A power distribution network carrying capacity multi-dimensional evaluation method and system based on probability evaluation

Abstract

This invention relates to a multi-dimensional evaluation method and system for the carrying capacity of distribution networks based on probabilistic assessment. The method includes: collecting distribution network operation data and constructing a distributed generation processing probability model and a load demand uncertainty model; performing sampling and power flow calculations based on the distributed generation processing probability model and the load demand uncertainty model; calculating and normalizing multi-dimensional indicators based on the calculation results at each sampling time; the multi-dimensional indicators include resilience indicators, including transient voltage stability index, islanding reconstruction power, and energy storage throughput margin; calculating the subjective and objective weights of the multi-dimensional indicators and fusing them to obtain a comprehensive weight; calculating the score for each sampling and the expected score based on the comprehensive weight and the normalization result; calculating the risk probability based on the expected scores of all samplings; and classifying the carrying capacity level based on the expected score and the risk probability. Compared with the prior art, this invention improves the reliability of carrying capacity evaluation for distribution networks with uncertainties.

Description

Technical Field

[0001] This invention relates to the field of distribution network carrying capacity assessment, and in particular to a multidimensional evaluation method and system for distribution network carrying capacity based on probability assessment. Background Technology

[0002] With the accelerated global transition to new energy, the installed capacity of distributed photovoltaic (PV) and wind power, among other new energy sources, is growing rapidly. In my country, distributed new energy capacity now accounts for more than 50% of total new energy capacity, making distribution network carrying capacity assessment a key technical bottleneck restricting the absorption of new energy. Distribution network carrying capacity refers to the maximum capacity of distributed new energy that a distribution network can accommodate under the constraints of safe and stable operation. Currently, scholars and engineers both domestically and internationally primarily employ traditional methods such as static security analysis, power flow calculation, and voltage stability analysis to assess the carrying capacity of distribution networks. Existing distribution network carrying capacity assessment methods are mainly divided into two categories: deterministic assessment methods and probabilistic assessment methods. Deterministic methods are typically based on the most stringent operating scenarios, while probabilistic methods consider some random factors but lack a systematic, multi-dimensional evaluation framework. One probabilistic method, such as Chinese patent application CN116542426A, provides a method for assessing the fault risk of distribution networks considering distributed generation. This method includes constructing an uncertainty model of the distribution network and generating wind and solar load output data based on the uncertainty model; modeling operating scenarios based on the wind and solar load output data, and then conducting risk assessments and generating corresponding risk indicators based on the operating scenarios; generating indicator weights for each risk indicator based on the entropy weight method and the analytic hierarchy process; calculating the comprehensive operating risk value of the distribution network based on the risk indicators and their corresponding weights; and achieving fault risk assessment and safety early warning for the distribution network based on the comprehensive operating risk value. While it solves the problem of accuracy in distribution network fault risk assessment and enables effective assessment and safety early warning of distribution network fault risks after the integration of distributed power sources, the method it provides can only assess the steady-state carrying capacity of the system under preset normal operating conditions, without addressing its dynamic resilience, especially the distribution network's ability to resist disturbances, absorb shocks, and recover quickly under disturbances. As a result, it cannot reflect the actual capabilities of the power grid in the real world, which is full of uncertainties and disturbance threats.

[0003] Therefore, improving the reliability of carrying capacity assessment for distribution networks with uncertainties is a technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a multi-dimensional evaluation method and system for the carrying capacity of distribution networks based on probability assessment. By calculating the resilience index of the distribution network after experiencing a fault, this invention can compensate for the problem that the existing carrying capacity assessment can only realize the carrying capacity under static normal operating conditions, thus limiting the reliability of the evaluation.

[0005] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a multi-dimensional evaluation method for the carrying capacity of a distribution network based on probability assessment is provided, comprising: Collect power distribution network operation data and construct distributed generation probabilistic models and load demand uncertainty models; Based on the distributed power source processing probability model and the load demand uncertainty model, sampling and power flow calculation are performed. At each sampling, multi-dimensional indicators are calculated and normalized based on the calculation results. The multi-dimensional indicators include safety indicators, economic indicators, flexibility indicators and resilience indicators. The resilience indicators include transient voltage stability index, islanding reconstruction power and energy storage throughput margin. Calculate the subjective and objective weights of the multidimensional indicators and fuse them to obtain a comprehensive weight; The score for each sample is calculated based on the comprehensive weight and normalization results, and the expected score is calculated through Monte Carlo simulation. The risk probability is then calculated based on the expected scores of all samples. The carrying capacity level is determined based on the expected score and risk probability.

[0006] As a preferred technical solution, the distributed power processing probability model follows a Beta distribution; the load demand uncertainty model includes a prediction term and a prediction error term, wherein the prediction error term follows a normal distribution.

