High-permeability power distribution network risk identification model and system based on dynamic power flow analysis

Abstract

The invention relates to the field of vulnerability assessment and pre-disaster risk management of a power system, in particular to a high-permeability power distribution network risk identification method based on dynamic power flow analysis. The existing vulnerability assessment method focuses on static analysis and ignores uncertainty of dynamic power flow and distributed power generation output, so that weak links in a power distribution network cannot be accurately identified. The invention provides a vulnerability assessment method based on dynamic power flow analysis. According to the method, a Latin hypercube sampling method is adopted to generate a plurality of distributed power generation output scenes, power grid power flow under different weather and load conditions is simulated, key components in the power distribution network are identified by combining power flow calculation and vulnerability analysis, and a scientific basis is provided for pre-disaster risk assessment and emergency response. Compared with a traditional method, the method has the advantages that the uncertainty of distributed generation output and the dynamic characteristics of the power distribution network can be considered more comprehensively, and the vulnerability identification accuracy of the power distribution network under the access of high-permeability renewable energy sources is improved. Besides, the vulnerability of the nodes and the branches in the power system is evaluated in a unified mode, more comprehensive and reliable technical support is provided for pre-disaster prevention and emergency response of a power grid, and the method has important theoretical value and practical application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of vulnerability assessment and pre-disaster risk management of power systems, and particularly to a risk identification method for a high-penetration distribution network based on dynamic power flow analysis, which is used to improve the safety, stability and reliability of the distribution network under the condition of high proportion of distributed generation access. Background Art

[0002] With the intensification of global climate change and the increase in the penetration rate of renewable energy, the operation of the distribution network faces unprecedented challenges. Especially the access of a high proportion of distributed generation (DG) makes the load and power flow of the power grid more uncertain and complex. Most of the existing distribution network vulnerability assessment methods focus on static topology analysis and ignore the dynamic impact of distributed generation on power flow, resulting in the inability to accurately evaluate the vulnerability of the distribution network under extreme climate or sudden disasters. Therefore, a new assessment method is needed that can comprehensively consider the uncertainty of DG output and the dynamic characteristics of the distribution network, so as to achieve accurate identification and vulnerability assessment of key components before disasters.

[0003] The present invention provides a risk identification method for a high-penetration distribution network based on dynamic power flow analysis. By combining complex network theory and the operating state of the power grid, based on the dynamic power flow analysis method, for different DG output scenarios, the dynamic vulnerability analysis of the nodes and branches in the distribution network is carried out. First, a distribution network model under the background of high proportion of new energy access is established, and the Latin hypercube sampling method is used to generate multiple DG output scenarios to simulate the power flow of the power grid under different meteorological and load conditions. Then, a power flow calculation tool is used to perform power flow calculations on the power grid, track the power flow under different output scenarios, and identify the key components affecting system stability. On this basis, an improved power flow betweenness index is proposed, which comprehensively considers the power flow directionality and the actual situation of power flow to comprehensively evaluate the vulnerability of the distribution network. Through multi-scenario analysis, combined with indicators such as power difference and node transmission capacity, the key nodes and branches in the distribution network are accurately identified, providing a scientific basis for pre-disaster risk prevention and emergency dispatching. The existing traditional methods fail to consider the dynamic power flow characteristics of high-penetration distribution networks and cannot effectively cope with the challenges brought by DG output fluctuations. By introducing a dynamic assessment mechanism and an improved power flow betweenness index, the present invention can timely identify the vulnerable components in the distribution network in a complex and changeable environment, avoiding the risk of cascading failures to the system during disasters. This method not only improves the accuracy of distribution network vulnerability identification, but also provides reliable technical support for pre-disaster prevention, emergency response and system optimization of the power grid, and has significant theoretical value and practical application prospects. Summary of the Invention

[0004] The present invention solves the problems of inaccurate vulnerability identification of distribution network components, insufficient handling of the uncertainty of distributed generation output, and incomplete assessment system in extreme climate and disaster scenarios in the existing distribution network vulnerability assessment methods under the condition of high-penetration new energy access. Specifically, the present invention provides a risk identification model for high-penetration distribution networks based on dynamic power flow analysis. By modeling the stochastic characteristics of high-proportion DG output, dynamically calculating power flow, and assessing vulnerability, a pre-disaster vulnerability identification scheme for high-penetration distribution networks is constructed, thus significantly improving the safety, stability, and resilience of the distribution network under extreme conditions. This method is based on an improved power flow betweenness index to conduct dynamic vulnerability analysis on the nodes and branches of the distribution network, providing a scientific basis for pre-disaster prevention and emergency dispatching. By simulating different DG output scenarios, identifying key components before disasters, and proposing optimization measures based on the evaluation results, the ability of the distribution network to cope with disaster risks is significantly enhanced.

[0005] The technical solution of the present invention includes the following steps:

[0006] 1. Distribution network modeling and data collection: Collect the basic data of the distribution network, including the grid topology structure, node voltage, branch parameters, etc. At the same time, establish a stochastic output model for distributed generations such as photovoltaic and wind power to simulate the power flow of the power grid under different meteorological and load conditions.

