A method and system for risk assessment of power distribution networks

Abstract

This invention discloses a method and system for risk assessment of distribution networks. The method includes: defining extreme high temperatures; constructing an assessment index system; using the indicators in the assessment index system to construct a model of the coupling relationship between extreme high temperatures and the temperature and load of the distribution network, a load surge risk assessment model, an equipment overload and failure probability assessment model, and a voltage instability assessment model, thereby obtaining the predicted load, overload probability, equipment failure probability, and voltage stability margin of the distribution network, respectively; and constructing a risk assessment model to assess the risk of the distribution network under extreme high temperatures. This invention, through the combination of accurate temperature and load coupling relationship modeling and the construction of a multi-dimensional risk assessment index system, solves the problems in existing technologies such as inaccurate load growth prediction under extreme high-temperature weather, inability to effectively assess the failure risk of distribution network equipment in high-temperature environments, and difficulty in comprehensively measuring voltage stability problems caused by extreme high temperatures.

Description

Technical Field

[0001] This invention relates to the field of distribution network risk assessment technology, and in particular to a distribution network risk assessment method and system that considers the coupling relationship between extreme high temperature and load. Background Technology

[0002] In the current power system operating environment, the stable operation of the distribution network plays a crucial role in the normal operation of social production and life. However, the trend of global warming has led to the frequent occurrence of extreme high-temperature weather, which has brought unprecedented challenges to the operation of the distribution network. Traditional distribution network risk assessment methods mainly focus on the operating status under normal climatic conditions.

[0003] With global warming, extreme heat events are becoming more frequent, posing numerous challenges to the safe and stable operation of power distribution networks. On the one hand, high temperatures cause a surge in temperature-controlled loads such as air conditioning, exceeding the traditional power distribution network planning and operation expectations based on average load and normal meteorological conditions, resulting in power supply imbalances. On the other hand, high temperatures accelerate equipment aging, affect heat dissipation, and reduce equipment reliability. Previous reliability assessments have not fully considered extreme high-temperature conditions, making it difficult to formulate effective maintenance strategies. At the same time, load fluctuations and equipment parameter changes caused by high temperatures also threaten voltage stability, which is not adequately considered by existing analysis methods.

[0004] Currently, there are numerous studies and technologies for power system operation risk assessment. For example, Chinese patent application CN117650516A discloses a method for assessing the operation risk of a high-proportion renewable energy power grid considering high-temperature weather. This method constructs an expression for the uncertain power output on the load side considering high-temperature weather, as well as the functional relationship and uncertain output expression between the output characteristics of photovoltaic and wind turbine units and temperature; it obtains typical power system operation scenarios under high-temperature weather through Monte Carlo sampling and scenario reduction; it establishes two evaluation indicators, line overload risk and node overvoltage risk, using risk theory, and performs system operation simulation and risk assessment indicator calculation based on an AC power flow model.

[0005] However, the existing technologies mentioned above only establish two evaluation indicators: line overload risk and node overvoltage risk, which cannot fully cover the various risks that the power grid may face under high temperature weather. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for risk assessment of power distribution networks, which partially solves or alleviates the above-mentioned shortcomings in the prior art. By combining accurate modeling of the coupling relationship between temperature and load with the construction of a multi-dimensional risk assessment index system, this invention solves the problems in the prior art, such as inaccurate prediction of load growth under extreme high temperature weather, inability to effectively assess the failure risk of power distribution network equipment under high temperature environment, and difficulty in comprehensively measuring voltage stability problems caused by extreme high temperature.

[0007] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution:

[0008] A first aspect of the present invention is to provide a method for risk assessment of a power distribution network, comprising:

[0009] Define extreme high temperature;

[0010] An evaluation index system is constructed, which includes temperature-related indexes, load-related indexes, equipment operation indexes, and voltage quality indexes;

[0011] By using the indicators in the evaluation index system, we can construct models for the coupling relationship between extreme high temperature and distribution network temperature and load, load surge risk assessment, equipment overload and failure probability assessment, and voltage instability assessment, thereby obtaining the predicted load, overload probability, equipment failure probability, and voltage stability margin of the distribution network, respectively.

[0012] A risk assessment model is constructed using predicted load, overload probability, equipment failure probability, and voltage stability margin to assess the risks of the distribution network under extreme high temperatures.

