network loss sensitivity data processing device provided by an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention;

[0066] Figure 4 It is a schematic diagram of the structure of a power grid topology model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Embodiment 1

[0069] See also Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for processing network loss sensitivity data provided by an embodiment of the present invention. Figure 1 As shown, this embodiment includes steps 101 to 104, and each step is specifically as follows:

[0070] Step 101: Acquire operating data of the power grid system to be tested, and construct a graph database model of the power grid system to be tested based on the operating data.

[0071] In this embodiment, the step of constructing a graph database model of the power grid system to be tested based on the operation data includes:

[0072] Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model;

[0073] Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses;

[0074] According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

[0075] It can be understood that in the graph database, the node network topology graph model can be constructed with the bus as the node and the relationship between the bus and the bus and the time as the edge. By injecting operating parameters into the nodes in the graph model and analyzing and solving them, the graph state estimation analysis of the power grid system can be realized.

[0076] In a specific embodiment, the busbar of the power grid system to be tested is defined as a node V, and the connection relationship between the devices is defined as a directed edge E, forming a graph database model G(V, E), (see Figure 4 )Enter each node ( Figure 4 The bus active power, bus reactive power, starting bus voltage amplitude, terminal bus voltage amplitude, line reactance of the inter-bus line, starting bus phase angle and terminal bus phase angle of ABCDEFGH in the diagram are recorded. The inter-bus line relationship and flow direction are entered as the principle for creating directed edges.

[0077] In a specific embodiment, taking the IEEE118 node standard model as an example:

[0078] The power grid topology graph model uses 118 buses to construct 118 topological nodes in the graph model. The node attributes include node voltage amplitude, node voltage phase angle, node unit attributes, node load attributes, node active power and node reactive power, etc.

[0079] And 186 directed edges are constructed with 186 transmission lines of the 118-node standard model, and the impedance and admittance properties, power flow direction and value of the lines are recorded on the edges;

[0080] Based on this, a power grid topology model is created for computational experiments.

[0081] Step 102: Calculate the admittance matrix and N-dimensional vector of the graph database model.

[0082] In this embodiment, the calculation of the admittance matrix and the N-dimensional vector of the graph database model includes:

[0083] The bus active power and bus reactive power of each node in the graph database model are input into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions:

[0084]

[0085] Where B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation;

[0086] The bus voltage amplitude, line reactance and bus phase angle of each node in the graph database model are input into the N-dimensional vector calculation formula, and the N-dimensional vector is obtained through graph parallel power flow calculation; wherein, the N-dimensional vector calculation formula satisfies the following conditions:

[0087]

[0088] Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

[0089] In a specific embodiment, during the calculation of the admittance matrix, after simplifying the derivative of the bus injection power with respect to the bus voltage phase angle and amplitude, it can be defined as In the formula, [B ′ ] is the bus susceptance matrix [(n-1)x(n-1)] formed by taking the inverse of the branch reactance; [B"] is the susceptance part of the bus admittance matrix [(n-1-p)x(n-1-p)], both of which are simplified power flow Jacobian matrices independent of the state quantity.

[0090] In a specific embodiment, during the calculation of the N-dimensional vector, a graph database model can be used to perform graph parallel power flow calculation and solution on each branch through an edge query method, so as to obtain the N-dimensional vector here.

[0091] Step 103: Calculate network loss sensitivity based on the admittance matrix and the N-dimensional vector.

[0092] In this embodiment, the calculating of network loss sensitivity based on the admittance matrix and the N-dimensional vector includes:

[0093] Substitute the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain the initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is:

[0094]

[0095] Where, [LF] is the initial network loss sensitivity matrix;

[0096] The initial network loss sensitivity matrix is ​​verified, and after the verification is passed, a final network loss sensitivity matrix is ​​determined, and the final network loss sensitivity matrix is ​​determined as the network loss sensitivity.

[0097] In a specific embodiment, the network loss sensitivity is calculated after the base state power flow converges, and the final network loss sensitivity matrix uses the matrix and graph topology stored in the graph database to obtain the network loss correction coefficient corresponding to the generator set or load on the bus i.

