Optical storage and charging micro-grid power distribution network safety assessment method, system and equipment based on continuous loading capacity assessment, and medium

Abstract

The invention relates to the technical field of power system safety assessment, and discloses an optical storage and charging micro-grid power distribution network safety assessment method, system and device based on continuous loading capacity assessment and a medium, and the method comprises the steps: building a probability distribution model of an optical storage and charging micro-grid through data preprocessing; calculating a continuous loading capacity index considering the time sequence characteristics; establishing a power flow model of the power distribution network, and performing voltage, thermal and transient stability analysis; carrying out probability safety evaluation by adopting Monte Carlo simulation; and optimizing the acceptance capability of the optical storage and charging micro-grid based on the security constraint. Accurate and efficient evaluation of the safety level of the power distribution network is achieved, and effective technical support is provided for safe operation of the power distribution network under high-proportion new energy access.

Description

Technical Field

[0001] This invention relates to the field of power system safety assessment technology, and in particular to a method, system, equipment and medium for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment. Background Technology

[0002] With the rapid development of distributed energy resources and electric vehicle charging facilities, photovoltaic-storage-charging microgrids, as an important component of new power systems, pose new challenges to the safe and stable operation of distribution networks due to their large-scale integration. Traditional distribution network security assessment methods, mainly based on deterministic power flow calculations and static security analysis, are unable to effectively handle the randomness, volatility, and time-varying characteristics of photovoltaic-storage-charging microgrid output. Existing technologies suffer from the following main shortcomings: Existing safety assessment methods primarily target traditional synchronous generators and static load designs, lacking effective modeling for the uncertainties in the output of distributed renewable energy. Photovoltaic power generation is significantly affected by weather conditions, energy storage systems exhibit complex and variable charging and discharging states, and electric vehicle charging loads possess strong randomness. These characteristics make it difficult for traditional assessment methods to accurately reflect the true operating state of the system.

[0003] Current safety assessment index systems mainly focus on static indicators such as the N-1 safety criterion and voltage stability margin, lacking dynamic assessment of the continuous load-carrying capacity of photovoltaic-storage-charging microgrids. Existing methods cannot effectively quantify the impact of photovoltaic-storage-charging microgrids on the safety of distribution networks under different operating scenarios, nor can they assess the maximum acceptance capacity of distribution networks for photovoltaic-storage-charging microgrids.

[0004] Existing probabilistic power flow calculation methods suffer from excessive computational complexity when dealing with high-dimensional random variables and complex correlations, making it difficult to meet the requirements of real-time security assessment. Furthermore, the lack of comprehensive assessment models that consider the operational constraints and control strategies of photovoltaic, energy storage, and charging equipment prevents them from accurately reflecting the actual operating characteristics of microgrid systems. Summary of the Invention

[0005] In view of the above-mentioned existing problems, the present invention provides a method, system, equipment and medium for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment.

[0006] Therefore, the technical problem solved by this invention is: to establish a dynamic security assessment model that considers the power output timing characteristics of photovoltaic-storage-charging microgrids, and to accurately quantify the impact of their continuous load-carrying capacity on the safe operation of the distribution network; to construct a comprehensive security assessment index system covering voltage stability, thermal stability and transient stability; and to develop an efficient probabilistic security assessment algorithm to achieve a rapid and accurate assessment of the distribution network's ability to accept photovoltaic-storage-charging microgrids.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a safety assessment method for a photovoltaic-storage-charging microgrid distribution network based on continuous load-carrying capacity evaluation, comprising: collecting meteorological data, electric vehicle travel patterns, charging habits, and user behavior data, as well as historical operating data of the energy storage system; cleaning, detecting outliers, and imputing missing values ​​in the data; establishing probability distribution models and multivariate correlation models for each component of the photovoltaic-storage-charging system; calculating the continuous load-carrying capacity index of the photovoltaic-storage-charging microgrid at different time scales based on the probability distribution models and historical operating data; establishing a mathematical model of the distribution network including the photovoltaic-storage-charging microgrid, considering network topology, line parameters, and transformer characteristics, and performing power flow calculations; based on the power flow calculation results, performing voltage stability, thermal stability, and transient stability analyses, and establishing a multidimensional safety assessment index system; and using the Monte Carlo simulation method to consider the randomness and correlation of photovoltaic-storage-charging power output for probabilistic safety assessment. An optimization model for the acceptance capacity of photovoltaic-storage-charging microgrids is established based on safety constraints. The maximum acceptance capacity of the distribution network for photovoltaic-storage-charging microgrids is evaluated. Considering time correlation, a dynamic safety assessment is conducted through an autoregressive model.