[0013] As an improvement, the extreme high temperature is defined as the daily maximum temperature exceeding the multi-year average daily maximum temperature plus k times the standard deviation for n consecutive days in a specific region and time period, or / and reaching the high temperature critical value and lasting for X days.

[0014] As an improvement, the temperature-related indicators include daily maximum temperature, number of consecutive days of high temperature, and temperature change rate; the load-related indicators include total load, peak load occurrence time, and load growth rate; the equipment operation-related indicators include equipment overload rate; and the voltage quality-related indicators include voltage deviation and voltage fluctuation rate.

[0015] As an improvement, the temperature-load coupling relationship model used to obtain the predicted load of the distribution network is as follows:

[0016] ;

[0017] in, For load prediction, T represents temperature, t represents time, and d represents date type; β0, β1, β2, and β3 are regression coefficients, and ε represents the random error term.

[0018] As an improvement, the load surge risk assessment model used to obtain the overload probability of the distribution network is as follows:

[0019] ;

[0020] Among them, P overload For overload probability, This is an indicator function that takes the value 1 when the condition is met, and 0 otherwise; Rk Let N be the k-th region in the distribution network, and N be the total number of regions in the distribution network. Let k be the rated capacity of the k-th region in the distribution network. Let be the predicted load of the k-th region in the distribution network.

[0021] As an improvement, the equipment overload and failure probability assessment model used to obtain the failure probability of distribution network equipment is as follows:

[0022] ;

[0023] Among them, P failure T represents the probability of equipment failure. max T(t) represents the upper limit of the equipment's temperature tolerance, T(t) represents the real-time temperature data, and m represents the shape parameter.

[0024] As an improvement, the voltage instability assessment model used to obtain the voltage stability margin of the distribution network is as follows:

[0025] ;

[0026] Where VSM is the voltage stability margin, V min V is the lower limit of voltage amplitude. rated This is the rated voltage.

[0027] As an improvement, the risk assessment model is as follows:

[0028] ;

[0029] Where R is the risk value, and R∈[0,1]; To predict load, P overload Let P be the overload probability. failure The device failure probability is represented by VSM, the voltage stability margin is represented by w1, w2, w3, and w4, which are weights.

[0030] As an improvement, when the risk value R is less than the medium risk threshold, the risk level of the distribution network is determined to be low risk;

[0031] If the risk value R is greater than or equal to the medium risk threshold and less than the high risk threshold, the risk level of the distribution network is determined to be medium risk.

[0032] If the risk value R is greater than or equal to the high-risk threshold, the risk level of the distribution network is determined to be high-risk.

[0033] The present invention also provides a power distribution network risk assessment system, comprising:

[0034] The extreme high temperature definition module is used to define extreme high temperatures;

[0035] The evaluation index system construction module is used to construct the evaluation index system, which includes temperature-related indexes, load-related indexes, equipment operation indexes, and voltage quality indexes.

[0036] The indicator acquisition module is used to construct extreme high temperature and distribution network temperature and load coupling relationship model, load surge risk assessment model, equipment overload and failure probability assessment model, and voltage instability assessment model using the indicators in the evaluation indicator system, so as to obtain the distribution network predicted load, overload probability, equipment failure probability, and voltage stability margin respectively.

[0037] The assessment module is used to build a risk assessment model using predicted load, overload probability, equipment failure probability, and voltage stability margin to assess the risks of the distribution network under extreme high temperatures.

[0038] Beneficial effects:

[0039] This invention constructs a comprehensive evaluation index system encompassing temperature, load, equipment operation, and voltage quality. It assesses the distribution network's operational status from multiple dimensions using temperature indicators such as daily maximum temperature and number of consecutive days with high temperatures, as well as load indicators such as total load and peak load occurrence time. In-depth evaluation of equipment overload and failure rates enables timely detection of potential overload hazards and prediction of failure risks, providing a basis for equipment maintenance and upgrades. Simultaneously, it evaluates voltage stability through indicators such as voltage deviation and voltage fluctuation rate, ensuring the normal operation of users' electrical equipment.

