Distributed photovoltaic consumption capacity evaluation method and system

Abstract

The application discloses a distributed photovoltaic consumption capacity evaluation method and system. The method comprises the following steps: evaluating the distributed photovoltaic consumption capacity based on a predetermined photovoltaic maximum consumption capacity; wherein the determination method of the photovoltaic maximum consumption capacity comprises the following steps: determining each simulation operation scene; constructing a relationship formula of the photovoltaic consumption rate, the system power loss, the transformer load rate, the line load rate and the weighted calculation value; constructing a comprehensive objective function based on the relationship formula and determining the constraint condition of the comprehensive objective function; based on the constraint condition and each simulation operation scene, the sea star optimization algorithm is used to solve the comprehensive objective function, so that each photovoltaic access scheme and the power flow calculation result of each photovoltaic access scheme under each simulation operation scene are obtained; based on each power flow calculation result and the constraint condition, data feature extraction is carried out, so that the margin index corresponding to each power flow calculation result is determined; based on each photovoltaic access scheme and each margin index, analysis is carried out, so that the photovoltaic maximum consumption capacity is obtained.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization technology, and in particular relates to a method and system for assessing the absorption capacity of distributed photovoltaic power. Background Technology

[0002] With the increasing global demand for renewable energy, distributed photovoltaic (PV) power generation technology is being applied more and more widely in power systems. PV power generation, with its clean, renewable, and distributed characteristics, has become an important component of the new power system. However, the high degree of randomness and temporal volatility of PV power generation presents new challenges to the stable operation of the power grid and the assessment of PV absorption capacity.

[0003] In existing technologies, on the one hand, traditional deterministic power flow analysis methods, when assessing photovoltaic (PV) grid absorption capacity, typically assume that the input parameters are fixed and fail to fully consider the randomness of PV output. Although this method is simple, it often fails to accurately reflect the dynamic characteristics of the system when facing unpredictable environmental changes in actual operation, thus leading to increased bias in the absorption capacity assessment. On the other hand, current optimization algorithms, in pursuing the global optimal solution, often get stuck in local optima, lack a comprehensive consideration of uncertain factors, and are difficult to reflect the true carrying capacity limit of the system. They are also prone to causing instability in grid operation and irrationality in planning. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing the distributed photovoltaic (PV) grid absorption capacity, thereby addressing at least one of the problems mentioned in the background art, such as large deviations in absorption capacity assessment results, low local optimization effects, difficulty in reflecting the true carrying capacity limit of the system, and the potential for instability in grid operation and unreasonable planning.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for assessing the grid absorption capacity of distributed photovoltaic power, comprising:

[0007] Assess the distributed photovoltaic (PV) grid connection capacity based on a predetermined maximum grid connection capacity;

[0008] The method for determining the maximum photovoltaic absorption capacity includes:

[0009] Determine the simulation scenarios;

[0010] The relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0011] Based on the aforementioned relation, a comprehensive objective function is constructed, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0012] Based on the constraints and the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function, and each photovoltaic access scheme in the solution process and the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario are obtained.

[0013] Based on the power flow calculation results and the constraints, data features are extracted to determine the margin index corresponding to each power flow calculation result.

[0014] Based on the analysis of each photovoltaic grid connection scheme and the margin index corresponding to each photovoltaic grid connection scheme, the maximum photovoltaic grid absorption capacity is obtained.

[0015] Optionally, the constraints include node voltage constraints, line power flow constraints, transformer capacity constraints, photovoltaic access capacity constraints, and line power flow balance constraints, and the margin indicators include the voltage margin of each node and the load rate margin of each branch.

[0016] Optionally, the step of analyzing each of the photovoltaic grid connection schemes and the corresponding margin indices to obtain the maximum photovoltaic grid absorption capacity includes:

[0017] Based on each of the photovoltaic access schemes, determine the total photovoltaic access capacity corresponding to each photovoltaic access scheme;

[0018] Based on the preset total photovoltaic access capacity range and the total photovoltaic access capacity, the photovoltaic access schemes are grouped to determine the set of photovoltaic access schemes corresponding to each total photovoltaic access capacity range;

[0019] Based on each set of photovoltaic access schemes and each of the margin indices corresponding to each photovoltaic access scheme, the minimum margin index corresponding to each set of photovoltaic access schemes is determined.

[0020] The maximum photovoltaic absorption capacity is determined by comparing a pre-set safety margin threshold with each of the minimum margin indices.

[0021] Optional, also includes:

[0022] Based on the pre-determined voltage over-limit indicators corresponding to each photovoltaic access scheme, the regional bottlenecks and time bottlenecks of each photovoltaic access scheme under each simulated operation scenario are analyzed.

[0023] Based on the pre-determined overload index of each equipment corresponding to each photovoltaic access scheme, the thermal bottleneck of each photovoltaic access scheme under each simulated operation scenario is analyzed.

[0024] Based on the predetermined grid loss indicators corresponding to each photovoltaic access scheme, the economics of each photovoltaic access scheme under each simulated operation scenario are analyzed.

[0025] Based on the predetermined margin indices corresponding to each photovoltaic access scheme, the robustness of each photovoltaic access scheme under each simulated operation scenario is analyzed.

[0026] Based on the predetermined photovoltaic access schemes and the corresponding voltage over-limit indicators, equipment overload indicators, network loss indicators, and margin indicators for each photovoltaic access scheme, the analysis examines the stable and sensitive locations of photovoltaic access for each scheme under various simulated operating scenarios.

[0027] Optionally, the methods for determining each of the voltage over-limit indicators, each of the equipment overload indicators, and each of the network loss indicators include:

[0028] Based on the power flow calculation results and the constraints, overload detection is performed to obtain voltage overload detection results and branch overload detection results corresponding to each power flow calculation result. The voltage overload detection results include the voltage overload node number, voltage overload time point, and voltage overload amplitude. The branch overload detection results include the overloaded line number, line overload time point, line overload rate, overloaded transformer number, transformer overload time point, and transformer overload rate.

[0029] Based on the voltage over-limit detection results, the voltage over-limit index corresponding to each power flow calculation result is extracted; the voltage over-limit index includes the total number of over-limit nodes, the total number of over-limit occurrences, the maximum over-limit amplitude, and the percentage of over-limit time.