[0007] As a preferred technical solution, the power flow calculation steps include: Meteorological data is obtained from the operational data, and the weather type for the target day is obtained from the meteorological data. Photovoltaic output is randomly sampled from the distributed power processing probability model based on the weather type. Various load forecasting errors are randomly sampled from the load demand uncertainty model, and the total load is calculated based on these various load forecasting errors. Based on the photovoltaic output and total load, power flow calculations are performed on the distribution network to obtain electrical quantities including the voltage of each node, the current of each branch, and the power.

[0008] As a preferred technical solution, the safety indicators include transformer load rate, voltage deviation rate, and voltage qualification rate, wherein the transformer load rate is calculated based on the sampled photovoltaic output and total load, as well as the transformer's rated capacity; the voltage deviation rate is calculated based on the node voltage obtained from power flow calculation and the corresponding nominal voltage value; and the voltage qualification rate is the ratio of the number of times the voltage deviation rate is less than a preset value to the total number of samplings.

[0009] As a preferred technical solution, the economic indicators include equipment utilization rate, capacity-to-load ratio suitability, and equipment age impact coefficient. The equipment utilization rate is the expected value of the transformer load rate; the capacity-to-load ratio suitability is calculated based on the sum of the electrical equipment capacity of all users in the distribution network area and the rated capacity of the transformer; and the equipment age impact coefficient is calculated using a piecewise function based on the equipment's years of operation.

[0010] As a preferred technical solution, the flexibility indicators include photovoltaic absorption capacity, load regulation margin, and new energy adaptability index. The photovoltaic absorption capacity is calculated based on the maximum photovoltaic output and the rated capacity of the transformer during the sampling time; the load regulation margin is calculated based on the transformer load rate; and the new energy adaptability index is calculated based on the photovoltaic absorption capacity and the load regulation margin.

[0011] As a preferred technical solution, the transient voltage stability index is calculated based on the stability score of a single fault voltage, and the calculation expression is as follows: , , in, This represents the stability score of a single fault voltage. This represents the severity weight of fault f, and , This indicates the apparent power surge caused by fault f. Indicates the rated capacity of the transformer; Indicates the maximum voltage drop depth, and , This represents the pre-fault voltage calculated using tidal methods. This represents the minimum voltage at which fault f occurs; Indicates the rated voltage; Indicates the recovery ability factor, and , and All represent the time dimension weighting coefficients. Indicates the rapid recovery time from fault f. This represents the reference time constant for rapid recovery from fault f. This represents the time from when fault f becomes completely stable. This represents the reference time constant for slow stabilization from fault f; Indicates the transient voltage stability index; This indicates the total number of faults.

[0012] As a preferred technical solution, the islanding reconfiguration success rate is the distribution network islanding reconstruction success rate, and its expression is: , , in, This represents the reconstruction score of the z-th substation. This represents the energy sufficiency index of region z, and , This represents the total capacity of distributed power sources within transformer area z. This represents the total energy storage capacity within the z-region. Indicates the duration of independent power supply by energy storage. This represents the apparent power of the total load in transformer area z; This indicates the number of critical load points in transformer zone z; Let c represent the weight of the critical load point c in transformer zone z, and , This represents the base weight of the critical load c. This represents the weight adjustment factor. This indicates the actual power outage time at critical load point c. This indicates the maximum permissible power outage time at critical load point c; Indicates characteristic functions; This indicates the critical power supply demand at the critical load point c. This represents the reconfiguration time of load c in transformer area z; This represents the reference reconstruction time of station z, and , Indicates the base reference time. Indicates the influence coefficient of switch operation. This indicates the number of switches required for the reconfiguration of the z-region. Indicates the maximum number of switch operations allowed in the distribution network; Indicates the total number of stations; This represents the total power of all critical load points in transformer zone z.

[0013] As a preferred technical solution, the calculation expression for the energy storage throughput margin is as follows: , , , , , , in, Indicates the energy storage throughput margin; Indicates the total duration; Indicates the rated power of the energy storage system; This represents the SOC regulation potential factor at time t. This represents the power regulation capability factor during time period t. This represents the health status factor at time t. This represents the available power during time period t; Indicates the state of charge of the stored energy at time t; express Maximum charging power at all times; express Maximum discharge power at any given moment; and Both represent the health status decay coefficient; express The rate of energy storage capacity decay at any given moment; Indicates the rated capacity of the energy storage system; This represents the increase in internal resistance of the energy storage at time t; This represents the initial internal resistance of the energy storage system; This indicates the energy storage power ramp-up limit at time t.

