Power distribution network risk assessment method and system

Abstract

The invention discloses a power distribution network risk assessment method and system. The method comprises the following steps: defining an extreme high temperature; constructing an evaluation index system; an extreme high temperature and power distribution network temperature and load coupling relation model, a load surge risk evaluation model, an equipment overload and fault probability evaluation model and a voltage instability evaluation model are constructed by using indexes in the evaluation index system, so that a power distribution network predicted load, an overload probability, an equipment fault probability and a voltage stability margin are obtained respectively; and constructing a risk assessment model for assessing the risk of the power distribution network at the extreme high temperature. According to the invention, through cooperation of accurate temperature and load coupling relation modeling and multi-dimensional risk assessment index system construction, the risk assessment accuracy is improved; the problems that in the prior art, load increase in extreme high temperature weather is not accurately estimated, the fault risk of power distribution network equipment in a high temperature environment cannot be effectively evaluated, and the voltage stability problem caused by extreme high temperature is difficult to comprehensively measure are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network risk assessment, and particularly to a distribution network risk assessment method and system considering the coupling relationship between extreme high temperature and load. Background Art

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

[0003] With global warming, extreme high temperature weather is becoming more and more frequent, bringing many challenges to the safe and stable operation of the distribution network. On the one hand, high temperature causes a sharp increase in temperature-controlled loads such as air conditioners, exceeding the expectations of traditional distribution network planning and operation based on average load and normal meteorological conditions, resulting in power supply imbalance. On the other hand, high temperature accelerates equipment aging, affects heat dissipation, and reduces equipment reliability. Previous reliability assessments did not fully consider extreme high temperature conditions, making it difficult to formulate effective maintenance strategies. At the same time, the load fluctuations and equipment parameter changes caused by high temperature also threaten voltage stability, and existing analysis methods do not adequately consider this.

[0004] Currently, there have been many 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 new energy power grid considering high temperature weather. This method constructs an expression for the uncertain electric power on the load side considering high temperature weather, as well as the functional relationship between the output characteristics of photovoltaic and wind turbine units and temperature and the expression for uncertain output; obtains typical operation scenarios of the power system under high temperature weather through Monte Carlo sampling and scenario reduction; establishes two evaluation indexes, namely line overload risk and node overvoltage risk, using risk theory, and conducts system operation simulation and calculation of risk evaluation indexes based on the AC power flow model.

[0005] However, only two evaluation indexes, namely line overload risk and node overvoltage risk, are established in the above-mentioned existing technologies, and various risks that the power grid may face under high temperature weather cannot be comprehensively covered. Summary of the Invention

[0006] The purpose of the present invention is to provide a distribution network risk assessment method and system to partially solve or alleviate the above deficiencies in the existing technology. Through the cooperation of accurate modeling of the coupling relationship between temperature and load and the construction of a multi-dimensional risk assessment index system, the problems in the existing technology, such as inaccurate prediction of load growth under extreme high temperature weather, inability to effectively evaluate the failure risk of distribution network equipment in a high temperature environment, and difficulty in comprehensively measuring the voltage stability problem caused by extreme high temperature, are solved.

[0007] In order to solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions: A first aspect of the present invention is to provide a distribution network risk assessment method, comprising: define extreme heat; Constructing an evaluation index system, the evaluation index system including temperature-related indicators, load-related indicators, equipment operation indicators and voltage quality indicators; Using the indicators in the evaluation index system, we build a temperature-load coupling relationship model for extreme high temperatures and distribution networks, a load surge risk assessment model, an equipment overload and failure probability assessment model, and a voltage instability assessment model to obtain the distribution network's predicted load, overload probability, equipment failure probability, and voltage stability margin. A risk assessment model is constructed using predicted load, overload probability, equipment failure probability, and voltage stability margin to evaluate the risks of distribution networks under extreme high temperatures.

[0008] As an improvement, the extreme high temperature refers to the situation in a specific region and time period where the maximum daily temperature exceeds the multi-year average maximum daily temperature plus k times the standard deviation for n consecutive days, or / and reaches the high temperature critical value and lasts for m days.