[0030] Based on the overload detection results of each branch, the equipment overload index corresponding to each power flow calculation result is extracted; the equipment overload index includes the total number of overloaded branches, the total number of overloads, and the maximum overload rate.

[0031] Based on the power flow calculation results, the network loss index corresponding to each power flow calculation result is extracted; the network loss index is the total active power loss value.

[0032] Optionally, the method for determining each of the simulated running scenarios includes:

[0033] Acquire historical photovoltaic power data and historical power grid load data;

[0034] The Monte Carlo method is used to calculate the historical photovoltaic power data to generate various photovoltaic power output scenarios;

[0035] The Monte Carlo method is used to calculate the historical power grid load data to generate various load scenarios;

[0036] Based on each of the photovoltaic power output scenarios and each of the load scenarios, each of the simulated operation scenarios is determined.

[0037] Optionally, the step of using the Monte Carlo method to calculate the historical photovoltaic power data and generate various photovoltaic output scenarios includes:

[0038] The historical photovoltaic power output ratio is determined by calculation based on the historical photovoltaic power data and the preset photovoltaic rated capacity.

[0039] Based on the historical photovoltaic power output ratio, the shape parameter of the beta distribution is fitted to determine the probability density function of the beta distribution of the historical photovoltaic power output ratio;

[0040] Based on the beta distribution probability density function of the historical photovoltaic power output ratio, the inverse transformation sampling method is used to generate samples of each photovoltaic power output ratio.

[0041] Calculate the actual output sample of each photovoltaic power output based on the photovoltaic power output ratio sample and the preset photovoltaic rated capacity;

[0042] Based on the preset photovoltaic panel temperature coefficient, the actual output samples of each photovoltaic panel are dynamically corrected to determine the output curve of each photovoltaic panel.

[0043] The photovoltaic power output curves are smoothed by the moving average filtering method to determine the smoothed photovoltaic power output curves.

[0044] Based on the smoothed photovoltaic output curves, the photovoltaic output scenarios are generated.

[0045] Optionally, the step of using the Monte Carlo method to calculate the historical power grid load data and generate various load scenarios includes:

[0046] Based on the historical power grid load data, various statistical characteristics are determined; wherein, the statistical characteristics include mean, maximum value, minimum value, standard deviation, median value, and quantiles;

[0047] Based on the historical power grid load data and the statistical characteristics, the optimal distribution and the corresponding optimal distribution parameters are determined.

[0048] Based on the optimal distribution and its corresponding optimal distribution parameters, the inverse transformation sampling method is used to generate various load scenarios.

[0049] Optionally, determining the optimal distribution and corresponding optimal distribution parameters based on the historical power grid load data and each of the statistical characteristics includes:

[0050] Based on the historical power grid load data and the statistical characteristics, the maximum likelihood estimation method is used to fit the distribution parameters of each distribution to obtain the distribution parameters of each distribution; wherein, the distributions include normal distribution, gamma distribution and Weibull distribution;

[0051] Based on the distribution parameters of each distribution, determine the empirical distribution function and the corresponding theoretical cumulative distribution function of each distribution;

[0052] Based on the empirical distribution function and the corresponding theoretical cumulative distribution function of each distribution, the Kolmogorov-Smirnov method is used to test and determine the optimal distribution and its corresponding optimal distribution parameters.

[0053] In a second aspect, the present invention provides a distributed photovoltaic (PV) grid connection capacity assessment system, comprising:

[0054] The evaluation module is used to assess the distributed photovoltaic grid connection capacity based on a predetermined maximum photovoltaic grid connection capacity.

[0055] The method for determining the maximum photovoltaic absorption capacity includes:

[0056] Determine the simulation scenarios;

[0057] The relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0058] Based on the aforementioned relation, a comprehensive objective function is constructed, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0059] Based on the constraints and the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function, and each photovoltaic access scheme in the solution process and the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario are obtained.

[0060] Based on the power flow calculation results and the constraints, data features are extracted to determine the margin index corresponding to each power flow calculation result.

[0061] Based on the analysis of each photovoltaic grid connection scheme and the margin index corresponding to each photovoltaic grid connection scheme, the maximum photovoltaic grid absorption capacity is obtained.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] The distributed photovoltaic (PV) grid absorption capacity assessment method provided by this invention addresses the problems of large deviations in absorption capacity assessment results, low local optimization effects, difficulty in reflecting the true carrying capacity limit of the system, and the potential for grid instability and unreasonable planning in existing technologies. It achieves the following beneficial effects: By constructing a multi-objective relationship considering PV absorption rate, system power loss, and equipment load rate, and combining Monte Carlo scenario generation and starfish optimization algorithms, it effectively solves the assessment deviation problems caused by neglecting the randomness of PV output and the single objective in traditional assessment methods; by dynamically adjusting the weights of each objective and performing weighted assessments under multiple scenarios, it achieves accurate assessment of PV absorption capacity while ensuring safe system operation; based on the power flow calculation results of each scheme obtained during the solution process, it extracts margin indicators to determine the maximum PV absorption capacity, improving the accuracy and engineering practicality of the assessment results and providing a scientific and reliable decision-making basis for distribution network PV planning.

[0064] Furthermore, by clarifying key operational constraints such as node voltage, line power flow, and transformer capacity, and specifying margin indicators as voltage margin and load factor margin, the problems of incomplete constraints and ambiguous safety boundaries in traditional assessments are effectively solved. At the same time, through refined constraints and quantitative margin analysis of key power grid operating parameters, it is ensured that the optimization scheme meets actual operating requirements and that the system's safety carrying capacity is accurately assessed, thereby improving the engineering applicability and safety reliability of the capacity assessment results.

[0065] Furthermore, by grouping photovoltaic access schemes according to capacity intervals and determining the maximum absorption capacity based on the comparison between the minimum margin index and the safety threshold within each capacity interval, the problem of conservative capacity estimation caused by individual extreme scenarios in traditional assessments is effectively solved. At the same time, by systematically analyzing the change law of safety margin under different capacity levels, the turning point from safe operation to over-limit critical state of the system can be accurately identified, thereby fully tapping the absorption potential of the distribution network under the premise of ensuring system safety, and improving the accuracy of capacity assessment results and engineering practical value.