[0008] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment described in this invention, the calculation of the continuous load capacity index includes: establishing a stochastic process model of the output of the photovoltaic-storage-charging microgrid, considering the volatility of photovoltaic power generation affected by weather conditions, the time-varying nature of the charging and discharging state of the energy storage system, and the randomness of the electric vehicle charging load, to obtain a continuous load capacity coefficient reflecting the microgrid's ability to maintain stable output within a specific time period.

[0009] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment described in this invention, the power flow calculation includes solving the power flow of the distribution network containing distributed generation sources by using an improved forward-backward substitution method, handling the mixed problem of PQ nodes, PV nodes and slack nodes, and considering the operating constraints of voltage limits, line thermal capacity limits and transformer capacity limits to obtain the node voltage and phase angle distribution.

[0010] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment described in this invention, the establishment of a multi-dimensional safety assessment index system includes: calculating voltage stability margin using the continuous power flow method, checking the load rate of all lines and transformers for thermal stability assessment, and using time-domain simulation method to assess the dynamic response of the system after a fault for transient stability assessment, thereby obtaining a comprehensive safety index covering the node level, equipment level, and system level.

[0011] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment described in this invention, the probabilistic safety assessment includes: setting a reasonable number of Monte Carlo simulations, statistically analyzing the safety indicators of each simulation, and calculating the probability of safety risks. In the formula, The probability of safety risk is represented by N, where N is the number of simulations. This is the safety status indication function for the k-th simulation. Establish a standard for classifying safety risk levels and obtain the confidence interval for safety risks: In the formula, This is the critical value for the standard normal distribution.

[0012] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment described in this invention, the assessment of the distribution network's maximum capacity to accommodate the photovoltaic-storage-charging microgrid includes describing the time correlation of photovoltaic-storage-charging output using an autoregressive model. In the formula, SI(t) is the safety index at time t. , These are the autoregressive coefficients. It is white noise.

[0013] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network security assessment method based on continuous load capacity assessment described in this invention, the dynamic security assessment includes a dynamic security assessment considering time correlation. In the formula, Let be the macroscopic security indicator at time t. For the macroscopic safety indicators at time t-1, For random disturbance terms, Let be the sustained load capacity coefficient at time t. The net output of the solar energy storage microgrid at time t.

[0014] This invention provides a safety assessment system for photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity evaluation.

[0015] As a preferred embodiment of the photovoltaic-storage-charging microgrid distribution network safety assessment system based on continuous load capacity assessment described in this invention, it includes a data management module, a core assessment module, and an optimization decision module. The data management module collects meteorological data such as photovoltaic radiation, temperature, and wind speed, user behavior data such as electric vehicle travel patterns and charging habits, and historical data of energy storage system operation. It also cleans the raw data, detects outliers, and fills in missing values, and establishes probability distribution models and multivariate correlation models for each component of photovoltaic, energy storage, and charging systems. The core evaluation module is connected to the data management module. By integrating the probability distribution model and the distribution network mathematical model, it calculates the continuous load-carrying capacity index and outputs the probabilistic safety risk assessment results and time-series dynamic safety index by comprehensively analyzing the voltage stability, thermal stability and transient stability. The optimization decision module is connected to the core evaluation module and is used to perform the acceptance capacity evaluation step. Based on the safety risk evaluation results and safety constraints output by the core evaluation module, an optimization model is established with the goal of maximizing the acceptance capacity of the photovoltaic-storage-charging microgrid. The maximum acceptance capacity of the distribution network for the photovoltaic-storage-charging microgrid and the optimal configuration scheme are obtained by solving the model.

