Dynamic evaluation method and system for operation risk of active power distribution network, equipment and medium

Abstract

The invention provides a dynamic evaluation method and system for the operation risk of an active power distribution network, equipment and a medium, and relates to the technical field of power distribution network operation analysis and control. The method comprises the following steps: generating a day-ahead wind-light load output scene set and calculating the occurrence probability of each operation scene based on wind-light load day-ahead output prediction data and a wind-light load prediction error probability distribution model in the active power distribution network; carrying out load flow calculation on each operation scene, and determining the occurrence possibility and the occurrence severity of a risk event corresponding to each risk index in the operation risk assessment index system; calculating the value of each risk index based on the occurrence probability, the occurrence possibility and the occurrence severity of each operation scene; and weighting each risk index based on the importance degree of each spatial hierarchy, and dynamically evaluating the day-ahead operation risk of the active power distribution network based on the value and weight of each risk index. The problem that the risk assessment accuracy and reliability of the existing distributed power supply access power distribution network are low is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation analysis and control technology, specifically to a dynamic assessment method, system, equipment, and medium for active power distribution network operation risk. Background Technology

[0002] With the large-scale application of distributed resources such as distributed photovoltaics and energy storage devices, the operational risks caused by random switching, voltage exceeding limits, and line overload have increased significantly. Simultaneously, the large-scale integration of nonlinear loads and impulsive loads (such as industrial electric furnaces and electric vehicle charging loads) into the distribution network further exacerbates problems such as voltage fluctuations and harmonic pollution, posing multiple challenges to the stability and safety of the distribution network. Therefore, accurate assessment of distribution network operational risks has become a core requirement for ensuring the safe and stable operation of the distribution network.

[0003] Currently, most risk assessment techniques for distributed generation (DG) integration into distribution networks employ analytical methods (such as network methods and state-space methods) and simulation methods (such as Monte Carlo simulation and Bayesian networks), enabling quantitative analysis of system risks. In terms of model building, dynamic simulation techniques are used to model DG integration scenarios, combined with stability criteria and disturbance analysis, to indirectly assess the grid's capacity to accommodate DG. However, these methods do not consider the cascading effects of uncertainties in renewable energy equipment and loads on system failures, making risk assessments difficult to adapt to the dynamic realities of distribution networks, resulting in low accuracy and reliability. Summary of the Invention

[0004] To overcome the problem of low accuracy and reliability in risk assessment of distributed power generation access to the distribution network, this invention provides a dynamic assessment method, system, equipment, and medium for active distribution network operation risk.

[0005] On the one hand, this invention provides a dynamic assessment method for the operational risks of active distribution networks, including: Based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, a set of day-ahead wind and solar load power output scenarios is generated and the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load power output scenarios is calculated. For each assessment period before the day, power flow calculation is performed on each operating scenario in the set of wind and solar load output scenarios before the day. Based on the power flow calculation results, the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system are determined. The values ​​of each risk indicator for each assessment period are calculated based on the probability of occurrence of each operational scenario and the likelihood and severity of the risk events corresponding to each risk indicator. Based on the importance of each spatial level in the aforementioned operational risk assessment index system, each risk index is weighted, and based on the value and weight of each risk index in each assessment period, the day-ahead operational risk of the active distribution network is dynamically assessed. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

[0006] Optionally, the multiple time scales of the operation risk assessment indicator system include real-time, short-term, and medium-term; the multiple spatial levels of the operation risk assessment indicator system include distribution area, feeder, and substation. The operational risk assessment index system includes real-time risk indicators for transformer substations, short-term risk indicators for transformer substations, medium-term risk indicators for transformer substations, real-time risk indicators for feeders, short-term risk indicators for feeders, medium-term risk indicators for feeders, and real-time risk indicators for substations, short-term risk indicators for substations, and medium-term risk indicators for substations.

[0007] Optionally, the transformer area-real-time risk indicators include transformer area heavy overload risk indicators and transformer area voltage exceeding limit risk indicators; the transformer area-short-term risk indicators include three-phase imbalance risk indicators; and the transformer area-medium-term risk indicators include equipment aging risk indicators. The feeder-real-time risk indicators include feeder power flow exceeding limit risk indicators and feeder voltage exceeding limit risk indicators; the feeder-short-term risk indicators include feeder fault risk indicators; and the feeder-medium-term risk indicators include feeder power flow fluctuation entropy risk indicators. The substation-real-time risk indicators include main transformer bus voltage exceeding limit risk indicators and main transformer overload risk indicators; the substation-short-term risk indicators include main transformer bus short-circuit current risk indicators and main transformer reactive power compensation margin risk indicators; and the substation-medium-term risk indicators include main transformer N-1 throughput risk indicators. The three-phase imbalance risk index is used to characterize the degree of imbalance of three-phase current / voltage in the transformer area. The equipment aging risk index is used to characterize the probability of equipment failure caused by the aging rate based on the equipment's years of operation and environmental conditions. The feeder power flow over-limit risk index is used to characterize the degree to which the active / reactive power flow of the line exceeds the thermal stability. The feeder failure risk index is used to characterize the feeder failure rate predicted based on historical feeder failure data and environmental factors. The feeder power flow fluctuation entropy risk index is used to characterize the power flow fluctuation uncertainty and disturbance rejection capability of the active distribution network. Its calculation formula is as follows: ; in, This represents the entropy of the feeder power flow fluctuation. The amplitude of the tidal fluctuation is in the first... f The probability distribution of each amplitude interval. The larger the value, the greater the uncertainty of the current fluctuation. The short-circuit current risk index of the main transformer bus is used to characterize the probability that the short-circuit current exceeds the breaking capacity of the circuit breaker. The main transformer reactive power compensation margin risk index is used to characterize the voltage regulation capability of the main transformer.

[0008] Optionally, the wind and solar load prediction error probability distribution model includes a wind power output prediction error probability distribution model, a photovoltaic power output prediction error probability distribution model, and a load prediction error probability distribution model; the step of generating a set of day-ahead wind and solar load output scenarios based on day-ahead wind and solar load output prediction data in the active distribution network and the wind and solar load prediction error probability distribution model includes: To address the historical power output errors of wind and solar loads in active power distribution networks, we construct probability distribution models for wind power output prediction errors, solar power output prediction errors, and load prediction errors. Based on the probability distribution models of wind power output prediction error, photovoltaic power output prediction error, and load prediction error, Monte Carlo sampling is used to generate wind power output prediction error, photovoltaic power output prediction error, and load prediction error. The wind power output prediction error, photovoltaic power output prediction error and load prediction error are superimposed on the corresponding day-ahead wind power output prediction data, day-ahead photovoltaic power output prediction data and day-ahead load prediction data to generate a day-ahead wind and solar load output scenario set. The daytime wind and solar power output scenario set includes operation scenarios under different combinations of wind and solar power output and load demand.

[0009] Optionally, after generating the day-ahead wind, solar, and load power output scene set, the following may also be included: The daytime wind and solar power output scenario set is reduced based on the K-medoids clustering algorithm to obtain a typical daytime wind and solar power output scenario set.

[0010] Optionally, the probability of occurrence of each operating scenario in the day-ahead wind and solar power output scenario set is as follows:

[0011] in, Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. Let t be the total number of samples in the set of day-ahead wind-solar-load power output scenarios predicted at time t; The first of the predicted day-ahead wind, solar and load power output scenarios at time t. lThe membership relationship between a sample and the class centered on the running scenario s, where a value of 1 indicates membership and a value of 0 indicates non-membership.

[0012] Optionally, the power flow calculation results include node voltage and line power data for each operating scenario; based on the power flow calculation results, the probability and severity of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system are determined, including: For each assessment period, based on the node voltage and line power data of each operating scenario and the definition of each risk indicator in the operating risk assessment indicator system, it is calculated whether the risk event corresponding to each risk indicator has occurred. Based on the node voltage and line power data for each operating scenario and the severity function of the risk events corresponding to each risk indicator, the severity of the occurrence of the risk events corresponding to each risk indicator is calculated. Among them, the severity function of each risk indicator is used to measure the degree to which the corresponding risk event exceeds the limit.

[0013] Optionally, the values ​​for each risk indicator are as follows: , ; , ; in, Let be the value of the voltage-related risk indicator for the i-th node predicted at time t. Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. This indicates whether the risk event corresponding to the voltage risk indicator has occurred as predicted at time t. A value of 1 indicates that it has occurred, and a value of 0 indicates that it has not occurred. Let be the voltage of node i in the predicted operating scenario s at time t. For voltage limits, The set of day-ahead wind, solar and load power output scenarios predicted at time t; This is a severity function for risk events corresponding to voltage-related risk indicators; e Represents the natural base; Let be the predicted power risk index value for line j at time t. This indicates whether the risk event corresponding to other risk indicators has occurred at time t, with a value of 1 indicating occurrence and a value of 0 indicating non-occurrence. Let t be the active power flowing through line j under the predicted operating scenario s at time t. Here is the power limit for line j; This is a severity function for the risk events corresponding to power-related risk indicators.

[0014] Optionally, the weights of each risk indicator include combined weights and hierarchical weights; the weights of each risk indicator are assigned based on the importance of each spatial level in the operational risk assessment indicator system, including: The objective weights of each risk indicator are calculated based on the entropy weight method, and the subjective weights of each risk indicator are determined based on the analytic hierarchy process. For each risk indicator, the corresponding subjective weight is adjusted using the objective weight of the risk indicator to obtain the combined weight of the risk indicators; Based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, the hierarchical weight of each risk indicator within each spatial level is determined.

