A method, system and device for identifying weak links of a power distribution network based on high proportion distributed photovoltaic access

Abstract

The application provides a weak link identification method, system and device of a power distribution network based on high-proportion distributed photovoltaic access, and belongs to the technical field of reliability technology analysis of the power distribution network. The scheme is as follows: a power flow calculation is performed on the power distribution network based on a Disflow power flow model; degree centrality parameters and betweenness centrality parameters of nodes and lines of the power distribution network are calculated; a power distribution network system static security risk model is constructed to calculate node voltage out-of-limit risks and line overload risks; a power distribution network system dynamic security risk model is constructed to calculate system power flow entropy vulnerability indexes; system node voltage out-of-limit risks, system line overload risks and system line overload risks are calculated; and system vulnerability indexes are used to evaluate the vulnerability of the power distribution network system. The application provides a weak node identification technology for voltage out-of-limit of the power distribution network under high-proportion distributed photovoltaic access, and improves the accuracy of weak link identification of the power distribution network.

Description

Technical Field

[0001] This invention relates to a method, system, and device for identifying weak links in distribution networks based on high-proportion distributed photovoltaic access, belonging to the field of reliability analysis technology for distribution networks. Background Technology

[0002] With the deepening implementation of the national "dual-carbon" strategic goals and the accelerated construction of new power systems, high-proportion distributed photovoltaic (PV) grid integration is gradually becoming an important development trend in new power distribution systems. The rapid growth of the distributed PV industry has played a positive role in promoting socio-economic development; however, this change has also brought new challenges to the safe and stable operation of the power grid.

[0003] Distributed photovoltaic (PV) power output exhibits significant volatility and intermittency, and displays anti-peak-shaving characteristics, meaning it outputs less during peak grid load periods and more during off-peak periods. This characteristic complicates grid dispatching and increases grid instability. Due to the large-scale integration of distributed PV, the distribution network structure has also changed significantly, exhibiting new characteristics such as multiple power sources and multiple power flow directions, making the original radial distribution network operation mode difficult to adapt to these changes.

[0004] With the integration of distributed photovoltaic (PV) power, the carrying capacity of the distribution network faces severe challenges. Specifically, problems such as disordered power flow distribution, bidirectional overload of distribution transformers and lines, bidirectional voltage exceeding limits, and excessive harmonics are becoming increasingly prominent. These problems not only threaten the safe and stable operation of the power grid but may also trigger large-scale power outages, causing significant disruption to social production and daily life.

[0005] Therefore, accurately identifying weak points in the distribution network is crucial for improving its safe operation. However, most existing technologies only identify weaknesses from a single perspective, such as the state vulnerability or structural vulnerability of the distribution network, failing to comprehensively analyze its vulnerability characteristics and thus making it difficult to effectively identify all potential weak links. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, and device for identifying weak links in distribution networks based on high-proportion distributed photovoltaic access. This invention comprehensively identifies weak links in distribution networks from both structural and state-related perspectives, thereby improving the accuracy of weak link identification.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] A method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic (PV) grid integration includes:

[0009] Input the overload probability structure and parameters of the network topology and lines, and perform power flow calculations on the distribution network based on the Disflow power flow model to obtain the voltage, current and power of each node and line;

[0010] Calculate the degree centrality parameters and median centrality parameters of the distribution network nodes and lines based on the voltage, current and power of each node and line;

[0011] A static security risk model for the distribution network system is constructed, and the node voltage over-limit risk and line overload risk are calculated based on the node degree centrality parameter, the intermediate centrality parameter, the line degree centrality parameter, and the intermediate centrality parameter.

[0012] A dynamic security risk model for the distribution network system is constructed, and the system power flow entropy vulnerability index is calculated based on voltage, current, and power.

[0013] After completing the calculations for all nodes in the system, the system node voltage over-limit risk and system line overload risk are calculated based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index.

[0014] Based on the risk of voltage exceeding the limit at system nodes and the risk of overload on system lines, system vulnerability indicators are calculated, and the vulnerability of the distribution network system is assessed through these indicators.

[0015] Preferably, the power flow calculation of the distribution network based on the Disflow power flow model is performed in the following manner:

[0016] Establish a Disflow power flow model and distribution network safety operation constraints, which include voltage safety constraints and branch power and current safety constraints.