[0007] 2. Output scenario generation: Use the Latin hypercube sampling method to generate scenarios covering different photovoltaic and wind power output situations, ensuring that the generated samples are representative and can cover the entire sample space of DG output, and accurately simulate the changing new energy output situation.

[0008] 3. Power flow calculation and tracking: Under multiple DG output scenarios, use a power flow calculation tool to calculate the power flow of the distribution network. By tracking the power flow, node voltage, and branch load conditions, ensure that the uncertainty and dynamic changes of DG output are fully considered.

[0009] 4. Vulnerability assessment: Based on the improved power flow betweenness index, conduct vulnerability assessment on the nodes and branches of the distribution network. Calculate the vulnerability index of each node and branch in the distribution network under different DG output scenarios, and conduct a comprehensive assessment in combination with factors such as power difference and node transmission capacity.

[0010] 5. Identification of key components before disasters: According to the calculated vulnerability index, sort the nodes and branches in the distribution network to identify the key components before disasters. By comparing the vulnerability index and the system load survival rate (LSR), verify the accuracy of the evaluation results.

[0011] The outstanding advantage of the present invention lies in that, through dynamic power flow calculation and stochastic output scenario generation, it comprehensively considers the uncertainty of DG output, and can accurately reflect the vulnerability of the distribution network under the background of high-penetration new energy access. In addition, by improving the power flow betweenness index, the present invention can combine the actual operation characteristics of the power grid, more accurately evaluate the vulnerability of distribution network nodes and branches, and thus provide a more scientific basis for the identification of key components before grid disasters. Compared with traditional static methods, the present invention can effectively identify the weak links of the distribution network under extreme scenarios, provide strong support for system optimization and dispatching, and improve the risk resistance ability and emergency response ability of the distribution network.

[0012] The present invention is realized through the following technical solutions:

[0013] In the first aspect, the present invention provides a risk identification method for a high-penetration distribution network based on dynamic power flow analysis, and the method includes the following steps:

[0014] Modeling and data collection of the high-penetration distribution network: Collect the basic data of the distribution network, including the grid topology structure, node voltage, branch parameters, etc., and establish distributed generation output models such as photovoltaic and wind power. Use the Latin hypercube sampling method to generate multiple DG output scenarios, simulate the power flow of the power grid under different meteorological and load conditions, and ensure that all possible output change situations are covered.

[0015] Wind turbine stochastic output model:

[0016] Wind energy has the characteristics of volatility and intermittency, which is related to the randomness of wind speed. Most existing literatures use the Weibull distribution to describe wind speed. The distribution function of the two-parameter Weibull distribution is:

[0017] F w (v) = 1 - exp[-(v / c) k

[0018] Among them, F w (v) is the distribution function of the two-parameter Weibull distribution, v is the wind speed; c is the Weibull distribution scale parameter; k is the Weibull distribution shape parameter.

[0019] In stability analysis, for the case of multiple wind turbines operating in parallel, one or more equivalent machines are often considered. For a single wind turbine, its active power output is directly affected by the wind speed, and the corresponding relationship is:

[0020]

[0021] Among them, P w (v) is the active power output of the wind turbine, v is the wind speed; v ci is the cut-in wind speed of the wind turbine; v r is the rated wind speed of the wind turbine; v​co is the cut-out wind speed of the wind turbine; P r is the rated output power of the wind turbine.

[0022] Photovoltaic random output model:

[0023] The output of a photovoltaic power generation unit has significant randomness, and its fluctuations are closely related to changes in light intensity. The light intensity follows a Beta distribution on both short and long time scales. The photovoltaic output also satisfies the Beta distribution, and its probability density function is:

[0024]

[0025] where Γ(·) is the gamma function, also known as the second Euler integral; f L (P a ) is the probability density function of the photovoltaic output P a ; α and β are the shape parameters of the Beta distribution; P max is the maximum output of the photovoltaic power generation unit.

[0026] Latin hypercube sampling for random output scenarios:

[0027] Using the Latin hypercube sampling method, scenarios covering different photovoltaic and wind power outputs are generated to ensure that the generated samples are representative and can cover the entire sample space of DG output, accurately simulating the variable new energy output. Specifically, the formula for the Latin hypercube sampling method is as follows:

[0028] 1) Divide the probability interval: According to the probability characteristics of the DG output distribution, divide it into N t = 1000 equal-probability intervals.

[0029] 2) Randomly draw sample points: Let 1 ≤ i ≤ N t , for any probability interval [(i - 1) / N t , i / N t , randomly draw a number w i , which can be expressed as:

[0030]

[0031] where r is a random variable uniformly distributed in the interval [0, 1], and N t is the number of equal-probability intervals.

[0032] 3) Inverse transformation sampling: The sample value corresponding to the probability interval can be obtained through the inverse transformation of the probability distribution function, expressed as:

[0033] x i = F -1 (w i )

[0034] Among them, x i is the sampling value; F -1 (·) is the inverse function of the probability distribution function.

[0035] Through the above steps, N t sampling values of the DG random output can be obtained. Multiple DG output scenarios obtained by the Latin hypercube sampling method. These sample values effectively cover the entire sample space of the DG output, making the evaluation results closer to the actual situation.

