Method for evaluating and quantifying operation risk and power supply capability
By introducing an upstream transmission risk model for topology and a risk-weighted power supply index, the problem of insufficient identification of weak links in traditional assessment methods is solved. This enables a scientific assessment and unified assessment framework for distribution network operation risk and power supply capacity, providing a scientific basis for scheduling and investment.
Patent Information
- Application Number
- CN202511590797.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-03
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies, when assessing the operational risks and power supply capacity of distribution networks, focus more on the condition of the components themselves and ignore the propagation effect of faults in the power grid, resulting in inaccurate assessment results and an inability to effectively identify weak links.
By adopting an upstream transmission risk model based on topology, combined with the analytic hierarchy process and fuzzy comprehensive evaluation, the risk value of components is quantified. Furthermore, by integrating the failure probability and failure consequences of components through risk-weighted power supply indicators, the barriers of traditional evaluation methods are broken.
It enables a scientific assessment of the operational risks and power supply capacity of the distribution network, accurately identifies weak links, provides a scientific basis for scheduling and investment, and has both adaptability and a unified risk and power supply capacity assessment framework.
Abstract
Description
Technical Field
[0001] This invention belongs to the field of distribution network risk management technology, specifically relating to a quantitative method for assessing distribution network operation risks and power supply capacity. Background Technology
[0002] The distribution network, located at the end of the power system and directly connected to users, directly impacts power supply security and users' overall evaluation of the power grid. However, distribution networks are complex in structure, contain numerous devices, and are widely exposed to the natural environment, facing threats from multiple risk factors such as equipment aging, severe weather, and load fluctuations. Therefore, scientifically and accurately assessing the operational risks of the distribution network and quantifying its true power supply capacity has become a crucial support for power grid companies in proactive operation and maintenance, precise investment, and emergency dispatch. Summary of the Invention
[0003] This invention addresses the shortcomings of existing technologies by providing a quantitative method for assessing operational risks and power supply capabilities.
[0004] To solve one or more or all of the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A quantitative method for assessing operational risk and power supply capacity includes: quantifying the risk factors of key components in a distribution network to obtain the risk value of each component; based on the distribution network topology, obtaining the upstream transmission risk value of each node and allocating the upstream transmission risk value to the risk value of the corresponding node component; using power flow recalculation to obtain the failure loss caused by the failure of the target component; using the risk value corresponding to the component as a weight to perform a weighted summation of the failure loss of the component to obtain a risk-weighted power supply index for the distribution network; assessing the operational risk of the distribution network using the risk value and failure loss of each component; and assessing the power supply capacity of the distribution network using the risk-weighted power supply index.
[0005] Furthermore, the method for quantifying the risk factors of key components in the distribution network and obtaining the risk value of each component includes: acquiring the risk factors of key components in the distribution network; generating subjective weights for each risk factor using the analytic hierarchy process (AHP); normalizing the original indicators of each risk factor after quantification; and multiplying the normalized results of the original indicators of the risk factors by the corresponding subjective weights to obtain the risk value corresponding to the component.
[0006] Furthermore, the risk factors include intrinsic risk factors and operational risk factors. The intrinsic risk factors include aging rate, number of historical failures, maintenance cycle, and voltage level. The operational risk factors include geographical environment, number of meteorological disasters, and number of emergency power outages.
[0007] Furthermore, the method for obtaining the upstream transmission risk value for each node includes: based on the target distribution network topology, starting from the top-level node of the target distribution network, if the current node has a top-level node in the target distribution network, then calculating the upstream transmission risk value corresponding to that node; the upstream transmission risk value is... N(v) is the set of parent nodes of the current node v in the target distribution network. Let be the risk value of node x, d(x,v) be the electrical distance from node x to node v, and L(v) be the set of upstream lines of node v in the target distribution network. Let y be the risk value of line y, d(y,v) be the electrical distance from line y to node v, and α and β be risk attenuation factors.
[0008] Furthermore, the electrical distance is the topology hop count.
[0009] Furthermore, the method for allocating upstream transmission risk values to the risk values of corresponding node components includes: obtaining each component contained in the node and its corresponding risk value, allocating upstream transmission risk values according to the proportion of risk values of each component, and accumulating the allocated risk values into the risk values of the corresponding components.
[0010] Furthermore, the method of obtaining the failure loss caused by the failure of the target component by using power flow recalculation includes: traversing the failure scenarios of individual components in the target component, calculating the power flow distribution of the system and evaluating the available power load in each traversal, and taking the difference between the available power load before failure and the available power load after failure as the failure loss corresponding to the failed component.