[0017] The second-order cone relaxation method is used to transform the Disflow power flow model into a mixed-integer second-order cone programming linear programming model;

[0018] The power flow distribution of the distribution network is calculated based on the Distflow power flow model with conversion parameters.

[0019] Preferably, the formula for the node centrality parameter is as follows:

[0020] ,

[0021] In the formula, Represents a node The node degree centrality parameter; , They are nodes and nodes The number of edges connected; This is the average number of edges connected to all nodes in the system; For nodes The set of all nodes connected by an edge;

[0022] The formula for the node geocentricity parameter is as follows:

[0023] ,

[0024] In the formula, For nodes The median centrality parameter, As the system's baseline capacity, For power nodes, For power node set, For load nodes, For load node set, This represents the actual power of the power node. This represents the actual power of the load node. For nodes A set of connected nodes. for After the node is connected to a unit current, the line Current on;

[0025] The formula for the line centrality parameter is as follows:

[0026] ,

[0027] In the formula, For the line Degree centrality parameter, For the line The impedance value, , They are nodes and nodes The node degree centrality parameter;

[0028] The formula for the line's intermediate centrality parameter is as follows:

[0029] ,

[0030] In the formula, For the line The median centrality parameter.

[0031] Preferably, the risk of node voltage exceeding limits and the risk of line overload are calculated based on the node degree centrality parameter, the intermediate centrality parameter, and the line degree centrality parameter, as follows:

[0032] Calculate node voltage over-limit parameters based on nodal degree centrality parameters and median centrality parameters; calculate line overload parameters based on line degree centrality parameters and median centrality parameters.

[0033] The effects of node voltage over-limit and line overload are calculated after normalizing the node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters.

[0034] Historical data is used to determine the likelihood of node voltage exceeding limits and line overload.

[0035] The risk of node voltage exceeding the limit is calculated based on the impact and probability of node voltage exceeding the limit, and the risk of line overload is calculated based on the impact and probability of line overload.

[0036] Preferably, the formula for the node voltage over-limit parameter is as follows:

[0037] ,

[0038] in, For nodes Node voltage over-limit parameters, For nodes Per-unit voltage value;

[0039] The formula for the line overload parameter is as follows:

[0040] ,

[0041] For the line Line overload parameters, This represents the ratio of the actual power flow to the rated power flow of the line.

[0042] The normalization formula is as follows:

[0043] ,

[0044] In the formula, It is the set of maximum values ​​for node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters in the system. For nodes The set of node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters. For nodes The set of normalized node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters.

[0045] The formula for calculating the impact of node voltage exceeding limits is as follows:

[0046] ,

[0047] In the formula, For nodes Node voltage exceeding limits affects performance indicators. For nodes The node voltage exceeding the limit affects the function value. , As the indicator weight, The nodes after normalization The node degree centrality parameter, The nodes after normalization The node median centrality parameter, For nodes after normalization Node voltage over-limit parameters;

[0048] The formula for calculating the impact of line overload is as follows:

[0049] ,

[0050] In the formula, For the line The impact of line overload on indicators, For the line The overload affects the function value. , As the indicator weight, The normalized circuit Line centrality parameters, The normalized circuit The line median centrality parameters, The normalized circuit Line overload parameters;

[0051] The formula for calculating the probability of node voltage exceeding the limit is as follows:

[0052] ,

[0053] In the formula, For nodes The probability value of node voltage exceeding the limit. For nodes The per-unit voltage value, and These are the upper and lower limits of the allowable voltage fluctuation range for node voltages as specified in national standards. The cumulative distribution function of node voltage;

[0054] The formula for calculating the probability of line overload is as follows:

[0055] ,

[0056] In the formula, For the line The probability value of line overload, For the line Active power flow This refers to the upper limit of active power flow on power lines as specified by national standards. Let be the cumulative power flow distribution function for line ij;

[0057] The formula for the risk of node voltage exceeding limits is as follows:

[0058] ,

[0059] In the formula, For nodes Risk of node voltage exceeding limit For nodes The possibility of node voltage exceeding the limit, For nodes The node voltage exceeding the limit affects the function value;

[0060] The formula for the risk of line overload is as follows:

[0061] ,

[0062] In the formula, For the line The risk of line overload For the line The possibility of line overload, For the line The effect of line overload on the function value;

[0063] The formula for the risk of system node voltage exceeding limits is as follows:

[0064] ,

[0065] In the formula, To mitigate the risk of system node voltage exceeding limits, , These are weight values, each set to 0.5;

[0066] The risk of system line overload is:

[0067] ,

[0068] In the formula, To mitigate the risk of system line overload, , The weights are 0.5 for each.