[0040] Furthermore, this invention provides a comprehensive assessment and detailed classification of distribution network risks, categorizing them into low, medium, and high risks. Corresponding response strategies are developed based on different risk levels, such as optimizing power distribution and promoting orderly electricity use, demonstrating strong practicality. The risk assessment results are output in the form of intuitive and easy-to-understand charts or detailed reports, providing distribution network operation and management personnel with clear decision-making support and facilitating timely measures to ensure the safe and stable operation of the power grid. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0042] Figure 1 This is a flowchart of Embodiment 1 of the present invention;

[0043] Figure 2This is a structural diagram of Embodiment 2 of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0046] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0047] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0048] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0049] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0050] Example 1:

[0051] like Figure 1 As shown in the figure, this embodiment provides a method for risk assessment of power distribution networks, including:

[0052] S1 defines extreme high temperature.

[0053] Specifically, in this embodiment, extreme high temperature is defined as: in a specific region and time period, the daily maximum temperature exceeds the multi-year average daily maximum temperature plus k times the standard deviation for n consecutive days, or / and reaches the high temperature critical value and lasts for X days.

[0054] An abnormally high temperature phenomenon, where the temperature is significantly higher than the historical average for the same period in the region and persists for a certain duration, is defined as the multi-year average daily maximum temperature as... The standard deviation is When continuous Daily high temperature satisfy ,in This is an empirical coefficient, typically taken as 2-3, and ), or / and, reaching and exceeding a predetermined high-temperature critical value (such as and above and continue If the temperature reaches 100 degrees Celsius (5 days), it is considered to be in an extreme high-temperature weather condition. Such extreme high-temperature conditions pose a serious threat to the stable operation of the power distribution network, and its impact needs to be studied in depth and effectively assessed.

[0055] Suppose a city's multi-year average daily maximum temperature is 30℃, with a standard deviation of 2℃, and k is 2.5. If the city's daily maximum temperatures for five consecutive days are 36℃, 37℃, 38℃, 37℃, and 36℃, the multi-year average daily maximum temperature plus k times the standard deviation equals: 30 + 2.5 × 2 = 35℃. Since the daily maximum temperatures for these five consecutive days all exceeded 35℃, satisfying the condition that "the daily maximum temperature exceeds the multi-year average daily maximum temperature plus k times the standard deviation for n consecutive days," it can be concluded that the city experienced extreme high temperatures during these five days.

[0056] Alternatively, a high-temperature threshold of 35℃ can be set, with m representing 3 days. If another city experiences daily maximum temperatures of 35℃, 36℃, 37℃, and 35℃ for four consecutive days, and these four days all reach a daily maximum temperature of 35℃ and last for more than three consecutive days, then by definition, the city is experiencing extreme high-temperature weather during these four days.

[0057] S2 constructs an evaluation index system, which includes temperature-related indexes, load-related indexes, equipment operation indexes, and voltage quality indexes.

[0058] Temperature-related indicators include:

[0059] Daily maximum temperature: Accurately recording the daily maximum temperature is crucial for understanding the frequency and intensity of extreme heat events and serves as important basic data for subsequent analysis.

[0060] Number of consecutive days of high temperature: Let the high temperature threshold be... Statistically continuous satisfaction Number of days This reflects the duration of extreme high-temperature events and is of great significance for testing the durability of power distribution network equipment;

[0061] Rate of temperature change: defined as ,in Indicates the time interval The amount of temperature change (e.g., per hour or per day) helps to understand the characteristics of temperature fluctuations. Rapid temperature fluctuations can exacerbate changes in thermal stress in power distribution equipment, thereby affecting the reliability of the equipment.

[0062] Load-related indicators include:

[0063] Total load: Real-time monitoring of the overall power load of the distribution network provides a clear picture of the power supply demand situation faced by the power system. The dynamic trend of total load change under extreme high temperature conditions is one of the core elements for risk assessment.

[0064] Peak load occurrence time: Determine the precise moment when the daily or weekly load reaches its peak. By analyzing the correlation with temperature data, the temporal relationship between high temperature and peak load can be revealed, providing key guidance for the early implementation of power dispatching and equipment maintenance strategies.

[0065] Load growth rate: Calculation method is as follows ,in and The load values ​​for different time periods, especially the comparison of load growth between extreme high-temperature periods and non-high-temperature periods, can clearly quantify the degree of influence of temperature on load growth, which is of indispensable value for accurately predicting future load trends.