[0009] As an improvement, the temperature-related indicators include the maximum daily temperature, the number of days with high temperature, and the temperature change rate; the load-related indicators include the total load, the time of peak load occurrence, and the load growth rate; the equipment operation-related indicators include the equipment overload rate; and the voltage quality-related indicators include the voltage deviation and the voltage fluctuation rate.

[0010] As an improvement, the temperature and load coupling relationship model used to obtain the distribution network forecast load is: ; in, is the predicted load, T is temperature, t is time, d is date type; β0, β1, β2, β3 are regression coefficients, and ε is the random error term.

[0011] As an improvement, the load surge risk assessment model used to obtain the overload probability of the distribution network is: ; Among them, P overload is the overload probability, is an indicator function, which takes the value 1 when the condition is met, otherwise it takes the value 0; R k is the kth region in the distribution network, N is the total number of regions in the distribution network, is the rated capacity of the kth area in the distribution network, is the predicted load of the kth area in the distribution network.

[0012] As an improvement, the equipment overload and fault probability assessment model for obtaining the fault probability of distribution network equipment is as follows: ; where P failure is the equipment fault probability, T max is the upper limit of the equipment's temperature tolerance, T(t) is the real-time temperature data, and m is the shape parameter.

[0013] As an improvement, the voltage instability assessment model for obtaining the voltage stability margin of the distribution network is as follows: ; where VSM is the voltage stability margin, V min is the lower limit of the voltage amplitude, and V rated is the rated voltage.

[0014] As an improvement, the risk assessment model is as follows: ; where R is the risk value, and R ∈ [0, 1]; is the predicted load, P overload is the overload probability, P failure is the equipment fault probability, VSM is the voltage stability margin, and w1, w2, w3, and w4 are weights.

[0015] 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 a low risk; when 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 a medium risk; when 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 a high risk.

[0016] The present invention also provides a distribution network risk assessment system, including: An extreme high temperature definition module for defining extreme high temperature; An evaluation index system construction module for constructing an evaluation index system, where the evaluation index system includes temperature-related indexes, load-related indexes, equipment operation indexes, and voltage quality indexes; An index acquisition module for using the indexes in the evaluation index system to construct a coupling relationship model of extreme high temperature and the temperature and load of the distribution network, a load surge risk assessment model, an equipment overload and fault probability assessment model, and a voltage instability assessment model to respectively obtain the predicted load, overload probability, equipment fault probability, and voltage stability margin of the distribution network; An evaluation module for using the predicted load, overload probability, equipment fault probability, and voltage stability margin to construct a risk assessment model for evaluating the risk of the distribution network under extreme high temperature.

[0017] Beneficial effects: The present invention constructs a comprehensive evaluation index system including temperature, load, equipment operation, and voltage quality. Through temperature indicators such as daily maximum temperature and the number of consecutive high-temperature days, as well as load indicators such as total load and the occurrence time of peak load, the operation status of the distribution network is evaluated from multiple dimensions. By deeply evaluating the equipment overload rate and failure rate, potential equipment overload hazards can be detected in a timely manner and the risk of failure can be predicted, providing a basis for equipment maintenance and upgrading. At the same time, voltage stability is evaluated through indicators such as voltage deviation and voltage fluctuation rate to ensure the normal operation of user electrical equipment.

[0018] In addition, the present invention comprehensively evaluates and finely classifies the risks of the distribution network, such as low, medium, and high risks, and formulates corresponding countermeasures according to different risk levels, such as optimizing power distribution and orderly power consumption, which has strong practicability. The risk assessment results are output in the form of intuitive and easy-to-understand charts or detailed reports, providing clear decision-making basis for distribution network operation management personnel and facilitating timely measures to ensure the safe and stable operation of the power grid. Description of the drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale. Obviously, the following-described drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of Embodiment 1 of the present invention; Figure 2 It is a structural diagram of Embodiment 2 of the present invention. Detailed implementation manners

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] In this article, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of describing the present invention and have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0023] In this text, terms such as "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0024] In this text, unless otherwise clearly defined and limited, terms such as "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" 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, and can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0025] As used herein, "and / or" includes any and all combinations of one or more of the listed related items.