[0069] Preferably, the system power flow entropy vulnerability index is calculated based on voltage, current, and power, specifically as follows:

[0070] The formula for calculating the power flow distribution entropy parameters at each node is as follows:

[0071] ,

[0072] In the formula, For nodes The power flow distribution entropy parameter, For nodes The power flow transfer ratio of branch ij When the current fluctuates, with the node Connecting branch roads The impact of the trend, For nodes Connected nodes, M is the number of nodes connected to. Number of connected branches;

[0073] The vulnerability index of the current flow entropy at each node is calculated based on the current flow distribution entropy parameter, using the following formula:

[0074] ,

[0075] In the formula, As an indicator of system power flow entropy vulnerability, For nodes Branch power flow channel variables;

[0076] The system's power flow entropy vulnerability index is calculated based on the power flow entropy vulnerability index of each node, using the following formula:

[0077] ,

[0078] In the formula, , These are weight values, each set to 0.5. It serves as an indicator of system power flow entropy vulnerability.

[0079] The preferred comprehensive system vulnerability index is calculated using the following formula:

[0080] ,

[0081] In the formula, , , For weight values, It serves as a comprehensive vulnerability indicator for the system.

[0082] Preferred, , , The values ​​are 0.2, 0.2, and 0.6.

[0083] A weak link identification system for distribution networks with high proportion of distributed photovoltaic access includes:

[0084] The data acquisition unit is used to collect information on the potential overload structure and parameters of network topology lines.

[0085] The power flow calculation unit performs power flow calculations on the distribution network based on the Disflow power flow model, and obtains the voltage, current and power of each node and line;

[0086] The centrality parameter calculation unit calculates the degree centrality parameter and median centrality parameter of the distribution network nodes and lines based on the voltage, current and power of each node and line;

[0087] The risk calculation unit calculates the risk of node voltage exceeding limits and line overload based on the node degree centrality parameter, the intermediate centrality parameter, and the line degree centrality parameter.

[0088] The power flow entropy vulnerability index calculation unit calculates the system's power flow entropy vulnerability index based on voltage, current, and power.

[0089] The system risk calculation unit completes the calculation for all nodes in the system, and calculates the system node voltage over-limit risk and system line overload risk based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index.

[0090] The assessment unit calculates system vulnerability indicators based on the risk of voltage exceeding limits at system nodes and the risk of overload on system lines, and assesses the vulnerability of the distribution network system through these indicators.

[0091] A weak link identification device for a distribution network based on high-proportion distributed photovoltaic access includes a processor and a memory storing program instructions. The processor is configured to execute the weak link identification method for the distribution network based on high-proportion distributed photovoltaic access when running the program instructions.

[0092] The advantages of this invention are as follows: Based on the characteristics of complex network structures, this invention identifies weak links in the distribution network and constructs a static security risk model for the distribution network system, achieving weak link identification from the perspective of structural vulnerability; based on entropy theory, it identifies weak links in the distribution network and constructs a dynamic security risk model for the distribution network system, achieving weak link identification from the perspective of structural vulnerability. This invention integrates the static and dynamic security risk models of the distribution network system, proposing a voltage-limit-crossing weak node identification technology for distribution networks with a high proportion of distributed photovoltaic access, thereby improving the accuracy of weak link identification in the distribution network. Attached Figure Description

[0093] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0094] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0095] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] Example 1

[0097] This embodiment provides a method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access, such as... Figure 1 As shown, it specifically includes:

[0098] S1: Input the network topology and line overload probability structure and parameters, perform power flow calculation on the distribution network based on the Disflow power flow model, and obtain the voltage, current and power of each node and line.

[0099] S2: Calculate the degree centrality parameters and median centrality parameters of the distribution network nodes and lines based on the voltage, current and power of each node and line.

[0100] S3: Construct a static security risk model for the distribution network system, and calculate the risk of node voltage exceeding the limit and the risk of line overload based on the degree centrality parameters and median centrality parameters of nodes and lines.