[0036] Power flow calculation and tracing:

[0037] Under multiple DG output scenarios, a power flow calculation tool is used to perform power flow calculations on the power grid to ensure that the impacts of different DG output scenarios on power flow, node voltage, and branch load are considered. By tracing the power grid states under different scenarios, the stability of the power grid under uncertain conditions is comprehensively evaluated. The power flow equation of the power grid can be expressed as:

[0038]

[0039] Among them, P i is the active power of the i-th node, V i and V j are the voltage amplitudes of the i-th and j-th nodes, θ i and θ j are the voltage phase angles of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the admittance matrix between node i and node j.

[0040] Vulnerability assessment:

[0041] Based on the improved power flow betweenness index, the nodes and branches of the distribution network are subjected to vulnerability assessment, and the vulnerability index of each node and branch in the distribution network is calculated. The improved power flow betweenness can be calculated by the following formula:

[0042]

[0043] In the formula: min(P g , P d ) is the weight factor of the single power flow betweenness, taking the smaller value of the actual output of the power source g and the actual power of the load d, indicating the maximum available transmission power between the power source and the load; P gd (l) is the active power transmitted by the power source g and the load d on the branch l; P gdThe active power transmitted from the power source g to the load d; G and D are the power source set and the load set respectively. BPFB(i) is the power flow betweenness of the l-th branch in the system; NPFB(i) is the power flow betweenness of the i-th node in the system; L i is the set of branches connected to node i in the network; B lk is the branch l k 's power flow betweenness; P i is the injection power of node i. NPFB i and BPFB i are the node and branch power flow betweenness of the system under the i-th scenario. INPFB and IBPFB are the improved node power flow betweenness and the improved branch power flow betweenness respectively; N t is the number of PV and wind power output scenarios generated by the Latin hypercube sampling method; p i and p j are the probabilities of the i-th and j-th scenarios respectively.

[0044] In a second aspect, the present invention also provides a risk identification system for a high-penetration distribution network based on dynamic power flow analysis. This system implements the above method, aiming to solve the dynamic power flow and uncertainty problems faced by the system after high-penetration new energy is connected to the distribution network, and improve the stability and risk resistance of the distribution network under extreme conditions. The system includes the following functional modules:

[0045] A data acquisition unit for obtaining the topological structure, node voltage, load information, and distributed generation output data of the distribution network.

[0046] An output scenario generation unit that uses the Latin hypercube sampling method to generate multiple DG output scenarios, ensuring that the generated samples can cover the entire sample space of DG output and accurately simulate the changing new energy output situation.

[0047] A power flow calculation unit that performs power flow calculations on the distribution network based on the generated DG output scenarios, tracking the power flow, node voltage, and branch load conditions under different scenarios.

[0048] A vulnerability assessment unit that uses the improved power flow betweenness to evaluate the vulnerability of each node and branch in the distribution network and identify the key components that are most likely to fail before a disaster.

[0049] A key component identification unit that ranks the key components before a disaster in the distribution network according to the vulnerability assessment results and outputs a list of components that need to be focused on.

[0050] Compared with the prior art, the system of the present invention has the following remarkable advantages:

[0051] The system of the present invention has significant technical advantages. It can comprehensively simulate the dynamic impact of distributed power generation such as photovoltaic and wind power on the power flow of the distribution network under the background of high penetration rate. By using the improved power flow betweenness method, it can accurately evaluate the vulnerability of each node and branch in the distribution network, providing a scientific basis for identifying key components before disasters. The DG output scenarios generated by the Latin hypercube sampling method can ensure that the evaluation results have wide applicability, covering different power system loads and environmental change conditions. Through this system, vulnerable components in the distribution network can be accurately identified before disasters, providing reliable data support for grid optimal scheduling and emergency response, significantly improving the ability of the distribution network to cope with extreme events, and providing a strong guarantee for the safe and stable operation of the system. Brief Description of the Drawings

[0052] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0053] Figure 1 It is the overall flowchart of the high-penetration distribution network risk identification model based on dynamic power flow analysis of the present invention;

[0054] Figure 2 It is a schematic diagram of the topological structure of the high-penetration distribution network system in Embodiment 1 of the present invention;

[0055] Figure 3 It is a schematic diagram of the quantification process of the uncertainty of distributed generation output in Embodiment 1 of the present invention;

[0056] Figure 4 It is the detailed flowchart of the high-penetration distribution network risk identification model based on dynamic power flow analysis of the present invention;

[0057] Figure 5 It is the functional structure block diagram of the high-penetration distribution network risk identification system based on dynamic power flow analysis of the present invention. Detailed Embodiments

[0058] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. The present invention proposes a risk identification model for a high-penetration distribution network based on dynamic power flow analysis, which mainly includes five links: distribution network modeling and data acquisition, output scenario generation, power flow calculation and tracking, vulnerability assessment, and key component identification. The first step is the modeling and data acquisition of the high-penetration distribution network, obtaining the basic data of the distribution network and establishing a stochastic output model for distributed generation; the second step is the output scenario generation, using the Latin hypercube sampling method to generate multiple output scenarios to ensure the representativeness of the samples and accurately simulate the variable new energy output; the third step is the power flow calculation and tracking, through the power flow calculation tool, calculating the grid state based on the generated output scenarios and evaluating the stability of the grid under different scenarios; the fourth step is the vulnerability assessment, using the improved power flow betweenness to evaluate the vulnerability of nodes and branches and identify the key components; the last step is the key component identification, identifying the distribution network components that need to be focused on before a disaster according to the vulnerability assessment results. Through comprehensive modeling and computational analysis, the present invention provides scientific support for the pre-disaster preparation and emergency response of the power grid.