[0014] According to a second aspect of the present invention, a multi-dimensional evaluation system for the carrying capacity of a distribution network based on probability assessment is provided, the system comprising: The data acquisition module is used to collect power distribution network operation data; The model building module constructs a distributed power processing probability model and a load demand uncertainty model based on the aforementioned operational data. The multi-dimensional index calculation module performs sampling and power flow calculation based on the distributed power source processing probability model and the load demand uncertainty model. During each sampling, it calculates and normalizes multi-dimensional indices based on the calculation results. The multi-dimensional indices include safety indices, economic indices, flexibility indices, and resilience indices. The multi-dimensional index fusion module performs the following steps: calculating the subjective weights of the multi-dimensional indexes using the analytic hierarchy process (AHP), calculating the objective weights of the multi-dimensional indexes using the entropy weight method based on the normalization results, and fusing the subjective and objective weights to obtain a comprehensive weight. The risk probability calculation module calculates the score for each sample based on the comprehensive weight and normalization results, calculates the expected score through Monte Carlo simulation, and calculates the risk probability based on the expected scores of all samples. The bearing capacity evaluation module classifies the bearing capacity level based on the expected score and risk probability.

[0015] Compared with existing technologies, this invention, when assessing the carrying capacity of distribution networks, introduces an independent resilience dimension, in addition to the traditional evaluation which only considers safety, economy, and flexibility under static normal operating conditions. It designs three specific, quantifiable indicators: transient voltage stability index, islanding reconstruction power, and energy storage throughput margin. The transient voltage stability index quantifies the probability of the system maintaining voltage stability after a fault; the islanding reconstruction power quantifies the system's ability to utilize distributed power sources to form islands and restore power to critical loads when a severe fault causes a main grid power outage; and the energy storage throughput margin quantifies the potential of the energy storage system as a rapid adjustment resource to support the system in coping with disturbances and during recovery. These three indicators achieve a more comprehensive and robust carrying capacity assessment that considers fault risk and recovery capability, improving the reliability of the evaluation results. Attached Figure Description

[0016] Figure 1 This is the method flow of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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 scope of protection of the present invention.

[0018] Example 1 To address the problems existing in the prior art, this invention proposes a multi-dimensional evaluation method for the carrying capacity of distribution networks based on probability assessment, the process of which is as follows: Figure 1 As shown, it includes: S1. Collect power distribution network operation data and construct a distributed power source processing probability model and a load demand uncertainty model.

[0019] S11. Collect power distribution network operation data.

[0020] Acquire power distribution network operation data, including: real-time monitoring data from the SCADA system, such as transformer load, three-phase voltage, and current; meteorological data, including irradiance, temperature, and weather type; photovoltaic monitoring data, including photovoltaic output and inverter status; and historical load data, including residential, industrial, and commercial load curves.

[0021] The preprocessing of the collected data includes: outlier identification, marking transformer load rates >120% or <0% as outliers and removing them; missing value handling, using linear interpolation for short-term missing values ​​(<3 hours) and filling long-term missing values ​​(≥3 hours) with the average value of the same type of transformer area; and data synchronization, unifying timestamps to the hourly granularity and adopting an hourly alignment strategy. The timestamp alignment method is to assign non-hourly data to the nearest hourly time according to the nearest principle to ensure that the time base of all data sources is consistent.

[0022] S12. Construct a distributed power source processing probability model.

[0023] The distributed power generation probabilistic model follows a Beta distribution, as follows: , in, This indicates the actual output of photovoltaic power, based on operational data. and These all represent the shape parameters of the Beta distribution, and their values ​​vary depending on the weather type. Indicates the rated power of photovoltaic installations; This represents the operation of the amma function.

[0024] Specifically, for sunny weather, there are At this time, the photovoltaic power output distribution is biased towards high output); for cloudy weather, there is At this time, the photovoltaic power output is relatively evenly distributed; for cloudy weather, there is At this time, the photovoltaic power output distribution is biased towards low output.

[0025] S13. Construct a load demand uncertainty model.

[0026] The load demand uncertainty model includes a forecast term and a forecast error term. The forecast error term follows a normal distribution, and its corresponding expression is: , This represents the total demand on the load side during time period t; This represents the weight of the i-th type of load. This represents the forecast term for the i-th type of load during time period t; Let represent the prediction error term for the i-th type of load, and have , This represents the variance of the prediction error term for the i-th type of load, which follows a normal distribution. The variance values ​​for different types of loads are also different.

[0027] Regarding variance, since business load is the most volatile and is greatly affected by business hours and holidays, the preset variance value is [value missing]. Industrial loads are the most stable, with strong production planning and a pre-set variance of [value missing]. The residents' workload is moderate, and due to the influence of their lifestyle habits, the preset variance is [value missing]. .

[0028] S2. Based on the distributed power source processing probability model and the load demand uncertainty model, sampling and power flow calculation are performed. At each sampling, multi-dimensional indicators are calculated and normalized based on the calculation results.

