A method, system, device and medium for long-term risk assessment of a mountainous distribution network

Abstract

The application discloses a kind of mountainous area distribution network long-term risk assessment method, system, equipment and medium, comprising: obtaining historical meteorological data, the average duration of each type of extreme weather and normal weather is counted, the Markov state transition rate is calculated, and the state transition rate set is constructed;Based on the set, the sequential Monte Carlo method is used to generate independent weather state sub-sequence for each type of extreme weather, and is merged according to unified time axis, to form the multi-type composite weather time sequence covering the long-term evaluation period;Combined with the sequence, the segmented modeling is obtained, and the element operating state scene set is aligned with the weather sequence;On this basis, active distribution network optimization operation simulation is executed every time period, the total cycle load shedding power and new energy power abandonment quantity are summarized, the power shortage expectation and new energy power abandonment expectation are calculated respectively, and the long-term risk assessment result is finally output.The application significantly improves the meteorological coupling, physical consistency and operation practicability of risk assessment.

Description

Technical Field

[0001] This invention relates to the field of distribution network risk assessment technology, and in particular to a method, system, equipment and medium for medium- and long-term risk assessment of distribution networks in mountainous areas. Background Technology

[0002] In recent years, due to global warming and changes in the Arctic region, the frequency and scope of extreme weather events have increased significantly. These extreme weather events have caused severe damage to power transmission and distribution systems, leading not only to widespread power shortages but also to huge economic losses. Particularly in mountainous areas where power grids are located, the complex natural geographical environment, frequent extreme weather events, and relatively weak grid structures make the entire power system more susceptible to their impact. The failure rate of electrical components such as generators and transmission lines also increases significantly with the arrival of extreme weather. Considering the randomness of extreme weather events and the uncertainty of electrical component failures, traditional deterministic safety assessment methods have gradually shifted to risk-based probabilistic safety assessments. Therefore, using historical statistical data to assess the probability of extreme weather events and random component failures, and further analyzing their actual impact on the power grid, is of significant practical importance.

[0003] However, traditional long-term reliability assessments of power systems often focus only on the transmission system, neglecting the distribution system. On the other hand, increasingly more renewable energy sources such as distributed photovoltaics (PV) and flexible resources like distributed energy storage systems (ESS) are being integrated into modern distribution networks (PDNs) to meet changing load demands and also to decarbonize the system. This is gradually transforming traditional distribution networks into active distribution networks (ADNs). While this transformation increases flexibility, it also presents new challenges to the safe operation of the system. Therefore, when conducting risk assessments, the optimized operation modes of various flexible resources within the ADN must be fully considered. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, equipment, and medium for long-term risk assessment of power distribution networks in mountainous areas to address the problem that existing power system risk assessment methods are unable to accurately reflect the real safety risks of modern power distribution networks under extreme weather conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for medium- and long-term risk assessment of power distribution networks in mountainous areas, comprising: By acquiring historical meteorological data, statistically analyzing the average duration of each type of extreme weather event and normal weather state, and calculating the corresponding Markov state transition rate, a set of state transition rates between various extreme weather events and normal weather is obtained. Based on the set of state transition rates, the sequential Monte Carlo algorithm is used to independently generate corresponding weather state subsequences for each type of extreme weather, and all weather state subsequences are merged along a unified time axis to obtain a multi-type composite weather state time series covering the entire medium- and long-term assessment period. Based on the multi-type composite weather state time series, a set of component operation state scenarios aligned with the complete weather series is obtained through segmented modeling. Based on the set of operating status scenarios of the components and the corresponding weather status time series, by performing active distribution network optimization operation simulation in each time period, and summarizing the load shedding power and renewable energy curtailment power in each time period throughout the entire cycle to calculate the expected power shortage and the expected renewable energy curtailment power, the final medium- and long-term risk assessment results are obtained.

[0007] As a preferred embodiment of the long-term risk assessment method for mountain power distribution networks described in this invention, the method involves acquiring historical meteorological data, statistically analyzing the average duration of each type of extreme weather event and normal weather condition, and calculating the corresponding Markov state transition rate to obtain a set of state transition rates between various extreme weather events and normal weather conditions, including: Each type of extreme weather event and normal weather is modeled as a two-state Markov process. Based on the average duration of corresponding extreme weather and the average duration of normal weather in historical meteorological data, the occurrence rate and recovery rate of extreme weather are calculated respectively. The occurrence rate and recovery rate are used to construct a state transition rate matrix for the corresponding extreme weather type.