[0015] Optionally, based on the values ​​and weights of each risk indicator in each assessment period, the day-ahead operational risk of the active distribution network is dynamically assessed, including: For each spatial level in each assessment period, the risk value of the spatial level in each assessment period is determined based on the ratio of the weighted risk value of each risk indicator within the spatial level to the theoretical maximum risk value. Based on the hierarchical weights and risk values ​​of each spatial level, the comprehensive risk value of the active distribution network for each assessment period is determined. Based on the time-series variation characteristics of the comprehensive risk value of the active distribution network over multiple assessment periods, the daily operational risk changes of the active distribution network are dynamically assessed. The total risk of the active distribution network is obtained by superimposing the comprehensive risk values ​​of the active distribution network for each assessment period before the current day; the risk level of the active distribution network is assessed based on the total risk of the active distribution network before the current day. The weighted risk value of each risk indicator within the spatial hierarchy is determined based on the value of each risk indicator within the spatial hierarchy and the combined weight of each risk indicator.

[0016] Optionally, after dynamically assessing the day-ahead operational risks of the active distribution network, the following may also be included: Overall risk warning for active distribution networks is conducted based on the day-ahead risk level of the active distribution network.

[0017] Optionally, after dynamically assessing the day-ahead operational risks of the active distribution network, the following may also be included: For each spatial level, the total risk of the spatial level is obtained by summing the risk values ​​of the spatial level over the current assessment period; based on the total risk of the spatial level, the risk level of each spatial level is determined. Visualized early warning of risk areas in active power distribution networks is conducted based on the current risk levels at each spatial level.

[0018] On the other hand, the present invention also provides a dynamic assessment system for the operational risks of active distribution networks, comprising: The scenario generation module is used to generate a set of day-ahead wind and solar load output scenarios based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, and to calculate the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load output scenarios. The risk factor calculation module is used to perform power flow calculations for each operating scenario in the daytime wind and solar load output scenario set for each assessment period before the daytime, and to determine the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system based on the power flow calculation results. The risk indicator calculation module is used to calculate the value of each risk indicator for each assessment period based on the probability of occurrence of each operational scenario and the probability and severity of the risk events corresponding to each risk indicator. The risk assessment module is used to assign weights to each risk indicator based on the importance of each spatial level in the operational risk assessment indicator system, and to dynamically assess the day-ahead operational risk of the active distribution network based on the value and weight of each risk indicator in each assessment period. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

[0019] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method described in any of the foregoing is implemented.

[0020] On the other hand, the present invention also provides a readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the method described in any one of the above.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a dynamic assessment method and system for the operational risks of active power distribution networks. By integrating a probability distribution model of wind and solar load prediction errors to generate a set of day-ahead wind and solar load output scenarios and calculating the probability of each scenario, the uncertainty of renewable energy output and load in the distribution network is quantified in the form of probabilistic scenarios, thus improving the accuracy of the assessment from the source. By constructing a spatiotemporally coupled risk indicator system with multiple time scales and spatial levels, the coupled impact of renewable energy output volatility and load temporal characteristics is quantified, ensuring the accuracy of the assessment results. Weighting each risk indicator based on the importance of each spatial level reflects the "bottom-up" risk transmission characteristics of the distribution network, making the assessment results closer to the actual situation and further improving the accuracy of the assessment results.

[0022] This invention combines probabilistic scenarios with precise power flow calculations to determine the probability and severity of risk events, achieving a quantitative coupling analysis of the "probability" and "severity" of risk events. This allows for the simulation of the impact of uncertainties on the consequences of failures, accurately depicting the dynamic evolution of risks and significantly enhancing the reliability of the assessment results. By calculating various risk indicators over multiple consecutive assessment periods, it achieves time-series rolling calculations of risk indicators, enabling the assessment results to dynamically capture the source-load fluctuations of the distribution network and guide the control and scheduling process of the distribution network. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an example of a dynamic assessment method for the operational risks of an active power distribution network according to the present invention. Figure 2 This is an example of the present invention, showing the indicator system at various levels and across multiple time scales, as well as the overall flowchart for indicator weighting and early warning. Figure 3 This is an example of an active power distribution network operation risk assessment index system and hierarchical assessment diagram. Figure 4 This is a flowchart illustrating an example of a dynamic assessment method for the operational risks of an active power distribution network according to the present invention. Figure 5 This is a block diagram of an electronic device according to the present invention. Detailed Implementation

[0024] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0025] Example 1 This invention provides a dynamic assessment method for the operational risks of active power distribution networks, as illustrated in the schematic diagram below. Figure 1 As shown, the method includes: Step S110: Based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, generate a set of day-ahead wind and solar load power output scenarios and calculate the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load power output scenarios. Step S120: For each assessment period before the day, perform power flow calculation for each operating scenario in the set of wind and solar load output scenarios before the day, and determine the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system based on the power flow calculation results. Step S130: Calculate the value of each risk indicator for each assessment period based on the probability of occurrence of each operational scenario and the probability and severity of the risk events corresponding to each risk indicator. Step S140: Assign weights to each risk indicator based on the importance of each spatial level in the operation risk assessment indicator system, and dynamically assess the day-ahead operation risk of the active distribution network based on the value of each risk indicator and the weight of each risk indicator in each assessment period. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

[0026] In this example implementation, a spatiotemporal coupled risk index system, i.e., an operational risk assessment index system, is first constructed based on the multi-timescale operational risk characteristics (e.g., real-time, short-term, medium-term) and multi-spatial-level characteristics (e.g., distribution areas, feeders, substations) of the active distribution network. Based on historical power output data of wind power, photovoltaics, and loads in the active distribution network, and a trained neural network (e.g., a backpropagation neural network), the day-ahead power output prediction data for wind power, photovoltaics, and loads is predicted. Simultaneously, combined with the prediction error probability distribution model for wind power, photovoltaics, and loads (which can be determined based on historical prediction errors; for example, the probability distribution of wind power prediction errors generally conforms to a non-standard normal distribution), multiple wind-solar-load output scenarios (formed by different combinations of wind power output, photovoltaic processing, and loads) are generated through sampling, and the probability of occurrence for each scenario is calculated. By quantifying the impact of source-load uncertainty on distribution network operation, it is ensured that the input data for risk assessment can cover multiple possible operating states. Next, for each day-ahead assessment period (e.g., the current hour or several hours), the assessment period refers to the rolling cycle of the day-ahead dynamic risk assessment process; for example, if a risk assessment is conducted every hour, then the assessment period is one hour. Each assessment can generate a new scenario set, or the same scenario set can be used for multiple assessments. Each assessment performs power flow calculations for each generated operating scenario to obtain node voltage and line power distribution data. Based on the power flow calculation results, according to the definitions of each risk indicator in the operational risk assessment indicator system (e.g., voltage exceeding limits, line overload, etc.), the probability of a risk event occurring (i.e., whether a risk event occurs) and its severity (e.g., the magnitude of voltage deviation from rated values ​​or the proportion of line power exceeding thermal stability limits) are determined. Then, combining the probability of occurrence of each operating scenario with the probability and severity of risk events, the values ​​of each risk indicator are comprehensively calculated, such as the voltage exceeding limit risk indicator value and the line overload risk indicator value. Since the calculation of risk indicator values ​​is a comprehensive calculation under each operating scenario in the day-ahead wind and solar load output scenario set, the comprehensiveness and representativeness of the indicator values ​​are ensured. Finally, based on the importance of each spatial level (e.g., distribution area, feeder, substation) in the operational risk assessment indicator system, each risk indicator is weighted, and the day-ahead operational risk of the active distribution network is dynamically assessed using the weights and risk indicator values. This example integrates a wind and solar load prediction error probability distribution model to quantify the uncertainties of new energy sources and loads in the form of probabilistic scenarios. Multiple operating scenarios are generated and probability calculations are performed, enabling risk assessment to cover various possible operating states, thereby improving the accuracy and reliability of the assessment. Power flow calculations are performed for each scenario, and combined with the probability and severity of risk events, quantitative coupled analysis of risk events is achieved, accurately depicting the dynamic evolution of risks such as voltage exceedances and line overloads. The construction of a spatiotemporal coupled index system ensures that risk analysis is conducted simultaneously across multiple time scales and spatial levels, avoiding the limitations of a single perspective and making the assessment results more comprehensive and systematic.The weighting mechanism based on spatial hierarchy reflects the risk transmission characteristics of different areas in the distribution network (e.g., bottom-up risk transmission), enhancing the relevance and practicality of the assessment. The dynamic assessment process, through rolling risk value calculations over multiple consecutive assessment cycles, enables real-time monitoring of distribution network operational risks. Uncertainty modeling, dynamic quantitative analysis, and spatiotemporal comprehensive evaluation effectively improve the accuracy and adaptability of active distribution network operational risk assessment, providing reliable data support for operation scheduling.

[0027] In some example implementations, the multiple time scales of the operational risk assessment indicator system include real-time, short-term, and medium-term; the multiple spatial levels of the operational risk assessment indicator system include distribution areas, feeders, and substations. The operational risk assessment index system includes real-time risk indicators for transformer substations, short-term risk indicators for transformer substations, medium-term risk indicators for transformer substations, real-time risk indicators for feeders, short-term risk indicators for feeders, medium-term risk indicators for feeders, and real-time risk indicators for substations, short-term risk indicators for substations, and medium-term risk indicators for substations.