[0101] S4: Construct a dynamic security risk model for the distribution network system and calculate the system power flow entropy vulnerability index based on voltage, current, and power.

[0102] S5: Complete the calculation of all nodes in the system, and calculate the system node voltage over-limit risk and system line overload risk based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index.

[0103] S6: Calculate system vulnerability indicators based on the risk of voltage exceeding system node limits and the risk of system line overload, and assess the vulnerability of the distribution network system through these indicators.

[0104] As a refinement of the above embodiments, the power flow calculation of the distribution network based on the Disflow power flow model in step S1 is specifically performed as follows:

[0105] S1-1: Establish the Disflow power flow model and distribution network safety operation constraints, which include voltage safety constraints, branch power and current safety constraints.

[0106] The Distflow power flow model is as follows;

[0107] ,

[0108] ,

[0109] ,

[0110] ,

[0111] In the formula: It is the set of all branches in the distribution network. , The lines are respectively exist Active and reactive power at any given time , The lines are respectively Resistance and reactance, For nodes exist Voltage at time, For the line exist Current at any moment , They are nodes exist Net active power and net reactive power at any given time , They are nodes Substation The active and reactive power generated at all times , They are located at the nodes respectively exist The active and reactive power of the load at any given time.

[0112] Constraints for the safe operation of power distribution networks include:

[0113] Voltage safety constraints:

[0114] ,

[0115] In the formula: , These are the lower and upper voltage limits for node i, respectively.

[0116] Branch power and current safety constraints:

[0117] ,

[0118] In the formula: For the line Current limit specified. For the line Apparent power For the line Apparent power rating.

[0119] S1-2: Using the second-order cone relaxation method, the Disflow power flow model is transformed into a mixed-integer second-order cone programming linear programming model. The specific formula is as follows:

[0120] ,

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] In the formula: for Time flows through the side road The square of the current, For nodes exist The square of the voltage at time 10:00. It is a 2-norm equation.

[0126] S1-3: Calculate the power flow distribution of the distribution network based on the Distflow power flow model of the conversion phase.

[0127] As a refinement of the above embodiment, the formula for the node centrality parameter in step S2 is as follows:

[0128] ,

[0129] In the formula, Represents a node The node degree centrality parameter; , They are nodes and nodes The number of edges connected; This is the average number of edges connected to all nodes in the system; For nodes The set of all nodes that are connected by an edge.

[0130] The formula for the node geocentricity parameter is as follows:

[0131] ,

[0132] In the formula, For nodes The median centrality parameter, As the system's baseline capacity, For power nodes, For power node set, For load nodes, For load node set, This represents the actual power of the power node. This represents the actual power of the load node. For nodes A set of connected nodes. for After the node is connected to a unit current, the line The current on it.

[0133] The formula for the line centrality parameter is as follows:

[0134] ,

[0135] In the formula, For the line Degree centrality parameter, For the line The impedance value, , They are nodes and nodes The node degree centrality parameter.

[0136] The formula for the line's intermediate centrality parameter is as follows:

[0137] ,

[0138] In the formula, For the line The median centrality parameter.

[0139] As a refinement of the above embodiments, step S3 calculates the node voltage over-limit risk and line overload risk based on the node degree centrality parameter, the intermediate centrality parameter, the line degree centrality parameter, and the intermediate centrality parameter, as follows:

[0140] S3-1: Calculate node voltage over-limit parameters based on node degree centrality parameters and median centrality parameters, and calculate line overload parameters based on line degree centrality parameters and median centrality parameters.

[0141] S3-2: Calculate the effects of node voltage over-limit and line overload after normalizing the node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters.

[0142] S3-3: Obtain the probability of node voltage exceeding the limit and the probability of line overload through historical data.

[0143] S3-4: Calculate the risk of node voltage exceeding the limit based on the impact and probability of node voltage exceeding the limit, and calculate the risk of line overload based on the impact and probability of line overload.

[0144] As a refinement of the above embodiment, the formula for the node voltage over-limit parameter in step S3-1 is as follows:

[0145] ,

[0146] in, For nodes Node voltage over-limit parameters, For nodes Per-unit voltage value.