[0066] Equipment operating parameters include:

[0067] Equipment overload rate: For lines or transformers in a power distribution network, let their actual load be... Rated load is Then the equipment overload rate ,when When the equipment is in an overload condition, continuous monitoring of the equipment overload rate can promptly detect potential equipment overload hazards caused by sudden load increases due to high temperatures, providing key information for equipment maintenance and upgrade decisions.

[0068] Equipment failure rate: This represents the equipment failure rate, which is calculated based on the number of failures of the equipment under high-temperature conditions. Total equipment runtime ,Right now By combining historical temperature and equipment operation data, a correlation model between equipment failure rate and temperature is constructed to predict the failure risk of equipment under different temperature environments. This allows for targeted equipment inspection plans and advance preparation of equipment replacement schemes, ensuring the reliability of the power distribution network.

[0069] Voltage quality indicators include:

[0070] Voltage deviation: defined as ,in The actual voltage at each node of the distribution network. The voltage is the rated voltage. Under extreme high temperature weather, the drastic fluctuation of the load can easily cause the voltage deviation to exceed the allowable limit, which will have an adverse effect on the normal operation of the user's electrical equipment. Therefore, voltage deviation is one of the key indicators for measuring power supply quality.

[0071] Voltage fluctuation rate: Let These represent the voltage values ​​at different times. Given the average voltage over a period of time, the voltage fluctuation rate is... High temperatures can cause load fluctuations that may lead to an increase in voltage volatility. Monitoring this indicator can help assess the voltage stability of the distribution network under extreme high-temperature conditions and provide a reference for decision-making on voltage regulation measures.

[0072] Temperature-related indicators (daily maximum temperature, number of consecutive days of high temperatures, and temperature change rate) provide fundamental temperature data dimensions for temperature-load coupling models, equipment overload and failure probability assessment models, and voltage instability assessment models. For example, in the temperature-load coupling model, daily maximum temperature and temperature change rate serve as key independent variables in the model's construction and calculation, quantifying the impact of temperature on load. Load-related indicators such as total load, peak load occurrence time, and load growth rate provide core load data for load surge risk assessment models and temperature-load coupling models. In the load surge risk assessment model, overload probability is calculated by comparing load changes based on load-related indicators in different regions under extreme high temperatures with the rated capacity of lines and transformers. Equipment operation indicators such as equipment overload rate and equipment failure rate provide data for equipment overload and failure probability assessment models, enabling the construction of a correlation model between equipment failure rate and temperature to determine the failure risk of equipment under different temperature environments. Voltage quality indicators such as voltage deviation and voltage fluctuation rate provide a data foundation for voltage instability assessment models. By combining these indicators with power flow calculations, the voltage stability of the distribution network under extreme high temperatures is assessed. The multi-dimensional indicator system determines that subsequent modeling needs to consider the problem from multiple perspectives. In the risk assessment and classification steps, based on the calculated values ​​of various assessment indicators (these values ​​are derived from the calculations of each model, and the model calculations are based on the indicator system data constructed in step S2), and referring to the pre-set risk level classification standards, the risks faced by the distribution network under extreme high temperature weather are comprehensively assessed and finely classified.

[0073] S3 Data Acquisition and Preprocessing.

[0074] Extensive collection of historical temperature data, covering daily maximum temperatures over many years. minimum temperature Average temperature In addition to detailed information on temperature changes, this was compared with the distribution network operation data (including load) for the same period. Accurate matching is performed on equipment status data and voltage data, among which... This represents the time index of historical data. For the collected data, data cleaning techniques are used to remove outliers and missing values, and data smoothing algorithms and interpolation methods are used to repair a small amount of incomplete data to ensure the accuracy, completeness and reliability of the data.

[0075] S4 utilizes the indicators in the evaluation index system to construct extreme high temperature and distribution network temperature-load coupling relationship model, load surge risk assessment model, equipment overload and failure probability assessment model, and voltage instability assessment model, thereby obtaining the distribution network predicted load, overload probability, equipment failure probability, and voltage stability margin respectively.

[0076] S41 uses multiple linear regression analysis to construct a mathematical function relationship between temperature and load, assuming the predicted load... There is a linear correlation between temperature T and other influencing factors (such as time factor t and date type factor d). The following model is constructed to obtain the temperature-load coupling relationship for obtaining the predicted load of the distribution network: , where β0, β1, β2, and β3 are regression coefficients, and ε is the random error term.