[0008] The beneficial effect of this preferred technical solution is that by constructing each type of extreme weather and normal weather as a two-state Markov process, and accurately calculating its occurrence rate and recovery rate based on historical meteorological data, quantitative modeling of the state transition characteristics of multiple types of extreme weather is realized, laying the foundation for generating high-fidelity medium- and long-term composite weather sequences.

[0009] As a preferred embodiment of the medium- and long-term risk assessment method for mountain power distribution networks described in this invention, the method involves: generating corresponding weather state subsequences independently for each type of extreme weather using a sequential Monte Carlo algorithm based on the state transition rate set, and merging all weather state subsequences along a unified time axis to obtain a multi-type composite weather state time series covering the entire medium- and long-term assessment period, including: Based on the geographical characteristics of mountainous areas, extreme weather types that are strongly correlated with power distribution network failures were selected, including icing, strong winds, and heavy rain. Independent state transition rate matrices are established for icing, strong winds, and heavy rain. During the Monte Carlo simulation, corresponding weather state subsequences are generated based on each independent state transition rate matrix, and the weather state subsequences of different extreme weather events are merged according to a unified time axis.

[0010] As a preferred embodiment of the long-term risk assessment method for mountain power distribution networks described in this invention, the step of obtaining a set of component operating state scenarios aligned with the complete weather sequence through segmented modeling based on the multi-type composite weather state time series includes: During normal weather periods, based on the historical average failure time and average repair time of components, a sequential Monte Carlo algorithm is used to generate a continuous time series of the operating and failure states of the components. Based on the component state at the end of the normal weather period, the initial state of the component at the start of the subsequent extreme weather event is set, and based on the segmented vulnerability curve of the corresponding extreme weather event and the component design tolerance threshold, multiple independent component failure state scenarios are generated through non-sequential Monte Carlo sampling. After the extreme weather event ends, all faulty components will be reset to the operating state as the initial component state for the next normal weather period.

[0011] As a preferred embodiment of the long-term risk assessment method for mountain power distribution networks described in this invention, the step of performing active power distribution network optimization operation simulation in each time period based on the set of component operating state scenarios and the corresponding weather state time series includes: For normal weather periods, a deterministic optimization simulation is performed based on the corresponding single component operating state scenario; For extreme weather events, optimization simulations are performed independently for each of the corresponding multiple component failure scenarios, and the optimal operational decision results for each scenario are retained. Throughout the entire optimized operation simulation process, the output of distributed photovoltaic power is modeled as a controllable variable. The maximum available power of distributed photovoltaic power is simultaneously limited by the current weather conditions and the connectivity status of the components.

[0012] As a preferred embodiment of the long-term risk assessment method for mountain power distribution networks described in this invention, the active power distribution network optimization operation simulation includes: The objective function is to minimize the sum of load shedding costs, curtailment penalties for solar and wind power, energy storage charging and discharging degradation costs, and external grid purchase costs. Based on the network topology determined by the current component operating status scenario, an improved branch power flow model is used to describe the active and reactive power flow relationship of the radial distribution network, and safety limit constraints are imposed on the line transmission power and node voltage amplitude. Under the improved branch power flow model, dynamic balance constraints on the state of charge and limits on the charging and discharging power are imposed on the energy storage system, while limiting the load shedding of each node to the upper limit of the corresponding load demand.

[0013] The beneficial effect of this preferred technical solution is that, by constructing a refined optimization model that integrates economic objectives and physical security constraints, it achieves optimal operation decisions with multi-source collaborative scheduling and controllable risks, while accurately reflecting the distribution network topology and operational limitations.

[0014] As a preferred embodiment of the long-term risk assessment method for mountain power distribution networks described in this invention, the step of summarizing the load shedding power and renewable energy curtailment power for each time period throughout the entire cycle to calculate the expected power shortage and the expected renewable energy curtailment power includes: During normal weather periods, the load shedding power obtained from a single optimized operation simulation and the power curtailment of new energy sources will be directly added to the total risk index. For periods of extreme weather events, first calculate the arithmetic average of the load shedding and renewable energy curtailment under multiple component failure scenarios, and then sum the obtained averages to the total risk index; The expected power shortage is the time-weighted sum of the load shedding power in each period of the entire assessment period, and the expected renewable energy curtailment power is the time-weighted sum of the renewable energy curtailment power in each period of the entire assessment period.

[0015] Secondly, the present invention provides a medium- and long-term risk assessment system for power distribution networks in mountainous areas, comprising: The multi-type extreme weather time series modeling module is used to acquire historical meteorological data, calculate the state transition rate between various types of extreme weather events and normal weather, and use the Monte Carlo algorithm to generate multi-type weather state time series covering the entire medium and long-term assessment period. The component operation status scenario generation module is used to construct component operation status scenarios of power system components in segments based on the weather state time series; The active distribution network optimization operation simulation module is used to perform distribution network optimization operation simulation based on each time period in the weather state time series and the corresponding component operation state scenario; The medium- and long-term comprehensive risk quantification module is used to calculate the expected power shortage and the expected curtailment of renewable energy based on the optimized operation simulation results under all time periods and all component operating status scenarios. These results serve as a comprehensive risk assessment indicator for the distribution network within the medium- and long-term assessment period, and output the final risk level.