[0029] Combining the established distributed power generation probabilistic model and load demand uncertainty model, Monte Carlo simulation sampling and power flow calculation are performed. The number of samplings is set, and the following steps are performed, where k-means represents the relevant parameters of different nodes. The steps include: S21. Based on the weather type of the day, randomly sample photovoltaic output from the Beta distribution established in step 3. .

[0030] S22. Randomly sample various load forecasting errors from the load demand uncertainty model. , , Calculate the total load .

[0031] S23, with and As input conditions, power flow calculations are performed on the distribution network to obtain electrical quantities such as voltage at each node, current and power in each branch.

[0032] S24. Based on the power flow calculation results of this sampling, calculate multi-dimensional index values, including security index, economic index, flexibility index and resilience index.

[0033] Specifically: i) Security indicators.

[0034] This indicator includes transformer load rate, voltage deviation rate, and voltage qualification rate. It is a negative indicator. Too high (>100%) indicates overload risk, and too low (<30%) indicates low utilization and poor economy. The optimal range is 70%-85%.

[0035] The transformer load factor is calculated based on the sampled photovoltaic output and total load, as well as the transformer's rated capacity, and its expression is: , in, The actual load power (kW) is the sampled at the kth time. Let be the photovoltaic output power (kW) of the kth sampling. This refers to the rated capacity of the transformer (kVA). The voltage deviation rate is calculated based on the node voltage obtained from power flow calculations and the corresponding nominal voltage value. This index is negative; the larger the value, the worse the voltage quality. According to the national standard GB / T 12325-2008, it should be controlled within ±7%. Its expression is: , in, , These are the actual three-phase voltage values ​​(V) obtained from the power flow calculation of the kth sampling; This is the nominal voltage value for a single phase.

[0036] The voltage qualification rate is the ratio of the number of times the voltage deviation rate is less than the preset value to the total number of samples. This indicator is a positive indicator; a higher value indicates better time stability of voltage quality and reflects the probability of voltage qualification in the distribution network. Its expression is: , Where I(·) is the indicative function, when the voltage deviation rate C 12 The value is 1 when ^{(k)}≤7%, otherwise it is 0; the numerator is the number of times the voltage is qualified in N samples; the denominator N is the total number of samples.

[0037] For the transformer load rate and voltage deviation rate in the safety indicators, calculate the corresponding expected values. , and C 13 As the final value of the safety indicator, due to C 13 It is already a statistical quantity, so it can be used directly.

[0038] ii) Economic indicators.

[0039] This indicator includes equipment utilization rate, load-capacity ratio suitability, and equipment age impact coefficient. Among them, equipment utilization rate is the expected value of transformer load rate, which is a positive indicator. The higher the value, the more fully the equipment is utilized and the better the economic efficiency. The corresponding expression is: , Among them, E[C 11 [This represents the calculated expected value of the transformer load rate.] This indicates the total number of samples taken.

[0040] The suitability of the capacity-to-load ratio is calculated based on the sum of the electrical equipment capacities of all users in the distribution network area and the rated capacity of the transformer. This indicator is a positive indicator; when R=1.6, the indicator value is 100%, indicating the optimum. The further R deviates from 1.6, the smaller the indicator value. A capacity-to-load ratio that is too small indicates insufficient installed capacity and limited development potential, while a ratio that is too large indicates low simultaneity and low demand coefficient. Its calculation expression is: , in, , R is the load ratio, which is the ratio of the installed capacity to the rated capacity; The sum of the electrical equipment capacity (kVA) of all M users in the distribution area is obtained from the user profile data; The rated capacity of the transformer (kVA); when When the load factor is at that time, it is the empirically optimal capacity ratio (according to the Technical Guidelines for Distribution Network Planning and Design DL / T 5729-2016).

[0041] The equipment age impact coefficient is calculated using a piecewise function based on the equipment's operational years. This index is a positive indicator, meaning a higher coefficient indicates better economic efficiency. It is also negatively correlated with operational years; that is, the older the equipment, the higher the failure rate, the greater the maintenance cost, the worse the economic efficiency, and the smaller the coefficient. Its expression is: , Where T represents the equipment's operational lifespan, which is the number of years from the equipment's commissioning date to the assessment date.

[0042] The piecewise function design takes into account the characteristics of the equipment life cycle: 0-5 years is the new equipment period with a coefficient of 1.0; 6-15 years is the maturity period with a coefficient of 0.9-0.7, which follows linear decay; 16-25 years is the aging period with a coefficient of 0.7-0.4, which follows accelerated decay; >25 years is the obsolescence period with a coefficient of 0.4.

[0043] IIi), flexibility index.