[0016] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a method for assessing the safety of a photovoltaic-storage-charging microgrid distribution network based on continuous load capacity assessment.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of a method for safety assessment of a photovoltaic-storage-charging microgrid distribution network based on continuous load capacity assessment.

[0018] The beneficial effects of this invention are as follows: Firstly, a continuous load-carrying capacity assessment model considering the dynamic characteristics of photovoltaic-storage-charging microgrids is established, which can accurately quantify the continuous power supply capacity of the microgrid system under different operating conditions; secondly, a multi-dimensional safety assessment index system covering voltage stability, thermal stability, and transient stability is constructed, realizing a comprehensive assessment of the safety level of the distribution network; thirdly, an efficient probabilistic safety assessment algorithm is developed, significantly improving computational efficiency and meeting the needs of real-time safety assessment; and fourthly, an optimization model for the acceptance capacity of photovoltaic-storage-charging microgrids is established, providing a scientific basis for distribution network planning and operation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0020] Figure 1This is a schematic diagram of a method for safety assessment of a photovoltaic-storage-charging microgrid distribution network based on continuous load capacity assessment, provided as an embodiment of the present invention. Detailed Implementation

[0021] 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.

[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for safety assessment of a photovoltaic-storage-charging microgrid distribution network based on continuous load-carrying capacity evaluation, including: S1: Collect meteorological data, electric vehicle travel patterns, charging habits and user behavior data, as well as historical data of energy storage system operation. Clean the data, detect outliers and fill in missing values, and establish probability distribution models and multivariate correlation models for each component of photovoltaic, energy storage and charging.

[0023] S2: Based on the probability distribution model and historical operating data, calculate the continuous load-carrying capacity index of the photovoltaic-storage-charging microgrid at different time scales.

[0024] S3: Establish a mathematical model of the distribution network that includes photovoltaic, energy storage and charging microgrids, and perform power flow calculations considering network topology, line parameters and transformer characteristics.

[0025] S4: Based on the power flow calculation results, voltage stability, thermal stability and transient stability analysis are performed, and a multi-dimensional safety assessment index system is established.

[0026] S5: The Monte Carlo simulation method is used to conduct a probabilistic safety assessment, taking into account the randomness and correlation of the power output of the photovoltaic storage system.

[0027] S6: Establish an optimization model for the acceptance capacity of photovoltaic-storage-charging microgrids based on security constraints, evaluate the maximum acceptance capacity of the distribution network for photovoltaic-storage-charging microgrids, consider time correlation, and conduct dynamic security assessment through an autoregressive model.

[0028] It should be noted that, compared with traditional distribution network assessment methods based on deterministic power flow and static security analysis, which are difficult to effectively cope with the randomness and volatility of power output of photovoltaic-storage-charging microgrids, this embodiment establishes a dynamic assessment model for its continuous load-carrying capacity, constructs a multi-dimensional index system covering voltage, thermal and transient stability, and adopts an efficient probabilistic assessment algorithm to achieve accurate and rapid quantification of the distribution network security level under uncertain environment, significantly improving the system's maximum capacity to accept distributed energy and the accuracy of operational risk assessment.

[0029] Example 2 is an embodiment of the present invention. Based on the above embodiment, a method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment is provided.

[0030] Furthermore, in this embodiment, step S1 collects meteorological data, electric vehicle travel patterns, charging habits and user behavior data, as well as historical operating data of the energy storage system. The data is then cleaned, outlier detection is performed, and missing value imputation is conducted. Probability distribution models and multivariate correlation models for each component of the photovoltaic, energy storage, and charging system are established. Specific steps include S101-S103: S101: Collect meteorological data such as photovoltaic radiation, temperature, and wind speed; user behavior data such as electric vehicle travel patterns and charging habits; and historical operation data of the energy storage system. Clean the raw data, detect outliers, and impute missing values. Establish probability distribution models for various devices, including the Beta distribution of photovoltaic output, the Weibull distribution of wind speed, and the Poisson distribution of electric vehicle arrival time. Establish a multivariate correlation model using Copula functions to describe the mutual influence between the photovoltaic, energy storage, and charging components.