[0059] Embodiment 1

[0060] Method flow

[0061] As Figure 1 shown, the risk identification model for a high-penetration distribution network based on dynamic power flow analysis of the present invention includes the following steps:

[0062] S1: Distribution network modeling and data acquisition

[0063] Obtain the basic information of the distribution network, including grid topology, node voltage, branch parameters, load information, etc., and the system topology structure is as Figure 2 shown. Establish a stochastic output model for distributed generation to ensure that it can cover the grid operation under different weather conditions and load changes.

[0064] S2: Output scenario generation

[0065] Through the Latin hypercube sampling method, generate multiple groups of scenarios covering different photovoltaic and wind power output situations. The specific method is to perform stratified random sampling on the equal-probability interval to ensure that the generated samples are representative and can cover the entire sample space of DG output, and accurately simulate the variable new energy output.

[0066] S3: Power flow calculation and tracking

[0067] Under multiple DG output scenarios, a power flow calculation tool is used to perform power flow calculations on the distribution network, tracking the power flow, node voltages, and branch loads in each scenario. Based on the calculation results of each output scenario, the operating state of the power grid is evaluated to ensure that the uncertainty of DG output and its impact on the stability of the distribution network are fully considered.

[0068] S4: Vulnerability assessment

[0069] An improved power flow betweenness index is used to assess the vulnerability of each node and branch in the distribution network. By calculating the vulnerability index under multiple output scenarios, the vulnerability of each node and branch under different power flow conditions is evaluated. Through this method, the key components that are most likely to fail under multiple DG output scenarios can be identified.

[0070] S5: Identification of key components

[0071] Based on the vulnerability assessment results, the key components in the distribution network are identified. By sorting the vulnerability index and verifying it in combination with the system load survival rate. Components with high improved power flow betweenness values are crucial for system stability and need to be strengthened in monitoring and maintenance before disasters. The identified key components will be the focus of power grid optimal scheduling and pre-disaster reinforcement.

[0072] In this embodiment, the distribution network modeling and data collection in S1 are completed through the following steps:

[0073] Obtain the basic data of the distribution network, including the power grid topology, node voltages, branch parameters, load information, and distributed generation output data, and establish output models for new energy sources such as photovoltaic and wind power. The Latin hypercube sampling method is used to generate multiple DG output scenarios, ensuring that the generated samples are representative and can cover the entire DG output sample space, accurately simulating the changing new energy output situation. In this process, first collect the historical output data of new energy sources such as photovoltaic and wind power, and combine the weather and load changes to construct a representative photovoltaic and wind power output model. The specific model is described by the following formula:

[0074] Random output model of wind turbines:

[0075] Wind energy has the characteristics of volatility and intermittency, which is related to the randomness of wind speed. Most existing literature uses the Weibull distribution to describe wind speed. The distribution function of the two-parameter Weibull distribution is:

[0076] F w (v) = 1 - exp[-(v / c) k

[0077] where, F w ​(v) is the distribution function of the two-parameter Weibull distribution, where v is the wind speed; c is the Weibull distribution scale parameter; k is the Weibull distribution shape parameter.

[0078] In stability analysis, for the case of multiple wind turbines operating in parallel, one or more equivalent machines are often considered. For a single wind turbine, its active power output is directly affected by the wind speed, and the corresponding relationship is:

[0079]

[0080] where, P w (v) is the active power output of the wind turbine; v ci is the cut-in wind speed of the wind turbine; v r is the rated wind speed of the wind turbine; v co is the cut-out wind speed of the wind turbine; P r is the rated output power of the wind turbine.

[0081] Photovoltaic random output model:

[0082] The output of photovoltaic power generation has significant randomness, and its fluctuations are closely related to the changes in light intensity. The light intensity follows the Beta distribution on both short-term and long-term time scales. The photovoltaic output also satisfies the Beta distribution, and the probability density function is:

[0083]

[0084] where, Γ(·) is the gamma function, also known as the second Euler integral; f L (P a ) is the probability density function of the photovoltaic output P a ; α and β are the shape parameters of the Beta distribution; P max is the maximum output of the photovoltaic power generation unit.

[0085] Next, obtain the operating parameters of various devices in the power grid, including transformers, lines, and energy storage systems, etc., and construct the working models of each device in the distribution network under different DG output scenarios. Specifically, through the above DG output scenarios, combined with the topological structure data of the power grid, based on the power balance principle, use the power flow calculation tool to perform power flow calculations on the distribution network, and real-time track the changes in power flow and node voltage, so as to comprehensively reflect the operating state of the power grid under different output scenarios.