[0026] As used herein, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0027] Embodiment 1: As Figure 1 shown, this embodiment provides a method for risk assessment of a distribution network, including: S1 Define extreme high temperature.

[0028] Specifically, in this embodiment, the definition of extreme high temperature is: within a specific region and time period, the daily maximum temperature continuously exceeds the multi-year average daily maximum temperature plus k times the standard deviation for n days, or / and, reaches the high temperature critical value and lasts for m days.

[0029] An abnormal high temperature climate phenomenon where the temperature is significantly higher than the average temperature level in the same historical period of this region and lasts for a certain duration. Let the multi-year average daily maximum temperature be , and the standard deviation be . When the daily maximum temperature of consecutive days satisfies , where is an empirical coefficient, generally taking 2 - 3, and ), or / and, reaches and exceeds the established high temperature critical value (such as and above and lasts for When the daily maximum temperature exceeds the average daily maximum temperature of the same period in previous years plus k times the standard deviation for n consecutive days (n is usually 3 - 5 days), it is determined that the area is in an extreme high - temperature weather state. Such extreme high - temperature situations pose a serious threat to the stable operation of the distribution network, and it is urgent to deeply study its impact and conduct effective evaluation.

[0030] Suppose the average daily maximum temperature of a certain city over the years is 30°C, the standard deviation is 2°C, and k is 2.5. If the daily maximum temperatures of this city for 5 consecutive days are 36°C, 37°C, 38°C, 37°C, and 36°C respectively. Calculate the value of the average daily maximum temperature plus k times the standard deviation: 30 + 2.5×2 = 35°C, and the daily maximum temperatures for these 5 consecutive days all exceed 35°C, meeting the condition of "the daily maximum temperature exceeds the average daily maximum temperature of the same period in previous years plus k times the standard deviation for n consecutive days", so it can be determined that this city is in extreme high - temperature weather for these 5 days.

[0031] Or set the high - temperature critical value as 35°C and m as 3 days. If the daily maximum temperatures of another city for 4 consecutive days are 35°C, 36°C, 37°C, and 35°C respectively, and the daily maximum temperatures for these 4 days all reach or exceed 35°C and the continuous number of days exceeds 3 days, according to the definition, this city is in extreme high - temperature weather for these 4 days.

[0032] S2 Construct an evaluation index system, and the evaluation index system includes temperature - related indicators, load - related indicators, equipment operation indicators, and voltage quality indicators.

[0033] The temperature - related indicators include: Daily maximum temperature: Precisely record the highest value of the daily temperature, which plays a key role in grasping the occurrence frequency and intensity of extreme high - temperature weather and is important basic data for subsequent analysis; Duration of high - temperature days: Set the high - temperature threshold as and count the number of consecutive days that meet The number of days reflects the duration of extreme high - temperature events and is of great significance for testing the durability of distribution network equipment; Temperature change rate: Defined as where represents the temperature change within the time interval (such as per hour or per day). This indicator helps to understand the fluctuation characteristics of temperature, and rapid temperature fluctuations will exacerbate the thermal stress changes of distribution network equipment, thereby affecting the reliability of the equipment.

[0034] The load - related indicators include: Total load: Real - time monitor the overall power load scale of the distribution network, which intuitively shows the power supply demand situation faced by the power system. The dynamic change trend of the total load in an extreme high - temperature environment is one of the core elements for risk assessment; Peak load occurrence time: Determine the exact moment when the daily or weekly load reaches its peak , through the correlation analysis with temperature data, it can reveal the time correlation between high temperature and load peak, providing key guidance for the early implementation of power distribution and equipment maintenance strategies; Load growth rate: The calculation method is , where and are the load values at different time periods respectively. Especially by comparing the load growth situations during extreme high temperature periods and non-high temperature periods, it can clearly quantify the impact degree of temperature on load growth, which has an indispensable value for accurately predicting the future load trend.