[0077] By fitting historical data, the least squares parameter estimation method is used to determine the values ​​of each coefficient, thereby accurately quantifying the combined impact of temperature and other factors on the load. For example, if only temperature is considered and the fitting results show... (unit: This means that for every 1°C increase in temperature, the load will increase. Based on this, and according to the definition of extreme high temperature, the temperature threshold T exceeding extreme high temperature is determined. threshold When the real-time temperature exceeds this threshold, the load growth rate can be accurately predicted based on the above functional relationship, providing a scientific basis for load forecasting and power dispatching in the distribution network.

[0078] S42 Given that the widespread use of air conditioning equipment during extreme high temperatures can cause a sharp increase in load in specific areas, which may lead to overload of lines or transformers, a load surge risk assessment model is constructed.

[0079] First, cluster analysis or region partitioning algorithms are used to divide the power distribution network into several different regions. Determine the load characteristics of each area. (Temperature-related load function) and temperature sensitivity parameters Then, combined with temperature prediction data Based on the temperature-load coupling model described above, predict the load changes in various regions under extreme high-temperature weather. The predicted load will be compared with the rated capacity of the lines and transformers. A comparison is made to calculate the overload probability. Specifically, the load surge risk assessment model used to obtain the overload probability of the distribution network is as follows:

[0080] ;

[0081] Among them, P overload For overload probability, This is an indicator function that takes the value 1 when the condition is met, and 0 otherwise; R k Let N be the k-th region in the distribution network, and N be the total number of regions in the distribution network. Let k be the rated capacity of the k-th region in the distribution network. Let be the predicted load of the k-th region in the distribution network.

[0082] By assessing the probability of overload, it is possible to develop response strategies in advance, such as optimizing power distribution schemes and implementing orderly power consumption measures, effectively reducing the potential risks brought about by load surges.

[0083] For equipment in the power distribution network, long-term operation in a high-temperature environment will cause its temperature to rise, thereby reducing its service life or even causing failure.

[0084] When constructing an equipment overload and failure probability assessment model, the upper limit of the equipment's temperature tolerance is first determined based on the equipment's thermophysical characteristics, heat dissipation mechanism, and material properties. Then, combined with real-time temperature data and equipment uptime The failure probability of equipment at high temperatures is calculated using lifetime distribution models (such as the Weibull distribution) from reliability theory. Specifically, the equipment overload and failure probability assessment model used to obtain the failure probability of distribution network equipment is as follows:

[0085] ;

[0086] Among them, P failure T represents the probability of equipment failure. max T(t) represents the upper limit of the equipment's temperature tolerance, and m represents the real-time temperature data. m is a shape parameter that can be determined through fitting analysis of the equipment's historical fault data.

[0087] By assessing the probability of equipment failure, we can rationally plan equipment maintenance schedules and replacement strategies, thereby effectively improving the overall reliability of the power distribution network.

[0088] Under extreme high temperature conditions (S44), large load fluctuations can cause grid voltage instability, affecting power supply quality. When constructing a voltage instability assessment model, a power flow calculation method is used, combined with the coupling relationship between temperature and load, to simulate the power flow distribution of the distribution network under different temperature conditions and calculate the voltage amplitude at each node. and phase angle Based on the voltage stability criterion (lower limit of voltage amplitude) (This is to assess the voltage stability of the distribution network under extreme high-temperature weather conditions.)

[0089] Specifically, define the voltage stability margin index. Using the formula:

[0090]

[0091] Calculate the voltage stability margin; where VSM is the voltage stability margin, V min V is the lower limit of voltage amplitude. ratedThis is the rated voltage.

[0092] When VSM is less than a certain threshold, it indicates that there is a risk of voltage instability in the distribution network. At this time, voltage regulation measures need to be taken, such as adjusting the capacity of reactive power compensation devices. Change the transformer tap position This is to ensure the stability of power supply quality.

[0093] S5 uses predicted load, overload probability, equipment failure probability, and voltage stability margin to build a risk assessment model to evaluate the risks of distribution networks under extreme high temperatures.

[0094] S51 will monitor temperature data in real time. Real-time data on distribution network operation (including load) Equipment status data, voltage data The preprocessed historical data (including historical temperature data and historical power distribution network operation data) are imported into the constructed evaluation model system.