[0147] The formula for the line overload parameter is as follows:

[0148] ,

[0149] For the line Line overload parameters, This represents the ratio of the actual power flow to the rated power flow of the line.

[0150] As a refinement of the above embodiment, the normalization formula in step S3-2 is as follows:

[0151] ,

[0152] In the formula, It is the set of maximum values ​​for node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters in the system. For nodes The set of node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters. For nodes The set of normalized node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters.

[0153] The formula for calculating the impact of node voltage exceeding limits is as follows:

[0154] ,

[0155] In the formula, For nodes Node voltage exceeding limits affects performance indicators. For nodes The node voltage exceeding the limit affects the function value. , As the indicator weight, The nodes after normalization The node degree centrality parameter, The nodes after normalization The node median centrality parameter, For nodes after normalization The node voltage over-limit parameter.

[0156] The formula for calculating the impact of line overload is as follows:

[0157] ,

[0158] In the formula, For the line The impact of line overload on indicators, For the line The overload affects the function value. , As the indicator weight, The normalized circuit Line centrality parameters, The normalized circuit The line median centrality parameters, The normalized circuit Line overload parameters;

[0159] As a refinement of the above embodiment, the formula for calculating the possibility of node voltage exceeding the limit in step S3-3 is as follows:

[0160] ,

[0161] In the formula, For nodes The probability value of node voltage exceeding the limit. For nodes The per-unit voltage value, and The upper and lower limits of the allowable voltage fluctuation range for node voltages as specified in the national standard are 1.07 and 0.93, respectively. This is the cumulative distribution function of node voltage.

[0162] The formula for calculating the probability of line overload is as follows:

[0163] ,

[0164] In the formula, For the line The probability value of line overload, For the line Active power flow This is the upper limit of the active power flow of a transmission line as specified by national standards, and is 1.1 times the expected power of the line under normal conditions. Let be the cumulative power flow distribution function for line ij;

[0165] As a refinement of the above embodiment, the formula for the node voltage over-limit risk in step S3-4 is as follows:

[0166] ,

[0167] In the formula, For nodes Risk of node voltage exceeding limit For nodes The possibility of node voltage exceeding the limit, For nodes The node voltage exceeding the limit affects the function value.

[0168] The formula for the risk of line overload is as follows:

[0169] ,

[0170] In the formula, For the line The risk of line overload For the line The possibility of line overload, For the line The effect of line overload on the function value.

[0171] The formula for the risk of system node voltage exceeding limits is as follows:

[0172] ,

[0173] In the formula, To mitigate the risk of system node voltage exceeding limits, , The weights are 0.5 for each value.

[0174] The risk of system line overload is:

[0175] ,

[0176] In the formula, To mitigate the risk of system line overload, , The weights are 0.5 for each.

[0177] As a refinement of the above embodiments, the calculation of the system power flow entropy vulnerability index based on voltage, current, and power in step S4 is carried out in the following manner:

[0178] S4-1: Calculate the power flow distribution entropy parameters at each node, using the following formula:

[0179] ,

[0180] In the formula, For nodes The power flow distribution entropy parameter, For nodes The power flow transfer ratio of branch ij When the current fluctuates, with the node Connecting branch roads The impact of the trend, For nodes Connected nodes, M is the number of nodes connected to. Number of connected branches;

[0181] S4-2: Calculate the power flow entropy vulnerability index of each node based on the power flow distribution entropy parameter, using the following formula:

[0182] ,

[0183] In the formula, As an indicator of system power flow entropy vulnerability, For nodes Branch power flow channel variables;

[0184] S4-3: Calculate the system's power flow entropy vulnerability index based on the power flow entropy vulnerability index of each node, using the following formula:

[0185] ,

[0186] In the formula, , The weights are 0.5 each. It serves as an indicator of system power flow entropy vulnerability.

[0187] As a refinement of the above embodiments, the system comprehensive vulnerability index in step S5 is calculated using the following formula:

[0188] ,

[0189] In the formula, , , For weight values, As a comprehensive vulnerability indicator for the system; , , The values ​​are 0.2, 0.2, and 0.6.

[0190] It should be noted that this invention patent proposes a method for identifying weak links in a distribution network by comprehensively assessing both structural and state vulnerability. By calculating the degree centrality parameters, medium centrality parameters, voltage over-limit risk, overload risk, and power flow entropy vulnerability index of distribution network nodes and lines, the importance and risk level of each node and line in the distribution network can be comprehensively assessed.