[0066] Furthermore, by systematically analyzing multi-dimensional indicators such as voltage overruns, equipment overloads, and network losses, a refined assessment of the photovoltaic absorption capacity of the distribution network is achieved. By identifying the spatiotemporal distribution characteristics of voltage overruns and the thermal bottlenecks of equipment overloads, the key links limiting absorption capacity can be accurately located. Combining the economic analysis of network loss indicators and the robustness assessment of margin indicators, the stable and sensitive locations of photovoltaic access can be effectively distinguished, providing clear guidance for distribution network transformation and photovoltaic site selection, and enhancing the practical value and decision support capability of the assessment results.

[0067] Furthermore, the Monte Carlo method was used to generate photovoltaic output scenarios and load scenarios respectively, and a simulated operation scenario incorporating the stochastic characteristics of both was constructed. This effectively solved the problem that traditional assessments, which use deterministic scenarios, cannot reflect the uncertainty of actual operation. At the same time, by fully considering the volatility of photovoltaic output and the randomness of load changes, the assessment scenario can comprehensively cover all possible operating states, improving the reliability and practicality of subsequent absorption capacity assessment results, and providing an analytical basis for power grid planning that is closer to actual operating conditions.

[0068] Furthermore, by fitting the photovoltaic output ratio through beta distribution, the uncertainty characteristics of photovoltaic power generation are accurately characterized. Inverse transformation sampling ensures the statistical representativeness of the generated scenario. At the same time, temperature coefficient correction and moving average filtering are introduced to make the generated photovoltaic output curve both conform to the actual physical characteristics and eliminate abnormal fluctuation interference, thereby improving the simulation accuracy of the photovoltaic scenario and providing a real and reliable input basis for subsequent absorption capacity assessment. This effectively overcomes the problem of overly idealistic scenario modeling in traditional methods.

[0069] Furthermore, by analyzing the multi-dimensional statistical characteristics of historical load data and determining the optimal probability distribution based on statistical tests, the random patterns of load changes can be accurately described. At the same time, the load scenarios generated by inverse transformation sampling retain the statistical characteristics of the actual load and cover the possible fluctuation range, effectively solving the problem of oversimplification of load models in traditional methods. This provides a real and reliable load change input for absorption capacity assessment, improving the accuracy of the assessment results and their practical guiding value.

[0070] Furthermore, by employing maximum likelihood estimation to fit parameters to various probability distributions and using the KS test to objectively select the optimal distribution model, the problem of distribution selection relying on prior assumptions and lacking statistical basis in traditional load modeling is effectively solved. At the same time, through a data-driven distribution optimization mechanism, it is ensured that the generated load scenario not only conforms to actual statistical laws but also accurately reflects the random characteristics of load fluctuations, thereby improving the simulation accuracy and reliability of the load scenario and providing a more scientific load input basis for subsequent absorption capacity assessment.

[0071] The distributed photovoltaic grid absorption capacity assessment system provided by this invention also solves the problems mentioned in the background section, such as large deviations in absorption capacity assessment results, low local optimal optimization effects, and the instability of grid operation and unreasonable planning. Attached Figure Description

[0072] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0073] Figure 1 A flowchart illustrating the method for determining the maximum photovoltaic absorption capacity in the distributed photovoltaic absorption capacity assessment method provided in this embodiment of the invention;

[0074] Figure 2 A flowchart of the starfish optimization algorithm for the distributed photovoltaic power absorption capacity assessment method provided in this embodiment of the invention;

[0075] Figure 3 A structural block diagram of an electronic device provided in an embodiment of the present invention;

[0076] Among them, 100 is electronic equipment; 101 is memory; 102 is processor; 103 is computer program; and 104 is communication bus. Detailed Implementation

[0077] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0078] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0079] Example 1

[0080] like Figures 1-2 As shown, in a first aspect, the present invention provides a method for assessing the distributed photovoltaic (PV) grid integration capacity, comprising:

[0081] S1: Assess the distributed photovoltaic absorption capacity based on the predetermined maximum photovoltaic absorption capacity;

[0082] The method for determining the maximum photovoltaic absorption capacity includes:

[0083] S11: Determine the simulation operation scenarios;

[0084] S12: Construct the relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations;

[0085] S13: Construct a comprehensive objective function based on the aforementioned relation, and determine the constraints of the comprehensive objective function; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0086] S14: Based on the constraints and the simulated operating scenarios, the comprehensive objective function is solved using the starfish optimization algorithm to obtain each photovoltaic access scheme in the solution process, as well as the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario;

[0087] S15: Based on the power flow calculation results and the constraints, extract data features and determine the margin index corresponding to each power flow calculation result;

[0088] S16: Based on the photovoltaic access schemes and the margin indicators corresponding to each photovoltaic access scheme, the maximum photovoltaic absorption capacity is obtained.

[0089] It should be noted that for a photovoltaic (PV) grid connection scheme, there is a corresponding power flow calculation result for each simulated operation scenario; for a PV grid connection scheme, there is a corresponding margin index for each simulated operation scenario; the maximum PV absorption capacity is the PV grid connection total capacity range corresponding to the minimum voltage margin of the PV grid connection total capacity range that first falls below the margin safety threshold; the power flow calculation result includes the voltage amplitude of each node, the active and reactive power flow of each branch, and the total network loss.

[0090] Therefore, the distributed photovoltaic (PV) absorption capacity assessment method provided by this invention solves the problems of large deviations in absorption capacity assessment results, low local optimization effects, difficulty in reflecting the true carrying capacity limit of the system, and easy instability of grid operation and unreasonable planning in existing technologies. It achieves the following beneficial effects: By constructing a multi-objective relationship considering PV absorption rate, system power loss, and equipment load rate, and combining Monte Carlo scenario generation and starfish optimization algorithms, it effectively solves the assessment deviation problem caused by neglecting the randomness of PV output and the single objective in traditional assessment methods; by dynamically adjusting the weights of each objective and performing weighted assessments under multiple scenarios, it achieves accurate assessment of PV absorption capacity while ensuring safe system operation; based on the power flow calculation results of each scheme obtained during the solution process, it extracts margin indicators to determine the maximum PV absorption capacity, thereby improving the accuracy and engineering practicality of the assessment results and providing a scientific and reliable decision-making basis for distribution network PV planning.