[0028] In this example implementation, such as Figure 2 As shown, the multiple time scales include real-time, short-term, and medium-term. The real-time scale is typically used at 15-minute intervals to characterize instantaneous risk events; the short-term scale is based on hours and focuses on risk changes over a medium time range; the medium-term scale is based on several hours (e.g., 4 hours) to assess long-term cumulative risks. The multiple spatial levels include distribution transformer areas, feeders, and substations. The distribution transformer area level involves user-side equipment in low-voltage distribution networks (e.g., 0.4kV); the feeder level represents medium-voltage (e.g., 10kV) distribution lines; and the substation level covers high-voltage (e.g., 110kV) equipment and busbar systems. The operational risk assessment indicator system is composed of cross-combinations of the above scales and levels, specifically including distribution transformer area-real-time risk indicators, distribution transformer area-short-term risk indicators, distribution transformer area-medium-term risk indicators, feeder area-real-time risk indicators, feeder area-short-term risk indicators, feeder area-medium-term risk indicators, and substation area-real-time risk indicators, substation area-short-term risk indicators, and substation area-medium-term risk indicators. This indicator system ensures that risk analysis covers the evolution from instantaneous to long-term in time and encompasses the scope from local equipment to the entire system in space. This spatiotemporal coupled structure avoids the one-sidedness of traditional single-scale or hierarchical assessments, making the assessment results closer to actual operating conditions. At the same time, the system provides a foundation for dynamic weighting and comprehensive assessment, reflecting the importance of different levels and scales through weight allocation, further enhancing the accuracy and practicality of risk assessment.

[0029] For example, the transformer area-real-time risk indicators include transformer area heavy overload risk indicators and transformer area voltage exceeding limit risk indicators; the transformer area-short-term risk indicators include three-phase imbalance risk indicators; and the transformer area-medium-term risk indicators include equipment aging risk indicators. The feeder-real-time risk indicators include feeder power flow exceeding limit risk indicators and feeder voltage exceeding limit risk indicators; the feeder-short-term risk indicators include feeder fault risk indicators; and the feeder-medium-term risk indicators include feeder power flow fluctuation entropy risk indicators. The substation-real-time risk indicators include main transformer bus voltage exceeding limit risk indicators and main transformer overload risk indicators; the substation-short-term risk indicators include main transformer bus short-circuit current risk indicators and main transformer reactive power compensation margin risk indicators; and the substation-medium-term risk indicators include main transformer N-1 throughput risk indicators.

[0030] In this exemplary embodiment, based on the spatial combinability of risks, the present invention first establishes a set of risk assessment system indicators at the spatial scale, as shown in Table 1. At the spatial level, based on the multi-time-scale operating characteristics of the distribution network and the multi-level equipment correlation between distribution stations, lines, and transformers, a total of 13 evaluation indicators are selected from events such as node voltage and line power flow exceeding limits to construct a multi-level risk assessment indicator system for the distribution network, and to conduct a comprehensive risk assessment of the active distribution network.

[0031] Table 1

[0032] For example, a risk indicator system at the transformer substation level is constructed, including substation overload risk indicators, substation voltage exceedance risk indicators, three-phase imbalance risk indicators, and equipment aging risk indicators. Among them: (1) Real-time scale (e.g., 15 minutes): overload risk index of transformer area and voltage over-limit risk index of transformer area.

[0033] The transformer area overload risk indicator reflects the risk that the real-time load rate of a transformer area will exceed its rated capacity, which may lead to equipment overheating or failure; transformer area overload situation. The calculation formula is: (1) in, This represents the real-time active power of the transformer substation. This refers to the rated capacity of the transformer substation.

[0034] The transformer area voltage exceedance risk index is used to characterize the probability that the voltage at transformer area nodes will exceed the allowable range. This index affects power quality and equipment lifespan; transformer area voltage exceedance situations... The calculation formula is: (2) in, Let t be the voltage sample value at time t, and T be the number of samples within the statistical period. , These are the upper and lower limits of the voltage in the transformer area.

[0035] (2) Short-term scale (e.g., 1 hour): The three-phase imbalance risk index is used to characterize the imbalance of three-phase current or voltage in the transformer area. This item may lead to additional losses and equipment vibration. The calculation formula is: (3) in, This is the average value of the three-phase current. , , The three-phase current of the transformer area; if the three-phase imbalance exceeds the national standard (2%), a corresponding risk event is considered to have occurred.

[0036] (3) Mid-term scale (e.g., 4 hours): Equipment aging risk indicators are used to assess the probability of failure associated with the aging rate based on the equipment's operating years and environmental conditions. Equipment aging status of a device within the distribution area. The calculation formula is: (4) in, This is the baseline failure rate for the equipment. The operating temperature of the equipment. This is the reference temperature for the device. This represents the cumulative operating time of the equipment. This refers to the original service life of the equipment. and This is the material aging coefficient of the equipment.

[0037] A risk indicator system for feeder layer operation is established, including power flow over-limit risk, voltage over-limit risk, fault risk, and power flow fluctuation entropy risk. Among these: (1) Real-time scale (15 minutes): Feeder power flow exceeding limits risk and feeder voltage exceeding limits risk. Feeder power flow exceeding limits risk: The degree to which the active / reactive power flow of the line exceeds thermal stability. Feeder power flow exceeding limits situation. The calculation formula is: (5) in, This represents the actual active power transmitted by the line. The active power limit for safe transmission of the line, when The system can promptly identify and address any corresponding risk events that occur.

[0038] Feeder voltage over-limit risk is used to characterize the extent to which node voltage exceeds voltage stability limits. Feeder voltage over-limit conditions. The calculation formula is: (6) in, Line voltage, For circuit voltage limits, when The system can promptly identify and address any corresponding risk events that occur.

[0039] (2) Short-term scale (1 hour): Feeder fault risk probability: The fault rate predicted by combining historical fault data with environmental factors (such as lightning and wind speed). Feeder fault risk situation. The calculation formula is: (7) in, The baseline failure rate per unit length of line. L For line length, This represents the influence of wind speed on the power line. is the weighting coefficient of the wind speed influence factor, used to quantify the degree of influence of wind speed on the probability of failure risk, and can be obtained empirically from historical data.

[0040] (3) Medium-term scale (4 hours): The feeder power flow fluctuation entropy risk index is used to characterize the uncertainty and disturbance resistance of power flow fluctuations in active distribution networks. The formula for calculating the feeder power flow fluctuation entropy is: (8) in, This represents the entropy of the feeder power flow fluctuation. Let f be the probability distribution of the power flow fluctuation amplitude in the f-th amplitude interval (the power fluctuation range is divided into multiple smaller intervals). The larger the value, the greater the uncertainty of the current fluctuation.

[0041] Step 1.3: Construct a substation-level operation risk indicator system, including bus voltage over-limit risk, main transformer overload risk, short-circuit current risk, reactive power compensation margin, and N-1 pass rate.

[0042] (1) Real-time scale (15 minutes): Bus voltage over-limit and main transformer overload; Bus voltage exceeding limits: This refers to the risk of the voltage on the high-voltage side of the main transformer deviating from its rated value, affecting grid stability. The calculation formula is similar to that for transformer substation voltage exceeding limits, but it is calculated specifically for bus nodes.

[0043] Main transformer overload: The main transformer load rate exceeds the short-term allowable value (e.g., 90%). Main transformer overload conditions. The calculation formula is: (9) in, Apparent power, This is the rated capacity.

[0044] (2) Short-term scale (1 hour): short-circuit current risk and reactive power compensation margin; Short-circuit current risk at the main transformer bus: The probability that the short-circuit current exceeds the breaking capacity of the circuit breaker. Short-circuit current risk situation at the main transformer bus. The calculation formula is: (10) in, The short-circuit current of the main transformer bus. For the circuit breaker interrupting current, if Traffic control measures need to be implemented.

[0045] Main transformer reactive power compensation margin: The difference between the current reactive power compensation capacity and the required capacity of the main transformer, reflecting its voltage regulation capability. The calculation formula is: (11) in, The current reactive power compensation capacity of the main transformer. Mainly for reactive power demand capacity. A negative value indicates insufficient reactive power, requiring additional equipment.

[0046] (3) Intermediate scale (4 hours): N-1 pass rate N-1 pass rate: The percentage of the main substation or busbar that meets the power supply capacity standard under N-1 fault conditions. The calculation formula is: (12) in, This represents the remaining capacity after the fault. For critical load demand, This represents the total number of main transformer devices in the system.

[0047] like Figure 2The indicators at each level and multiple time scales shown above, based on the risk conditions calculated by equations (1) to (12) above, determine the occurrence of the risk event corresponding to the risk indicator when the risk situation exceeds its corresponding risk threshold. This refined risk indicator design significantly improves the accuracy and practicality of risk assessment by specifically evaluating specific risk events. The indicators at the transformer substation level focus on user-side issues, such as heavy overload and voltage exceeding limits, helping to detect local risks early; the indicators at the feeder level focus on line-level issues, such as power flow exceeding limits and fault risks, realizing risk monitoring at the meso level; the indicators at the substation level cover system-level issues, such as short-circuit current and reactive power margin, ensuring overall stability. The connection of indicators at multiple time scales allows risk assessment to be extended from instantaneous events (such as real-time overload) to long-term trends (such as equipment aging), thereby providing a comprehensive risk view. For example, the power flow fluctuation entropy risk indicator quantifies uncertainty through information entropy, directly reflecting the randomness impact brought by the access of new energy sources, and enhancing the assessment of the system's anti-interference capability. Overall, these indicators work synergistically to provide a multi-dimensional and multi-level quantitative basis for the operational risks of active power distribution networks, supporting more accurate early warning and decision-making.

[0048] In some example implementations, the step of generating a set of day-ahead wind and solar load output scenarios based on day-ahead power output prediction data of the active power distribution network and a probability distribution model of wind and solar load prediction errors includes: To address the historical power output errors of wind and solar loads in active power distribution networks, we construct probability distribution models for wind power output prediction errors, solar power output prediction errors, and load prediction errors. Based on the probability distribution models of wind power output prediction error, photovoltaic power output prediction error, and load prediction error, Monte Carlo sampling is used to generate wind power output prediction error, photovoltaic power output prediction error, and load prediction error. The wind power output prediction error, photovoltaic power output prediction error and load prediction error are superimposed on the corresponding day-ahead wind power output prediction data, day-ahead photovoltaic power output prediction data and day-ahead load prediction data to generate a day-ahead wind and solar load output scenario set. The daytime wind and solar power output scenario set includes operation scenarios under different combinations of wind and solar power output and load demand.