[0011] Furthermore, the risk-weighted power supply index is , This represents the maximum powerable load under fault-free conditions. The risk value of component k. Let K be the failure loss of component k.
[0012] Furthermore, the method for assessing the operational risk of the distribution network using the risk value and failure loss of each component includes: obtaining indicators such as high-risk components, average risk, risk loss weighted average, and risk index based on the risk value and failure loss of each component, and assessing the operational risk of the distribution network based on each indicator.
[0013] Furthermore, methods for evaluating the power supply capacity of a distribution network using risk-weighted power supply indicators include: obtaining indicators such as power supply capacity degradation rate, power supply reliability, and risk power supply ratio, and evaluating the power supply capacity of the distribution network based on each indicator.
[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: Traditional risk assessment methods for distribution networks often focus on the condition of individual components, neglecting the propagation effect of faults within the grid. This invention innovatively introduces an upstream transmission risk model based on topology, considering the attenuation of risk with electrical distance. This allows the assessment results to reflect not only the health of the components themselves but also their criticality within the network. The risk values of some critical components can significantly increase due to the cumulative effect of upstream risks, thus enabling accurate identification of true weak points and avoiding the omissions common in traditional methods.
[0015] This invention proposes a risk-weighted power supply index, which integrates the "failure probability" and "failure consequences" of components through a weighted summation. This index can quantify the potential power supply capacity loss of the power grid under the current risk distribution, providing a more scientific and conservative basis for dispatching plans and power trading.
[0016] In the quantification of risk factors, this invention combines the subjective weighting of the analytic hierarchy process (AHP) with the objective normalization of fuzzy comprehensive evaluation. It not only incorporates the profound understanding of the relative importance of different risk factors by domain experts, but also handles the standardization problem of data with different dimensions through membership functions. It combines the wisdom of expert systems with the rigor of data-driven approaches, making the model highly adaptable to distribution networks of different regions and structures.
[0017] This invention places operational risk assessment and power supply capacity assessment within the same framework, breaking down the traditional barriers between these two independent analytical fields. The resulting risk index and risk-weighted power supply index enable managers to weigh the safety and economy of the distribution network on a unified scale, providing strong data support for formulating operational modes and development plans that balance safety and efficiency. Detailed Implementation
[0018] To better understand the present invention, the following embodiments further illustrate the content of the invention, but the scope of protection of the present invention is not limited to the following embodiments. Numerous specific details are set forth in the following description to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without one or more of these details.
[0019] Example 1: The purpose of this example is to provide a quantitative method for assessing operational risk and power supply capacity. The method includes: S1. Quantify the risk factors of key components in the power distribution network to obtain the risk value of each component.
[0020] Identify the risk factors for key components such as equipment and lines in the power distribution network. Risk factors include inherent risk factors and operational risk factors. For example, inherent risk factors include aging rate (the ratio of usage time to lifespan), number of historical failures, maintenance cycle (the shorter the maintenance cycle, the higher the risk), and voltage level. Operational risk factors include geographical environment, number of weather disasters, and number of emergency power outages. All components should be assessed using the same risk factors to avoid inconsistencies in risk assessment standards between components.
[0021] The Analytic Hierarchy Process (AHP) was used to generate subjective weights for each risk factor. Specifically, experts compared risk factors pairwise, labeled the risk level using a 1-9 scale, and generated a judgment matrix. The eigenvector corresponding to the largest eigenvalue of the judgment matrix was normalized to obtain a subjective weight vector, with each element corresponding to the subjective weight of each risk factor.
[0022] Fuzzy comprehensive evaluation is used to normalize the original indicators of each risk factor. Since the original indicators of risk factors belong to different dimensions, they need to be quantified and then normalized. Specifically, a membership function is constructed for each risk factor; the risk value of each component and each risk factor is calculated using the corresponding membership function; and the risk values are normalized within the same risk factor.
[0023] Finally, the normalized result of the risk factor risk value (original indicator) is multiplied by the corresponding subjective weight to obtain the risk value corresponding to the element.
[0024] Membership functions of risk factors are used to quantify corresponding analytical indicators into risk values. For example, the membership function of aging rate can be defined as follows: x is the aging rate. Let be the risk inflection point (e.g., a value of 0.8, meaning the risk increases sharply after 80% of the lifespan is reached), and k be the curve steepness (e.g., a value of 15, used to control the rate of risk increase). For example, the membership function of frequency-based risk factors such as the number of historical failures, the number of meteorological disasters, and the number of emergency power outages can be defined as... x is the number of times. The baseline offset is 0 (default value, used to eliminate noise from low-frequency occurrences), k is the risk sensitivity coefficient (e.g., range of 1-2, the larger the value, the steeper the curve), and p is the growth pattern coefficient (e.g., range of 1-3, controlling the inflection point of the curve). The membership functions of other indicators are not detailed here.