[0091] In step S1: The distributed photovoltaic absorption capacity is evaluated based on the predetermined maximum photovoltaic absorption capacity.

[0092] Therefore, by directly applying the pre-determined maximum photovoltaic absorption capacity for evaluation, the problems of complexity and low computational efficiency in traditional evaluation processes are effectively solved. The complex multi-objective optimization process is placed in the capacity determination stage, so that a scientific and reliable capacity reference value can be directly obtained during actual evaluation. This simplifies the on-site evaluation process and forms an evaluation method based on sufficient prior calculations. It not only ensures the accuracy of the results but also improves the evaluation efficiency. It provides grid planners with a clear and reliable capacity basis and effectively supports the rational layout and scientific decision-making of distributed photovoltaics.

[0093] In step S11: Determine each simulation scenario.

[0094] Therefore, by identifying multiple simulated operating scenarios, the problem of one-sided evaluation results caused by using a single typical scenario in traditional evaluations is effectively solved. At the same time, by considering the probability distribution of various operating conditions such as different light intensities and load levels, the evaluation process can truly reflect the uncertainties in actual operation, providing a comprehensive and reliable scenario basis for subsequent optimization evaluations. This ensures that the evaluation results are both statistically representative and accurately reflect the system's performance under various possible operating states, thereby improving the accuracy and practicality of absorption capacity evaluation.

[0095] In one embodiment, the method for determining each of the simulated running scenarios includes:

[0096] Acquire historical photovoltaic power data and historical power grid load data;

[0097] The Monte Carlo method is used to calculate the historical photovoltaic power data to generate various photovoltaic power output scenarios;

[0098] The Monte Carlo method is used to calculate the historical power grid load data to generate various load scenarios;

[0099] Based on each of the photovoltaic power output scenarios and each of the load scenarios, each of the simulated operation scenarios is determined.

[0100] Therefore, by generating photovoltaic output scenarios and load scenarios separately using the Monte Carlo method, and constructing a simulated operating scenario that incorporates the stochastic characteristics of both, the problem of traditional assessments failing to reflect the uncertainty of actual operation due to the use of deterministic scenarios is effectively solved. At the same time, by fully considering the volatility of photovoltaic output and the randomness of load changes, the assessment scenario can comprehensively cover all possible operating states, improving the reliability and practicality of subsequent absorption capacity assessment results, and providing an analytical basis for power grid planning that is closer to actual operating conditions.

[0101] In one embodiment, the step of using the Monte Carlo method to calculate the historical photovoltaic power data and generate various photovoltaic output scenarios includes:

[0102] The historical photovoltaic power output ratio is determined by calculation based on the historical photovoltaic power data and the preset photovoltaic rated capacity.

[0103] Based on the historical photovoltaic power output ratio, the shape parameter of the beta distribution is fitted to determine the probability density function of the beta distribution of the historical photovoltaic power output ratio;

[0104] Based on the beta distribution probability density function of the historical photovoltaic power output ratio, the inverse transformation sampling method is used to generate samples of each photovoltaic power output ratio.

[0105] Calculate the actual output sample of each photovoltaic power output based on the photovoltaic power output ratio sample and the preset photovoltaic rated capacity;

[0106] Based on the preset photovoltaic panel temperature coefficient, the actual output samples of each photovoltaic panel are dynamically corrected to determine the output curve of each photovoltaic panel.

[0107] The photovoltaic power output curves are smoothed by the moving average filtering method to determine the smoothed photovoltaic power output curves.

[0108] Based on the smoothed photovoltaic output curves, the photovoltaic output scenarios are generated.

[0109] Therefore, by fitting the photovoltaic output ratio through beta distribution, the uncertainty characteristics of photovoltaic power generation are accurately characterized. Inverse transformation sampling ensures the statistical representativeness of the generated scenario. At the same time, temperature coefficient correction and moving average filtering are introduced to make the generated photovoltaic output curve both conform to the actual physical characteristics and eliminate abnormal fluctuation interference, thereby improving the simulation accuracy of the photovoltaic scenario and providing a real and reliable input basis for subsequent absorption capacity assessment. This effectively overcomes the problem of overly idealistic scenario modeling in traditional methods.

[0110] In one embodiment, the historical photovoltaic output ratio is determined based on the historical photovoltaic power data and the preset photovoltaic rated capacity using the following formula:

[0111] ;

[0112] in, x The historical photovoltaic power output ratio is mentioned above. x The range is [0,1]. P pv The historical photovoltaic power data, P rated The rated capacity of the photovoltaic system is [value missing].

[0113] In one embodiment, the formula used to determine the probability density function of the beta distribution of the historical photovoltaic power output ratio by fitting the shape parameters of the beta distribution based on the historical photovoltaic power output ratio is as follows:

[0114] ;

[0115] in, Let be the beta distribution probability density function of the historical photovoltaic power output ratio. For beta functions, α The first shape parameter, β For the second shape parameter, x The historical photovoltaic power output ratio is mentioned above.

[0116] It should be noted that, α and β It mainly controls the distribution pattern, such as skewness and peak value; This is used to ensure that the probability integral is 1.

[0117] In one embodiment, the beta distribution probability density function based on the historical photovoltaic power output ratio is sampled using an inverse transformation sampling method to generate samples of each photovoltaic power output ratio. The formula used is:

[0118] ;

[0119] in, x k For the first k The photovoltaic output ratio sample of each simulated operating scenario For beta functions, N This represents the total number of preset simulation scenarios.

[0120] It should be noted that, x k It follows a Beta distribution.

[0121] In one embodiment, the formula used to calculate the actual output sample of each photovoltaic power generation based on each of the photovoltaic power output ratio samples and the preset photovoltaic rated capacity is as follows:

[0122] ;

[0123] in, P pv,k For the first k The actual photovoltaic power output samples of the simulated operating scenarios x k For the first k The photovoltaic output ratio sample of each simulated operating scenario P rated The rated capacity of the photovoltaic system is [value missing].

[0124] In one embodiment, the dynamic correction of the actual photovoltaic output samples based on a preset photovoltaic panel temperature coefficient is performed using the following formula:

[0125] ;

[0126] in, For the first k A dynamically corrected sample of actual photovoltaic power output for a simulated operating scenario. For the first k The actual photovoltaic power output samples of the simulated operating scenarios The temperature coefficient of the photovoltaic panel. This refers to the actual ambient temperature. This is the standard test temperature.