[0016] Thirdly, the present invention provides an electronic device, comprising: Memory, used to store programs; A processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the long-term risk assessment method for the mountain power distribution network.

[0017] Fourthly, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the medium- and long-term risk assessment method for mountain power distribution networks.

[0018] The beneficial effects of this invention are as follows: This invention constructs a Markov state transition model for multiple types of extreme weather based on historical meteorological data and combines it with the Monte Carlo algorithm to generate a composite weather state time series covering the medium- and long-term assessment period, thereby achieving high-fidelity time series simulation of typical mountain disasters (such as icing, strong winds, and rainstorms) and their superimposed or alternating scenarios; by establishing a sequential Monte Carlo component state sequence under normal weather conditions and a non-sequential multi-scenario failure model based on a piecewise vulnerability curve under extreme weather conditions in segments, and by introducing state inheritance and post-event forced repair mechanisms, it achieves continuous and physical consistency modeling of the operating state of power system components under long-term scales and multi-hazard disturbances; by coupling network topology, improved branch power flow constraints, and flexible resource scheduling strategies in each time period to perform refined optimization operation simulation, and calculating the expected power shortage and expected renewable energy curtailment based on scenario-based results, it achieves quantitative assessment and proactive operation support for the medium- and long-term comprehensive risks of the distribution network. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the basic process of a medium- and long-term risk assessment method for a mountain power distribution network, provided as an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for medium- and long-term risk assessment of power distribution networks in mountainous areas is provided, comprising: S100: Obtain historical meteorological data, calculate the corresponding Markov state transition rate by statistically analyzing the average duration of each type of extreme weather event and normal weather state, and obtain the set of state transition rates between various types of extreme weather and normal weather. S200: Based on the state transition rate set, the sequential Monte Carlo algorithm is used to independently generate corresponding weather state subsequences for each type of extreme weather, and all weather state subsequences are merged according to a unified time axis to obtain a multi-type composite weather state time series covering the entire medium and long-term assessment period. S300: Based on the time series of multiple types of composite weather conditions, a set of component operation status scenarios aligned with the complete weather series is obtained through segmented modeling. S400: Based on the set of component operating status scenarios and the corresponding weather status time series, it performs active distribution network optimization operation simulation in each time period, and summarizes the load shedding power and renewable energy curtailment power in each time period throughout the entire cycle to calculate the expected power shortage and renewable energy curtailment power, and obtains the final medium- and long-term risk assessment results.

[0022] It should be noted that existing power system risk assessment methods face a series of challenges during operation. These include: most methods simplify weather conditions to static or single disaster scenarios, making it difficult to characterize the superposition and alternation of multiple types of extreme weather such as icing, strong winds, and heavy rain over time, leading to distortion of meteorological driving factors; on the other hand, component reliability models usually adopt a uniform failure rate assumption, failing to distinguish the differences in failure mechanisms under normal and extreme weather conditions, and ignoring the physical characteristics of component vulnerability dynamically changing with meteorological intensity; furthermore, existing assessment frameworks often separate operation simulation from risk quantification processes, lacking a collaborative optimization mechanism that integrates network topology changes, flexible resource scheduling, and uncertainties in multiple scenarios at medium- to long-term scales. This makes it difficult for key risk indicators such as power shortages and renewable energy curtailment to accurately reflect the actual operating status, weakening the ability of risk assessment to support distribution network planning and operation decisions.

[0023] Therefore, in response to the problem that existing power system risk assessment methods cannot accurately reflect the real safety risks of modern distribution networks under extreme weather conditions, the S100-S400 steps, through the integration of multi-type extreme weather time series modeling, dual-modal component state scenario generation, and medium- and long-term distribution network optimization operation simulation, have achieved accurate quantification of the expected power shortage and new energy curtailment in mountainous distribution networks, significantly improving the physical consistency, meteorological coupling, and operational practicality of medium- and long-term comprehensive risk assessment.