[0031] Modeling the output characteristics of a photovoltaic-storage-charging microgrid is fundamental to its safety assessment. The photovoltaic power generation output model considers the effects of solar radiation intensity and temperature. In the formula, Let be the photovoltaic power generation at time t. The rated power of the photovoltaic system, For the efficiency of photovoltaic systems, Let be the solar radiation intensity at time t. Radiation intensity under standard test conditions For power temperature coefficient, For battery temperature, This is a reference temperature.

[0032] S102: The charging and discharging power of the energy storage system is constrained by the state of charge. In the formula, Let be the power of the energy storage system at time t. For maximum charge and discharge power, Let t be the state of charge. , These are the upper and lower limits of the state of charge, respectively. For the rated capacity of the energy storage system, , These represent charge and discharge efficiencies, For time intervals.

[0033] S103: Electric vehicle charging load is described using a probabilistic model: In the formula, Let be the total charging power at time t. For the number of electric vehicles, The charging power for the i-th vehicle. This is a function indicating the charging status of the i-th vehicle.

[0034] Furthermore, in this embodiment of the application, step S2 calculates the continuous load-carrying capacity index of the photovoltaic-storage-charging microgrid at different time scales based on the probability distribution model and historical operating data. Specific steps include S201-S203: S201: Based on historical operational data and meteorological forecasts, calculate the continuous load-carrying capacity of a photovoltaic-storage-charging microgrid at different time scales. A rolling time window method is used to calculate the continuous load-carrying capacity indices at the hourly, daily, and seasonal levels. Considering the charging and discharging constraints of the energy storage system, the time-series characteristics of electric vehicle charging demand, and the volatility of photovoltaic power generation, a dynamic continuous load-carrying capacity assessment model is established.

[0035] S202: Continuous load capacity reflects the ability of a photovoltaic-storage-charging microgrid to maintain stable power output within a specific time period. Definition of continuous load capacity index: In the formula, The continuous load-carrying capacity coefficient is represented by T, which is the evaluation time window. Available power, Let t0 be the required power, and t0 be the initial time.

[0036] In an optional embodiment, the calculation of the continuous load-carrying capacity index can also be achieved through segmented evaluation within a fixed time window. Specifically, the evaluation period is pre-divided into fixed time periods for evaluation, with a day fixedly divided into 24 independent hour segments and a month fixedly divided into the first, middle, and last ten days, and the continuous load-carrying capacity index is calculated for each of these fixed time periods.

[0037] In another alternative embodiment, the calculation of the continuous load-carrying capacity index can also be achieved through event-triggered evaluation. Specifically, the continuous load-carrying capacity evaluation for the current operating state is initiated only when the system detects specific events such as a sudden change in photovoltaic output exceeding a threshold, a state of charge of energy storage falling below a critical value, or a sharp increase in load.

[0038] S203: Available power considering the operational constraints of each component in the photovoltaic, energy storage, and charging system: In the formula, For energy storage discharge power, This represents the system's power loss.

[0039] To quantify the uncertainty of sustained load capacity, a confidence level assessment is introduced: In the formula, For minimum acceptable continuous load capacity, The confidence level.

[0040] Furthermore, in this embodiment of the application, step S3 establishes a mathematical model of the distribution network including the photovoltaic-storage-charging microgrid, considering network topology, line parameters, and transformer characteristics, and performs power flow calculations. Specific steps include: A mathematical model of a distribution network incorporating a photovoltaic-storage-charging microgrid is established, considering network topology, line parameters, and transformer characteristics. An improved forward-backward substitution method is employed to solve the power flow problem of the distribution network containing distributed generation, addressing the mixed problem involving PQ nodes, PV nodes, and slack nodes. Operating constraints for the distribution network are established, including voltage limits, line thermal capacity limits, and transformer capacity limits.

[0041] Power flow equations for distribution networks considering the integration of photovoltaic, energy storage, and charging microgrids: In the formula, P i , Inject active and reactive power into node i. , To provide power to the generator, , For normal load, , Contribute to photovoltaic power , To contribute to energy storage, , For electric vehicle load.

[0042] The node voltage and phase angle satisfy: In the formula, , Let i be the voltage magnitude and phase angle. , Let n be the magnitude and phase angle of the admittance matrix elements, and n be the number of nodes.