[0095] Based on the input data, S52 utilizes the aforementioned temperature-load coupling relationship model, load surge risk assessment model, equipment overload and failure probability assessment model, and voltage instability assessment model to derive values ​​for various assessment indicators, including load forecast values. Equipment overload probability Equipment failure probability Voltage stability margin .

[0096] S53 requires standardization due to the different dimensions and value ranges of its various parameters. (Load forecast value) Mapped to the [0, 1] interval via normalization; device overload probability. and equipment failure probability No additional processing is needed in the range [0, 1]; voltage stability margin Standardize to the [0, 1] interval using a specific formula.

[0097] S54 constructs a risk assessment model.

[0098] The risk assessment model is as follows:

[0099] ;

[0100] Where R is the risk value, and R∈[0,1]; To predict load, P overload Let P be the overload probability. failure R represents the probability of equipment failure, VSM represents the voltage stability margin, and w1, w2, w3, and w4 are weights. The larger the R value, the higher the risk.

[0101] S55 determines the parameter weights. In this embodiment, the Analytic Hierarchy Process (AHP) is used. A judgment matrix is ​​constructed by power industry experts comparing the relative importance of the four parameters pairwise. Then, the largest eigenvalue of the judgment matrix and its corresponding eigenvector are calculated and normalized to obtain the weight vectors w1, w2, w3, and w4.

[0102] Based on the calculated evaluation indicators and referring to the pre-set risk level classification standards, S56 comprehensively assesses and finely classifies the risks faced by the distribution network under extreme high-temperature weather. For example, the risk level is divided into three levels: low risk, medium risk, and high risk. If the risk value R is less than the medium risk threshold, the distribution network risk level is determined to be low risk; if the risk value R is greater than or equal to the medium risk threshold but less than the high risk threshold, the distribution network risk level is determined to be medium risk; if the risk value R is greater than or equal to the high risk threshold, the distribution network risk level is determined to be high risk.

[0103] When a certain indicator exceeds its corresponding high-risk threshold, it indicates that the power distribution network is in a high-risk state and it is urgent to activate the corresponding risk response plan in a timely manner.

[0104] S57 outputs the results of risk assessments in an intuitive and easy-to-understand format (such as charts and detailed reports), providing distribution network operation and management personnel with strong decision-making support. Based on the risk level and specific assessment indicator details, corresponding risk response strategies are tailored, such as flexibly adjusting power dispatch plans, strengthening equipment inspection and maintenance, and implementing effective demand-side management measures to ensure that the distribution network can operate safely, stably, and reliably under extreme high-temperature weather conditions, and continuously provide users with high-quality power services.

[0105] Example 2:

[0106] like Figure 2 As shown, this embodiment also provides a power distribution network risk assessment system, including:

[0107] The extreme high temperature definition module is used to define extreme high temperatures;

[0108] The evaluation index system construction module is used to construct the evaluation index system, which includes temperature-related indexes, load-related indexes, equipment operation indexes, and voltage quality indexes.

[0109] The indicator acquisition module is used to construct extreme high temperature and distribution network temperature and load coupling relationship model, load surge risk assessment model, equipment overload and failure probability assessment model, and voltage instability assessment model using the indicators in the evaluation indicator system, so as to obtain the distribution network predicted load, overload probability, equipment failure probability, and voltage stability margin respectively.

[0110] The assessment module is used to build a risk assessment model using predicted load, overload probability, equipment failure probability, and voltage stability margin to assess the risks of the distribution network under extreme high temperatures.

[0111] This invention will be demonstrated through two examples.

[0112] Example 1: Risk assessment of power distribution network in urban commercial area.

[0113] Background information:

[0114] A commercial district in a certain city frequently experiences extreme heat during the summer. This area is characterized by numerous high-rise buildings, a dense population, and frequent commercial activity, resulting in a large and fluctuating demand for electricity from air conditioning and other cooling equipment. The power distribution network covers multiple large shopping malls, office buildings, and hotels, making its wiring and equipment complex and requiring high power supply reliability.

[0115] Evaluation process:

[0116] 1. Data Collection: Historical temperature data for the commercial area over the past 10 years was collected, including daily maximum, minimum, and average temperatures, as well as temperature variations. Simultaneously, power distribution network operation data for the same period was obtained, such as hourly load data, load status of major lines and transformers, voltage monitoring data, and equipment fault records.