[0024] Example 2, this is an embodiment of the present invention, which provides a method for medium- and long-term risk assessment of mountain power distribution networks based on the previous embodiment, including: In this embodiment of the application, step S100 involves acquiring historical meteorological data, calculating the corresponding Markov state transition rate by statistically analyzing the average duration of each type of extreme weather event and normal weather state, and obtaining a set of state transition rates between various extreme weather events and normal weather, including: Each type of extreme weather event and normal weather is modeled as a two-state Markov process. Based on the average duration of corresponding extreme weather and the average duration of normal weather in historical meteorological data, the occurrence rate and recovery rate of extreme weather are calculated respectively. The occurrence rate and recovery rate are used to construct the state transition rate matrix for the corresponding extreme weather type.

[0025] In this embodiment of the application, the time series modeling of extreme weather events in step S100 involves constructing each type of extreme weather (such as icing, strong winds, and heavy rain) and normal weather as a two-state Markov process, calculating the occurrence rate and recovery rate based on historical data to statistically analyze the average duration, and then using the sequential Monte Carlo method to sample and generate the duration of each weather state according to the exponential distribution, and merging multiple types of weather subsequences according to a unified time axis to form a composite weather state time series covering the medium and long-term assessment period.

[0026] In an optional implementation, the time series modeling method for extreme weather events in step S100 can also be based on historical meteorological data to fit the empirical distribution of the actual duration of various extreme weather and normal weather (such as Weibull or log-normal distribution), and in S200, the state duration is directly sampled from the empirical distribution using the sequential Monte Carlo algorithm, while the remaining steps (subsequence merging, component state modeling, optimization simulation and risk assessment) remain unchanged.

[0027] In an optional implementation, the time series modeling method for extreme weather events in step S100 can also utilize historical meteorological sequences to train an Hidden Model (HMM), dividing the weather into multiple latent states (such as no disaster, mild, moderate, and severe extreme events), learning their state transition matrix and observation probability distribution, and generating a continuous weather state time series containing multiple intensity levels in step S200 through forward sampling or Viterbi decoding, replacing the original binary "normal / extreme" subsequence. The subsequent steps S300–S400 adapt to the multi-state input to complete the component state modeling and risk assessment.

[0028] In this embodiment, a two-state model is used to model extreme weather events, where the occurrence process of extreme weather can be considered to follow a Poisson distribution. Based on this, the second... The rate of state transition between extreme weather and normal weather conditions – including occurrence rate (i.e., failure rate). With recovery rate (i.e., repair rate) —This can be calculated based on historical meteorological statistics and satisfies the following relationship: in, Indicates the first The average duration of extreme weather events, This indicates the average duration of normal weather.

[0029] In this embodiment of the application, step S200, based on the state transition rate set, generates a corresponding weather state subsequence for each type of extreme weather independently using the sequential Monte Carlo algorithm, and merges all weather state subsequences along a unified time axis to obtain a multi-type composite weather state time series covering the entire medium- and long-term assessment period, including: Based on the geographical characteristics of mountainous areas, extreme weather types that are strongly correlated with power distribution network failures were selected, including icing, strong winds, and heavy rain. Independent state transition rate matrices are established for icing, strong winds, and heavy rain. During the Monte Carlo simulation, corresponding weather state subsequences are generated based on each independent state transition rate matrix, and the weather state subsequences of different extreme weather events are merged according to a unified time axis.

[0030] In this embodiment of the application, the duration of various weather conditions follows an exponential distribution, the th... Duration of extreme weather-like conditions The probability density function is expressed as: Duration of normal weather conditions The probability density function is expressed as: Based on the above distribution, the state duration is generated using the sequential Monte Carlo method, specifically as follows: in, and denoted as the duration of normal weather state and extreme weather state respectively during the sequential Monte Carlo simulation of the i-th type of extreme weather event. and It is a random number that is uniformly distributed between [0,1].

[0031] In this application embodiment, when evaluating the reliability of electrical components under normal weather conditions, the impact of random failures is primarily considered. This invention employs a two-state model to quantify this impact. According to this model, for the first state in a power system... Each component has a failure rate and repair rate It can be calculated based on historical operating data.

[0032] Under normal weather conditions, the first The failure rate and repair rate of each component are as follows: in, and They represent the first in the power system. Mean time to failure and mean time to repair for each component.

[0033] In this embodiment of the application, step S300, based on the multi-type composite weather state time series, obtains a set of component operating state scenarios aligned with the complete weather series through segmented modeling, including: During normal weather periods, based on the historical average failure time and average repair time of components, a sequential Monte Carlo algorithm is used to generate a continuous time series of the operating and failure states of the components. Based on the component state at the end of a normal weather period, the initial state of the component at the start of a subsequent extreme weather event is set. Based on the piecewise vulnerability curve of the corresponding extreme weather event and the component design tolerance threshold, multiple independent component failure state scenarios are generated through non-sequential Monte Carlo sampling. The piecewise vulnerability curve is used to characterize the piecewise response characteristics of the component failure probability as the extreme weather intensity parameter changes from low to high, exhibiting low constant value, monotonically increasing value, and high constant value below the tolerable threshold, in the transition range, and in the over-limit range, respectively. After the extreme weather event ends, all faulty components are reset to operational status, serving as the initial component state for the next normal weather period.