[0049] In this example implementation, the wind and solar load prediction error probability distribution model includes a wind power output prediction error probability distribution model, a solar power output prediction error probability distribution model, and a load prediction error probability distribution model. For example, based on historical wind power output errors, the wind power output prediction error probability distribution model generally conforms to a non-standard normal distribution, as shown below: (13) (14) In the formula, Let x1 be the probability distribution function of wind power output prediction error. The independent variable; This represents the standard deviation of wind power forecasting error. , These are the prediction parameters, determined by the prediction duration and the size of the wind farm, respectively. Predict power output for wind power units; This refers to the rated installed capacity of the wind turbine.

[0050] Based on the historical power output error data of photovoltaic power generation, the probability distribution of photovoltaic power output prediction error generally conforms to a non-standard normal distribution with a mean of 0. Therefore, a non-standard normal distribution with a mean of 0 is used for modeling, as shown below: (15) (16) In the formula, Let x2 be the probability distribution function of the photovoltaic power output prediction error. The independent variable, This represents the standard deviation of the photovoltaic prediction error; This refers to photovoltaic prediction errors; This refers to the installed capacity of photovoltaic (PV) units.

[0051] Based on historical load error data, the probability distribution of load power prediction error is obtained. It is also modeled using a non-standard normal distribution with a mean of 0, as shown below: (17) (18) In the formula, Let x3 be the probability distribution function of the load power prediction error. The independent variable, This represents the standard deviation of the load forecasting error. For load forecasting error, This is the rated power of the load.

[0052] It should be noted that, in addition to using the specific distribution mentioned above to fit the wind and solar load prediction error, other continuous distribution models of wind and solar load prediction error probabilities can also be used, such as t-distribution, log-normal distribution, γ-distribution, Gaussian distribution, etc.

[0053] This example first constructs the three probability distribution models mentioned above, based on historical output errors of wind and solar loads in active distribution networks. Then, using Monte Carlo sampling, it generates wind power output prediction errors, solar power output prediction errors, and load prediction errors from these distributions. Next, these error values ​​are superimposed onto the corresponding day-ahead wind power output prediction data, day-ahead solar power output prediction data, and day-ahead load prediction data, thereby generating multiple wind and solar load output scenarios. These scenarios cover different combinations of wind and solar power output and load demand, such as high solar power output with low load or low wind power output with high load, ensuring the diversity and representativeness of the scenario set. This method significantly improves the adaptability of risk assessment to uncertainty by constructing probability distribution models and generating diverse scenarios. The probability distribution models are built based on historical data, ensuring the accuracy of error simulation and fully considering the randomness of source and load characteristics. Through error modeling and scenario generation, reliable input is provided for dynamic risk assessment, enhancing the robustness of distribution network operation risk management.

[0054] In some example implementations, after generating the day-ahead wind and solar power load scenario set, the method further includes: The daytime wind and solar power output scenario set is reduced based on the K-medoids clustering algorithm to obtain a typical daytime wind and solar power output scenario set.

[0055] In this example implementation, while generating a large set of wind and solar power output scenarios through extensive sampling provides a more accurate simulation of uncertain variables, it also significantly increases the computational burden. Considering this issue, this example reduces the number of scenarios to accelerate the process while maintaining the accuracy of the random variable simulation. This example uses the K-medoids clustering method for scenario reduction. Compared to the K-means clustering algorithm, which uses the mean of samples within a cluster as the data point, the K-medoids clustering method selects the point closest to the cluster center (the object at the cluster center) as the representative point of the cluster. It is less sensitive to isolated points and noisy data, effectively avoiding distortion of the clustering results. Specifically, the process first selects k initial cluster centers according to the "maximum-minimum distance principle," that is, selecting k samples from the scenario set as initial centers. The selection principle is to maximize the minimum distance from the candidate sample points to the known cluster centers, ensuring that the initial centers are dispersed and highly representative. The distance between scenarios is defined using Euclidean distance (2-norm), which measures the similarity of the wind and solar power output vectors. Next, based on the minimum distance principle, the remaining samples are assigned to cluster centers, forming k clusters, and the objective function value is calculated, which is the sum of the distances from all samples to their cluster centers. Then, the cluster centers are updated: for each cluster, the sum of the distances between all sample points within the cluster is calculated, and the sample with the smallest sum of distances is selected as the new cluster center, which reduces the impact of noise and outliers. The assignment and update steps are repeated until the objective function no longer changes or the maximum number of iterations is reached, finally outputting a typical scenario set (i.e., cluster centers), which is the typical scenario set of day-to-day wind and solar power output.

[0056] Next, the probability of occurrence of each operating scenario in the day-ahead wind and solar power output scenario set is calculated as follows: (19) in, Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. Let t be the total number of samples in the set of day-ahead wind-solar-load power output scenarios predicted at time t; The first of the predicted day-ahead wind, solar and load power output scenarios at time t. l The membership relationship between a sample and the class centered on the operating scenario s is calculated, with a value of 1 indicating membership and a value of 0 indicating non-membership. The calculation process for the probability of occurrence of typical scenarios in the current set of typical wind and solar power output scenarios is similar.

[0057] The above example demonstrates how clustering algorithms reduce a large number of scenarios into representative typical scenarios, thereby reducing computational burden while maintaining simulation accuracy. Ultimately, the typical scenario set retains the main characteristics of the original scenarios while significantly reducing the number of scenarios, thus improving the efficiency of subsequent power flow calculations and risk analysis. Scenario reduction optimizes the use of computational resources while maintaining the accuracy of risk assessment.

[0058] In some example implementations, the probability and severity of risk events corresponding to each risk indicator in the operational risk assessment index system of the active distribution network are determined based on power flow calculation results, including: For each assessment period, based on the node voltage and line power data of each operating scenario and the definition of each risk indicator in the operating risk assessment indicator system, it is calculated whether the risk event corresponding to each risk indicator has occurred. Based on the node voltage and line power data for each operating scenario and the severity function of the risk events corresponding to each risk indicator, the severity of the occurrence of the risk events corresponding to each risk indicator is calculated. Among them, the severity function of each risk indicator is used to measure the degree to which the corresponding risk event exceeds the limit.

[0059] In this example implementation, for each assessment cycle, the power flow calculation results include node voltage and line power data for each operating scenario, such as node voltage matrix and line active and reactive power matrix. Based on these data, the process of determining the probability and severity of the occurrence of risk events corresponding to each risk indicator in the operation risk assessment index system includes: First, according to the definition of each risk indicator, the occurrence of a risk event is calculated using node voltage and line power data. For example, for voltage over-limit risk, the probability of node voltage exceeding the allowable range (such as upper and lower limits) is determined by the power flow calculation results. This probability value is compared with a preset risk threshold. If the probability value is greater than the corresponding risk threshold, the occurrence of the corresponding voltage over-limit event is determined. A 0-1 flag variable can be used to represent whether the event occurs (1) or does not occur (0). Then, based on the node voltage and line power data and the severity function of each risk event, the occurrence severity is calculated. The distribution network operation risk considered in this example is a comprehensive measure of the probability and severity of the consequences of events such as voltage over-limit, line overload, and load loss caused by solar radiation intensity, wind speed, load demand, and load uncertainty. The general expression is as follows: (20) In the formula, In order to operate under the following conditions The risk indicator value below, For the first Potential risk events include voltage exceeding limits, line overload, and loss of load. for The probability of an event occurring. For the operating conditions are Next event The severity of the event. The severity function is used to quantify the severity of the consequences of a risk event. For example, risk indicators can be divided into two categories: voltage-related (risk indicators calculated based on voltage values) and power-related (risk indicators calculated based on power values). The formulas for calculating the values ​​of the risk indicators for these two categories are as follows: , ; , ; in, Let be the value of the voltage-related risk index for the i-th node predicted at time t. Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. This indicates whether the risk event corresponding to the voltage risk indicator has occurred as predicted at time t. A value of 1 indicates that it has occurred, and a value of 0 indicates that it has not occurred. Let be the voltage of node i in the predicted operating scenario s at time t. For voltage limits, The set of day-ahead wind, solar and load power output scenarios predicted at time t; This is a severity function for risk events corresponding to voltage-related risk indicators; Let be the predicted value of the power-related risk index for line j at time t. This indicates whether the risk event corresponding to other risk indicators has occurred at time t, with a value of 1 indicating occurrence and a value of 0 indicating non-occurrence. Let t be the active power flowing through line j under the predicted operating scenario s at time t. Here is the power limit for line j; This is a severity function for the risk events corresponding to power-related risk indicators.

[0060] This example demonstrates a quantitative assessment of risk events through precise power flow data and a severity function, significantly improving the accuracy and detail of risk analysis. The introduction of the severity function allows risk assessment to not only focus on whether an event has occurred but also quantify the severity of its consequences, thus providing a more comprehensive reflection of operational risks.

[0061] In some example implementations, the weights of each risk indicator include combined weights and hierarchical weights; the weighting of each risk indicator is based on the importance of each spatial level in the operational risk assessment indicator system, including: The objective weights of each risk indicator are calculated based on the entropy weight method, and the subjective weights of each risk indicator are determined based on the analytic hierarchy process. For each risk indicator, the corresponding subjective weight is adjusted using the objective weight of the risk indicator to obtain the combined weight of the risk indicators; Based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, the hierarchical weight of each risk indicator within each spatial level is determined.