[0127] It should be noted that the temperature coefficient of the photovoltaic panel is taken as -0.3% / °C, and the standard test temperature is taken as 25°C; based on the above-mentioned... k The photovoltaic output curves are determined by dynamically correcting the actual photovoltaic output samples of each simulated operating scenario.

[0128] In one embodiment, the smoothing of each photovoltaic power output curve using a moving average filtering method to determine the smoothed photovoltaic power output curve is achieved using the following formula:

[0129] ;

[0130] in, For the first k The smoothed photovoltaic output curves for each simulated operating scenario For smoothing coefficients, For the first k The photovoltaic output curves in each simulated operating scenario are Solar power output at all times For the first k The photovoltaic output curves in each simulated operating scenario are t Solar power output at all times For the first k The photovoltaic output curves in each simulated operating scenario are Solar power output at all times t Indicates time, It is the time step.

[0131] In one embodiment, the expressions for each of the photovoltaic power output scenarios are as follows:

[0132] ;

[0133] in, For the first k In a simulated running scenario t Smoothed photovoltaic output over time N This represents the total number of preset simulation scenarios.

[0134] In one embodiment, the calculation of the historical power grid load data using the Monte Carlo method to generate various load scenarios includes:

[0135] Based on the historical power grid load data, various statistical characteristics are determined; wherein, the statistical characteristics include mean, maximum value, minimum value, standard deviation, median value, and quantiles;

[0136] Based on the historical power grid load data and the statistical characteristics, the optimal distribution and the corresponding optimal distribution parameters are determined.

[0137] Based on the optimal distribution and its corresponding optimal distribution parameters, the inverse transformation sampling method is used to generate various load scenarios.

[0138] Therefore, by analyzing the multi-dimensional statistical characteristics of historical load data and determining the optimal probability distribution based on statistical tests, the random patterns of load changes can be accurately described. At the same time, the load scenarios generated by inverse transformation sampling not only retain the statistical characteristics of the actual load but also cover the possible fluctuation range, effectively solving the problem of oversimplification of load models in traditional methods. This provides a real and reliable load change input for absorption capacity assessment, improving the accuracy of the assessment results and their practical guiding value.

[0139] In one embodiment, the step of generating various load scenarios by sampling using an inverse transformation sampling method based on the optimal distribution and the corresponding optimal distribution parameters includes:

[0140] Based on the optimal distribution and the corresponding optimal distribution parameters, the inverse transformation sampling method is used to generate various load curves.

[0141] Based on the aforementioned load curves, each load scenario is generated.

[0142] In one embodiment, the expressions for each of the load scenarios are:

[0143] ;

[0144] in, For the first k The load curves of the simulated operating scenarios, t Indicates time, N This represents the total number of preset simulation scenarios.

[0145] In one embodiment, determining the optimal distribution and corresponding optimal distribution parameters based on the historical power grid load data and each of the statistical characteristics includes:

[0146] Based on the historical power grid load data and the statistical characteristics, the maximum likelihood estimation method is used to fit the distribution parameters of each distribution to obtain the distribution parameters of each distribution; wherein, the distributions include normal distribution, gamma distribution and Weibull distribution;

[0147] Based on the distribution parameters of each distribution, determine the empirical distribution function and the corresponding theoretical cumulative distribution function of each distribution;

[0148] Based on the empirical distribution function and the corresponding theoretical cumulative distribution function of each distribution, the Kolmogorov-Smirnov method is used to test and determine the optimal distribution and its corresponding optimal distribution parameters.

[0149] Therefore, by using maximum likelihood estimation to fit parameters of various probability distributions and applying the KS test to objectively select the optimal distribution model, the problem of distribution selection relying on prior assumptions and lacking statistical basis in traditional load modeling is effectively solved. At the same time, through a data-driven distribution optimization mechanism, the generated load scenario is ensured to not only conform to actual statistical laws but also accurately reflect the random characteristics of load fluctuations, thereby improving the simulation accuracy and reliability of load scenarios and providing a more scientific load input basis for subsequent absorption capacity assessment.

[0150] In one embodiment, based on the historical power grid load data and the statistical characteristics, the distribution parameters of each distribution are fitted using the maximum likelihood estimation method to obtain the distribution parameters of each distribution. The formula used is as follows:

[0151] ;

[0152] in, Let be the likelihood function. Let be the probability density function. For the first i Historical power grid load data, For distribution parameters, n This represents the total number of historical power grid load data.

[0153] In one embodiment, the empirical distribution function and the corresponding theoretical cumulative distribution function based on each of the distributions are tested using the Kolmogorov-Smirnov method to determine the optimal distribution and the corresponding optimal distribution parameters, including:

[0154] Calculate the KS statistic based on the empirical distribution function and the corresponding theoretical cumulative distribution function of each of the aforementioned distributions;

[0155] The optimal distribution and its corresponding optimal distribution parameters are determined based on the KS statistic.

[0156] In one embodiment, the KS statistic is calculated based on the empirical distribution function and the corresponding theoretical cumulative distribution function of each distribution, using the following formula:

[0157] ;

[0158] in, The KS statistic is mentioned above. Let be the empirical distribution function of the distribution. This is the corresponding theoretical cumulative distribution function.

[0159] In step S12: the relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0160] Therefore, by constructing a synergistic relationship between photovoltaic absorption rate, system power loss, and equipment load rate, an evaluation model that can reflect the multi-objective balance requirements was established. At the same time, by dynamically adjusting the weights of each indicator according to the iteration process, the focus is on improving absorption capacity in the early stage of optimization, while taking into account the economic operation of the system in the later stage. This effectively solves the limitation of the traditional fixed weight method, which is difficult to adapt to different optimization stages. This ensures that the evaluation process maximizes photovoltaic absorption and ensures the safety and stability of system operation, providing a scientific and reasonable evaluation benchmark for subsequent optimization solutions.

[0161] In one embodiment, the relationship is as follows:

[0162] ;

[0163] in, The weighted calculated value, The weight of the photovoltaic absorption rate, As the weight of system power loss, As the weight of transformer load rate, As the weight of the line load rate, The photovoltaic absorption rate is mentioned above. The power loss of the system, The transformer load rate, The line load rate is [value].