[0034] In this embodiment of the application, the failure modeling method of the component under extreme weather conditions in step S300 is as follows: In an optional implementation, the failure modeling method of the component under extreme weather conditions in step S300 can also be based on a direct comparison between the component's mechanical or electrical withstand limits (such as wind resistance strength, insulation withstand voltage threshold) and actual extreme weather intensity parameters (such as wind speed, ice thickness). When the weather intensity exceeds the critical value, the component is deterministically set to a fault state; otherwise, it remains in operation, and the rest of the process remains unchanged.

[0035] In an optional implementation, the failure modeling method of components under extreme weather conditions in step S300 can also utilize a pre-trained machine learning model (such as random forest or graph neural network) to output the failure probability of each component based on the current extreme weather intensity, component attributes and geographical environment characteristics as input, and generate multiple component state scenarios through non-sequential Monte Carlo sampling accordingly, while the rest of the process remains unchanged.

[0036] In this embodiment, the component failure probability under extreme weather conditions is characterized by a piecewise fragility curve, the mathematical expression of which is: in, Key parameters representing extreme weather include wind speed for typhoons or ice thickness for ice storms; and These represent the design-tolerable weather parameters and the maximum tolerable weather parameters for the component, respectively. and The failure rate and repair rate of components under normal operating conditions; To define in the interval A monotonically non-decreasing continuous function on the weather surface is used to describe the transient characteristics of the component failure probability increasing with weather intensity.

[0037] In this embodiment of the application, under normal weather conditions, the first Duration of operation of each component Duration of the fault state Generated using the sequential Monte Carlo method, satisfying: in, and These are mutually independent, uniformly distributed random numbers generated in the interval [0,1]. Under extreme weather conditions, for the first In the first scenario Operating status of each component Determined through non-sequential Monte Carlo sampling, satisfying the following: in, This is a binary variable; a value of 1 indicates that the component is in operation, and a value of 0 indicates that the component is in a fault state. For uniformly distributed random numbers generated in the interval [0,1], To correspond to extreme weather parameters The probability of component failure under the following conditions; The extreme weather event simulation uses the following two assumptions: the state of each electrical component in each scenario is either operational or faulty; and the extreme weather parameters used in each scenario are... The intensity value is taken from the average intensity value of similar extreme weather events in history.

[0038] In this embodiment of the application, step S400, based on the set of component operating state scenarios and the corresponding weather state time series, includes performing an active distribution network optimization operation simulation in each time period, including: For normal weather periods, a deterministic optimization simulation is performed based on the corresponding single component operating state scenario; For extreme weather events, optimization simulations are performed independently for each of the corresponding multiple component failure scenarios, and the optimal operational decision results for each scenario are retained. Throughout the entire optimized operation simulation process, the output of distributed photovoltaic power is modeled as a controllable variable. The maximum available power of distributed photovoltaic power is simultaneously limited by the current weather conditions and the connectivity status of the components.

[0039] In this embodiment of the application, step S400, active distribution network optimization operation simulation, includes: The objective function is to minimize the sum of load shedding costs, curtailment penalties for solar and wind power, energy storage charging and discharging degradation costs, and external grid purchase costs. Based on the network topology determined by the current component operating status scenario, an improved branch power flow model is used to describe the active and reactive power flow relationship of the radial distribution network, and safety limit constraints are imposed on the line transmission power and node voltage amplitude. Under the improved branch power flow model, dynamic balance constraints on the state of charge and limits on the charging and discharging power are imposed on the energy storage system, while limiting the load shedding of each node to the upper limit of the corresponding load demand.

[0040] In this embodiment of the application, the solution strategy for the active distribution network optimization operation simulation in step S400 is to construct a deterministic optimization model with the goal of minimizing load shedding, power curtailment, energy storage degradation and electricity purchase cost for each time period and each component state scenario. The improved branch power flow constraints and operation constraints such as energy storage, voltage and power are used to solve the mixed integer programming problem to obtain the optimal scheduling decision for each time period and the corresponding load shedding and new energy output results.

[0041] In an optional implementation, the solution strategy for the active distribution network optimization operation simulation in step S400 can also construct a two-stage robust optimization model: the first-stage decision (such as the energy storage baseline charge and discharge plan) is determined before the start of the extreme weather event, and the second-stage scheduling variables (such as load shedding and new energy output) are adjusted under the given component state and the worst realization within the uncertainty set to minimize the maximum possible operating cost. The other risk indicators are summarized in a way that is adapted to the robust solution result.