[0035] Equipment operation indicators include: Equipment overload rate: For line or transformer equipment in the distribution network, let its actual load be , and the rated load be , then the equipment overload rate , when , the equipment is in an overloaded condition. Continuously monitoring the equipment overload rate can timely detect the potential equipment overload hazards induced by the sudden increase in load caused by high temperature, providing a key basis for equipment maintenance and upgrade decisions; Equipment failure rate: Let represent the equipment failure rate, which is calculated based on the number of equipment failures under high temperature conditions and the total operation duration of the equipment, that is . Combining historical temperature and equipment operation data, constructing a correlation model between equipment failure rate and temperature, and using this to predict the equipment failure risk in different temperature environments, so as to arrange equipment inspection plans and prepare equipment replacement plans in advance to ensure the reliability of the distribution network.

[0036] Voltage quality indicators include: Voltage deviation: Defined as , where is the actual voltage of each node in the distribution network, is the rated voltage. In extreme high temperature weather, the violent fluctuation of the load is likely to cause the voltage deviation to exceed the allowable limit, which has an adverse impact on the normal operation of user electrical equipment. Therefore, voltage deviation is one of the key indicators to measure power supply quality; Voltage fluctuation rate: Let be the voltage values at different times, be the average voltage over a period of time, then the voltage fluctuation rate . The load changes caused by high temperature may lead to an increase in the voltage fluctuation rate. By monitoring this indicator, the voltage stability of the distribution network under extreme high temperature conditions can be evaluated, providing a decision-making reference for taking voltage regulation measures.

[0037] Temperature-related indicators (daily maximum temperature, number of high-temperature days, and temperature change rate) provide fundamental temperature data for the temperature-load coupling model, the equipment overload and failure probability assessment model, and the voltage instability assessment model. For example, in the temperature-load coupling model, data such as daily maximum temperature and temperature change rate serve as key independent variables in model construction and calculation to quantify 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 the load surge risk assessment model and the temperature-load coupling model. 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 the lines and transformers. Equipment operating indicators such as equipment overload rate and equipment failure rate provide data for the equipment overload and failure probability assessment model, which is used to construct 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 basis for the voltage instability assessment model. The voltage stability of the distribution network under extreme high temperatures is evaluated by combining these indicators through power flow calculation. The multi-dimensional indicator system determines that subsequent modeling needs to consider the problem from multiple angles. In the risk assessment and classification steps, based on the calculated values of various evaluation indicators (these values are obtained by each model operation, and the model operation is 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 evaluated and finely classified.

[0038] S3 data collection and preprocessing.

[0039] Extensive collection of historical temperature data, covering daily maximum temperatures over many years , minimum temperature , average temperature and temperature change details, and compare them with the distribution network operation data (including load , device status data, voltage data) for precise matching, where Represents the time index of historical data. For the collected data, data cleaning technology is 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, integrity and reliability of the data.

[0040] S4 uses the indicators in the evaluation index system to construct a temperature-load coupling relationship model for extreme high temperatures and distribution networks, a load surge risk assessment model, an equipment overload and failure probability assessment model, and a voltage instability assessment model to obtain the distribution network's predicted load, overload probability, equipment failure probability, and voltage stability margin respectively.

[0041] S41 uses the statistical method of multiple linear regression analysis to construct the mathematical function relationship between temperature and load. Assume the predicted load has a linear correlation with temperature T and other influencing factors (such as time factor t and date type factor d), and the temperature-load coupling relationship model for obtaining the predicted load of the distribution network is constructed as follows: , where β0, β1, β2, and β3 are regression coefficients, and ε is a random error term.

[0042] By fitting the historical data, the least squares method parameter estimation method is used to determine the values of each coefficient, so as to accurately quantify the comprehensive influence of temperature and other factors on the load. For example, if only the temperature factor is considered and the fitting result shows (unit: ), it means that for every 1°C increase in temperature, the load will increase . On this basis, according to the definition of extreme high temperature, the temperature threshold T threshold exceeding the extreme high temperature is determined. When the real-time temperature exceeds this threshold, the growth rate of the load can be accurately predicted according to the above function relationship, providing a scientific basis for the load prediction and power dispatching of the distribution network.