[0164] In one embodiment, the expression for the photovoltaic absorption rate is as follows:

[0165] ;

[0166] in, For photovoltaic power absorption rate, For the first The actual grid connection power of each photovoltaic grid connection point For the first The theoretical access power of each photovoltaic access point This refers to the collection of photovoltaic access points.

[0167] In one embodiment, the expression for the system power loss is as follows:

[0168]

[0169] in, For system power loss, For the line The resistance; For the line Active power on For the line reactive power on For nodes voltage amplitude, A collection of routes.

[0170] In one embodiment, the expression for the transformer load rate is as follows:

[0171]

[0172] In the formula: For transformer load rate, For the first The apparent power of the transformer; For the first The rated capacity of the transformer.

[0173] In one embodiment, the line load rate is expressed as follows:

[0174]

[0175] In the formula: The line load rate, For the line Apparent power; For the line The rated apparent power.

[0176] In step S13: a comprehensive objective function is constructed based on the relation, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0177] Therefore, by constructing a comprehensive objective function from multi-objective relationships and setting corresponding constraints, the problem of difficulty in uniformly quantifying multiple performance indicators in traditional evaluation is effectively solved. By adopting a multi-scenario weighted average optimization objective, the final solution can maintain good performance under various possible operating conditions, while meeting the safety constraints of actual power grid operation. This balances photovoltaic absorption capacity with system operation economy and safety, and ensures the feasibility of the optimization scheme in actual engineering, providing a clear and reliable optimization direction for subsequent algorithm solutions.

[0178] In one embodiment, the expression for the comprehensive objective function is:

[0179]

[0180] in, This is the weighted average of the weighted calculation values ​​for each simulated scenario. This is the weighted calculation value for the Nth simulated running scenario. The weights are the weights of the weighted calculation values ​​for the Nth simulation scenario. .

[0181] In one embodiment, the constraints include node voltage constraints, line power flow constraints, transformer capacity constraints, photovoltaic access capacity constraints, and line power flow balance constraints.

[0182] Therefore, by establishing a complete constraint system that includes node voltage, line power flow, transformer capacity, photovoltaic access capacity, and power flow balance, the problem of insufficient feasibility of schemes caused by incomplete constraints in traditional assessments is effectively overcome. It comprehensively covers the key limiting factors for the safe operation of the distribution network, ensuring that the optimized photovoltaic access scheme meets power quality requirements, complies with equipment safety operation specifications, and guarantees system power balance. This significantly improves the practical operability of the assessment results and provides a truly valuable technical solution for photovoltaic access projects.

[0183] In one embodiment, the expression for the line power flow balance constraint is as follows:

[0184] ;

[0185] in, For nodes The active power input to the system, For nodes The reactive power of the input system, For nodes Active load at the location; For nodes reactive load at the location, For nodes voltage amplitude, For nodes voltage amplitude, For nodes and nodes The electrical conductance between them For nodes and nodes The susceptance between them For nodes and nodes The phase difference between them For nodes A set of.

[0186] In one embodiment, the expressions for the node voltage constraint, the line power flow constraint, the transformer capacity constraint, and the photovoltaic access capacity constraint are as follows:

[0187]

[0188] in, This is the lower limit of the node voltage. For nodes voltage amplitude, This is the upper limit of the node voltage. For the line Apparent power For the line Rated apparent power, For the first Apparent power of the transformer For the first The rated capacity of the transformer For the first The access capacity of each photovoltaic access point For the first The maximum access capacity of each photovoltaic access point.

[0189] In step S14: Based on the constraints and the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function to obtain each photovoltaic access scheme in the solution process, as well as the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario.

[0190] Therefore, by solving the comprehensive objective function using the starfish optimization algorithm, the problem of traditional optimization methods easily getting trapped in local optima when dealing with high-dimensional nonlinear problems is effectively solved. Through its unique exploration and development mechanism, under the premise of satisfying various operational constraints, it can systematically obtain a variety of feasible photovoltaic access schemes and their detailed power flow calculation results. It not only ensures the global search capability of the optimization scheme, but also provides rich decision data for subsequent analysis, so that the final determined absorption capacity assessment results not only meet the requirements of system safe operation, but also have good engineering applicability.

[0191] In one embodiment, based on the constraints and each of the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function to obtain each photovoltaic access scheme in the solution process, and the power flow calculation results of each photovoltaic access scheme under each simulated operating scenario, including:

[0192] Initialization is performed based on the constraints to generate an initial starfish population; wherein the initial starfish population includes multiple starfish, and the position of each starfish represents one of the photovoltaic access schemes.

[0193] Based on the initial starfish population and the pre-constructed simulated operating scenarios, iterative updates are performed to obtain each photovoltaic access scheme in the solution process, as well as the power flow calculation results of each photovoltaic access scheme under each simulated operating scenario; wherein, the fitness function of the starfish optimization algorithm is the comprehensive objective function.

[0194] It should be noted that for each starfish, a current flow calculation is performed in each simulated operating scenario to obtain the weighted count value of each starfish in each simulated operating scenario. The weighted average of the weighted count values ​​of each starfish in each simulated operating scenario is taken as the fitness value of the starfish in all simulated operating scenarios.

[0195] In one embodiment, the formula used to generate the initial starfish population in the starfish optimization algorithm is:

[0196]

[0197] in, For the first i Only the starfish j Dimensional position, For the first j Upper bound of a dimensional variable For the first j The lower bound of a dimensional variable. r These are random numbers uniformly distributed in [0,1].

[0198] It should be noted that the position of each starfish is composed of multiple decision variables, including photovoltaic access location, access capacity, etc. The upper and lower bounds of each decision variable are clearly defined, and the initial position of each starfish is randomly generated based on the upper and lower bounds of each decision variable.

[0199] In one embodiment, in the starfish optimization algorithm, the fitness of each starfish in a single simulated running scenario is calculated using a fitness function, the expression of which is:

[0200] ;

[0201] in, Let the fitness function be... The weight of the photovoltaic absorption rate, As the weight of system power loss, As the weight of transformer load rate, As the weight of the line load rate, The photovoltaic absorption rate is mentioned above. The power loss of the system, The transformer load rate, The line load rate is [value].