[0086] In this embodiment, the generation of the S2 distributed generation random output scenario is completed through the following strategy:

[0087] The present invention uses the Latin hypercube sampling method to generate multiple output scenarios, ensuring that the generated samples are representative and can cover the entire sample space of DG output, and accurately simulating the variable new energy output situation. The Latin hypercube sampling method performs stratified random sampling in the equal probability interval to ensure that each sampling point is evenly distributed, and can reflect the actual operation of the distribution network under different weather, load changes, and new energy power generation output fluctuations. The multiple output scenarios generated will cover the power generation characteristics of different photovoltaics and wind power, ensuring that the vulnerability of the distribution network can be comprehensively evaluated under uncertain conditions. In this process, it is specifically implemented through the following steps:

[0088] 1) Divide the probability interval: According to the probability characteristics of the DG output distribution, divide N t = 1000 equal probability intervals.

[0089] 2) Randomly extract sample points: Let 1 ≤ i ≤ N t , for any probability interval [(i - 1) / N t , i / N t , randomly extract a number w i , which can be expressed as:

[0090]

[0091] where r is a random variable uniformly distributed in the interval [0, 1], and N t is the number of equal probability intervals.

[0092] 3) Inverse transformation sampling: The sample value corresponding to the probability interval can be obtained through the inverse transformation of the probability distribution function, which is expressed as:

[0093] x i = F -1 (w i )

[0094] where x i is the sampling value; F -1 (·) is the inverse function of the probability distribution function.

[0095] Through the above steps, N t sampling values of the DG random output can be obtained. Figure 3 shows multiple DG output scenarios obtained by the Latin hypercube sampling method. These sample values effectively cover the entire sample space of DG output, and can make the evaluation results closer to the actual situation.

[0096] In this embodiment, the partial power flow calculation and tracing in S3 are completed through the following strategy:

[0097] Power flow calculation and tracing model: Based on the previously obtained distribution network data and multiple DG output scenarios, a power flow calculation tool is used to perform power flow calculations on the distribution network. The goal is to evaluate the stability of the power grid under different DG output conditions and load conditions through multi-scenario analysis. The objective function is to minimize power losses and maintain system stability, and the constraints include voltage range, power balance, line transmission capacity, power flow constraints, etc. The specific steps are as follows:

[0098] First, according to the established DG access and processing model, obtain the multi-scenario output data of photovoltaic and wind power, as well as the electrical parameters of each node and branch (such as admittance matrix, branch resistance, etc.). Then, according to different DG output scenarios, construct the power flow equation of the power grid, and calculate the voltage amplitude and phase angle of each node in the distribution network, power flow, etc. The power flow calculation formula is as follows:

[0099]

[0100] where, P i is the active power of the i-th node, V i and V j are the voltage amplitudes of the i-th and j-th nodes, θ i and θ j are the voltage phase angles of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the admittance matrix between node i and node j. By performing power flow calculations on different output scenarios, the power flow, node voltage, and branch load conditions can be traced in real time to ensure the stability of the power grid under different situations.

[0101] In addition, the following constraints should be considered during the power flow calculation process:

[0102] Generator output constraint

[0103]

[0104] In the formula: and are the maximum theoretical outputs of photovoltaic and wind power at time t, and P t w is the wind power output.

[0105] Thermal power unit ramp rate constraint

[0106] -r i,down ≤P i,t -P i,(t-1) ≤r i,up

[0107] In the formula: r i,up and r i,down are the maximum upward ramp rate and maximum downward ramp rate of the thermal power unit respectively, and Pi,t and P i,(t-1) are the thermal power output at time t and the thermal power output at time t-1, respectively.

[0108] Line transmission capacity constraint

[0109] |B i,j (θ i,t -θ j,t )| ≤ P i,j,Lmax

[0110] Where: B i,j (S) represents the susceptance between nodes i and j, θ i,t , θ j,t represent the voltage phase angles of nodes i and j, respectively, and P i,j,Lmax represents the per-unit value of the maximum allowable transmission power of the line between nodes i and j.

[0111] Reserve constraint

[0112]

[0113] Where: is the maximum available power for grid connection of unit i at time t, is the actual power for grid connection of unit i at time t, λ is the reserve coefficient, is the load power of node i at time t.

[0114] In this embodiment, the vulnerability assessment of part S4 is completed through the following process:

[0115] First, use the improved current flow betweenness to conduct vulnerability assessment on each node and branch in the distribution network. Based on the improved current flow betweenness index, conduct vulnerability assessment on the nodes and branches of the distribution network, calculate the vulnerability indices of each node and branch in the distribution network. The flow chart for identifying and evaluating vulnerable components in a high-penetration distribution network is as Figure 4 shown. The improved current flow betweenness can be calculated by the following formula:

[0116]

[0117] Where: min(P g , P d ) is the weight factor of the single current flow betweenness, taking the smaller value of the actual output of power source g and the actual power of load d, representing the maximum available transmission power between the power source and the load; P gd (l) is the active power transmitted by power source g and load d on branch l; P gd is the active power transmitted from power source g to load d; G and D are the power source set and the load set, respectively. BPFB(i) is the current flow betweenness of the l-th branch in the system; NPFB(i) is the current flow betweenness of the i-th node in the system; Li The set of branches connected to node i in the network; B lk is the branch l k 's current transfer index; P i is the injection power of node i. NPFB i and BPFB i are the node and branch current transfer indices of the system under the ith scenario. INPFB and IBPFB are the improved node current transfer index and the improved branch current transfer index respectively; N t is the number of photovoltaic and wind power output scenarios generated by the Latin hypercube sampling method; p i and p j are the probabilities of the ith and jth scenarios respectively.