[0044] This indicator includes photovoltaic (PV) absorption capacity, load regulation margin, and new energy adaptability index. PV absorption capacity is calculated based on the maximum PV output and the transformer's rated capacity during the sampling time. This is a positive indicator; the larger the value, the closer it is to 100%, indicating sufficient remaining capacity and stronger PV absorption capacity. The corresponding expression is: , in, Let be the expected peak power of the photovoltaic system (kW), and we have: , It can be seen that this value is the expectation of the maximum value taken over time for each sample. The expected average load power (kW); This refers to the rated capacity of the transformer (kVA).

[0045] Load regulation margin is calculated based on transformer load rate. This is a positive indicator; a larger value indicates a wider load fluctuation range, greater regulation flexibility, and greater benefit from demand response and other means of absorbing renewable energy. The corresponding expression is: , in, The maximum load rate among N samples reflects the peak load level. E[C] represents the minimum load rate across N samples, reflecting the valley load level; 11 [ ] represents the expected load factor, which serves as the normalization benchmark.

[0046] The renewable energy adaptability index is calculated based on photovoltaic absorption capacity and load regulation margin. This index is a positive indicator that comprehensively reflects the distribution network's adaptability to renewable energy access. A higher value indicates better adaptability. Its calculation expression is as follows: , Among them, C 31 Photovoltaic absorption capacity (%); C 32 Load adjustment margin (%); This represents the theoretical maximum value of the load regulation margin.

[0047] iiii), resilience index.

[0048] This indicator is evaluated under multiple disturbance scenarios, including N-1 faults, extreme weather, and load surges. It quantifies the system's resilience across the entire chain of prevention, mitigation, recovery, and adaptation under disturbances, including the transient voltage stability index, islanding reconstruction power, and energy storage throughput margin. The transient voltage stability index is calculated based on the stability score of a single fault voltage and is a positive indicator. Its calculation expression is as follows: , , in, This represents the stability score of a single fault voltage. This represents the severity weight of fault f, and , This indicates the apparent power surge caused by fault f. Indicates the rated capacity of the transformer; Indicates the maximum voltage drop depth, and , This represents the pre-fault voltage calculated using tidal methods. This represents the minimum voltage at which fault f occurs; Indicates the rated voltage; Indicates the recovery ability factor, and , and All represent the time dimension weighting coefficients. Indicates the rapid recovery time from fault f. This represents the reference time constant for rapid recovery from fault f. This represents the time from when fault f becomes completely stable. This represents the reference time constant for slow stabilization from fault f; Indicates the transient voltage stability index; This indicates the total number of faults.

[0049] The islanding reconfiguration success rate is the distribution network islanding reconstruction success rate, which is a positive indicator, and its expression is: , , in, This represents the reconstruction score of the z-th substation. This represents the energy sufficiency index of region z, and , This represents the total capacity of distributed power sources within transformer area z. This represents the total energy storage capacity within the z-region. Indicates the duration of independent power supply by energy storage. This represents the apparent power of the total load in transformer area z; This indicates the number of critical load points in transformer zone z; Let c represent the weight of the critical load point c in transformer zone z, and , This represents the base weight of the critical load c. This represents the weight adjustment factor. This indicates the actual power outage time at critical load point c. This indicates the maximum permissible power outage time at critical load point c; Indicates characteristic functions; This indicates the critical power supply demand at the critical load point c. This represents the reconfiguration time of load c in transformer area z; This represents the reference reconstruction time of station z, and , Indicates the base reference time. Indicates the influence coefficient of switch operation. This indicates the number of switches required for the reconfiguration of the z-region. Indicates the maximum number of switch operations allowed in the distribution network; Indicates the total number of stations; This represents the total power of all critical load points in transformer zone z.

[0050] Energy storage throughput margin is a positive indicator, and its calculation formula is as follows: , , , , , , in, Indicates the energy storage throughput margin; Indicates the total duration; Indicates the rated power of the energy storage system; This represents the SOC regulation potential factor at time t. This represents the power regulation capability factor during time period t. This represents the health status factor at time t. This represents the available power during time period t; Indicates the state of charge of the stored energy at time t; express Maximum charging power at all times; express Maximum discharge power at any given moment; and Both represent the health status decay coefficient; express The rate of energy storage capacity decay at any given moment; Indicates the rated capacity of the energy storage system; This represents the increase in internal resistance of the energy storage at time t; This represents the initial internal resistance of the energy storage system; This indicates the energy storage power ramp-up limit at time t.

[0051] S25. Calculate the probability distribution of each indicator using Monte Carlo simulation: , in, ] represents the expected value of the indicator, which serves as its statistical representative value. The index value calculated for the k-th sampling includes , , ..., N represents the total number of Monte Carlo samplings.

[0052] S26, Indicator normalization processing.

[0053] The expected values ​​of the 12 indicators were normalized to unify the indicators of different dimensions and orders of magnitude into the [0,1] interval, which facilitates subsequent weighted calculations.