[0043] S42 In view of the fact that the extensive use of air conditioning refrigeration equipment under extreme high temperature weather will cause the load in a specific area to rise sharply and concentrate, which may lead to the situation of line or transformer overload, a load surge risk assessment model is constructed.

[0044] First, use the clustering analysis or regional division algorithm to divide the distribution network into several different regions , determine the load characteristics (load function related to temperature) and temperature sensitivity parameter of each region. Then, combine the temperature prediction data and the above temperature-load coupling relationship model to predict the load change situation of each region under extreme high temperature weather , and compare the predicted load with the rated capacity of the line and transformer to calculate the overload probability. Specifically, the load surge risk assessment model for obtaining the overload probability of the distribution network is: ; where P overload is the overload probability, is an indicator function, which takes the value of 1 when the condition is satisfied, otherwise 0; R k is the kth region in the distribution network, N is the total number of regions in the distribution network, is the rated capacity of the kth region in the distribution network, is the predicted load of the kth region in the distribution network.

[0045] By evaluating the overload probability, coping strategies can be formulated in advance, such as optimizing the power distribution plan and implementing orderly power consumption measures, effectively reducing the potential risks brought by the sudden increase in load.

[0046] S43 For the equipment in the distribution network, long-term operation in a high-temperature environment will cause its temperature to rise, thereby reducing its service life and even causing failures.

[0047] When constructing the evaluation model of equipment overload and failure probability, first determine the upper limit of the temperature tolerance of the equipment based on the thermophysical properties, heat dissipation mechanism and material properties of the equipment , and then, combined with the real-time temperature data and the operating time of the equipment , use the life distribution model (such as Weibull distribution) in reliability theory to calculate the failure probability of the equipment at high temperature. Specifically, the evaluation model of equipment overload and failure probability for obtaining the failure probability of distribution network equipment is: ; where P failure is the equipment failure probability, T max is the upper limit of the temperature tolerance of the equipment, T(t) is the real-time temperature data; m is the shape parameter, which can be determined by fitting analysis of the equipment historical failure data.

[0048] By evaluating the equipment failure probability, the maintenance plan and replacement strategy of the equipment can be reasonably planned, effectively improving the overall reliability of the distribution network.

[0049] S44 Under extreme high-temperature conditions, the large fluctuations in load will cause the instability of the grid voltage and affect the power supply quality. When constructing the voltage instability evaluation model, the power flow calculation method is adopted, combined with the coupling relationship between temperature and load, to simulate the power flow distribution trend of the distribution network under different temperature conditions and calculate the voltage amplitude of each node and phase angle , and evaluate the voltage stability of the distribution network under extreme high-temperature weather according to the voltage stability criterion (lower limit of voltage amplitude ).

[0050] Specifically, define the voltage stability margin index ; use the formula:

[0051] to calculate the voltage stability margin; where, VSM is the voltage stability margin, V min is the lower limit of voltage amplitude, V rated is the rated voltage.

[0052] When the VSM is less than a specific 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 the reactive power compensation device , changing the tap position of the transformer , to ensure the stability of the power supply quality.

[0053] S5 constructs a risk assessment model using predicted load, overload probability, equipment failure probability, and voltage stability margin to evaluate the risk of the distribution network under extreme high temperatures.

[0054] S51 imports the real-time monitored temperature data , the real-time operation data of the distribution network (including load , equipment status data, voltage data ), and the preprocessed historical data (covering historical temperature data and historical operation data of the distribution network) into the constructed evaluation model system.

[0055] S52 Based on the input data, using the above-established temperature-load coupling relationship model, load surge risk assessment model, equipment overload and failure probability assessment model, and voltage instability assessment model, the values of various evaluation indicators are obtained, including the load prediction value , equipment overload probability , equipment failure probability , voltage stability margin .

[0056] S53 Since the dimensions and value ranges of each parameter are different, standardization is required. The load prediction value is mapped to the interval [0, 1] through normalization; the equipment overload probability and the equipment failure probability themselves are in [0, 1] and do not require additional processing; the voltage stability margin is standardized to the interval [0, 1] according to a specific formula.