[0118] By considering multiple distributed generation output scenarios, the vulnerability indices of each node and branch are calculated. Then, for each output scenario, the vulnerability indices of each node and branch are calculated. The vulnerability index comprehensively considers factors such as power flow distribution, power transmission, and node voltage to evaluate the reliability and potential risks of each component.

[0119] In this embodiment, the identification of some key components in S5 is completed through the following process:

[0120] First, based on the aforementioned vulnerability assessment results, by sorting the vulnerability indices, the potential key components in the distribution network are identified. These components show high vulnerability under multiple output scenarios and are usually the weak links in the distribution network. By calculating the vulnerability indices of each node and branch, the nodes and branches that are prone to failure under different power flow conditions can be identified, and these components are crucial for the stability of the distribution network. Then, the results of vulnerability identification are verified using the system load survival rate. LSR is a measure of the ability of the distribution network to maintain the system load when a component fails, and the calculation formula is:

[0121]

[0122] where LSR is the system load survival rate; ∑P load is the total load of the system when it is operating normally; P loss is the load loss caused by the failure of a node or branch. Under the condition of the same number of failed components, the lower the load survival rate, the more effective the distribution network vulnerability identification strategy is, and it can identify the vulnerable components in the distribution system more accurately and quickly.

[0123] Finally, combining the vulnerability index and LSR, comprehensively evaluate the risk levels of various components in the distribution network, and output a list of key components that need to be monitored and strengthened. Through the accurate identification and verification of key components, this method ensures that the distribution network can better cope with possible catastrophic failures, providing a scientific basis for the pre-disaster preparation and emergency response of the power grid.

[0124] Embodiment 2

[0125] Risk Identification System for High-Penetration Distribution Network Based on Dynamic Power Flow Analysis

[0126] As Figure 5 shown, this embodiment provides a risk identification system for a high-penetration distribution network based on dynamic power flow analysis. This system implements the above method and includes the following functional modules:

[0127] Distribution Network Modeling and Data Acquisition Unit: Collect the topological structure, node voltage, branch parameters, load information, and distributed generation (DG) output data of the distribution network. Combining these basic data, generate multiple DG output scenarios through the Latin hypercube sampling method to simulate different weather conditions, load changes, and new energy output fluctuations, providing basic data support for subsequent power flow calculations and vulnerability assessments.

[0128] DG Output Scenario Generation Unit: Use the Latin hypercube sampling method to generate scenarios covering different photovoltaic and wind power output situations, ensuring that the generated samples are representative and can cover the entire sample space of DG output, accurately simulating the variable new energy output situation.

[0129] Power Flow Calculation and Tracking Unit: Based on the obtained distribution network data and multiple DG output scenarios, use a power flow calculation tool to perform power flow calculations. During the calculation process, track the power flow, node voltage, and branch load conditions under different scenarios, and evaluate the stability of the power grid under different situations to ensure that the operating states of all power grid components meet the safety constraints.

[0130] Vulnerability Assessment Unit: Use the improved power flow betweenness index to evaluate the vulnerability of each node and branch in the distribution network. By calculating the vulnerability index under multiple DG output scenarios, identify the key components that are most likely to fail under different power flow conditions. Through this module, the most vulnerable nodes and branches in the power grid can be effectively determined, providing a scientific basis for pre-disaster reinforcement and optimization.

[0131] Key Component Identification Unit: Sort the key components in the distribution network based on the vulnerability assessment results, and verify the effect of vulnerability identification in combination with the system load survival rate. Components with high vulnerability index values usually have a higher load capacity when the system fails, so they need to be monitored and protected preferentially. Through this module, the key components that need to be focused on and strengthened before a disaster can be accurately identified.

[0132] Application Cases

[0133] This paper verifies the effectiveness of the proposed improved power flow intermediate number in the vulnerability assessment of key components of the distribution network, and connects different types of DGs in the IEEE 33-node system. Specifically, a 1MW photovoltaic generator set and a 0.8MW wind generator set are connected to nodes 15 and 30 respectively. The photovoltaic generator set and the wind turbine set adopt constant power factor control, and the power factor of all DGs is set to 0.9, thereby obtaining an improved IEEE 33-node system. During normal operation, the operating status of the distribution network switch is shown in Figure 1. Figure 2 The branch marked with solid lines. The system voltage level is 12.66kV, the benchmark capacity is 10MV·A, and the new energy penetration rate is 48%, reflecting the scenario of high penetration rate of renewable energy access to the distribution network. The present invention uses Matpower 7.1 software as a power flow calculation tool based on the MATLAB R2023a platform, and uses a computer equipped with an Intel Core-i5-13400F 4.6GHz processor and 16GB memory for calculation and verification. The identification results of vulnerable components in high penetration distribution networks are shown in Table 1.