[0062] In this example implementation, the authorization process is as follows: Figure 2 As shown in the operational risk weighting section, the objective weights of each risk indicator are first calculated using the entropy weight method. The entropy weight method determines the weights based on the degree of variation in indicator values; the greater the variation, the higher the weight, reflecting the inherent discriminative power of the data. Next, the subjective weights of each risk indicator are determined using the analytic hierarchy process (AHP). The AHP constructs a judgment matrix through expert evaluation to calculate the relative importance of each indicator, reflecting domain knowledge and experience. Then, for each risk indicator, the subjective weights are adjusted using the objective weights, for example, through weighted averaging or product methods, to obtain a combined weight, balancing subjective experience and objective data. Finally, based on the importance of each spatial level (such as distribution area, feeder, substation), the hierarchical weights of each risk indicator within each level are determined, for example, by using the AHP to assess the relative importance of each level and assign weights. Specifically, in the entropy weighting method, the values ​​of each risk indicator are first standardized, and then the entropy value and weight are calculated. In the analytic hierarchy process (AHP), a judgment matrix is ​​constructed and eigenvectors are calculated as weights. Combined weights integrate subjective and objective weights to ensure their scientific validity and rationality. Hierarchical weights are allocated according to the strategic position of each level in the distribution network; for example, substations may be assigned higher weights due to their wider influence. Finally, the weights are used to calculate the weighted risk value, achieving a comprehensive risk assessment. This weighting method, through multi-dimensional coupling, provides a scientific and practical weighting foundation for dynamic risk assessment, supporting fairer and more effective risk management and early warning.

[0063] In some example implementations, the day-ahead operational risk of the active distribution network is dynamically assessed based on the values ​​and weights of each risk indicator for each assessment period, including: For each spatial level in each assessment period, the risk value of the spatial level in each assessment period is determined based on the ratio of the weighted risk value of each risk indicator within the spatial level to the theoretical maximum risk value. Based on the hierarchical weights and risk values ​​of each spatial level, the comprehensive risk value of the active distribution network for each assessment period is determined. Based on the time-series variation characteristics of the comprehensive risk value of the active distribution network over multiple assessment periods, the daily operational risk changes of the active distribution network are dynamically assessed. The total day-ahead risk of the active distribution network is obtained by superimposing the comprehensive risk values ​​of the active distribution network for each assessment period before the current day; the day-ahead risk level of the active distribution network is assessed based on the total day-ahead risk of the active distribution network.

[0064] In this example implementation, the evaluation period can be set according to actual conditions. For example, the evaluation period can be set to one hour or several hours, allowing for dynamic evaluation as needed to guide the dispatching of the distribution network. For example, as Figure 3 As shown, taking a 1-hour assessment period as an example, dynamic assessment of operational risk is performed. This means that the values ​​of each risk indicator for the current day can be calculated every hour, for example, if the initial time is t and the ending time is t. N The evaluation times are t+1, t+2, ..., t N -2,t N -1,t N Based on the values ​​and weights of each risk indicator at each assessment time, the day-ahead operational risk of the active distribution network is dynamically assessed. This dynamic assessment process can include the dynamic assessment of the total risk of the distribution network system and the assessment of the time-series characteristics of system risks. The total risk assessment includes: First, for each spatial level (e.g., distribution area, feeder, substation), the risk value of that level is calculated. Specifically, based on the values ​​of each risk indicator within that level and the combined weights of each indicator (determined through the entropy weight method and the analytic hierarchy process), a weighted risk value is calculated. This weighted risk value is then compared with the theoretical highest risk value of that level (e.g., the weighted sum of all indicators at their maximum values), and the ratio of the two is taken as the risk value of that spatial level. Then, based on the level weights and risk values ​​of each spatial level, the comprehensive risk value of the active distribution network is calculated, for example, by weighted summation of the risks of each spatial level to obtain the comprehensive risk value of the distribution network. The total risk of the active distribution network is obtained by overlaying (e.g., summing) the comprehensive risk values ​​from multiple consecutive assessment periods for the current day. Then, risk levels (e.g., low, medium, high) are assigned based on the total risk value to assess the overall risk status of the distribution network for the current day. Assessing the temporal characteristics of risk includes analyzing the temporal variation characteristics of the comprehensive risk value of the distribution network across multiple consecutive assessment periods. This can be achieved, for example, by plotting risk curves for consecutive assessment periods or calculating volatility indices, dynamically assessing the temporal changes in operational risk for the current day, and identifying risk patterns and critical periods. This example significantly improves the comprehensiveness and real-time nature of risk management through dynamic and cumulative assessment. The calculation of hierarchical risk values ​​allows for spatial refinement of risk assessment, helping to locate risk sources; the comprehensive risk value integrates multi-level information, providing an overall view; and time-series analysis identifies the dynamic characteristics of risks, supporting proactive early warning.

[0065] In some example implementations, after dynamically assessing the day-ahead operational risks of the active distribution network, the following is also included: Overall risk warning for active distribution networks is conducted based on the day-ahead risk level of the active distribution network.

[0066] In this example implementation, the current risk level is categorized by the total risk value, such as low risk (total risk value below the lower threshold), medium risk (between the upper and lower thresholds), or high risk (above the upper threshold), and corresponding warnings are issued accordingly. The warning level is adjusted based on the risk level; for example, an alert is issued for low risk, and an emergency response is triggered for high risk. Warning signals may be displayed through the dispatch system interface to help operators quickly identify the overall risk status.

[0067] In some example implementations, after dynamically assessing the day-ahead operational risks of the active distribution network, the following is also included: For each spatial level, the total risk of the spatial level is obtained by summing the risk values ​​of the spatial level over the current assessment period; based on the total risk of the spatial level, the risk level of each spatial level is determined. Visualized early warning of risk areas in active power distribution networks is conducted based on the current risk levels at each spatial level.

[0068] In this example implementation, for each spatial level, the total risk for that level is obtained by superimposing the risk values ​​of that level across assessment periods. Then, based on the total risk values ​​for each level, risk levels (e.g., low, medium, high) are assigned, determining the risk level for each spatial level. Next, based on these levels, a visual early warning system for the day-ahead risk areas of the active distribution network is implemented. For example, in a Geographic Information System (GIS) or dispatch interface, color coding (e.g., green, yellow, red) is used to identify the risk status of distribution areas, feeders, or substations, highlighting high-risk areas. This spatial-level risk visualization significantly improves the intuitiveness and location capabilities of risk management. Operators can quickly identify high-risk areas, prioritize addressing localized issues, and prevent risk spread. Visualized early warning enhances situational awareness, supporting refined control and emergency management.

[0069] For example, the core task of the distribution network operation risk early warning system of this invention is to accurately identify power grid risk factors, determine their risk level through risk analysis and assessment of their severity, and issue early warnings. The specific process is shown in the appendix. Figure 4 As shown.

[0070] (1) Input the basic parameters of the distribution network, photovoltaic, wind power and load, as well as the parameters of the wind-solar-load prediction error probability distribution model.

[0071] (2) Based on the historical power output data of wind and solar load, the day-ahead wind and solar load data are predicted using a BP neural network.

[0072] (3) Based on the probability distribution model of wind and solar load prediction error, Monte Carlo sampling is used to generate wind and solar load prediction error, which is then superimposed on the predicted day-ahead wind and solar load output data to generate a set of wind and solar load output scenarios.

[0073] (4) Based on the K-medoids clustering algorithm, the wind and solar load output scene set is reduced to output the final determined typical scene and calculate the occurrence probability of the typical scene.

[0074] (5) Perform power flow calculations for each scenario at time t (evaluation period / evaluation cycle) to obtain the output node voltage matrix and line active and reactive power matrix. If risk events exist, calculate the severity of each risk event. (6) Determine whether all scenarios have been traversed. If so, calculate the values ​​of each indicator in the risk indicator system of the distribution network at time t (evaluation period); otherwise, proceed to (4) to calculate the next scenario. (7) Determine whether all assessment periods have been traversed. If so, calculate the day-ahead comprehensive operation risk of the distribution network; otherwise, proceed to (4) to calculate the next assessment period.

[0075] (8) Based on the risk assessment of the daytime distribution network operation, determine whether the conditions for early warning are met. If so, issue an early warning; otherwise, proceed to (2) to continue forecasting the daytime wind and solar power output.

[0076] To address the multiple risk factors and day-ahead forecasting errors in active power distribution networks, this invention's technical solution predicts day-ahead wind, solar, and load data, calculates early warning indicators based on time-series risk assessment, and classifies risks according to early warning grading standards. This effectively identifies the risk level and risk areas of the distribution network in different time periods, enabling predictive perception and visualized early warning of distribution network risks. This technical solution can effectively determine the risk level and risk areas in different time periods before the day-ahead, providing visualized early warnings. It empowers dispatching and operation personnel to have a real-time, comprehensive understanding of the power grid's operational safety and stability.

[0077] With the widespread integration of distributed generation (DG), the booming development of electric vehicles (EVs), and power market reforms, uncertainties in distribution networks are constantly increasing, adversely affecting their operation. Quantifying grid risks has become a crucial step in ensuring the safe and stable operation of the power grid. Traditional distribution network risk assessment primarily employs a deterministic analysis method based on the N-1 principle. This method assumes a single component failure and assesses grid security by calculating indicators such as power flow distribution and voltage levels. First, a mathematical model of the distribution network is established, encompassing line parameters, transformer parameters, and load distribution. Then, for each potentially faulty component, a fault simulation is performed, and power flow calculation algorithms are used to solve for the grid's operating state parameters after the fault, thereby determining whether the grid meets safe operating conditions. However, this N-1 principle-based deterministic analysis method cannot effectively address the current complex and ever-changing distribution network operating environment. With the continuous expansion of distribution network scale and increasing structural complexity, the single component failure criterion is no longer sufficient to comprehensively assess the operational risks of the distribution network. In actual power grid operation, there are often various uncertainties, such as the intermittency and randomness of renewable energy generation and load fluctuations. These factors are difficult to consider using the simple N-1 principle, resulting in assessment results that cannot accurately reflect the true risk level of the distribution network. There are also distribution network local risk early warning methods based on single component status monitoring. These methods only focus on the status of local components, without considering the electrical coupling relationships between components, and cannot assess the risk of system-level cascading failures. They are mostly based on steady-state data, making it difficult to capture the temporal risks caused by dynamic changes in load and power supply. They also fail to consider the uncertainties of distributed resources; for example, fluctuations in photovoltaic output may cause overloads in adjacent components through power flow transmission, but existing models only analyze the state of the component itself in isolation.