[0098] It should be noted that the final network loss sensitivity matrix is ​​solved by using graph node parallelism and forward back substitution. The graph node parallel algorithm here solves and calculates the matrix in a parallel manner, thereby improving the calculation speed and efficiency.

[0099] In this embodiment, the verifying of the initial network loss sensitivity matrix includes:

[0100] In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node;

[0101] Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix;

[0102] According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified;

[0103] If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0104] If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0105] Otherwise, the verification is judged as failed.

[0106] In a specific embodiment, after calculating and solving the network loss sensitivity, verification is performed, specifically as follows:

[0107] 1. With the help of the characteristics of the graph database model, the mapping relationship between the network loss sensitivity coefficient and the bus node can be stored at the same time when the network loss sensitivity coefficient matrix is ​​obtained;

[0108] 2. Based on the balance node, query and classify each busbar node according to the line flow direction to determine the electrical status and location of each node;

[0109] 3. By performing edge path queries on the graph database, determine the matching between the electrical relationship between busbars and the network loss sensitivity coefficient: divided into two steps: direction verification and measurement verification, to obtain the electrical distance relationship between the power generation node, the balancing node and the load node.

[0110] In the calculation, the balancing node is selected as the reference zero point. For the electrical relationship where the flow direction is the power generation node-balancing node-load node, the network loss sensitivity of the power generation node should be positive; if the flow direction is the electrical relationship where the balancing node-power generation node-load node, the network loss sensitivity of the power generation node should be negative. If the positive and negative relationship of the network loss sensitivity of the power generation node is contrary to this logic, the calculation result is considered to be incorrect.

[0111] With the help of the edge search algorithm of the graph database, the network loss sensitivity measurement of the power generation nodes on the line can be verified. The value of the network loss sensitivity should decrease along the line flow direction, otherwise it is judged that the calculation is wrong.

[0112] Step 104: Optimizing the dispatching of generator operation for the power grid system to be tested according to the power grid loss sensitivity.

[0113] In this embodiment, the output distribution, start-stop strategy, upper and lower limits, and load distribution of the generator are set according to the network loss sensitivity. After obtaining the network loss sensitivity, the user can adaptively set the output distribution, start-stop strategy, upper and lower limits, and load distribution of the generator based on the operating background of the power grid system to be tested.

[0114] See also Figure 2 , Figure 2 It is a structural schematic diagram of a network loss sensitivity data processing device provided by an embodiment of the present invention, comprising: a data acquisition module 201, a first calculation module 202, a second calculation module 203 and an optimization module 204;

[0115] The data acquisition module is used to acquire the operating data of the power grid system to be tested, and to construct a graph database model of the power grid system to be tested based on the operating data;

[0116] The first calculation module is used to calculate the admittance matrix and N-dimensional vector of the graph database model;

[0117] The second calculation module is used to calculate the network loss sensitivity based on the admittance matrix and the N-dimensional vector;

[0118] The optimization module is used to optimize the scheduling of generator operation for the power grid system to be tested according to the network loss sensitivity.

[0119] As an improvement of the above solution, the step of constructing a graph database model of the power grid system to be tested based on the operation data includes:

[0120] Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model;

[0121] Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses;

[0122] According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

[0123] As an improvement of the above solution, the first calculation module includes: a first calculation unit and a second calculation unit;

[0124] The first calculation unit is used to input the bus active power and bus reactive power of each node in the graph database model into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions:

[0125]

[0126] Where B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation;

[0127] The second calculation unit is used to input the bus voltage amplitude of each node, the line reactance of the bus line, and the bus phase angle in the graph database model into the N-dimensional vector calculation formula, and obtain the N-dimensional vector through the graph parallel power flow calculation; wherein the N-dimensional vector calculation formula satisfies the following conditions:

[0128]

[0129] Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

[0130] As an improvement of the above solution, the second calculation module includes: a data calculation unit and a verification unit;

[0131] The data calculation unit is used to bring the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain an initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is:

[0132]

[0133] Where, [LF] is the initial network loss sensitivity matrix;

[0134] The verification unit is used to calculate and verify the initial network loss sensitivity matrix, and after the verification is passed, determine the final network loss sensitivity matrix, and determine the final network loss sensitivity matrix as the network loss sensitivity.