[0043] Furthermore, in this embodiment, step S4, based on the power flow calculation results, performs voltage stability, thermal stability, and transient stability analysis to establish a multi-dimensional security assessment index system. Specific steps include S401-S404: S401: Based on the power flow calculation results, voltage stability, thermal stability, and transient stability analyses are performed respectively. Voltage stability analysis uses the continuous power flow method to calculate the PV curve and critical point; thermal stability analysis checks the load rate of all lines and transformers; and transient stability analysis uses time-domain simulation to evaluate the system's dynamic response after a fault. A multi-level safety assessment index system is established, including node-level, equipment-level, and system-level indicators.

[0044] S402: Perform voltage stability assessment, and calculate the voltage stability margin based on the PV curve: In the formula, Let i be the voltage stability margin at node i. This is the critical load power. This refers to the operating load power.

[0045] The system voltage stability index is defined as: In the formula, This is the set of all load nodes.

[0046] Corrected voltage stability parameters considering the impact of photovoltaic-storage-charging microgrids: In the formula, , These are the standardized variances of photovoltaic and energy storage outputs, respectively.

[0047] S403: Line thermal stability margin calculation: In the formula, This represents the thermal stability margin of line ij. This is the maximum allowable current of the line. This represents the operating current of the line.

[0048] Transformer thermal stability assessment: In the formula, This refers to the rated capacity of the transformer. This is the transformer load.

[0049] S404: Evaluating transient stability using the extended equal-area criterion: In the formula, For the maximum power angle deviation, This is the maximum power angle after the fault. This is the initial work angle.

[0050] Transient stability margin: In the formula, This is the critical work angle.

[0051] In an optional embodiment, transient stability can also be assessed through time-domain simulation. Specifically, numerical integration is performed by constructing a simplified power system differential equation model. The dynamic models of key generator units and loads are retained, while some minor dynamic processes are ignored. Stability is determined by directly observing whether the power angle converges after calculating the trajectory of the system state variables following fault clearing.

[0052] In another alternative embodiment, transient stability can also be assessed using a transient energy function. This is achieved by constructing the system's transient energy function. The numerical relationship between the transient energy accumulated during the fault and the potential energy barrier after fault clearance is calculated, and system stability is assessed by determining whether the transient energy exceeds a critical value.

[0053] Furthermore, in this embodiment, step S5 employs the Monte Carlo simulation method to consider the randomness and correlation of the power output of the optical storage system and to conduct a probabilistic security assessment. Specific steps include S501-S503: S501: The Monte Carlo simulation method is used for probabilistic safety assessment, considering the randomness and correlation of photovoltaic energy storage charging and output. A reasonable number of simulations is set to ensure the convergence and reliability of the statistical results. Statistical analysis is performed on the safety indicators for each simulation, calculating the probability of safety risks, the expected value of safety margins, and variance. A standard for classifying safety risk levels is established.

[0054] S502: Establish a multi-dimensional security assessment indicator system: In the formula, For comprehensive safety indicators, Let be the weighting coefficient, satisfying .

[0055] Modified safety parameters considering continuous load capacity: S503: Probabilistic security assessment using Monte Carlo simulation: In the formula, The probability of safety risk is represented by N, where N is the number of simulations. This is the safety status indication function for the k-th simulation.

[0056] Confidence intervals for risk indicators: In the formula, This is the critical value for the standard normal distribution.

[0057] In an alternative embodiment, probabilistic security assessment can also be performed using Latin hypercube sampling, which involves stratifying the probability distribution of each random variable and ensuring that each stratum is sampled only once, thereby achieving more effective coverage of the tails of the distribution with fewer samplings.

[0058] In another alternative embodiment, probabilistic security assessment can also be achieved through point estimation. Specifically, a few key feature points (such as the mean and ±1 standard deviation points) are selected for each input random variable for calculation. By combining the deterministic power flow results of these points and weighted summing, the moments of each order of the output variable are approximated, and the probabilistic characteristics of the security index are estimated.