[0078] To address the aforementioned issues in risk assessment for high-proportion distributed photovoltaic (PV) grid integration, and considering the randomness of distributed PV power generation and load output, this invention divides the distribution network into three levels based on voltage: 110kV substation level, 10kV feeder level, and 0.4kV transformer substation level. It proposes a dynamic risk early warning method for distribution networks based on entropy-corrected analytic hierarchy process (AHP) and multi-dimensional coupled weighting, aiming to solve the problem of insufficient adaptability of traditional single-index weighting methods in scenarios with high renewable energy penetration. First, considering the coupled impact of PV output volatility and load temporal characteristics, a multi-level risk index system of "transformer substation-feeder-station" is constructed: in the time dimension, a multi-timescale framework for assessment is established, encompassing real-time, short-term, and medium-term assessments. Compared with traditional evaluation methods, this invention innovatively adopts a hierarchical coupled weighting mechanism: the hierarchical weight factors for transformer substations, feeders, and substations are determined through AHP, reflecting the "bottom-up" risk transmission characteristics of the distribution network; combined with entropy weighting, the index weights are dynamically corrected, providing a quantitative basis for risk assessment and early warning.

[0079] To address the issue that the operational risks of active distribution networks largely stem from inaccurate forecast data due to the uncertainty of wind and solar loads, this invention combines multi-level risk coupling analysis of "station-line-transformer" and multiple time scales to reflect the temporal risks caused by dynamic changes in load and power supply. It can effectively quantify the cascading impact of uncertainties in distributed resources and new energy equipment on system failures. This invention sequentially establishes a probability distribution model of wind and solar load forecasting errors, assesses the multi-level temporal risk levels of the distribution network, and formulates corresponding risk control measures. This effectively determines the risk level and risk areas for each time period before the day-ahead, providing visualized early warnings. This enables dispatching personnel to have a real-time, comprehensive understanding of the grid's operational safety and stability, providing guidance for the operation and dispatching of active distribution networks.

[0080] Example 2 Based on the same inventive concept, this invention also provides a dynamic assessment method system for the operational risks of active distribution networks, comprising: The scenario generation module is used to generate a set of day-ahead wind and solar load output scenarios based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, and to calculate the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load output scenarios. The risk factor calculation module is used to perform power flow calculations for each operating scenario in the daytime wind and solar load output scenario set for each assessment period before the daytime, and to determine the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system based on the power flow calculation results. The risk indicator calculation module is used to calculate the value of each risk indicator for each assessment period based on the probability of occurrence of each operational scenario and the probability and severity of the risk events corresponding to each risk indicator. The risk assessment module is used to assign weights to each risk indicator based on the importance of each spatial level in the operational risk assessment indicator system, and to dynamically assess the day-ahead operational risk of the active distribution network based on the value and weight of each risk indicator in each assessment period. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

[0081] In one possible implementation, the operational risk assessment indicator system includes multiple time scales such as real-time, short-term, and medium-term; and the operational risk assessment indicator system includes multiple spatial levels such as distribution area, feeder, and substation. The operational risk assessment index system includes real-time risk indicators for transformer substations, short-term risk indicators for transformer substations, medium-term risk indicators for transformer substations, real-time risk indicators for feeders, short-term risk indicators for feeders, medium-term risk indicators for feeders, and real-time risk indicators for substations, short-term risk indicators for substations, and medium-term risk indicators for substations.

[0082] In one possible implementation, the transformer area-real-time risk indicators include transformer area heavy overload risk indicators and transformer area voltage exceeding limit risk indicators; the transformer area-short-term risk indicators include three-phase imbalance risk indicators; and the transformer area-medium-term risk indicators include equipment aging risk indicators. The feeder-real-time risk indicators include feeder power flow exceeding limit risk indicators and feeder voltage exceeding limit risk indicators; the feeder-short-term risk indicators include feeder fault risk indicators; and the feeder-medium-term risk indicators include feeder power flow fluctuation entropy risk indicators. The substation-real-time risk indicators include main transformer bus voltage exceeding limit risk indicators and main transformer overload risk indicators; the substation-short-term risk indicators include main transformer bus short-circuit current risk indicators and main transformer reactive power compensation margin risk indicators; and the substation-medium-term risk indicators include main transformer N-1 throughput risk indicators. The three-phase imbalance risk index is used to characterize the degree of imbalance of three-phase current / voltage in the transformer area. The equipment aging risk index is used to characterize the probability of equipment failure caused by the aging rate based on the equipment's years of operation and environmental conditions. The feeder power flow over-limit risk index is used to characterize the degree to which the active / reactive power flow of the line exceeds the thermal stability. The feeder failure risk index is used to characterize the feeder failure rate predicted based on historical feeder failure data and environmental factors. The feeder power flow fluctuation entropy risk index is used to characterize the power flow fluctuation uncertainty and disturbance rejection capability of the active distribution network. Its calculation formula is as follows: ; in, This represents the entropy of the feeder power flow fluctuation. The amplitude of the tidal fluctuation is in the first... f The probability distribution of each amplitude interval. The larger the value, the greater the uncertainty of the current fluctuation. The short-circuit current risk index of the main transformer bus is used to characterize the probability that the short-circuit current exceeds the breaking capacity of the circuit breaker. The main transformer reactive power compensation margin risk index is used to characterize the voltage regulation capability of the main transformer.

[0083] In one possible implementation, the wind-solar-load prediction error probability distribution model includes a wind power output prediction error probability distribution model, a photovoltaic power output prediction error probability distribution model, and a load prediction error probability distribution model; the scenario generation module includes: The error model construction submodule is used to construct probability distribution models for wind power output prediction error, photovoltaic power output prediction error, and load prediction error, based on the historical output error of wind and solar loads in active distribution networks. The error generation submodule is used to generate wind power output prediction error, photovoltaic output prediction error and load prediction error based on the probability distribution models of wind power output prediction error, photovoltaic output prediction error and load prediction error, using Monte Carlo sampling. The scenario generation submodule is used to superimpose the wind power output prediction error, photovoltaic power output prediction error and load prediction error onto the corresponding day-ahead wind power output prediction data, day-ahead photovoltaic power output prediction data and day-ahead load prediction data to generate a set of day-ahead wind, solar and load output scenarios. The daytime wind and solar power output scenario set includes operation scenarios under different combinations of wind and solar power output and load demand.

[0084] In one possible implementation, the scene generation module further includes: The scene reduction submodule is used to reduce the daytime wind and solar load output scene set based on the K-medoids clustering algorithm to obtain a typical daytime wind and solar load output scene set.

[0085] In one possible implementation, the probability of occurrence of each operating scenario in the day-ahead wind and solar power output scenario set is as follows:

[0086] in, Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. Let t be the total number of samples in the set of day-ahead wind-solar-load power output scenarios predicted at time t; The first of the predicted day-ahead wind, solar and load power output scenarios at time t. l The membership relationship between a sample and the class centered on the running scenario s, where a value of 1 indicates membership and a value of 0 indicates non-membership.

[0087] In one possible implementation, the power flow calculation results include node voltage and line power data for each operating scenario; the risk factor calculation module includes: The event probability calculation submodule is used to calculate, for each evaluation period, whether the risk event corresponding to each risk indicator has occurred, based on the node voltage and line power data of each operating scenario and the definition of each risk indicator in the operating risk assessment indicator system. The event severity calculation submodule is used to calculate the severity of the occurrence of risk events corresponding to each risk indicator based on the node voltage and line power data of each operating scenario and the severity function of each risk indicator corresponding to the risk event. Among them, the severity function of each risk indicator is used to measure the degree to which the corresponding risk event exceeds the limit.

[0088] In one possible implementation, the values ​​of the risk indicators are as follows: , ; , ; in, Let be the value of the voltage-related risk indicator for the i-th node predicted at time t. Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. This indicates whether the risk event corresponding to the voltage risk indicator has occurred as predicted at time t. A value of 1 indicates that it has occurred, and a value of 0 indicates that it has not occurred. Let be the voltage of node i in the predicted operating scenario s at time t. For voltage limits, The set of day-ahead wind, solar and load power output scenarios predicted at time t; This is a severity function for risk events corresponding to voltage-related risk indicators; Let be the predicted power risk index value for line j at time t. This indicates whether the risk event corresponding to other risk indicators has occurred at time t, with a value of 1 indicating occurrence and a value of 0 indicating non-occurrence. Let t be the active power flowing through line j under the predicted operating scenario s at time t. Here is the power limit for line j; This is a severity function for the risk events corresponding to power-related risk indicators.

[0089] In one possible implementation, the weights of each risk indicator include combined weights and hierarchical weights; the risk assessment module includes a weighting submodule, which is specifically used for: The objective weights of each risk indicator are calculated based on the entropy weight method, and the subjective weights of each risk indicator are determined based on the analytic hierarchy process. For each risk indicator, the corresponding subjective weight is adjusted using the objective weight of the risk indicator to obtain the combined weight of the risk indicators; Based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, the hierarchical weight of each risk indicator within each spatial level is determined.