[0042] In an optional implementation, the solution strategy for the active distribution network optimization operation simulation in step S400 can also construct a two-stage stochastic programming model for each extreme weather event and its Monte Carlo scenario set: the first stage determines the day-ahead scheduling decision (such as the energy storage charging and discharging plan) before the event begins, and the second stage adjusts the recourse variables such as load shedding and new energy output in real time according to the specific component status scenario to minimize the expected total operating cost, and calculates the risk index based on the stochastic optimization result.

[0043] In this embodiment, the above optimization problem is solved to obtain the load shedding power for each time period. Actual output of new energy Wait for the results; In this embodiment of the application, the objective of the optimization model is to minimize the operating cost, which consists of the load shedding cost. The cost of curtailing solar and wind power Energy storage system operating costs and electricity purchase cost Composition. Specifically, total cost It can be represented as: The cost items are defined as follows: Shedding Costs Calculated in the following way: in, Represents the set of nodes in an active distribution network; Represents a set of time periods; It is a simulated time interval; and Representing nodes respectively At any moment The unit load shedding cost and load shedding power.

[0044] Curtailment of solar and wind power penalties Defined as: in, Represents a set of generators; The penalty cost per unit for curtailing solar and wind power; and They are the first Each new energy unit is at all times Maximum output power and actual output power.

[0045] Energy storage system operating costs It is given by the following formula: in, Represents a collection of energy storage systems; The charging and discharging cost coefficient for energy storage systems; These are technical degradation parameters; and These represent the charging power and discharging power of the energy storage system at a given time.

[0046] Electricity purchase cost The calculation is as follows: in, for Electricity price at any time Indicates the distribution network at time Electricity purchased.

[0047] In this embodiment, the improved branch power flow model is based on the DistFlow form, and its operational constraints include: And meet the line capacity and voltage safety constraints: in, , They are time points branch road → +1 active and reactive power; , For nodes Load power; , Inject power into the energy storage system; , To contribute to the power generation of new energy units; , This refers to the load shedding power; , For branch resistance and reactance; , These are the upper and lower limits of the node voltage amplitude; This represents the upper limit of the transmission power of the branch.

[0048] The operational constraints of energy storage systems include the dynamic balance between power output and state of charge (SOC), as shown in the following equation: in, For the first The rated apparent power of the energy storage system; Its rated energy; , These are the charging and discharging efficiencies, respectively. , This is the range allowed by SOC.

[0049] The load shedding constraint assumes a proportional relationship between reactive and active load shedding, as shown in the following formula: in, For a moment node The load shedding rate.

[0050] In this embodiment of the application, the aggregation method of the risk index in step S400 is to directly accumulate the load shedding power and renewable energy curtailment power obtained from a single optimization during normal weather periods, while for each extreme weather event, the arithmetic average (i.e. the expected value) of the corresponding power under multiple Monte Carlo scenarios is first calculated, and then the average value is accumulated to the total risk index of the whole cycle by time weighting.

[0051] In an optional implementation, the aggregation method of risk indicators in step S400 can also be to no longer use equal weighted arithmetic average for multiple Monte Carlo scenarios of each extreme weather event, but instead perform weighted summation based on the occurrence probability of each scenario (such as determined by the joint probability of component failure combinations or importance sampling weight) to obtain the probability-weighted expected load shedding power and abandoned power, and then accumulate them to the full-cycle risk indicators.

[0052] In an optional implementation, the aggregation method of risk indicators in step S400 may also be to no longer calculate a simple average for multiple Monte Carlo scenarios of each extreme weather event, but instead solve the CVaR optimization or post-processing model based on a preset confidence level (such as 95%) to quantify the conditional expected value of high-loss scenarios at the tail, and incorporate the CVaR value as the risk contribution of that period into the full-cycle comprehensive risk assessment.

[0053] In this embodiment of the application, based on the operation results, the expected power shortage (EENS) and expected renewable energy curtailment (EARES) are calculated for normal weather conditions and extreme weather conditions, respectively, to quantify the system operation risk.