[0191] Compared to existing technologies that mostly identify weaknesses from a single dimension of structure or state, this invention comprehensively considers multiple factors such as the complex network structure characteristics of the distribution network, voltage over-limit risk, overload risk, and dynamic power flow changes, and constructs static and dynamic security risk models for the distribution network system. By integrating these two models, the accuracy of identifying weak links in the distribution network is improved.

[0192] With the widespread integration of high-proportion distributed photovoltaic power, the operation mode of the distribution network has undergone significant changes, exhibiting characteristics such as multiple power sources and multiple power flow directions. This invention addresses these characteristics of the new distribution network by proposing an effective method for identifying weak links, providing strong support for the safe and stable operation of the distribution network.

[0193] The identification method of this invention can accurately locate weak links in the distribution network, providing a scientific basis for subsequent distribution network optimization and transformation, fault prevention and emergency handling, and helping to improve the overall operating efficiency and reliability of the distribution network.

[0194] Example 2

[0195] This disclosure also provides a weak link identification device for a distribution network based on high-proportion distributed photovoltaic (PV) access, including a processor and a memory. Optionally, the device may further include a communication interface and a bus. The processor, communication interface, and memory can communicate with each other via the bus. The communication interface can be used for information transmission. The processor can call logical instructions in the memory to execute the weak link identification method for a distribution network based on high-proportion distributed PV access described in the above embodiments.

[0196] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0197] Memory, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor executes the program instructions / modules stored in the memory to perform functional applications and data processing, thereby realizing the weak link identification method for the distribution network based on high-proportion distributed photovoltaic access in the above embodiments.

[0198] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory.

[0199] Example 3

[0200] This disclosure provides a weak link identification system for a distribution network based on a high proportion of distributed photovoltaic access, comprising:

[0201] The data acquisition unit is used to collect information on the potential overload structure and parameters of network topology lines.

[0202] The power flow calculation unit performs power flow calculations on the distribution network based on the Disflow power flow model, and obtains the voltage, current and power of each node and line.

[0203] The centrality parameter calculation unit calculates the degree centrality parameter and median centrality parameter of the distribution network nodes and lines based on the voltage, current and power of each node and line.

[0204] The risk calculation unit calculates the risk of node voltage exceeding limits and line overload based on the node degree centrality parameter, the intermediate centrality parameter, and the line degree centrality parameter.

[0205] The power flow entropy vulnerability index calculation unit calculates the system's power flow entropy vulnerability index based on voltage, current, and power.

[0206] The system risk calculation unit completes the calculation for all nodes in the system, and calculates the system node voltage over-limit risk and system line overload risk based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index.

[0207] The assessment unit calculates system vulnerability indicators based on the risk of voltage exceeding limits at system nodes and the risk of overload on system lines, and assesses the vulnerability of the distribution network system through these indicators.

[0208] This disclosure provides a weak link identification system for distribution networks with high-proportion distributed photovoltaic (PV) access. Based on the characteristics of complex network structures, it identifies weak links in the distribution network and constructs a static security risk model for the distribution network system, achieving weak link identification from a structural vulnerability perspective. Based on entropy theory, it also identifies weak links in the distribution network and constructs a dynamic security risk model for the distribution network system, achieving weak link identification from a structural vulnerability perspective. This invention integrates the static and dynamic security risk models of the distribution network system, proposing a voltage-limit-exceeding weak node identification technology for distribution networks with high-proportion distributed PV access, thereby improving the accuracy of weak link identification.

[0209] The power flow calculation unit is also used to: establish a power flow model and distribution network safety operation constraints, which include voltage safety constraints and branch power and current safety constraints.

[0210] The Disflow power flow model is transformed into a mixed-integer second-order cone programming linear programming model using the second-order cone relaxation method.

[0211] The power flow distribution of the distribution network is calculated based on the transformed Distflow power flow model.

[0212] The risk calculation unit is also used to: calculate node voltage over-limit parameters based on node degree centrality parameters and median centrality parameters, and calculate line overload parameters based on line degree centrality parameters and median centrality parameters.

[0213] The effects of node voltage exceedance and line overload are calculated after normalizing the node voltage exceedance parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters.