[0054] Among them, for positive indicators, There are 10 in total, including: , For negative indicators, C 11 C 12 There are 2 in total: , in, The normalized index value ranges from [0,1], where 0 represents the worst and 1 represents the best. The corresponding value for the indicator; and These represent the maximum and minimum values ​​of the indicator.

[0055] S3. Calculate the subjective and objective weights of the multidimensional indicators and integrate them to obtain the comprehensive weight.

[0056] S31, Subjective weight calculation.

[0057] The Analytic Hierarchy Process (AHP) was used to determine the subjective weights of 12 indicators. Specifically, the AHP method constructs a judgment matrix to reflect the relative importance of the indicators, and then calculates the weights of each indicator. Based on the power distribution network safety operation specifications, new energy access technology requirements, and work experience, a judgment matrix for the 12 indicators was constructed. The judgment matrix adopts the Saaty 1-9 scaling method, where This indicates the degree of importance of indicator i relative to indicator j.

[0058] Obtain the judgment matrix Then, calculate the largest eigenvalue. The corresponding feature vectors are normalized to obtain subjective weights, which are then calculated using the power method, including: S311. Set the initial vector: .

[0059] S312, Perform iterative calculations, and for the ... Second iteration : .

[0060] S313, Normalization process: .

[0061] S314. Based on the convergence criterion, determine whether convergence has occurred, specifically by calculating the difference between two adjacent iterations: ,when When the iteration converges, This represents the threshold.

[0062] S315, Normalization, as follows: , Obtain the subjective weight vector: .

[0063] S32, Calculation of objective weights.

[0064] Based on the normalized index value matrix (Evaluation scores for m transformer substations) are calculated using the entropy weight method to determine objective weights. These objective weights reflect the information content of the data itself and include the following steps: S321. First, calculate the normalized weight of the j-th indicator: , in, Let m be the weight of the i-th evaluation object on the j-th indicator, and m be the number of substations; when all When the sum is 0, meaning all objects perform the same on this metric, it is defined as follows: .

[0065] S322. Next, calculate the entropy value of the j-th index: , in, Let the information entropy of the j-th indicator be [0,1], and the entropy value be... The smaller the value, the greater the difference between different distribution areas in this indicator, and the more information the indicator provides.

[0066] S323. Calculate the objective weights, the expression of which is: , in, Let be the objective weight of the j-th indicator (j=1,2,...,12).

[0067] The entropy weight method calculates weights entirely based on the objective distribution characteristics of the data, and is not affected by subjective factors. The greater the difference in the indicator data, the smaller the entropy value and the greater the weight; this complements the subjective weight.

[0068] S33, Calculation of comprehensive weight.

[0069] The obtained subjective weight vector With the obtained objective weight vector Performing linear weighted fusion yields a comprehensive weight vector, which includes: in, This is the subjective weighting coefficient, ranging from 0 to 1, and can be set according to actual evaluation needs. It is typically set to [value missing]. In order to achieve a balance between subjective and objective factors.

[0070] S4. Calculate the score for each sample based on the comprehensive weight and normalization results, and calculate the expected score through Monte Carlo simulation. Calculate the risk probability based on the expected scores of all samples.

[0071] S41, Calculation of expected score.

[0072] The multi-dimensional index calculation is based on N Monte Carlo samplings, each corresponding to different photovoltaic output and load demand. Therefore, N scoring samples {S1, S2, ..., S_N} are obtained, reflecting the probability distribution characteristics of the scores. For each sample k (k=1,2,...,N), the corresponding score is calculated based on the index value calculated in that sampling. , in, The score corresponding to the k-th sample; The index value calculated in the kth sampling is normalized; weight Maintain consistency across all samples.

[0073] The expected score is calculated using Monte Carlo simulation; this value is a statistically representative value of the bearing capacity score. . Calculate the standard deviation of the ratings, which reflects the degree of dispersion of the ratings: , in, The expected score reflects the average level of carrying capacity; The standard deviation is denoted by N, where a larger value indicates a higher degree of uncertainty in the load-bearing capacity; N represents the number of samples.

[0074] S42, Risk Probability Calculation.

[0075] Based on the obtained N rating samples Statistical analysis was conducted to quantitatively assess the uncertainty risk of bearing capacity, including: S421. Calculate the 95% confidence interval.

[0076] rating samples Sort by size from smallest to largest to obtain an ordered sequence. ， in The corresponding confidence interval can be expressed as: , in, The confidence interval is 95%. Corresponding to the 2.5th percentile; It corresponds to the 97.5th percentile.

[0077] S422. Calculate the probability of insufficient bearing capacity: ,in, The probability of insufficient bearing capacity is defined as [0,1]. This is the bearing capacity threshold; anything below this value is considered insufficient bearing capacity. For the characteristic function, when It takes the value 1 when it is active, and 0 otherwise. It reflects the likelihood of insufficient carrying capacity of the distribution network; the higher the value, the higher the risk.