[0134] Table 1 Identification results of vulnerable components in distribution network

[0135]

[0136]

[0137] It can be found that in multiple DG output scenarios, the vulnerability assessment is performed using the improved power flow betweenness, and it is found that nodes 1, node 2, node 3, node 15, node 30 and branches 1-2, branch 2-3, branch 3-23, branch 23-24, and branch 14-15 show high vulnerability in multiple scenarios. These nodes and branches are identified as key components in the distribution network. Specifically, the vulnerability assessment method based on node power flow betweenness and branch power flow betweenness quickly identifies high-vulnerability nodes and branches located in the backbone network in the vulnerability assessment, as shown in Table 2. Therefore, by failing the assessed high-vulnerability components, the system load survival rate drops sharply, verifying the effectiveness of the vulnerability identification results. Through this assessment method, the distribution network can identify vulnerable components in advance and take targeted measures in extreme cases to serve the grid dispatching and fault emergency response, thereby improving the system's resilience and ability to cope with emergencies.

[0138] Table 2 System load survival rate after vulnerable nodes and branches fail

[0139]

[0140] Among them, the execution process of each unit can be carried out according to the process steps of the high - permeability distribution network risk identification model based on dynamic power flow analysis in Embodiment 1, and will not be elaborated one by one in this embodiment.

[0141] Those skilled in the art should know that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memory, CD - ROM, optical memory, etc.) containing computer - usable program code.

[0142] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general - purpose computer, a special - purpose computer, an embedded processor, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data - processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0143] These computer program instructions can also be stored in a computer - readable memory that can direct a computer or other programmable data - processing device to work in a specific manner, so that the instructions stored in the computer - readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data - processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer - implemented process. Thus, the instructions executed on the computer or other programmable device provide for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of functions specified in one or more boxes. The above-described specific embodiments have further elaborated on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Risk identification model and system for high - permeability distribution network based on dynamic power flow analysis, characterized in that The model includes: Obtain the basic data of the distribution network, including the grid topology structure, node voltage, branch parameters, etc., and establish a stochastic output model for distributed generations such as photovoltaic and wind power; Use the Latin hypercube sampling method to generate multiple DG output scenarios, and simulate the power flow of the power grid under different meteorological and load conditions; use a power flow calculation tool to perform power flow calculations, track the power flow, node voltage, and branch load conditions to ensure full consideration of the uncertainty and dynamic changes of DG output; based on the improved power flow betweenness index (IPFB), evaluate the vulnerability of nodes and branches in the distribution network, calculate the vulnerability index of each node and branch, and conduct a comprehensive evaluation in combination with factors such as power difference and node transmission capacity; According to the calculated vulnerability index, sort the nodes and branches in the distribution network, identify the key components before a disaster, and provide an optimal scheduling plan before a disaster; output the identification results of the key components before a disaster, and propose optimization measures according to the evaluation results to significantly improve the disaster resistance and risk response capabilities of the distribution network.

2. The risk identification model and system for a high-penetration distribution network based on dynamic power flow analysis according to claim 1, characterized in that The output scenario generation model includes: Wind turbine stochastic output model: Wind energy has the characteristics of volatility and intermittency, which is related to the randomness of wind speed. Most existing literature uses the Weibull distribution to describe wind speed. The distribution function of the two-parameter Weibull distribution is: F w (v) = 1 - exp[-(v / c) k ​ where F w (v) is the distribution function of the two-parameter Weibull distribution, v is the wind speed; c is the Weibull distribution scale parameter; k is the Weibull distribution shape parameter. In stability analysis, for the case of multiple wind turbines operating in parallel, one or more equivalent machines are often considered. For a single wind turbine, its active power output is directly affected by wind speed, and the corresponding relationship is: Among them, P w (v) is the active power output of the wind turbine, and v is the wind speed; v ci is the cut-in wind speed of the wind turbine; v r is the rated wind speed of the wind turbine; v co is the cut-out wind speed of the wind turbine; P r is the rated output power of the wind turbine. Photovoltaic stochastic output model: The output of photovoltaic power generation units has significant randomness, and its fluctuations are closely related to the changes in light intensity. Light intensity follows the Beta distribution on both short-term and long-term time scales. Photovoltaic output also satisfies the Beta distribution, and the probability density function is: where Γ(·) is the gamma function, also known as the second Euler integral; f L (P a ) is the probability density function of the photovoltaic output P a ; α and β are the shape parameters of the Beta distribution; P max is the maximum output of the photovoltaic power generation unit. Next, obtain the operating parameters of various devices in the power grid, including transformers, lines, and energy storage systems, etc., and construct the working models of each device in the distribution network under different DG output scenarios. Specifically, through the above DG output scenarios, combined with the topology structure data of the power grid, based on the power balance principle, use a power flow calculation tool to perform power flow calculations on the distribution network, and continuously track the changes in power flow and node voltage, so as to comprehensively reflect the operating state of the power grid under different output scenarios.