[0090] In one possible implementation, the risk assessment module includes: The overall risk assessment submodule is used to determine the risk value of each spatial level in each assessment period based on the ratio of the weighted risk value of each risk indicator within the spatial level to the theoretical maximum risk value; determine the comprehensive risk value of the active distribution network in each assessment period based on the level weight and the risk value of each spatial level; sum the comprehensive risk values ​​of the active distribution network in each assessment period for the current day to obtain the total risk of the active distribution network for the current day; and assess the current day risk level of the active distribution network based on the total risk of the current day. The time-series assessment submodule is used to dynamically assess the changes in the day-ahead operational risk of the active distribution network based on the time-series variation characteristics of the comprehensive risk value of the active distribution network over multiple assessment periods for the day-ahead. The weighted risk value of each risk indicator within the spatial hierarchy is determined based on the value of each risk indicator within the spatial hierarchy and the combined weight of each risk indicator.

[0091] In one possible implementation, it also includes: The early warning module is used to provide overall risk warnings for the active distribution network based on the day-ahead risk level of the active distribution network.

[0092] In one possible implementation, an early warning module is also included, the early warning module being used for: For each spatial level, the total risk of the spatial level is obtained by summing the risk values ​​of the spatial level over the current assessment period; based on the total risk of the spatial level, the risk level of each spatial level is determined. Visualized early warning of risk areas in active power distribution networks is conducted based on the current risk levels at each spatial level.

[0093] Example 3 like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0094] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the dynamic assessment method for active power distribution network operation risk in the above embodiments.

[0095] Example 4 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). An electronic device readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both the built-in storage medium within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the dynamic assessment method for active power distribution network operation risks in the above embodiments.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A dynamic assessment method for operational risks of active power distribution networks, characterized in that, include: Based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, a set of day-ahead wind and solar load power output scenarios is generated and the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load power output scenarios is calculated. For each assessment period before the day, power flow calculation is performed on each operating scenario in the set of wind and solar load output scenarios before the day. Based on the power flow calculation results, the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system are determined. The values ​​of each risk indicator for each assessment period are calculated based on the probability of occurrence of each operational scenario and the likelihood and severity of the risk events corresponding to each risk indicator. Based on the importance of each spatial level in the aforementioned operational risk assessment index system, each risk index is weighted, and based on the value and weight of each risk index in each assessment period, the day-ahead operational risk of the active distribution network is dynamically assessed. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

2. The method according to claim 1, characterized in that, The operational risk assessment indicator system has multiple time scales, including real-time, short-term, and medium-term; and multiple spatial levels, including distribution area, feeder, and substation. The operational risk assessment index system includes real-time risk indicators for transformer substations, short-term risk indicators for transformer substations, medium-term risk indicators for transformer substations, real-time risk indicators for feeders, short-term risk indicators for feeders, medium-term risk indicators for feeders, and real-time risk indicators for substations, short-term risk indicators for substations, and medium-term risk indicators for substations.

3. The method according to claim 2, characterized in that, The real-time risk indicators for transformer substations include transformer substation overload risk indicators and transformer substation voltage limit exceedance risk indicators; the short-term risk indicators for transformer substations include three-phase imbalance risk indicators; and the medium-term risk indicators for transformer substations include equipment aging risk indicators. The real-time risk indicators for feeders include feeder power flow limit exceedance risk indicators and feeder voltage limit exceedance risk indicators; the short-term risk indicators for feeders include feeder fault risk indicators; and the medium-term risk indicators for feeders include feeder power flow fluctuation entropy risk indicators. The real-time risk indicators for substations include main transformer bus voltage limit exceedance risk indicators and main transformer overload risk indicators; the short-term risk indicators for substations include main transformer bus short-circuit current risk indicators and main transformer reactive power compensation margin risk indicators; and the medium-term risk indicators for substations include main transformer N-1 throughput risk indicators. The three-phase imbalance risk index is used to characterize the degree of imbalance of three-phase current / voltage in the transformer area. The equipment aging risk index is used to characterize the probability of equipment failure caused by the aging rate based on the equipment's years of operation and environmental conditions. The feeder power flow over-limit risk index is used to characterize the degree to which the active / reactive power flow of the line exceeds the thermal stability. The feeder failure risk index is used to characterize the feeder failure rate predicted based on historical feeder failure data and environmental factors. The feeder power flow fluctuation entropy risk index is used to characterize the power flow fluctuation uncertainty and disturbance rejection capability of the active distribution network. Its calculation formula is as follows: ； in, This represents the entropy of the feeder power flow fluctuation. The amplitude of the tidal fluctuation is in the first... f The probability distribution of each amplitude interval. The larger the value, the greater the uncertainty of the current fluctuation. The short-circuit current risk index of the main transformer bus is used to characterize the probability that the short-circuit current exceeds the breaking capacity of the circuit breaker. The main transformer reactive power compensation margin risk index is used to characterize the voltage regulation capability of the main transformer.

4. The method according to claim 2, characterized in that, The wind-solar load prediction error probability distribution model includes a wind power output prediction error probability distribution model, a photovoltaic power output prediction error probability distribution model, and a load prediction error probability distribution model; the generation of a set of day-ahead wind-solar load output scenarios based on day-ahead wind-solar load output prediction data in the active distribution network and the wind-solar load prediction error probability distribution model includes: To address the historical power output errors of wind and solar loads in active power distribution networks, we construct probability distribution models for wind power output prediction errors, solar power output prediction errors, and load prediction errors. Based on the probability distribution models of wind power output prediction error, photovoltaic power output prediction error, and load prediction error, Monte Carlo sampling is used to generate wind power output prediction error, photovoltaic power output prediction error, and load prediction error. The wind power output prediction error, photovoltaic power output prediction error and load prediction error are superimposed on the corresponding day-ahead wind power output prediction data, day-ahead photovoltaic power output prediction data and day-ahead load prediction data to generate a day-ahead wind and solar load output scenario set. The daytime wind and solar power output scenario set includes operation scenarios under different combinations of wind and solar power output and load demand.

5. The method according to claim 4, characterized in that, After generating the set of wind, solar and load-bearing scenes, it also includes: The daytime wind and solar power output scenario set is reduced based on the K-medoids clustering algorithm to obtain a typical daytime wind and solar power output scenario set.

6. The method according to claim 4, characterized in that, The probability of occurrence of each operating scenario in the current daytime wind and solar power output scenario set is as follows: in, Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. Let t be the total number of samples in the set of day-ahead wind-solar-load power output scenarios predicted at time t; The first of the predicted day-ahead wind, solar and load power output scenarios at time t. l The membership relationship between a sample and the class centered on the running scenario s, where a value of 1 indicates membership and a value of 0 indicates non-membership.

7. The method according to claim 2, characterized in that, The power flow calculation results include node voltage and line power data for each operating scenario; Based on power flow calculation results, the probability and severity of risk events corresponding to each risk indicator in the operational risk assessment index system for active distribution networks are determined, including: For each assessment period, based on the node voltage and line power data of each operating scenario and the definition of each risk indicator in the operating risk assessment indicator system, it is calculated whether the risk event corresponding to each risk indicator has occurred. Based on the node voltage and line power data for each operating scenario and the severity function of the risk events corresponding to each risk indicator, the severity of the occurrence of the risk events corresponding to each risk indicator is calculated. Among them, the severity function of each risk indicator is used to measure the degree to which the corresponding risk event exceeds the limit.

8. The method according to claim 7, characterized in that, The values ​​of each risk indicator are as follows: ， ； ， ； in, Let be the value of the voltage-related risk indicator for the i-th node predicted at time t. Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. This indicates whether the risk event corresponding to the voltage risk indicator has occurred as predicted at time t. A value of 1 indicates that it has occurred, and a value of 0 indicates that it has not occurred. Let be the voltage of node i in the predicted operating scenario s at time t. For voltage limits, The set of day-ahead wind, solar and load power output scenarios predicted at time t; This is a severity function for risk events corresponding to voltage-related risk indicators; e Represents the natural base; Let be the predicted power risk index value for line j at time t. This indicates whether the risk event corresponding to other risk indicators has occurred at time t, with a value of 1 indicating occurrence and a value of 0 indicating non-occurrence. Let t be the active power flowing through line j under the predicted operating scenario s at time t. Here is the power limit for line j; This is a severity function for the risk events corresponding to power-related risk indicators.

9. The method according to claim 2, characterized in that, The weights of each risk indicator include combined weights and hierarchical weights; Weights are assigned to each risk indicator based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, including: The objective weights of each risk indicator are calculated based on the entropy weight method, and the subjective weights of each risk indicator are determined based on the analytic hierarchy process. For each risk indicator, the corresponding subjective weight is adjusted using the objective weight of the risk indicator to obtain the combined weight of the risk indicators; Based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, the hierarchical weight of each risk indicator within each spatial level is determined.

10. The method according to claim 8, characterized in that, Based on the values ​​and weights of each risk indicator in each assessment period, the day-ahead operational risk of the active distribution network is dynamically assessed, including: For each spatial level in each assessment period, the risk value of the spatial level in each assessment period is determined based on the ratio of the weighted risk value of each risk indicator within the spatial level to the theoretical maximum risk value. Based on the hierarchical weights and risk values ​​of each spatial level, the comprehensive risk value of the active distribution network for each assessment period is determined. Based on the time-series variation characteristics of the comprehensive risk value of the active distribution network over multiple assessment periods, the daily operational risk changes of the active distribution network are dynamically assessed. The total risk of the active distribution network is obtained by superimposing the comprehensive risk values ​​of the active distribution network for each assessment period before the current day; the risk level of the active distribution network is assessed based on the total risk of the active distribution network before the current day. The weighted risk value of each risk indicator within the spatial hierarchy is determined based on the value of each risk indicator within the spatial hierarchy and the combined weight of each risk indicator.

11. The method according to claim 10, characterized in that, Following the dynamic assessment of the day-ahead operational risks of the active distribution network, the following is also included: Overall risk warning for active distribution networks is conducted based on the day-ahead risk level of the active distribution network.