[0214] Historical data is used to determine the likelihood of node voltage exceeding limits and line overload.

[0215] The risk of node voltage exceeding the limit is calculated based on the impact and probability of node voltage exceeding the limit, and the risk of line overload is calculated based on the impact and probability of line overload.

[0216] The current tidal entropy vulnerability index calculation unit is also used to: calculate the current tidal distribution entropy parameter of each node; calculate the current tidal entropy vulnerability index of each node based on the current tidal distribution entropy parameter; and calculate the system current tidal entropy vulnerability index based on the current tidal entropy vulnerability index of each node.

[0217] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0218] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0219] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and 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. In addition, the coupling or direct coupling or communication connection shown or discussed between each other may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. 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 may be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0220] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access, characterized in that, include: Input the overload probability structure and parameters of the network topology and lines, and perform power flow calculations on the distribution network based on the Disflow power flow model to obtain the voltage, current and power of each node and line; Calculate the degree centrality parameters and median centrality parameters of the distribution network nodes and lines based on the voltage, current and power of each node and line; A static security risk model for the distribution network system is constructed, and the node voltage over-limit risk and line overload risk are calculated based on the node degree centrality parameter, the intermediate centrality parameter, the line degree centrality parameter, and the intermediate centrality parameter. A dynamic security risk model for the distribution network system is constructed, and the system power flow entropy vulnerability index is calculated based on voltage, current, and power. After completing the calculations for all nodes in the system, the system node voltage over-limit risk and system line overload risk are calculated based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index. Based on the risk of voltage exceeding the limit at system nodes and the risk of overload on system lines, system vulnerability indicators are calculated, and the vulnerability of the distribution network system is assessed through these indicators. The static security risk model for the power distribution network system calculates the risk of node voltage exceeding limits and the risk of line overload, as detailed below: Calculate node voltage over-limit parameters based on nodal degree centrality parameters and median centrality parameters; calculate line overload parameters based on line degree centrality parameters and median centrality parameters. The effects of node voltage over-limit and line overload are calculated after normalizing the node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters. Historical data is used to determine the likelihood of node voltage exceeding limits and line overload. The risk of node voltage exceeding the limit is calculated based on the impact and probability of node voltage exceeding the limit, and the risk of line overload is calculated based on the impact and probability of line overload. The formula for the node voltage over-limit parameter is as follows: ， in, For nodes Node voltage over-limit parameters, For nodes Per-unit voltage value; The formula for the line overload parameter is as follows: ， For the line Line overload parameters, This is the ratio of the actual power flow to the rated power flow of the line. The normalization formula is as follows: ， In the formula, It is the set of maximum values ​​for node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters in the system. For nodes The set of node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters. For nodes The set of normalized node voltage over-limit parameters, line overload parameters, node degree centrality parameters and median centrality parameters, and line degree centrality parameters and median centrality parameters. The formula for calculating the impact of node voltage exceeding limits is as follows: ， In the formula, For nodes Node voltage exceeding limits affects performance indicators. For nodes The node voltage exceeding the limit affects the function value. , As the indicator weight, The nodes after normalization The node degree centrality parameter, The nodes after normalization The node median centrality parameter, For nodes after normalization Node voltage over-limit parameters; The formula for calculating the impact of line overload is as follows: ， In the formula, For the line The impact of line overload on indicators, For the line The overload affects the function value. , As the indicator weight, The normalized circuit Line centrality parameters, The normalized circuit The line median centrality parameter, The normalized circuit Line overload parameters; The formula for calculating the probability of node voltage exceeding the limit is as follows: ， In the formula, For nodes The probability value of node voltage exceeding the limit. For nodes The per-unit voltage value, and These are the upper and lower limits of the allowable voltage fluctuation range for node voltages as specified in national standards. The cumulative distribution function of node voltage; The formula for calculating the probability of line overload is as follows: ， In the formula, For the line The probability value of line overload, For the line Active power flow This refers to the upper limit of active power flow on power lines as specified by national standards. For the line The cumulative power flow distribution function; The formula for the risk of node voltage exceeding limits is as follows: ， In the formula, For nodes Risk of node voltage exceeding limit For nodes The possibility of node voltage exceeding the limit, For nodes The node voltage exceeding the limit affects the function value; The formula for the risk of line overload is as follows: ， In the formula, For the line The risk of line overload For the line The possibility of line overload, For the line The effect of line overload on the function value; The formula for the risk of system node voltage exceeding limits is as follows: ， In the formula, Risk of system node voltage exceeding limits; The risk of system line overload is: ， In the formula, To mitigate the risk of system line overload, , This is the weight value.