[0059] Furthermore, in this embodiment, step S6 establishes an optimization model for the acceptance capacity of the photovoltaic-storage-charging microgrid based on security constraints, evaluates the maximum acceptance capacity of the distribution network for the photovoltaic-storage-charging microgrid, considers time correlation, and performs dynamic security assessment through an autoregressive model. Specific steps include S601-S603: S601: An optimization model for the acceptance capacity of a photovoltaic-storage-charging microgrid is established based on security constraints, with maximizing the acceptance capacity as the objective function. Intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are used to solve the acceptance capacity optimization problem. The impact of different access locations and different capacity ratios on the security of the distribution network is considered to determine the optimal photovoltaic-storage-charging microgrid configuration scheme.

[0060] S602: Maximum capacity of the distribution network to accommodate photovoltaic-storage-charging microgrids: In the formula, To maximize acceptance capacity, Provide power to the optical storage of node i. , , These are the minimum allowable values ​​for each stability indicator. , These are the upper and lower limits of the voltage.

[0061] In an optional embodiment, the acceptance capability can also be achieved through sequential linear programming. Specifically, the original nonlinear optimization problem is linearized at the initial solution, and the optimal solution is gradually approximated by iteratively solving a series of linear programming problems.

[0062] In an alternative embodiment, the acceptance capability can also be achieved through gradient projection, specifically by using the gradient information of the objective function and constraints to find the optimal solution by searching along the boundary of the feasible region.

[0063] S603: Time-series correlation is described by an autoregressive model: In the formula, , These are the autoregressive coefficients. White noise. Dynamic security assessment considering time correlation: In the formula, Let be the macroscopic security indicator at time t. For the macroscopic safety indicators at time t-1, For random disturbance terms, Let be the sustained load capacity coefficient at time t. The net output of the solar energy storage microgrid at time t.

[0064] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a safety assessment system for photovoltaic-storage-charging microgrid distribution networks based on continuous load capacity assessment, including: a data management module, a core assessment module, and an optimization decision module; The data management module collects meteorological data such as photovoltaic radiation, temperature, and wind speed, user behavior data such as electric vehicle travel patterns and charging habits, and historical data of energy storage system operation. It also cleans the raw data, detects outliers, and fills in missing values, and establishes probability distribution models and multivariate correlation models for each component of photovoltaic, energy storage, and charging systems. The core evaluation module is connected to the data management module. By integrating the probability distribution model and the distribution network mathematical model, it calculates the continuous load-carrying capacity index and outputs the probabilistic safety risk assessment results and time-series dynamic safety index by comprehensively analyzing the voltage stability, thermal stability and transient stability. The optimization decision module is connected to the core evaluation module and is used to perform the acceptance capacity evaluation step. Based on the safety risk evaluation results and safety constraints output by the core evaluation module, an optimization model is established with the goal of maximizing the acceptance capacity of the photovoltaic-storage-charging microgrid. The maximum acceptance capacity of the distribution network for the photovoltaic-storage-charging microgrid and the optimal configuration scheme are obtained by solving the model.

[0065] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the photovoltaic-storage-charging microgrid distribution network safety assessment method based on continuous load capacity assessment proposed in the above embodiment.

[0066] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the photovoltaic-storage-charging microgrid distribution network security assessment method based on continuous load capacity assessment proposed in the above embodiments.

[0067] The storage medium proposed in this embodiment and the method for assessing the safety of photovoltaic-storage-charging microgrid distribution networks based on continuous load capacity assessment 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.

[0068] 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. 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.

[0069] 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.

[0070] Example 4 is the first embodiment of the present invention, used to verify the safety assessment method of photovoltaic-storage-charging microgrid distribution network based on continuous load capacity assessment.

[0071] This embodiment uses a simulation verification based on the IEEE 33-node distribution system, employing a MATLAB R2023a and PSS / E co-simulation platform. The simulation system includes three photovoltaic-storage-charging microgrid access points, with a photovoltaic installed capacity of 2.5MW, an energy storage capacity of 1.5MWh, and 50 electric vehicle charging piles. The experiment uses actual operating data from an industrial park throughout 2023, including 8760 hours of continuous operation data. Photovoltaic output data is based on radiation and temperature records from a local meteorological station; the energy storage operation strategy adopts a peak-valley electricity price arbitrage model; and electric vehicle charging behavior is modeled based on actual user survey data.