[0202] It should be noted that the fitness function is determined by the relational expression and is used to evaluate the merits of each starfish in a single simulated operating scenario, that is, the superiority of each photovoltaic access scheme in a single simulated operating scenario. The fitness value of each starfish in a single simulated operating scenario is the weighted count value of each photovoltaic access scheme in a single simulated operating scenario.

[0203] In one embodiment, the weights of the photovoltaic absorption rate, system power loss, transformer load rate, and line load rate are dynamically adjusted according to the number of iterations, using the following expressions:

[0204]

[0205] in, The weight of the photovoltaic absorption rate, As the weight of system power loss, As the weight of transformer load rate, As the weight of the line load rate, T Current iteration number, T max This represents the maximum number of iterations.

[0206] In one embodiment, the formula used in the five-dimensional search mode of the starfish optimization algorithm during the exploration phase is:

[0207]

[0208] in, For the first T During the nth iteration i The starfish in the first p The updated position on the dimension For the first T During the nth iteration i The starfish in the first p Current position on the dimension For the first T In the nth iteration, the optimal starfish is at the... p Current position on the dimension p for D Five dimensions are randomly selected from the five dimensions. For coefficients, For random angles, r Represents a random number between [0, 1]. T This represents the current iteration number. T max This represents the maximum number of iterations.

[0209] It should be noted that, D The dimension is the number of decision variables in the design. When there are more than 5 decision variables to be optimized, a five-dimensional search mode is adopted. In this embodiment, for example, the decision variables include photovoltaic access capacity, access location, energy storage charging and discharging power, voltage regulation equipment, and power factor.

[0210] In one embodiment, the formula used in the one-dimensional search mode of the starfish optimization algorithm during the exploration phase is:

[0211]

[0212] in, For the first T During the nth iteration i The starfish in the first p The updated position on the dimension For the first T During the nth iteration i The starfish in the first p Current position on the dimension For the first T The first one is randomly selected during iteration. k 1 starfish p Position on the dimension For the first t The first one is randomly selected in the nth iteration. k 2 starfish p Position on the dimension As the first random weight, As the second random weight, p forD A dimension is randomly selected from the dimensionality. For the energy of starfish, For random angles, T This represents the current iteration number. T max This represents the maximum number of iterations.

[0213] It should be noted that, among them, .

[0214] In one embodiment, the formula used for predation behavior during the development phase of the starfish optimization algorithm is:

[0215]

[0216] in, For the first T During the nth iteration i The updated location of the starfish For the first T During the nth iteration i The current location of the starfish. and A random number between [0,1] and for Two distances are randomly selected from the data. The distances between the five optimal starfish and the randomly selected starfish. For the first T The current position of the optimal starfish in the next iteration. Five randomly selected starfish, For the first T During the nth iteration The current location of the starfish. T This represents the current iteration number.

[0217] In one embodiment, the formula used for the regeneration behavior during the development phase of the starfish optimization algorithm is:

[0218]

[0219] in, For the first T During the nth iteration i The updated location of the starfish The total population size For the first T During the nth iteration i The current location of the starfish. T This represents the current iteration number. T max This represents the maximum number of iterations.

[0220] It should be noted that, It is a natural exponential function used to achieve a dynamic decay effect.

[0221] In one embodiment, the formula used in the development phase of the starfish optimization algorithm to calculate and correct out-of-bounds variables is:

[0222]

[0223] in, For the first T During the nth iteration i The updated location of the starfish For the first i Only the starfish j Dimensional position, For the first j Upper bound of a dimensional variable For the first j The lower bound of a dimensional variable.

[0224] In one embodiment, the starfish optimization algorithm includes a penalty term added during the development phase. The expression for the penalty term is:

[0225]

[0226] in, C The penalty value. For nodes voltage amplitude, This represents the upper limit of the node voltage.

[0227] It should be noted that the penalty item is used to deduct the fitness of each photovoltaic access scheme under the simulated operation scenario that exceeds the limit.

[0228] In one embodiment, in the starfish optimization algorithm, the fitness value of each starfish in all simulated operating scenarios is the weighted average of the weighted count values ​​of each simulated operating scenario for each photovoltaic access scheme, using the following formula:

[0229]

[0230] in, This is the weighted average of the weighted calculation values ​​for each simulated scenario. Let i be the current position of the i-th starfish. This is the weighted calculation value for the Nth simulated running scenario. The weights are the weights of the weighted calculation values ​​for the Nth simulation scenario. .

[0231] In one embodiment, the starfish optimization algorithm terminates when the maximum number of iterations is reached or the fitness change is less than 1% after 10 consecutive iterations. The output is the globally optimal starfish, and the expression for the globally optimal starfish is:

[0232]

[0233] in, The globally optimal starfish, The comprehensive objective function is... Let be the current position of the i-th starfish.

[0234] It should be noted that the globally optimal starfish represents the optimal photovoltaic grid connection scheme under all simulated operating scenarios.

[0235] In step S15: Data features are extracted based on the power flow calculation results and the constraints to determine the margin index corresponding to each power flow calculation result.

[0236] Therefore, by extracting key operational margin indicators from the power flow calculation results of various scenarios, the shortcomings of traditional assessments that only focus on boundary values ​​and ignore system safety margins are effectively overcome. Through quantitative analysis of constraints such as voltage deviation and load rate, implicit system safety information is transformed into comparable margin parameters, realizing the transformation from simple compliance judgment to refined safety level assessment. Multi-scenario panoramic data feature extraction is achieved, providing key data support for accurately quantifying the system's absorption potential and improving the accuracy and engineering guidance value of capacity assessment results.

[0237] In one embodiment, the margin metrics include the voltage margin at each node and the load factor margin at each branch.

[0238] Therefore, by introducing node voltage margin and branch load factor margin as core evaluation indicators, the traditional binary judgment of whether the limit is exceeded is transformed into a continuous quantitative evaluation of the system's safety boundary. This can accurately reveal the system's safety buffer space under different operating conditions, effectively overcoming the limitation of traditional evaluations that only focus on extreme states and ignore the degree of safety. At the same time, through in-depth quantification of voltage levels and equipment load states, it provides grid operators with a more intuitive perception of the safety status, so that the absorption capacity evaluation results can not only reflect the system's ultimate carrying capacity, but also reflect its actual safety redundancy level in operation, providing key decision-making basis for grid safety regulation and preventive control.