[0025] S2. Based on the distribution network topology, obtain the upstream transmission risk value of each node and allocate the upstream transmission risk value to the risk value of the corresponding node element.
[0026] In a distribution network, a fault in an upstream node or line will directly affect downstream nodes. Therefore, the risks of upstream nodes and lines will be transmitted to downstream nodes. The upstream transmission risk value in this step is the risk value transmitted by the upstream node and line of the current node. Based on the distribution network topology, starting from the topmost node, if the current node has an upstream node in the target distribution network, the upstream transmission risk value corresponding to that node is calculated.
[0027] Upstream transmission risk value N(v) is the set of parent nodes of the current node v in the target distribution network. Let be the risk value of node x, d(x,v) be the electrical distance from node x to the current node v, and L(v) be the set of upstream lines of the current node in the target distribution network. Let d(y,v) be the risk value of line y, and d(y,v) be the electrical distance from line y to the current node v. α and β are risk attenuation factors, representing the degree to which risk decreases with increasing electrical distance. For ease of calculation, the electrical distance can be expressed as the number of topology hops or the number of layers apart. The risk value of a node is the sum of the risk values of all devices at that node (excluding inter-node connecting lines).
[0028] After the current node obtains the corresponding upstream transmission risk value, it obtains the risk values of each component contained in the node and their corresponding risk values. It then allocates the upstream transmission risk value according to the proportion of the risk value of each component and adds the allocated risk value to the risk value of the corresponding component.
[0029] Finally, the risk values of all components are normalized (within the range of 0-1), and the normalized result is used as the new risk value of the component.
[0030] S4. Use power flow recalculation to obtain the failure loss caused by the failure of the target component.
[0031] Failure of some components in the power system may cause power outages, resulting in power loss.
[0032] In practice, the target component can be any component with a risk value, or any component with a risk value exceeding a set threshold.
[0033] This step iterates through the failure scenarios of individual components in the target components (N-1 failures), and uses methods such as DC power flow or piecewise linear AC power flow to calculate the power flow distribution of the system and assess the available power load. The difference between the power supply load before failure and the available power load after failure is the failure loss corresponding to that component.
[0034] The method for power flow calculation is a conventional technique in this field and will not be described in detail here.
[0035] S5. Using the risk value corresponding to the component as a weight, the failure loss of the component is weighted and summed to obtain the risk-weighted power supply index of the distribution network.
[0036] Using formula Calculate risk-weighted power supply index , This represents the maximum powerable load under fault-free conditions. This is the risk value of component k (after normalization). Let K be the failure loss of component k.
[0037] S6. Assess the operational risks of the power distribution network using the risk values and failure losses of each component.
[0038] Based on the risk value and failure loss of each component, indicators such as high-risk component, average risk, weighted average risk loss, and risk index are obtained, and the operation risk of the distribution network is assessed based on these indicators.
[0039] High-risk components are those selected based on their risk values, which are significantly higher than the average level or the risk threshold. These components are weak links in the distribution network and require priority maintenance or continuous monitoring.
[0040] Average risk reflects the overall risk level of the distribution network; a low average risk indicates a lower overall operational risk. In specific assessments, comparisons with historical indicators are used to determine the overall health of the distribution network and its changing trends.
[0041] The risk loss weighted average is This indicator considers both the magnitude of risk and the consequences of failure, with high-risk, high-loss components contributing more. If the weighted average of risk and loss is significantly higher than the average risk, it indicates that high-risk components often correspond to large losses, and these high-risk components should be addressed first.
[0042] Risk index is This provides a clear visual representation of potential power supply losses. By comparing with historical indicators, the changing trends of power outages can be observed. Furthermore, the ratio of the risk index to the total load can be used as a relative risk index, facilitating comparisons between different distribution networks.
[0043] During the assessment, power supply areas can be divided according to the power grid topology. By calculating the average risk or relative risk index of each area, the spatial distribution of risk can be analyzed, and high-risk areas can be identified.
[0044] S7. Use risk-weighted power supply indicators to assess the power supply capacity of the distribution network.
[0045] The system acquires indicators such as power supply capacity degradation rate, power supply reliability, and risk power supply ratio, and evaluates the power supply capacity of the distribution network based on these indicators.
[0046] Power supply capacity degradation rate This indicates the percentage reduction in available power load due to risk factors. By comparing this indicator with historical data, it can be determined whether the impact of risks on power supply capacity is intensifying. If the indicator exceeds a set threshold, emergency dispatch needs to be initiated.