[0054] For the The duration is Under normal weather conditions, its power consumption is less than expected. Compared with the expected amount of abandoned renewable energy Calculated separately as follows: For the The duration is Extreme weather conditions, based on generated A collection of Monte Carlo scenes Its power is insufficient to meet expectations. Compared with the expected amount of abandoned renewable energy Calculated by averaging across scenarios: in, , These are the set of nodes in the active distribution network and the set of new energy generating units, respectively. This is a simulated time interval; This is a collection of Monte Carlo scenes corresponding to extreme weather events. This represents the load shedding power of node i at time t in the w-th component state scenario generated for the n-th extreme weather event; and Respectively, new energy units The maximum power output and actual output at time t; In this embodiment of the application, step S400, which summarizes the load shedding power and renewable energy curtailment power for each time period throughout the entire cycle to calculate the expected power shortage and the expected renewable energy curtailment power, includes: During normal weather periods, the load shedding power obtained from a single optimized operation simulation and the power curtailment of new energy sources will be directly added to the total risk index. For periods of extreme weather events, first calculate the arithmetic average of the load shedding and renewable energy curtailment under multiple component failure scenarios, and then sum the obtained averages to the total risk index; The expected power shortage is the time-weighted sum of the load shedding power in each period of the entire assessment period, and the expected renewable energy curtailment power is the time-weighted sum of the renewable energy curtailment power in each period of the entire assessment period.

[0055] Example 3 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a long-term risk assessment system for power distribution networks in mountainous areas.

[0056] It should be noted that the technical solution of the medium- and long-term risk assessment system for mountain power distribution networks is based on the same concept as the technical solution of the medium- and long-term risk assessment method for mountain power distribution networks described above. For details not described in detail in the technical solution of the medium- and long-term risk assessment system for mountain power distribution networks in this embodiment, please refer to the description of the technical solution of the medium- and long-term risk assessment method for mountain power distribution networks described above.

[0057] This embodiment describes a medium-to-long-term risk assessment system for mountain power distribution networks, comprising: The state transition modeling module is used to acquire historical meteorological data, and by statistically analyzing the average duration of each type of extreme weather event and normal weather state and calculating the corresponding Markov state transition rate, a set of state transition rates between various types of extreme weather and normal weather is obtained. The time series generation module is used to generate corresponding weather state subsequences for each type of extreme weather independently based on the state transition rate set and through the sequential Monte Carlo algorithm, and to merge all weather state subsequences according to a unified time axis to obtain a multi-type composite weather state time series covering the entire medium and long-term assessment period. The scenario construction module is used to obtain a set of component operation status scenarios aligned with the complete weather sequence based on the multi-type composite weather state time series through a segmented modeling method. The risk quantification and assessment module is used to calculate the expected power shortage and expected renewable energy curtailment based on the set of operating status scenarios of the components and the corresponding weather status time series, by performing active distribution network optimization operation simulation in each time period, and summarizing the load shedding power and renewable energy curtailment power in each time period throughout the entire cycle, so as to obtain the final medium and long-term risk assessment results.

[0058] This embodiment also provides an electronic device applicable to a long-term risk assessment method for a mountainous power distribution network, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a method for medium- and long-term risk assessment of a mountain power distribution network, as proposed in the above embodiments.

[0059] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for medium- and long-term risk assessment of a mountain power distribution network as proposed in the above embodiments.

[0060] The storage medium proposed in this embodiment and the method for implementing medium- and long-term risk assessment of mountain power distribution networks proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0061] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for medium- and long-term risk assessment of power distribution networks in mountainous areas, characterized in that, include: By acquiring historical meteorological data, statistically analyzing the average duration of each type of extreme weather event and normal weather state, and calculating the corresponding Markov state transition rate, a set of state transition rates between various extreme weather events and normal weather is obtained. Based on the set of state transition rates, the sequential Monte Carlo algorithm is used to independently generate corresponding weather state subsequences for each type of extreme weather, and all weather state subsequences are merged along a unified time axis to obtain a multi-type composite weather state time series covering the entire medium- and long-term assessment period. Based on the multi-type composite weather state time series, a set of component operation state scenarios aligned with the complete weather series is obtained through segmented modeling. Based on the set of operating status scenarios of the components and the corresponding weather status time series, by performing active distribution network optimization operation simulation in each time period, and summarizing the load shedding power and renewable energy curtailment power in each time period throughout the entire cycle to calculate the expected power shortage and the expected renewable energy curtailment power, the final medium- and long-term risk assessment results are obtained.

2. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 1, characterized in that: The acquisition of historical meteorological data involves statistically analyzing the average duration of each type of extreme weather event and normal weather conditions, and calculating the corresponding Markov state transition rate to obtain a set of state transition rates between various extreme weather events and normal weather conditions. This set includes: Each type of extreme weather event and normal weather is modeled as a two-state Markov process. Based on the average duration of corresponding extreme weather and the average duration of normal weather in historical meteorological data, the occurrence rate and recovery rate of extreme weather are calculated respectively. The occurrence rate and recovery rate are used to construct a state transition rate matrix for the corresponding extreme weather type.

3. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 1 or 2, characterized in that: Based on the state transition rate set, a sequential Monte Carlo algorithm is used to independently generate corresponding weather state subsequences for each type of extreme weather. All weather state subsequences are then merged along a unified time axis to obtain a multi-type composite weather state time series covering the entire medium- to long-term assessment period, including: Based on the geographical characteristics of mountainous areas, extreme weather types that are strongly correlated with power distribution network failures were selected, including icing, strong winds, and heavy rain. Independent state transition rate matrices are established for icing, strong winds, and heavy rain. During the Monte Carlo simulation, corresponding weather state subsequences are generated based on each independent state transition rate matrix, and the weather state subsequences of different extreme weather events are merged according to a unified time axis.

4. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 3, characterized in that: Based on the multi-type composite weather state time series, a set of component operating state scenarios aligned with the complete weather series is obtained through segmented modeling, including: During normal weather periods, based on the historical average failure time and average repair time of components, a sequential Monte Carlo algorithm is used to generate a continuous time series of the operating and failure states of the components. Based on the component state at the end of the normal weather period, the initial state of the component at the start of the subsequent extreme weather event is set, and based on the segmented vulnerability curve of the corresponding extreme weather event and the component design tolerance threshold, multiple independent component failure state scenarios are generated through non-sequential Monte Carlo sampling. After the extreme weather event ends, all faulty components will be reset to the operating state as the initial component state for the next normal weather period.

5. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 4, characterized in that: The method based on the set of operating state scenarios of the components and the corresponding weather state time series, includes performing active distribution network optimization operation simulation in each time period, including: For normal weather periods, a deterministic optimization simulation is performed based on the corresponding single component operating state scenario; For extreme weather events, optimization simulations are performed independently for each of the corresponding multiple component failure scenarios, and the optimal operational decision results for each scenario are retained. Throughout the entire optimized operation simulation process, the output of distributed photovoltaic power is modeled as a controllable variable. The maximum available power of distributed photovoltaic power is simultaneously limited by the current weather conditions and the connectivity status of the components.

6. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 5, characterized in that: The active distribution network optimization operation simulation includes: The objective function is to minimize the sum of load shedding costs, curtailment penalties for solar and wind power, energy storage charging and discharging degradation costs, and external grid purchase costs. Based on the network topology determined by the current component operating status scenario, an improved branch power flow model is used to describe the active and reactive power flow relationship of the radial distribution network, and safety limit constraints are imposed on the line transmission power and node voltage amplitude. Under the improved branch power flow model, dynamic balance constraints on the state of charge and limits on the charging and discharging power are imposed on the energy storage system, while limiting the load shedding of each node to the upper limit of the corresponding load demand.

7. The method for medium- and long-term risk assessment of mountain power distribution networks as described in claim 6, characterized in that: The summation of load shedding and renewable energy curtailment for each time period throughout the entire cycle is used to calculate the expected power shortage and expected renewable energy curtailment, including: During normal weather periods, the load shedding power obtained from a single optimized operation simulation and the power curtailment of new energy sources will be directly added to the total risk index. For periods of extreme weather events, first calculate the arithmetic average of the load shedding and renewable energy curtailment under multiple component failure scenarios, and then sum the obtained averages to the total risk index; The expected power shortage is the time-weighted sum of the load shedding power in each period of the entire assessment period, and the expected renewable energy curtailment power is the time-weighted sum of the renewable energy curtailment power in each period of the entire assessment period.

8. A medium- and long-term risk assessment system for mountain power distribution networks, using the method described in any one of claims 1-7, characterized in that, include: The state transition modeling module is used to acquire historical meteorological data, and by statistically analyzing the average duration of each type of extreme weather event and normal weather state and calculating the corresponding Markov state transition rate, a set of state transition rates between various types of extreme weather and normal weather is obtained. The time series generation module is used to generate corresponding weather state subsequences for each type of extreme weather independently based on the state transition rate set and through the sequential Monte Carlo algorithm, and to merge all weather state subsequences according to a unified time axis to obtain a multi-type composite weather state time series covering the entire medium and long-term assessment period. The scenario construction module is used to obtain a set of component operation status scenarios aligned with the complete weather sequence based on the multi-type composite weather state time series through a segmented modeling method. The risk quantification and assessment module is used to calculate the expected power shortage and expected renewable energy curtailment based on the set of operating status scenarios of the components and the corresponding weather status time series, by performing active distribution network optimization operation simulation in each time period, and summarizing the load shedding power and renewable energy curtailment power in each time period throughout the entire cycle, so as to obtain the final medium and long-term risk assessment results.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the steps of the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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