[0057] S54 constructs a risk assessment model.

[0058] The risk assessment model is as follows: ; where R is the risk value, and R ∈ [0, 1]; is the predicted load, P overload is the overload probability, P failure is the equipment failure probability, VSM is the voltage stability margin, and w1, w2, w3, w4 are weights. The larger the R value, the higher the risk.

[0059] S55 Determine the parameter weights. In this embodiment, the analytic hierarchy process (AHP) is adopted. Through the comparison of the relative importance of the four parameters in pairs by experts in the power field, a judgment matrix is constructed. Then, the maximum eigenvalue of the judgment matrix and its corresponding eigenvector are calculated, and after normalization, the weight vectors w1, w2, w3, and w4 are obtained.

[0060] S56 Based on the calculated evaluation indicators, referring to the pre-set risk level classification criteria, comprehensively evaluate and finely classify 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. When the risk value R is less than the medium risk threshold, it is determined that the risk level of the distribution network is low risk; when the risk value R is greater than or equal to the medium risk threshold and less than the high risk threshold, it is determined that the risk level of the distribution network is medium risk; when the risk value R is greater than or equal to the high risk threshold, it is determined that the risk level of the distribution network is high risk.

[0061] When a certain indicator exceeds its corresponding high risk threshold, it indicates that the distribution network is in a relatively high risk state, and it is urgent to initiate the corresponding risk response plan in a timely manner.

[0062] S57 Output the results of the risk assessment in an intuitive and easy-to-understand form (such as chart display, detailed report), providing a strong decision-making basis for the operation and management personnel of the distribution network. According to the risk level and the details of specific evaluation indicators, customize the corresponding risk response strategies, such as flexibly adjusting the power dispatching plan, strengthening the inspection and maintenance efforts of equipment, and implementing effective demand-side management measures, to ensure that the distribution network can operate safely, stably, and reliably under extreme high temperature weather and continuously provide high-quality power services for users.

[0063] Embodiment 2: As Figure 2 shown, this embodiment also provides a distribution network risk assessment system, including: An extreme high temperature definition module for defining extreme high temperature; An evaluation index system construction module for constructing an evaluation index system, where the evaluation index system includes temperature-related indicators, load-related indicators, equipment operation indicators, and voltage quality indicators; An index acquisition module for using the indicators in the evaluation index system to construct a temperature and load coupling relationship model between extreme high temperature and the distribution network, a load surge risk assessment model, an equipment overload and failure probability assessment model, and a voltage instability assessment model, so as to obtain the predicted load, overload probability, equipment failure probability, and voltage stability margin of the distribution network respectively; An evaluation module for using the predicted load, overload probability, equipment failure probability, and voltage stability margin to construct a risk assessment model to evaluate the risk of the distribution network under extreme high temperature.

[0064] The implementation of the present invention is demonstrated through two examples.

[0065] Example 1: Risk assessment of the distribution network in the urban commercial area.

[0066] Background information: In the commercial area of a certain city, extremely high temperature weather often occurs in summer. There are many high-rise buildings, dense population, frequent commercial activities, and a large number of refrigeration equipment such as air conditioners are used, resulting in a great demand for electricity and obvious fluctuations. The distribution network coverage includes multiple commercial buildings such as large shopping malls, office buildings, and hotels. The lines and equipment are relatively complex, and the power supply reliability requirements are high.

[0067] Assessment process: 1. Data collection: Historical temperature data of the commercial area in the past 10 years was collected, including information such as the daily maximum temperature, minimum temperature, average temperature, and temperature change. At the same time, the operation data of the distribution network in the same period was obtained, such as the hourly load data, the load conditions of the main lines and transformers, the voltage monitoring data, and the equipment failure records.