[0117] 2. Model Establishment: A model of the coupling relationship between temperature and load was established using regression analysis. By fitting historical data, the load (…) was determined. ) and temperature ( The functional relationship between them is (unit: This means that for every increase in temperature The load will increase Simultaneously, based on the equipment's thermal characteristics and operating data, an overload and failure probability assessment model was constructed, and the transformer's temperature tolerance limit was determined to be... Furthermore, the relationship between equipment failure rate and temperature was obtained by fitting the Weibull distribution. In addition, a voltage instability assessment model was established using power flow calculation methods, taking into account the influence of temperature on parameters such as line resistance and load power.

[0118] 3. Risk Assessment: Prior to a predicted extreme heatwave, the risk to the power distribution network was assessed using the aforementioned model based on forecast temperature data. The predicted maximum temperature for the day was expected to reach... Based on the temperature-load coupling model, the maximum load for the day is expected to increase compared to normal weather conditions. Using an equipment overload and failure probability assessment model, the overload probability of some transformers is calculated to be as high as [missing information]. The probability of failure also increases significantly. Voltage instability assessment models show that the voltage stability margin in some areas will decrease to [a lower percentage]. The voltage is below the safety threshold, posing a risk of voltage instability.

[0119] 4. Results and Measures:

[0120] Based on the risk assessment results, the power sector took a series of proactive measures. Power dispatch plans were adjusted, and additional power resources were allocated from surrounding areas to meet the increased load demand. Transformers potentially prone to overload were closely monitored, and emergency repair teams were on standby. Simultaneously, voltage stability of the power grid was improved by adjusting reactive power compensation devices. During periods of extreme heat, although the distribution network faced significant load pressure, the proactive risk assessment and contingency measures successfully prevented large-scale power outages and ensured normal power supply to commercial areas.

[0121] Example 2: Risk assessment of power distribution network in industrial clusters.

[0122] Background information:

[0123] A certain industrial park houses numerous factories and enterprises with diverse production processes, resulting in a continuous and substantial demand for electricity. During periods of extreme summer heat, the cooling needs of production equipment increase, and air conditioning and other cooling devices in some workshops are also heavily utilized, leading to complex load variations in the power distribution network. Furthermore, the power distribution network equipment in this industrial park operates under high loads for extended periods, exhibiting poor tolerance to high temperatures and significant aging issues.

[0124] Evaluation process:

[0125] 1. Data Collection: Detailed temperature data and power distribution network operation data for the industrial cluster over the past five years were collected, including electricity load curves, equipment types and parameters, and line losses for each factory. The data was thoroughly categorized and organized to better analyze the relationship between load characteristics and temperature in different factories.

[0126] 2. Model Establishment: A more complex load forecasting model was established using multiple linear regression analysis, considering factors such as temperature, time, and weekdays / holidays. For example, for a large steel plant, its load ( ) and temperature ( ),time( The relationship is (unit: and To address equipment overload and failure issues, a reliability assessment model was established based on the actual operating conditions and thermal aging model of the equipment, determining the failure rate variation curves of key equipment under different temperatures. Using power system simulation software, a power flow calculation model for the distribution network considering the effects of temperature was constructed to evaluate voltage stability and power loss.

[0127] 3. Risk Assessment: During an extreme heatwave, real-time monitoring of temperature and power distribution network operation data was conducted and input into an established model for risk assessment. It was found that as temperatures rose, the load on some factories increased beyond expectations, leading to overload conditions on some lines and transformers, with an overload rate reaching [percentage missing]. Meanwhile, the equipment reliability assessment model predicted that the failure probability of some key equipment would increase under high temperatures. Voltage stability analysis showed that the voltage at some nodes fluctuated significantly, with voltage deviations exceeding [a certain threshold]. The permissible range.

[0128] 4. Results and Measures: Based on the risk assessment results, emergency power rationing measures were implemented, requiring the suspension or adjustment of production schedules for some non-critical production processes to reduce load demand. Emergency capacity expansion and heat dissipation measures were carried out on overloaded lines and transformers, such as adding fans for cooling and adjusting transformer tap changes. The frequency of inspections of critical equipment was increased to promptly identify and address potential faults. These measures effectively alleviated the operational pressure on the distribution network under extreme high temperatures, ensured the basic power needs of the industrial zone, reduced economic losses caused by power outages, and provided important reference data for subsequent distribution network upgrades and renovations.