[0072] Simulation verification is divided into three levels: comparative verification of basic safety indicators, risk analysis verification under different operating scenarios, and verification of the accuracy of time-series dynamic evaluation. Through comparison with traditional static safety assessment methods, the superiority and practicality of the method in this embodiment are fully verified.

[0073] Table 1: Comparison of Safety Assessment Indicators for Different Methods

[0074] As shown in Table 1, the method in this embodiment significantly outperforms traditional methods in all key safety indicators. Voltage stability margin is improved by 30.3%, indicating that considering continuous load capacity allows for a more accurate assessment of the system's voltage support capability; line thermal stability margin is improved by 27.1%, demonstrating the advantages of dynamic load modeling; and system safety margin is improved by 32.2%, proving the effectiveness of the comprehensive evaluation indicators. Most importantly, the maximum capacity of the photovoltaic-storage-charging microgrid increases from 3.2MW to 4.5MW, an increase of 40.6%, providing a scientific basis for the distribution network to accommodate more clean energy. Simultaneously, computational efficiency is improved by nearly 50%, meeting the needs of real-time safety assessment.

[0075] Table 2: Security Risk Assessment Results under Different Operating Scenarios

[0076] Table 2 illustrates the risk identification capability of the method in this embodiment under different operating scenarios. The results show that system safety risk is negatively correlated with the continuous load-bearing capacity coefficient; when the continuous load-bearing capacity coefficient is below 0.8, the safety risk increases sharply. Under normal operating conditions (high load on sunny days, medium load on cloudy days, and low load on rainy days), the system safety risk remains at a low level, all less than 6%. Under fault conditions, the safety risk rises to 12.5% ​​during an N-1 fault and reaches 18.7% under extreme weather conditions, but is still within an acceptable range. This refined risk quantification provides important decision support for operators.

[0077] Table 3: Results of Accuracy Verification of Time-Series Dynamic Evaluation

[0078] Table 3 shows the time-series dynamic evaluation results, verifying the prediction accuracy advantage of the method in this embodiment at different time periods. The most significant accuracy improvement is observed during the sunrise and sunset periods when photovoltaic output fluctuates dramatically, reaching 37.5% and 40.7% respectively. This is attributed to the accurate modeling of the dynamic characteristics of photovoltaic-storage-charging by the continuous load capacity model. The accuracy improvement during the high-radiation period is 38.9%, demonstrating the method's accurate grasp of peak photovoltaic output periods. Although the accuracy improvement is relatively small during the nighttime period (34.0%), the prediction confidence level is the highest (97.1%), indicating good stability of the method during periods of low variation. The average accuracy improvement throughout the day is 38.2%, with a prediction confidence level of 95.4%, fully demonstrating the reliability of the method in this embodiment.

[0079] Further sensitivity analysis showed that as the penetration rate of photovoltaic-storage-charging microgrids increased from 10% to 40%, the evaluation error of the traditional method increased exponentially, while the method in this embodiment maintained a low error level, demonstrating good adaptability. In 1000 Monte Carlo simulations, the convergence speed of the method in this embodiment was about 45% faster than that of the traditional method, and the confidence interval of the statistical results was narrower, indicating that the evaluation results were more reliable.

Claims

1. A method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity evaluation, characterized in that: include, Collect meteorological data, electric vehicle travel patterns, charging habits and user behavior data, as well as historical data of energy storage system operation. Clean the data, detect outliers and fill in missing values, and establish probability distribution models and multivariate correlation models for each component of photovoltaic, energy storage and charging. Based on the probability distribution model and historical operation data, the continuous load-carrying capacity index of the photovoltaic-storage-charging microgrid is calculated at different time scales. A mathematical model of the distribution network including photovoltaic, energy storage and charging microgrids is established, and power flow calculations are performed considering network topology, line parameters and transformer characteristics; Based on the power flow calculation results, voltage stability, thermal stability and transient stability are analyzed, and a multi-dimensional safety assessment index system is established. The Monte Carlo simulation method is used to conduct a probabilistic safety assessment, taking into account the randomness and correlation of the power output of the photovoltaic energy storage system. An optimization model for the acceptance capacity of photovoltaic-storage-charging microgrids is established based on safety constraints. The maximum acceptance capacity of the distribution network for photovoltaic-storage-charging microgrids is evaluated. Considering time correlation, a dynamic safety assessment is conducted through an autoregressive model.

2. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 1, characterized in that: The calculation of the continuous load-carrying capacity index includes establishing a stochastic process model of the output of the photovoltaic-storage-charging microgrid, taking into account the volatility of photovoltaic power generation affected by weather conditions, the time-varying nature of the charging and discharging state of the energy storage system, and the randomness of the electric vehicle charging load, to obtain a continuous load-carrying capacity coefficient that reflects the microgrid's ability to maintain stable output within a specific time period.

3. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 2, characterized in that: The power flow calculation includes solving the power flow of the distribution network containing distributed generation sources using an improved forward-backward substitution method, handling the mixed problem of PQ nodes, PV nodes and slack nodes, and considering the operating constraints of voltage limits, line thermal capacity limits and transformer capacity limits to obtain the node voltage and phase angle distribution.

4. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 3, characterized in that: The establishment of a multi-dimensional safety assessment index system includes calculating voltage stability margin using the continuous power flow method, assessing thermal stability by checking the load rate of all lines and transformers, and assessing transient stability by using time-domain simulation to evaluate the dynamic response of the system after a fault, thereby obtaining comprehensive safety indicators covering the node level, equipment level, and system level.

5. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 4, characterized in that: The probabilistic security assessment includes setting a reasonable number of Monte Carlo simulations, statistically analyzing the security indicators of each simulation, and calculating the probability of security risks. In the formula, The probability of safety risk is represented by N, where N is the number of simulations. This is the safety status indication function for the k-th simulation. Establish a standard for classifying safety risk levels and obtain the confidence interval for safety risks: In the formula, This is the critical value for the standard normal distribution.

6. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 5, characterized in that: The assessment of the distribution network's maximum capacity to accommodate photovoltaic-storage-charging microgrids includes describing the time correlation of photovoltaic-storage-charging output using an autoregressive model. In the formula, SI(t) is the safety index at time t. , These are the autoregressive coefficients. It is white noise.

7. The method for safety assessment of photovoltaic-storage-charging microgrid distribution networks based on continuous load-carrying capacity assessment as described in claim 6, characterized in that: The dynamic security assessment includes a dynamic security assessment that considers time correlation. In the formula, Let be the macroscopic security indicator at time t. For the macroscopic safety indicators at time t-1, For random disturbance terms, Let be the sustained load capacity coefficient at time t. The net output of the solar energy storage microgrid at time t.

8. A photovoltaic-storage-charging microgrid distribution network security assessment system based on continuous load capacity assessment, employing the method for photovoltaic-storage-charging microgrid distribution network security assessment based on continuous load capacity assessment as described in any one of claims 1 to 7, characterized in that... include: Data management module, core evaluation module, and optimization decision-making module; The data management module collects meteorological data such as photovoltaic radiation, temperature, and wind speed, user behavior data such as electric vehicle travel patterns and charging habits, and historical data of energy storage system operation. It also cleans the raw data, detects outliers, and fills in missing values, and establishes probability distribution models and multivariate correlation models for each component of photovoltaic, energy storage, and charging systems. The core evaluation module is connected to the data management module. By integrating the probability distribution model and the distribution network mathematical model, it calculates the continuous load-carrying capacity index and outputs the probabilistic safety risk assessment results and time-series dynamic safety index by comprehensively analyzing the voltage stability, thermal stability and transient stability. The optimization decision module is connected to the core evaluation module and is used to perform the acceptance capacity evaluation step. Based on the safety risk evaluation results and safety constraints output by the core evaluation module, an optimization model is established with the goal of maximizing the acceptance capacity of the photovoltaic-storage-charging microgrid. The maximum acceptance capacity of the distribution network for the photovoltaic-storage-charging microgrid and the optimal configuration scheme are obtained by solving the model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic-storage-charging microgrid distribution network security assessment method based on continuous load capacity assessment as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic-storage-charging microgrid distribution network security assessment method based on continuous load capacity assessment as described in any one of claims 1 to 7.

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