[0047] Power supply reliability is By comparing it with traditional reliability indicators, if the indicator is significantly lower than the traditional indicators, it indicates that there are potential risks that have not been captured by the traditional indicators, and it is necessary to strengthen the maintenance of risk perception in the traditional way.
[0048] Risk power supply ratio is The risk-to-power ratio represents the amount of power loss corresponding to a unit of risk value; a higher value indicates a greater risk impact. This ratio can be used to assess the return on risky investments. If this indicator is too high, it means that a significant improvement in power supply can be achieved by investing a certain amount of risk to reduce overall investment, making it a worthwhile priority for investment.
[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be covered within the scope of the claims of the present invention.
Claims
1. A quantitative method for assessing operational risk and power supply capacity, characterized in that, include: The risk factors of key components in the power distribution network are quantified to obtain the risk value of each component; Based on the distribution network topology, the upstream transmission risk value of each node is obtained, and the upstream transmission risk value is allocated to the risk value of the corresponding node element; The failure loss caused by the failure of the target component is obtained by recalculating the power flow. By using the risk value corresponding to the component as a weight, the failure loss of the component is weighted and summed to obtain the risk-weighted power supply index of the distribution network; The risk of power distribution network operation is assessed by using the risk values and failure losses of each component; The power supply capacity of the distribution network is assessed using risk-weighted power supply indicators.
2. The method for assessing and quantifying operational risks and power supply capacity according to claim 1, characterized in that, Methods for quantifying the risk factors of key components in a power distribution network and obtaining the risk value of each component include: Identify the risk factors of key components in the power distribution network; The analytic hierarchy process (AHP) is used to generate subjective weights for each risk factor. The original indicators of each risk factor are quantified and then normalized. The risk value corresponding to the element is obtained by multiplying the normalized result of the original indicator of the risk factor by the corresponding subjective weight.
3. The method for assessing and quantifying operational risks and power supply capacity according to claim 1, characterized in that, The risk factors include intrinsic risk factors and operational risk factors. The intrinsic risk factors include aging rate, number of historical failures, maintenance cycle, and voltage level. The operational risk factors include geographical environment, number of meteorological disasters, and number of emergency power outages.
4. The method for assessing and quantifying operational risks and power supply capacity according to claim 1, characterized in that, Methods for obtaining the upstream propagation risk value for each node include: Based on the target distribution network topology, starting from the top-level node of the target distribution network, if the current node has a top-level node in the target distribution network, then the upstream transmission risk value corresponding to that node is calculated. Upstream transmission risk value N(v) is the set of parent nodes of the current node v in the target distribution network. Let be the risk value of node x, d(x,v) be the electrical distance from node x to node v, and L(v) be the set of upstream lines of node v in the target distribution network. Let y be the risk value of line y, d(y,v) be the electrical distance from line y to node v, and α and β be risk attenuation factors.
5. The method for quantifying operational risk and power supply capacity according to claim 4, characterized in that, The electrical distance is the topology hop count.
6. The method for quantifying operational risk and power supply capacity according to claim 1, characterized in that, The method for allocating upstream transmission risk values to the risk values of corresponding node components includes: obtaining each component contained in the node and its corresponding risk value, allocating upstream transmission risk values according to the proportion of risk values of each component, and accumulating the allocated risk values into the risk values of the corresponding components.
7. The method for quantifying operational risk and power supply capacity according to claim 1, characterized in that, The method of obtaining the failure loss caused by the failure of the target component by power flow recalculation includes: traversing the failure scenarios of individual components in the target component, calculating the power flow distribution of the system and evaluating the available power load in each traversal, and taking the difference between the available power load before failure and the available power load after failure as the failure loss corresponding to the failed component.
8. The method for quantifying operational risk and power supply capacity according to claim 1, characterized in that, The risk-weighted power supply index is: , This represents the maximum powerable load under fault-free conditions. The risk value of component k. Let K be the failure loss of component k.
9. The method for quantifying operational risk and power supply capacity according to claim 1, characterized in that, The method for assessing the operational risk of a distribution network using the risk values and failure losses of each component includes: obtaining indicators such as high-risk components, average risk, risk loss weighted average, and risk index based on the risk values and failure losses of each component, and assessing the operational risk of the distribution network based on each indicator.
10. The method for assessing and quantifying operational risks and power supply capacity according to claim 1, characterized in that, Methods for assessing the power supply capacity of a distribution network using risk-weighted power supply indicators include: obtaining indicators such as power supply capacity degradation rate, power supply reliability, and risk power supply ratio, and assessing the power supply capacity of the distribution network based on each indicator.