[0068] 2. Model establishment: A coupling relationship model between temperature and load was established by using the regression analysis method. Through the fitting of historical data, the functional relationship between the load ( ) and the temperature ( ) was determined as (unit: ), which means that for every increase in temperature, the load will increase by . At the same time, according to the thermal characteristics and operation data of the equipment, an evaluation model for equipment overload and failure probability was constructed, and the temperature tolerance limit of the transformer was determined to be , and the relationship between the equipment failure rate and temperature was obtained through Weibull distribution fitting. In addition, a voltage instability evaluation model was established by using the power flow calculation method, considering the influence of temperature on parameters such as line resistance and load power.

[0069] 3. Risk assessment: Before the arrival of a predicted extremely high temperature weather, according to the temperature data in the weather forecast, the risk of the distribution network was evaluated by using the above models. It was predicted that the maximum temperature on that day would reach . According to the temperature-load coupling relationship model, it was estimated that the maximum load on that day would increase by compared with normal weather. Through the equipment overload and failure probability evaluation model, it was calculated that the overload probability of some transformers would reach , and the failure probability also increased significantly. The voltage instability evaluation model showed that the voltage stability margin in some areas would drop to , below the safety threshold, there was a risk of voltage instability.

[0070] 4. Results and Measures: Based on the risk assessment results, the power department took a series of measures in advance. The power dispatching plan was adjusted, and additional power resources were allocated from surrounding areas to meet the demand for load growth. Key monitoring was carried out on transformers that might be overloaded, and emergency repair teams were arranged to standby. At the same time, by adjusting the reactive power compensation device, the voltage stability of the power grid was improved. During the extremely high-temperature weather, although the distribution network endured great load pressure, through the early risk assessment and response measures, large-scale power outages were successfully avoided, ensuring the normal power supply in the commercial area.

[0071] Example 2: Risk assessment of the distribution network in an industrial cluster.

[0072] Background Information: An industrial cluster has many factory enterprises with diverse production processes and a continuous and large demand for electricity. In the extremely high-temperature weather in summer, the heat dissipation demand of production equipment in factories increases, and at the same time, cooling equipment such as air conditioners in some workshops is also heavily used, resulting in complex load changes in the distribution network. Moreover, the distribution network equipment in the industrial cluster has been operating at high load for a long time, with poor tolerance to high temperatures and prominent equipment aging problems.

[0073] Assessment Process: 1. Data Collection: Detailed temperature data and distribution network operation data for the past 5 years in the industrial cluster were collected, including the electricity load curves of each factory, equipment types and parameters, line loss conditions, etc. The data was classified and sorted in detail to better analyze the relationship between the load characteristics of different factories and temperature.

[0074] 2. Model Establishment: Multiple linear regression analysis was used, considering factors such as temperature, time, working day / holiday, etc., to establish a more complex load prediction model. For example, for a large steel plant, the relationship between its load ( ), temperature ( ), and time ( ) is (unit: and ). Regarding equipment overload and failure problems, combined with the actual operating conditions of the equipment and the thermal aging model, an equipment reliability assessment model was established to determine the failure rate change curve of key equipment at different temperatures. Using power system simulation software, a distribution network power flow calculation model considering the influence of temperature was constructed to evaluate voltage stability and power loss.

[0075] 3. Risk Assessment: During an extreme high-temperature weather event, the air temperature and the operating data of the distribution network were monitored in real time and input into the established model for risk assessment. It was found that as the temperature increased, the load growth of some factories exceeded expectations, resulting in overload conditions in some lines and transformers, with the overload rate reaching . At the same time, through the equipment reliability assessment model, it was predicted that the failure probability of some key equipment increased under high temperature by . The voltage stability analysis showed that the voltage of some nodes fluctuated significantly, and the voltage deviation exceeded the allowable range.

[0076] 4. Results and Measures: Based on the risk assessment results, emergency power curtailment measures were taken, requiring some non-critical production processes to suspend or adjust production times to reduce the load demand. Emergency capacity expansion and heat dissipation treatments were carried out on the overloaded lines and transformers, such as increasing fan cooling and adjusting the transformer tap. The inspection frequency of key equipment was strengthened to promptly detect and handle potential fault hazards. Through these measures, the operating pressure of the distribution network under extreme high-temperature weather was effectively alleviated, the basic production electricity demand of the industrial concentration area was guaranteed, the economic losses caused by power outages were reduced, and at the same time, important reference basis was provided for the subsequent upgrade and transformation of the distribution network.