[0135] As an improvement of the above solution, the calculation and verification of the initial network loss sensitivity matrix includes:

[0136] In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node;

[0137] Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix;

[0138] According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified;

[0139] If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0140] If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed;

[0141] Otherwise, the verification is judged as failed.

[0142] Beneficial Effects

[0143] This embodiment acquires the operating data of the power grid system to be tested, constructs a graph database model of the power grid system to be tested based on the operating data; calculates the admittance matrix and N-dimensional vector of the graph database model; calculates the network loss sensitivity based on the admittance matrix and N-dimensional vector; and optimizes the dispatching of the generator operation of the power grid system to be tested according to the network loss sensitivity. The present invention can better reflect the topological relationship and data dependency relationship between the components in the system by constructing a graph database model for the power grid system to be tested, and can improve the accuracy of data calculation by calculating the admittance matrix and N-dimensional vector based on the graph database model, thereby improving the calculation accuracy of the network loss sensitivity, thereby making the dispatching optimization of the generator operation more accurate, which is beneficial to improving the operating efficiency of the power grid system to be tested.

[0144] Embodiment 2

[0145] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of a terminal device provided in one embodiment of the present invention.

[0146] A terminal device of this embodiment includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the steps of the above-mentioned network loss sensitivity data processing method in the embodiment are implemented, for example: Figure 1 Alternatively, when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are realized, for example: Figure 2 All modules of the network loss sensitivity data processing device are shown.

[0147] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the network loss sensitivity data processing method described in any of the above embodiments.

[0148] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0149] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 301 is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0150] The memory 302 can be used to store the computer program and / or module. The processor 301 implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0151] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0152] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0153] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for processing network loss sensitivity data, characterized in that: include: Acquire operation data of the power grid system to be tested, and construct a graph database model of the power grid system to be tested based on the operation data; Calculating the admittance matrix and N-dimensional vector of the graph database model; Calculating network loss sensitivity based on the admittance matrix and the N-dimensional vector; According to the network loss sensitivity, the dispatching optimization of the generator operation is performed on the power grid system to be tested.

2. The network loss sensitivity data processing method according to claim 1, characterized in that: The step of constructing a graph database model of the power grid system to be tested based on the operation data includes: Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model; Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses; According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

3. The network loss sensitivity data processing method according to claim 2, characterized in that: The calculating the admittance matrix and the N-dimensional vector of the graph database model includes: The bus active power and bus reactive power of each node in the graph database model are input into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions: In the formula, B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation; The bus voltage amplitude, line reactance and bus phase angle of each node in the graph database model are input into the N-dimensional vector calculation formula, and the N-dimensional vector is obtained through graph parallel power flow calculation; wherein, the N-dimensional vector calculation formula satisfies the following conditions: Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

4. The network loss sensitivity data processing method according to claim 3 is characterized in that: The calculating the network loss sensitivity based on the admittance matrix and the N-dimensional vector includes: Substitute the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain the initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is: Where, [LF] is the initial network loss sensitivity matrix; The initial network loss sensitivity matrix is ​​verified, and after the verification is passed, a final network loss sensitivity matrix is ​​determined, and the final network loss sensitivity matrix is ​​determined as the network loss sensitivity.

5. The network loss sensitivity data processing method according to claim 4, characterized in that: The verifying the initial network loss sensitivity matrix includes: In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node; Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix; According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified; If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed; If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed; Otherwise, the verification is judged as failed.