[0078] S5. The carrying capacity level is determined based on the expected score and the risk probability.

[0079] In summary, this invention has the following advantages: 1) Advantages in probabilistic assessment technology: Based on Beta and normal distribution, it accurately models the uncertainty of photovoltaic output and load demand, and uses Monte Carlo simulation to comprehensively characterize the probability distribution of assessment indicators, significantly improving the reliability of assessment results and decision-making reference value; 2) Advantages in multi-dimensional comprehensive evaluation: It constructs a four-dimensional evaluation framework including safety, economy, flexibility, and resilience. On the basis of ensuring the safety, economy, and flexibility of the system, it introduces quantifiable resilience indicators, which can more comprehensively and profoundly reflect the comprehensive characteristics and anti-disturbance and rapid recovery capabilities of the distribution network under high-proportion renewable energy access; 3) Multi-scenario and multi-angle resilience assessment: The resilience indicators comprehensively consider the response capability of the distribution network when facing compound disturbances such as extreme weather, equipment failure, and renewable energy fluctuations, including prevention capability, resistance capability, recovery capability, and adaptive capability. This indicator constructs hierarchical resilience indicators from equipment level, regional level to system level, reflecting the resilience requirements at different levels. The resilience assessment not only considers the ideal operating state, but also focuses on the system performance under extreme disturbances, which is in line with the actual power grid operation requirements.

[0080] Example 2 In this embodiment, a multi-dimensional evaluation system for distribution network carrying capacity based on probability assessment is provided to implement the above-mentioned method, including: The data acquisition module is used to collect power distribution network operation data; The model building module constructs a distributed power processing probability model and a load demand uncertainty model based on operational data.

[0081] The multidimensional index calculation module performs sampling and power flow calculation based on the distributed power source processing probability model and the load demand uncertainty model. At each sampling, it calculates and normalizes multidimensional indices based on the calculation results. The multidimensional indices include safety indicators, economic indicators, flexibility indicators and resilience indicators.

[0082] The multi-dimensional index fusion module performs the following steps: it uses the analytic hierarchy process (AHP) to calculate the subjective weights of the multi-dimensional indexes, uses the entropy weight method to calculate the objective weights of the multi-dimensional indexes based on the normalization results, and then merges the subjective and objective weights to obtain the comprehensive weight.

[0083] The risk probability calculation module calculates the score for each sample based on the comprehensive weight and normalization results, calculates the expected score through Monte Carlo simulation, and calculates the risk probability based on the expected scores of all samples.

[0084] The bearing capacity evaluation module classifies bearing capacity levels based on a combination of expected score and risk probability.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0086] Furthermore, the present invention provides an electronic device including a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0087] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] The processing unit executes the various methods and processes described above, such as methods S1 to S5. For example, in some embodiments, methods S1 to S5 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S5 by any other suitable means (e.g., by means of firmware).

[0089] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0090] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-dimensional evaluation method for the carrying capacity of a distribution network based on probability assessment, characterized in that, include: Collect power distribution network operation data and construct distributed generation probabilistic models and load demand uncertainty models; Based on the distributed power source processing probability model and the load demand uncertainty model, sampling and power flow calculation are performed. At each sampling, multi-dimensional indicators are calculated and normalized based on the calculation results. The multi-dimensional indicators include safety indicators, economic indicators, flexibility indicators and resilience indicators. The resilience indicators include transient voltage stability index, islanding reconstruction power and energy storage throughput margin. Calculate the subjective and objective weights of the multidimensional indicators and fuse them to obtain a comprehensive weight; The score for each sample is calculated based on the comprehensive weight and normalization results, and the expected score is calculated through Monte Carlo simulation. The risk probability is then calculated based on the expected scores of all samples. The carrying capacity level is determined based on the expected score and risk probability.

2. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The distributed power processing probability model follows a Beta distribution; the load demand uncertainty model includes a prediction term and a prediction error term, wherein the prediction error term follows a normal distribution.

3. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The steps for power flow calculation include: Meteorological data is obtained from the operational data, and the weather type for the target day is obtained from the meteorological data. Photovoltaic output is randomly sampled from the distributed power processing probability model based on the weather type. Various load forecasting errors are randomly sampled from the load demand uncertainty model, and the total load is calculated based on these various load forecasting errors. Based on the photovoltaic output and total load, power flow calculations are performed on the distribution network to obtain electrical quantities including the voltage of each node, the current of each branch, and the power.

4. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The safety indicators include transformer load rate, voltage deviation rate, and voltage qualification rate. The transformer load rate is calculated based on the sampled photovoltaic output and total load, as well as the transformer's rated capacity. The voltage deviation rate is calculated based on the node voltage obtained from power flow calculation and the corresponding nominal voltage value. The voltage qualification rate is the ratio of the number of times the voltage deviation rate is less than a preset value to the total number of samples.

5. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The economic indicators include equipment utilization rate, capacity-to-load ratio suitability, and equipment age impact coefficient. The equipment utilization rate is the expected value of the transformer load rate. The capacity-to-load ratio suitability is calculated based on the sum of the electrical equipment capacity of all users in the distribution network area and the rated capacity of the transformer. The equipment age impact coefficient is calculated using a piecewise function based on the equipment's years of operation.

6. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The aforementioned flexibility indicators include photovoltaic absorption capacity, load regulation margin, and new energy adaptability index. The photovoltaic absorption capacity is calculated based on the maximum photovoltaic output and the rated capacity of the transformer at the sampling time. The load regulation margin is calculated based on the transformer load rate. The new energy adaptability index is calculated based on the photovoltaic absorption capacity and the load regulation margin.

7. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The transient voltage stability index is calculated based on the stability score of a single fault voltage, and the calculation expression is as follows: ， ， in, This represents the stability score of a single fault voltage. This represents the severity weight of fault f, and , This indicates the apparent power surge caused by fault f. Indicates the rated capacity of the transformer; Indicates the maximum voltage drop depth, and , This represents the pre-fault voltage calculated using tidal methods. This represents the minimum voltage at which fault f occurs; Indicates the rated voltage; Indicates the recovery ability factor, and , and All represent the time dimension weighting coefficients. Indicates the rapid recovery time from fault f. This represents the reference time constant for rapid recovery from fault f. This represents the time from when fault f becomes completely stable. This represents the reference time constant for slow stabilization from fault f; Indicates the transient voltage stability index; This indicates the total number of faults.

8. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The islanding reconfiguration success rate is the distribution network islanding reconstruction success rate, and its expression is: ， ， in, This represents the reconstruction score of the z-th substation. This represents the energy sufficiency index of region z, and , This represents the total capacity of distributed power sources within transformer area z. This represents the total energy storage capacity within the z-region. Indicates the duration of independent power supply by energy storage. This represents the apparent power of the total load in transformer area z; This indicates the number of critical load points in transformer zone z; Let c represent the weight of the critical load point c in transformer zone z, and , This represents the base weight of the critical load c. This represents the weight adjustment factor. This indicates the actual power outage time at critical load point c. This indicates the maximum permissible power outage time at critical load point c; Indicates characteristic functions; This indicates the critical power supply demand at the critical load point c. This represents the reconfiguration time of load c in transformer area z; This represents the reference reconstruction time of station z, and , Indicates the base reference time. Indicates the influence coefficient of switch operation. This indicates the number of switches required for the reconfiguration of the z-region. Indicates the maximum number of switch operations allowed in the distribution network; Indicates the total number of stations; This represents the total power of all critical load points in transformer zone z.

9. The multi-dimensional evaluation method for distribution network carrying capacity based on probability assessment according to claim 1, characterized in that, The formula for calculating the energy storage throughput margin is as follows: ， ， ， ， ， ， in, Indicates the energy storage throughput margin; Indicates the total duration; Indicates the rated power of the energy storage system; This represents the SOC regulation potential factor at time t. This represents the power regulation capability factor during time period t. This represents the health status factor at time t. This represents the available power during time period t; Indicates the state of charge of the stored energy at time t; express Maximum charging power at all times; express Maximum discharge power at any given moment; and Both represent the health status decay coefficient; express The rate of energy storage capacity decay at any given moment; Indicates the rated capacity of the energy storage system; This represents the increase in internal resistance of the energy storage at time t; This represents the initial internal resistance of the energy storage system; This indicates the energy storage power ramp-up limit at time t.

10. A multi-dimensional evaluation system for the carrying capacity of a distribution network based on probability assessment, characterized in that, The system includes: The data acquisition module is used to collect power distribution network operation data; The model building module constructs a distributed power processing probability model and a load demand uncertainty model based on the aforementioned operational data. The multi-dimensional index calculation module performs sampling and power flow calculation based on the distributed power source processing probability model and the load demand uncertainty model. During each sampling, it calculates and normalizes multi-dimensional indices based on the calculation results. The multi-dimensional indices include safety indices, economic indices, flexibility indices, and resilience indices. The multi-dimensional index fusion module performs the following steps: calculating the subjective weights of the multi-dimensional indexes using the analytic hierarchy process (AHP), calculating the objective weights of the multi-dimensional indexes using the entropy weight method based on the normalization results, and fusing the subjective and objective weights to obtain a comprehensive weight. The risk probability calculation module calculates the score for each sample based on the comprehensive weight and normalization results, calculates the expected score through Monte Carlo simulation, and calculates the risk probability based on the expected scores of all samples. The bearing capacity evaluation module classifies the bearing capacity level based on the expected score and risk probability.

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