12. The method according to claim 10, characterized in that, Following the dynamic assessment of the day-ahead operational risks of the active distribution network, the following is also included: For each spatial level, the total risk of the spatial level is obtained by summing the risk values ​​of the spatial level over the current assessment period; based on the total risk of the spatial level, the risk level of each spatial level is determined. Visualized early warning of risk areas in active power distribution networks is conducted based on the current risk levels at each spatial level.

13. A dynamic assessment system for operational risks of active power distribution networks, characterized in that, include: The scenario generation module is used to generate a set of day-ahead wind and solar load output scenarios based on the day-ahead power output prediction data of wind and solar load in the active power distribution network and the probability distribution model of wind and solar load prediction error, and to calculate the probability of occurrence of each operating scenario in the set of day-ahead wind and solar load output scenarios. The risk factor calculation module is used to perform power flow calculations for each operating scenario in the daytime wind and solar load output scenario set for each assessment period, and to determine the probability and severity of the occurrence of risk events corresponding to each risk indicator in the active distribution network operation risk assessment index system based on the power flow calculation results. The risk indicator calculation module is used to calculate the value of each risk indicator for each assessment period based on the probability of occurrence of each operational scenario and the probability and severity of the risk events corresponding to each risk indicator. The risk assessment module is used to assign weights to each risk indicator based on the importance of each spatial level in the operational risk assessment indicator system, and to dynamically assess the day-ahead operational risk of the active distribution network based on the value and weight of each risk indicator in each assessment period. The operational risk assessment index system is a spatiotemporal coupled risk index system constructed based on the multi-timescale operational risk characteristics and multi-spatial-level characteristics of the active distribution network; the day-ahead power output prediction data of wind, solar and load is predicted based on the historical power output data of the active distribution network.

14. The system according to claim 13, characterized in that, The operational risk assessment indicator system has multiple time scales, including real-time, short-term, and medium-term; and multiple spatial levels, including distribution area, feeder, and substation. The operational risk assessment index system includes real-time risk indicators for transformer substations, short-term risk indicators for transformer substations, medium-term risk indicators for transformer substations, real-time risk indicators for feeders, short-term risk indicators for feeders, medium-term risk indicators for feeders, and real-time risk indicators for substations, short-term risk indicators for substations, and medium-term risk indicators for substations.

15. The system according to claim 14, characterized in that, The real-time risk indicators for transformer substations include transformer substation overload risk indicators and transformer substation voltage limit exceedance risk indicators; the short-term risk indicators for transformer substations include three-phase imbalance risk indicators; and the medium-term risk indicators for transformer substations include equipment aging risk indicators. The real-time risk indicators for feeders include feeder power flow limit exceedance risk indicators and feeder voltage limit exceedance risk indicators; the short-term risk indicators for feeders include feeder fault risk indicators; and the medium-term risk indicators for feeders include feeder power flow fluctuation entropy risk indicators. The real-time risk indicators for substations include main transformer bus voltage limit exceedance risk indicators and main transformer overload risk indicators; the short-term risk indicators for substations include main transformer bus short-circuit current risk indicators and main transformer reactive power compensation margin risk indicators; and the medium-term risk indicators for substations include main transformer N-1 throughput risk indicators. The three-phase imbalance risk index is used to characterize the degree of imbalance of three-phase current / voltage in the transformer area. The equipment aging risk index is used to characterize the probability of equipment failure caused by the aging rate based on the equipment's years of operation and environmental conditions. The feeder power flow over-limit risk index is used to characterize the degree to which the active / reactive power flow of the line exceeds the thermal stability. The feeder failure risk index is used to characterize the feeder failure rate predicted based on historical feeder failure data and environmental factors. The feeder power flow fluctuation entropy risk index is used to characterize the power flow fluctuation uncertainty and disturbance rejection capability of the active distribution network. Its calculation formula is as follows: ； in, This represents the entropy of the feeder power flow fluctuation. The amplitude of the tidal fluctuation is in the first... f The probability distribution of each amplitude interval. The larger the value, the greater the uncertainty of the current fluctuation. The short-circuit current risk index of the main transformer bus is used to characterize the probability that the short-circuit current exceeds the breaking capacity of the circuit breaker. The main transformer reactive power compensation margin risk index is used to characterize the voltage regulation capability of the main transformer.

16. The system according to claim 14, characterized in that, The wind-solar-load prediction error probability distribution model includes a wind power output prediction error probability distribution model, a photovoltaic power output prediction error probability distribution model, and a load prediction error probability distribution model; the scenario generation module includes: The error model construction submodule is used to construct probability distribution models for wind power output prediction error, photovoltaic power output prediction error, and load prediction error, based on the historical output error of wind and solar loads in active distribution networks. The error generation submodule is used to generate wind power output prediction error, photovoltaic output prediction error and load prediction error based on the probability distribution models of wind power output prediction error, photovoltaic output prediction error and load prediction error, using Monte Carlo sampling. The scenario generation submodule is used to superimpose the wind power output prediction error, photovoltaic power output prediction error and load prediction error onto the corresponding day-ahead wind power output prediction data, day-ahead photovoltaic power output prediction data and day-ahead load prediction data to generate a set of day-ahead wind, solar and load output scenarios. The daytime wind and solar power output scenario set includes operation scenarios under different combinations of wind and solar power output and load demand.

17. The system according to claim 16, characterized in that, The scene generation module also includes: The scene reduction submodule is used to reduce the daytime wind and solar load output scene set based on the K-medoids clustering algorithm to obtain a typical daytime wind and solar load output scene set.

18. The system according to claim 16, characterized in that, The probability of occurrence of each operating scenario in the current daytime wind and solar power output scenario set is as follows: in, Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. Let t be the total number of samples in the set of day-ahead wind-solar-load power output scenarios predicted at time t; The first of the predicted day-ahead wind, solar and load power output scenarios at time t. l The membership relationship between a sample and the class centered on the running scenario s, where a value of 1 indicates membership and a value of 0 indicates non-membership.

19. The system according to claim 14, characterized in that, The power flow calculation results include node voltage and line power data for each operating scenario; The risk factor calculation module includes: The event probability calculation submodule is used to calculate, for each evaluation period, whether the risk event corresponding to each risk indicator has occurred, based on the node voltage and line power data of each operating scenario and the definition of each risk indicator in the operating risk assessment indicator system. The event severity calculation submodule is used to calculate the severity of the occurrence of risk events corresponding to each risk indicator based on the node voltage and line power data of each operating scenario and the severity function of each risk indicator corresponding to the risk event. Among them, the severity function of each risk indicator is used to measure the degree to which the corresponding risk event exceeds the limit.

20. The system according to claim 19, characterized in that, The values ​​of each risk indicator are as follows: ， ； ， ； in, Let be the value of the voltage-related risk indicator for the i-th node predicted at time t. Let be the probability of scenario s occurring in the set of day-ahead wind and solar power output scenarios predicted at time t. This indicates whether the risk event corresponding to the voltage risk indicator has occurred as predicted at time t. A value of 1 indicates that it has occurred, and a value of 0 indicates that it has not occurred. Let be the voltage of node i in the predicted operating scenario s at time t. For voltage limits, The set of day-ahead wind, solar and load power output scenarios predicted at time t; This is a severity function for risk events corresponding to voltage-related risk indicators; e Represents the natural base; Let be the predicted power risk index value for line j at time t. This indicates whether the risk event corresponding to other risk indicators has occurred at time t, with a value of 1 indicating occurrence and a value of 0 indicating non-occurrence. Let t be the active power flowing through line j under the predicted operating scenario s at time t. Here is the power limit for line j; This is a severity function for the risk events corresponding to power-related risk indicators.

21. The system according to claim 14, characterized in that, The weights of each risk indicator include combined weights and hierarchical weights; the risk assessment module includes a weighting submodule, which is specifically used for: The objective weights of each risk indicator are calculated based on the entropy weight method, and the subjective weights of each risk indicator are determined based on the analytic hierarchy process. For each risk indicator, the corresponding subjective weight is adjusted using the objective weight of the risk indicator to obtain the combined weight of the risk indicators; Based on the importance of each spatial level in the aforementioned operational risk assessment indicator system, the hierarchical weight of each risk indicator within each spatial level is determined.

22. The system according to claim 20, characterized in that, The risk assessment module includes: The overall risk assessment submodule is used to determine the risk value of each spatial level in each assessment period based on the ratio of the weighted risk value of each risk indicator within the spatial level to the theoretical maximum risk value; determine the comprehensive risk value of the active distribution network in each assessment period based on the level weight and the risk value of each spatial level; sum the comprehensive risk values ​​of the active distribution network in each assessment period for the current day to obtain the total risk of the active distribution network for the current day; and assess the current day risk level of the active distribution network based on the total risk of the current day. The time-series assessment submodule is used to dynamically assess the changes in the day-ahead operational risk of the active distribution network based on the time-series variation characteristics of the comprehensive risk value of the active distribution network over multiple assessment periods for the day-ahead. The weighted risk value of each risk indicator within the spatial hierarchy is determined based on the value of each risk indicator within the spatial hierarchy and the combined weight of each risk indicator.

23. The system according to claim 22, characterized in that, Also includes: The early warning module is used to provide overall risk warnings for the active distribution network based on the day-ahead risk level of the active distribution network.

24. The system according to claim 22, characterized in that, It also includes an early warning module, which is used for: For each spatial level, the total risk of the spatial level is obtained by summing the risk values ​​of the spatial level over the current assessment period; based on the total risk of the spatial level, the risk level of each spatial level is determined. Visualized early warning of risk areas in active power distribution networks is conducted based on the current risk levels at each spatial level.

25. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method as described in any one of claims 1 to 12 is implemented.

26. A readable storage medium, characterized in that, It contains an executable program, which, when executed, implements the method as described in any one of claims 1 to 12.

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