[0077] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including that element.

[0078] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, 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 for causing a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0079] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A method for risk assessment of a distribution network, characterized in that Including: Defining extreme high temperature; Constructing an evaluation index system, which includes temperature-related indexes, load-related indexes, equipment operation indexes and voltage quality indexes; Using the indexes in the evaluation index system to construct a temperature-load coupling relationship model between extreme high temperature and distribution network, a load surge risk assessment model, an equipment overload and fault probability assessment model, and a voltage instability assessment model, so as to obtain the predicted load, overload probability, equipment fault probability, and voltage stability margin of the distribution network respectively; Using the predicted load, overload probability, equipment fault probability, and voltage stability margin to construct a risk assessment model for assessing the risk of the distribution network under extreme high temperature.

2. The method for risk assessment of a distribution network according to claim 1, wherein: The extreme high temperature means that within a specific region and time period, the daily maximum temperature continuously 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 m days.

3. The risk assessment method for a distribution network according to claim 1, wherein: The temperature-related indexes include the daily maximum temperature, the number of days of high temperature persistence, and the temperature change rate; the load-related indexes include the total load, the time of occurrence of the peak load, and the load growth rate; the equipment operation-related indexes include the equipment overload rate; the voltage quality-related indexes include the voltage deviation and the voltage fluctuation rate.

4. The method and system for risk assessment of a distribution network according to claim 1, characterized in that The temperature-load coupling relationship model for obtaining the predicted load of the distribution network is: ； wherein, is the predicted load, T is the temperature, t is the time, and d is the date type; β0, β1, β2, and β3 are regression coefficients, and ε is a random error term.

5. A method and system for risk assessment of a distribution network according to claim 1, characterized in that The load surge risk assessment model for obtaining the overload probability of the distribution network is: ； Among them, P overload is the overload probability, is the indicator function, which takes the value of 1 when the condition is satisfied and 0 otherwise; R k is the k-th area in the distribution network, N is the total number of areas in the distribution network, is the rated capacity of the k-th area in the distribution network, is the predicted load of the k-th area in the distribution network.

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

7. A method for risk assessment of a distribution network according to claim 1, characterized in that The voltage instability assessment model for obtaining the voltage stability margin of the distribution network is: ； Among them, VSM is the voltage stability margin, V min is the lower limit of voltage amplitude, V rated is the rated voltage.

8. The method for risk assessment of a distribution network according to claim 1, characterized in that The risk assessment model is: ； Among them, R is the risk value, and R ∈ [0, 1]; is the predicted load, P overload is the overload probability, P failure is the equipment failure probability, VSM is the voltage stability margin, and w1, w2, w3, and w4 are weights.

9. A method for assessing the risk of a distribution network according to claim 8, characterized in that: When the risk value R is less than the medium risk threshold, it is determined that the risk level of the distribution network is low risk; When the risk value R is greater than or equal to the medium risk threshold and less than the high risk threshold, it is determined that the risk level of the distribution network is medium risk; When the risk value R is greater than or equal to the high risk threshold, it is determined that the risk level of the distribution network is high risk.

10. A distribution network risk assessment system, characterized in that Including: An extreme high temperature definition module for defining extreme high temperature; An evaluation index system construction module for constructing an evaluation index system, which includes temperature-related indexes, load-related indexes, equipment operation indexes and voltage quality indexes; An index acquisition module for using the indexes in the evaluation index system to construct a temperature-load coupling relationship model between extreme high temperature and distribution network, a load surge risk assessment model, an equipment overload and fault probability assessment model, and a voltage instability assessment model, so as to obtain the predicted load, overload probability, equipment fault probability, and voltage stability margin of the distribution network respectively; An evaluation module for using the predicted load, overload probability, equipment fault probability, and voltage stability margin to construct a risk assessment model for assessing the risk of the distribution network under extreme high temperature.

Images