2. The method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in claim 1, characterized in that, The specific method for calculating power flow in the distribution network based on the Disflow power flow model is as follows: Establish a Disflow power flow model and distribution network safety operation constraints, which include voltage safety constraints and branch power and current safety constraints. The second-order cone relaxation method is used to transform the Disflow power flow model into a mixed-integer second-order cone programming linear programming model; The power flow distribution of the distribution network is calculated based on the transformed Distflow power flow model.

3. The method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in claim 1, characterized in that, The formula for the node centrality parameter is as follows: ， In the formula, Represents a node The node degree centrality parameter; , They are nodes and nodes The number of edges connected; This is the average number of edges connected to all nodes in the system; For nodes The set of all nodes connected by an edge; The formula for the node geocentricity parameter is as follows: ， In the formula, For nodes The median centrality parameter, As the system's baseline capacity, For power nodes, For power node set, For load nodes, For load node set, This represents the actual power of the power node. This represents the actual power of the load node. For nodes A set of connected nodes. for After the node is connected to a unit current, the line Current on; The formula for the line centrality parameter is as follows: ， In the formula, For the line Degree centrality parameter, For the line The impedance value, , They are nodes and nodes The node degree centrality parameter; The formula for the line's intermediate centrality parameter is as follows: ， In the formula, For the line The median centrality parameter.

4. The method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in claim 1, characterized in that, The dynamic security risk model for distribution network systems calculates the system's power flow entropy vulnerability index, as detailed below: The formula for calculating the power flow distribution entropy parameters at each node is as follows: ， In the formula, For nodes The power flow distribution entropy parameter, For nodes The power flow transfer ratio of branch ij When the current fluctuates, with the node Connecting branch roads The impact of the trend, For nodes Connected nodes, M is the number of nodes connected to. Number of connected branches; The vulnerability index of the current flow entropy at each node is calculated based on the current flow distribution entropy parameter, using the following formula: ， In the formula, As an indicator of system power flow entropy vulnerability, For nodes Branch power flow channel variables; The system's power flow entropy vulnerability index is calculated based on the power flow entropy vulnerability index of each node, using the following formula: ， In the formula, , For weight values, It serves as an indicator of system power flow entropy vulnerability.

5. The method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in claim 4, characterized in that, The comprehensive vulnerability index of the system is calculated using the following formula: ， In the formula, , , For weight values, It serves as a comprehensive vulnerability indicator for the system.

6. The method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in claim 5, characterized in that, , , The values ​​are 0.2, 0.2, and 0.6 respectively.

7. A weak link identification system for a distribution network based on high-proportion distributed photovoltaic access, implementing the method described in any one of claims 1-6, characterized in that, include: The data acquisition unit is used to collect information on the potential overload structure and parameters of network topology lines. The power flow calculation unit performs power flow calculations on the distribution network based on the Disflow power flow model, and obtains the voltage, current and power of each node and line; The centrality parameter calculation unit calculates the degree centrality parameter and median centrality parameter of the distribution network nodes and lines based on the voltage, current and power of each node and line; The risk calculation unit calculates the risk of node voltage exceeding limits and line overload based on the node degree centrality parameter, the intermediate centrality parameter, and the line degree centrality parameter. The power flow entropy vulnerability index calculation unit calculates the system's power flow entropy vulnerability index based on voltage, current, and power. The system risk calculation unit completes the calculation for all nodes in the system, and calculates the system node voltage over-limit risk and system line overload risk based on the node voltage over-limit risk, line overload risk and node power flow entropy vulnerability index. The assessment unit calculates system vulnerability indicators based on the risk of voltage exceeding limits at system nodes and the risk of overload on system lines, and assesses the vulnerability of the distribution network system through these indicators.

8. A weak link identification device for a distribution network based on high-proportion distributed photovoltaic access, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, the method for identifying weak links in a distribution network based on a high proportion of distributed photovoltaic access as described in any one of claims 1-5.

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