[0129] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0131] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A power distribution network risk assessment method characterized by Comprise: Define extreme high temperature; Build evaluation index system, the evaluation index system includes temperature related index, load related index, equipment operation index and voltage quality index; Utilize the index in the evaluation index system to build the temperature and load coupling relationship model of extreme high temperature and distribution network, load surge risk assessment model, equipment overload and failure probability assessment model, voltage instability assessment model to obtain distribution network predicted load, overload probability, equipment failure probability, voltage stability margin respectively; Utilize predicted load, overload probability, equipment failure probability, voltage stability margin to build risk assessment model to evaluate the risk of distribution network under extreme high temperature; The temperature and load coupling relationship model for obtaining distribution network predicted load is: ； wherein, for predicting load, T is temperature, t is time, d is date type; β0, β1, β2, β3 are regression coefficients, and ε is a random error term. The load surge risk assessment model for obtaining distribution network overload probability is: ； where P overload is the overload probability, is an indicator function that takes value 1 when the condition is satisfied and 0 otherwise; R k is the k-th region in the distribution network, N is the total number of regions in the distribution network, is the rated capacity of the k-th region in the distribution network, is the predicted load of the k-th region in the distribution network.

2. The power distribution network risk assessment method of claim 1, wherein: The extreme high temperature is that the daily maximum temperature exceeds the average daily maximum temperature plus k times standard deviation for n consecutive days, or / and, reaches the high temperature critical value and lasts for X days in a specific region and period.

3. The power distribution network risk assessment method of claim 1, wherein: The temperature related index includes daily maximum temperature, high temperature duration, temperature change rate; The load related index includes total load, peak load occurrence time, load growth rate; The equipment operation related index includes equipment overload rate; The voltage quality related index includes voltage deviation, voltage fluctuation rate.

4. The power distribution network risk assessment method of claim 1, wherein The equipment overload and failure probability assessment model for obtaining distribution network equipment failure probability is: ； where P failure is the probability of equipment failure, T max is the upper limit of temperature tolerance of the equipment, T(t) is real-time temperature data, and m is a shape parameter.

5. The power distribution network risk assessment method of claim 1, wherein The voltage instability assessment model for obtaining distribution network voltage stability margin is: ； where VSM is the voltage stability margin, V min is the lower limit of the voltage amplitude, V rated is the rated voltage.

6. The power distribution network risk assessment method of claim 1, wherein The risk assessment model is: ； wherein R is a risk value, and R ∈ [0, 1]; P is a predicted load, overload P is an overload probability, failure VSM is a voltage stability margin, and w1, w2, w3, w4 are weights.

7. The distribution network risk assessment method according to claim 6, characterized in that: In the case that the risk value R is less than the medium risk threshold value, it is determined that the risk level of the distribution network is low risk; In the case that the risk value R is greater than or equal to the medium risk threshold value and less than the high risk threshold value, it is determined that the risk level of the distribution network is medium risk; In the case that the risk value R is greater than or equal to the high risk threshold value, it is determined that the risk level of the distribution network is high risk.

8. A power distribution network risk assessment system characterized by Comprise: Extreme high temperature definition module, used for defining extreme high temperature; Evaluation index system construction module, used for constructing evaluation index system, the evaluation index system includes temperature related index, load related index, equipment operation index and voltage quality index; Index acquisition module, for utilizing the index in the evaluation index system to build the temperature and load coupling relationship model of extreme high temperature and distribution network, load surge risk assessment model, equipment overload and failure probability assessment model, voltage instability assessment model to obtain distribution network predicted load, overload probability, equipment failure probability, voltage stability margin respectively; Evaluation module, for utilizing predicted load, overload probability, equipment failure probability, voltage stability margin to build risk assessment model to evaluate the risk of distribution network under extreme high temperature; The temperature and load coupling relationship model for obtaining distribution network predicted load is: ； wherein, for predicting load, T is temperature, t is time, d is date type; β0, β1, β2, β3 are regression coefficients, and ε is a random error term. The load surge risk assessment model for obtaining distribution network overload probability is: ； where P overload is the overload probability, is an indicator function that takes value 1 when the condition is satisfied and 0 otherwise; R k is the k-th region in the distribution network, and N is the total number of regions in the distribution network, is the rated capacity of the k-th region in the distribution network, is the predicted load of the k-th region in the distribution network.

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