6. A network loss sensitivity data processing device, characterized in that: include: A data acquisition module, a first calculation module, a second calculation module and an optimization module; The data acquisition module is used to acquire the operating data of the power grid system to be tested, and to construct a graph database model of the power grid system to be tested based on the operating data; The first calculation module is used to calculate the admittance matrix and N-dimensional vector of the graph database model; The second calculation module is used to calculate the network loss sensitivity based on the admittance matrix and the N-dimensional vector; The optimization module is used to optimize the scheduling of generator operation for the power grid system to be tested according to the network loss sensitivity.

7. The network loss sensitivity data processing device according to claim 6, characterized in that: The step of constructing a graph database model of the power grid system to be tested based on the operation data includes: Determine a number of buses of the power grid system to be tested as a number of nodes corresponding to the graph database model, and determine the connection states between a number of devices as a number of directed edges corresponding to the graph database model; Determine the principle of creating directed edges based on the line relationship between buses and the flow direction between buses; According to the operating data, the plurality of nodes, the plurality of directed edges and the directed edge creation principle, a graph database model of the power grid system to be tested is constructed; wherein the operating data includes a plurality of bus operating parameters, and each bus operating parameter corresponds one-to-one to each of the nodes; each of the operating parameters includes: bus active power, bus reactive power, bus voltage amplitude, line reactance of inter-bus lines and bus phase angle.

8. The network loss sensitivity data processing device according to claim 7, characterized in that: The first calculation module includes: a first calculation unit and a second calculation unit; The first calculation unit is used to input the bus active power and bus reactive power of each node in the graph database model into the power flow equation to calculate the admittance matrix; wherein the power flow equation satisfies the following conditions: In the formula, B′ is the admittance matrix, P is the node active power, Q is the node reactive power, and f p () is the active power flow equation, V is the voltage amplitude of the initial node, θ is the voltage phase angle of the initial node, V N is the voltage amplitude at node N, θ N is the voltage phase angle at node N, f q () is the reactive power flow equation; The second calculation unit is used to input the bus voltage amplitude of each node, the line reactance of the bus line, and the bus phase angle in the graph database model into the N-dimensional vector calculation formula, and obtain the N-dimensional vector through the graph parallel power flow calculation; wherein the N-dimensional vector calculation formula satisfies the following conditions: Where, is an N-dimensional vector, v i is the bus voltage amplitude of the ith node, v j is the bus voltage amplitude of the jth node, G ij is the line reactance corresponding to the line between the i-th node and the j-th node, θ i is the bus phase angle of the ith node, θ j is the bus phase angle of the jth node.

9. The network loss sensitivity data processing device according to claim 8, characterized in that: The second computing module includes: a data computing unit and a verification unit; The data calculation unit is used to bring the line susceptance matrix and the N-dimensional vector into the network loss sensitivity calculation formula to obtain an initial network loss sensitivity matrix; wherein the network loss sensitivity calculation formula is: Where, [LF] is the initial network loss sensitivity matrix; The verification unit is used to calculate and verify the initial network loss sensitivity matrix, and after the verification is passed, determine the final network loss sensitivity matrix, and determine the final network loss sensitivity matrix as the network loss sensitivity.

10. The network loss sensitivity data processing device according to claim 9, characterized in that: The calculating and verifying the initial network loss sensitivity matrix includes: In the graph database model, a node is selected as a balancing node, the balancing node is used as a reference zero point, and each node is queried and classified according to the line flow direction to determine the electrical state of each node and the location of each node; wherein the electrical state includes: power generation node, balancing node and load node; Determine the network loss sensitivity value corresponding to each node according to the network loss sensitivity matrix; According to the electrical state of each node, the location of each node and the network loss sensitivity value corresponding to each node, according to the preset electrical logic and edge search algorithm, it is determined whether the network loss sensitivity matrix has been verified; If the flow direction determined based on the location of the node is balancing node → power generation node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is negative, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed; If the flow direction determined based on the location of the node is power generation node → balancing node → load node, and the network loss sensitivity value of the power generation node in the current flow direction is positive, and the network loss sensitivity value of the power generation node determined by the edge search algorithm decreases along the flow direction, then the verification is judged to be passed; Otherwise, the verification is judged as failed.

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