3. The risk identification model and system for a high-penetration distribution network based on dynamic power flow analysis according to claim 1, characterized in that The Latin hypercube sampling technique includes: Perform stratified random sampling in the equal-probability interval to ensure that each sampling point is evenly distributed and can reflect the actual operating conditions of the distribution network under different weather, load changes, and new energy power generation output fluctuations. The multiple output scenarios generated will cover the power generation characteristics of different photovoltaics and wind powers to ensure a comprehensive evaluation of the vulnerability of the distribution network under uncertain conditions. In this process, it is specifically realized through the following steps: 1) Divide the probability intervals: According to the probability characteristics of the DG output distribution, divide it into N t = 1000 equal-probability intervals. 2) Randomly select sample points: Let 1 ≤ i ≤ N t , for any probability interval [(i - 1) / N t , i / N t , randomly select a number w i , which can be expressed as: where r is a random variable uniformly distributed in the interval [0, 1], and N t is the number of equiprobable intervals. 3) Inverse transformation sampling: The sample values corresponding to the probability intervals can be obtained through the inverse transformation of the probability distribution function, which is expressed as: x i = F -1 (w i ) where x i is the sampling value; F -1 (·) is the inverse function of the probability distribution function. The N sampling values of the DG random output can be obtained through the above steps t The figure 3 shows multiple DG output scenarios obtained by the Latin hypercube sampling method. These sample values effectively cover the entire sample space of the DG output, making the evaluation results closer to the actual situation.

4. The risk identification model and system for a high - permeability distribution network based on dynamic power flow analysis according to claim 1, wherein The power flow calculation and tracking include: Power flow calculation and tracking model: According to the distribution network data obtained in the early stage and multiple DG output scenarios, a power flow calculation tool is used to perform power flow calculations on the distribution network. The goal is to evaluate the stability of the power grid under different DG output conditions and load conditions through multi-scenario analysis. The objective function is to minimize power losses and maintain system stability, and the constraints include voltage range, power balance, line transmission capacity, power flow constraints, etc. The specific steps are as follows: First, obtain the multi-scenario output data of photovoltaic and wind power, as well as the electrical parameters of each node and branch (such as admittance matrix, branch resistance, etc.) according to the established DG reach and processing model. Then, according to different DG output scenarios, construct the power flow equations of the power grid, and calculate the voltage amplitude and phase angle of each node in the distribution network, power flow, etc. The power flow calculation formula is as follows: where, P i is the active power of the i-th node, V i and V j are the voltage amplitudes of the i-th and j-th nodes, θ i and θ j are the voltage phase angles of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the admittance matrix between node i and node j. By performing power flow calculations for different output scenarios, the power flow, node voltages, and branch loads can be tracked in real time to ensure the stability of the power grid under different conditions. In addition, the following constraints need to be considered during the power flow calculation process: Generator output constraint Where: and are the maximum theoretical output powers of PV and wind power at time t, and P t w is the wind power output. Thermal power unit ramp rate constraint -r i,down ≤P i,t -P i,(t-1) ≤r i,up Where: r i,up and r i,down are the maximum upward ramping rate and the maximum downward ramping rate of the thermal power unit respectively, P i,t and P i,(t-1) are the thermal power output at time t and the thermal power output at time t - 1 respectively. Line transmission capacity constraint Where: B i,j (S) represents the susceptance between nodes i and j, θ i,t , θ j,t respectively represent the voltage phase angles of nodes i and j, P i,j,Lmax represents the per-unit value of the maximum allowable transmission power of the line between nodes i and j. Reserve constraint Wherein: is the maximum available grid-connected power of unit i at time t, is the actual grid-connected power of unit i at time t, and λ is the reserve coefficient, is the load power of node i at time t.

5. The risk identification model and system for a high-penetration distribution network based on dynamic power flow analysis according to claim 1, characterized in that The vulnerability assessment process includes: Based on the improved power flow betweenness index, conduct vulnerability assessments on the nodes and branches of the distribution network, and calculate the vulnerability indices of each node and branch in the distribution network. The improved power flow betweenness can be calculated through the following formula: where: min(P g , P d ) is the weight factor of the single-line flow mediation number, taking the smaller value of the actual output of power source g and the actual power of load d, representing the maximum available transmission power between the power source and the load; P gd (l) is the active power transmitted by power source g and load d on branch l; P gd is the active power transmitted from power source g to load d; G and D are the power source set and the load set respectively. BPFB(i) is the flow mediation number of the l-th branch in the system; NPFB(i) is the flow mediation number of the i-th node in the system; L i is the set of branches connected to node i in the network; B lk is the flow mediation number of branch l k ; P i is the injection power of node i. NPFB i and BPFB i are the node and branch flow mediation numbers of the system under the i-th scenario. INPFB and IBPFB are the improved node flow mediation number and the improved branch flow mediation number respectively; N t is the number of photovoltaic and wind power output scenarios generated by the Latin hypercube sampling method; p i and p j are the probabilities of the i-th and j-th scenarios respectively.

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