[0239] In one embodiment, the step of extracting data features based on each of the power flow calculation results and the constraints to determine the margin index corresponding to each power flow calculation result includes:

[0240] Based on the voltage magnitude of each node, the preset maximum voltage value, and the preset minimum voltage value of each power flow calculation result, the voltage margin of each node corresponding to each power flow calculation result is determined.

[0241] Based on the active and reactive power flow of each branch and the preset maximum branch transmission power according to the power flow calculation results, the load margin of each branch corresponding to each power flow calculation result is determined.

[0242] In one embodiment, the voltage margin of each node corresponding to each power flow calculation result is determined based on the node voltage magnitude, preset maximum voltage value, and preset minimum voltage value of each power flow calculation result, using the following formula:

[0243] Y t,n =min( V t,n - V min , V max - V t,n )

[0244] in, V t,n For the first t The first moment n The voltage value of each node, V max The preset maximum voltage value, V min The preset minimum voltage value, Y t,n For the first t The first moment n The node voltage margin of each node.

[0245] In one embodiment, the active and reactive power flows of each branch in the power flow calculation results and the preset maximum branch transmission power are used to determine the load margin of each branch corresponding to each power flow calculation result, using the following formula:

[0246] M t,l =(1- S t,l ) / S t,e

[0247] in, S t,l For the first t Time of the first l The power of each branch, S t,e The maximum transmission power of the preset branch. Mt,l For the first t The first moment l Branch load margin of each branch.

[0248] In step S16: Based on each photovoltaic access scheme and the margin index corresponding to each photovoltaic access scheme, the maximum photovoltaic absorption capacity is obtained.

[0249] Therefore, by comprehensively analyzing different photovoltaic access schemes and their corresponding system operation margin indicators, this method overcomes the limitations of traditional methods that determine the absorption capacity based on a single typical scenario. At the same time, by establishing the correlation between photovoltaic access capacity and system safety margin, it can accurately identify the maximum carrying capacity of the system under various safety constraints. This avoids the waste of resources caused by overly conservative capacity estimates and prevents the operational risks that may be caused by overly optimistic assessments, providing an accurate and reliable quantitative basis for the scientific decision-making of photovoltaic access capacity in distribution networks.

[0250] In one embodiment, the step of analyzing each photovoltaic (PV) grid connection scheme and the corresponding margin index for each PV grid connection scheme to obtain the maximum PV grid absorption capacity includes:

[0251] Based on each of the photovoltaic access schemes, determine the total photovoltaic access capacity corresponding to each photovoltaic access scheme;

[0252] Based on the preset total photovoltaic access capacity range and the total photovoltaic access capacity, the photovoltaic access schemes are grouped to determine the set of photovoltaic access schemes corresponding to each total photovoltaic access capacity range;

[0253] Based on each set of photovoltaic access schemes and each of the margin indices corresponding to each photovoltaic access scheme, the minimum margin index corresponding to each set of photovoltaic access schemes is determined.

[0254] The maximum photovoltaic absorption capacity is determined by comparing a pre-set safety margin threshold with each of the minimum margin indices.

[0255] It should be noted that the analysis observes the change in minimum voltage margin as the total photovoltaic capacity increases; as the total photovoltaic capacity increases, the voltage margin gradually decreases and changes from a positive value to a negative value, and the voltage begins to exceed the limit.

[0256] Therefore, by grouping photovoltaic access schemes according to capacity intervals and determining the maximum absorption capacity based on the comparison between the minimum margin index and the safety threshold within each capacity interval, the problem of conservative capacity estimation caused by individual extreme scenarios in traditional assessments is effectively solved. At the same time, by systematically analyzing the change law of safety margin under different capacity levels, the turning point of the system from safe operation to over-limit critical state can be accurately identified. Thus, under the premise of ensuring system safety, the absorption potential of the distribution network is fully explored, improving the accuracy of capacity assessment results and engineering practical value.

[0257] In this embodiment, for example, determining the maximum photovoltaic absorption capacity by comparing a pre-set safety margin threshold with each of the minimum margin indices includes:

[0258] A scatter plot was drawn based on each of the minimum margin indices and each of the total photovoltaic access capacity ranges;

[0259] The margin safety threshold is plotted in the scatter plot to determine the maximum photovoltaic absorption capacity.

[0260] In one embodiment, it also includes:

[0261] Based on the pre-determined voltage over-limit indicators corresponding to each photovoltaic access scheme, the regional bottlenecks and time bottlenecks of each photovoltaic access scheme under each simulated operation scenario are analyzed.

[0262] Based on the pre-determined overload index of each equipment corresponding to each photovoltaic access scheme, the thermal bottleneck of each photovoltaic access scheme under each simulated operation scenario is analyzed.

[0263] Based on the predetermined grid loss indicators corresponding to each photovoltaic access scheme, the economics of each photovoltaic access scheme under each simulated operation scenario are analyzed.

[0264] Based on the predetermined margin indices corresponding to each photovoltaic access scheme, the robustness of each photovoltaic access scheme under each simulated operation scenario is analyzed.

[0265] Based on the predetermined photovoltaic access schemes and the corresponding voltage over-limit indicators, equipment overload indicators, network loss indicators, and margin indicators for each photovoltaic access scheme, the analysis examines the stable and sensitive locations of photovoltaic access for each scheme under various simulated operating scenarios.

[0266] Therefore, by systematically analyzing multi-dimensional indicators such as voltage overruns, equipment overloads, and network losses, a refined assessment of the photovoltaic absorption capacity of the distribution network is achieved. By identifying the spatiotemporal distribution characteristics of voltage overruns and the thermal bottlenecks of equipment overloads, the key links limiting absorption capacity can be accurately located. Combining the economic analysis of network loss indicators and the robustness assessment of margin indicators, the stable and sensitive locations of photovoltaic access can be effectively distinguished, providing clear guidance for distribution network transformation and photovoltaic site selection, and enhancing the practical value and decision support capability of the assessment results.

[0267] In one embodiment, the method for determining each of the voltage over-limit indicators, each of the equipment overload indicators, and each of the network loss indicators includes:

[0268] Based on the power flow calculation results and the constraints, overload detection is performed to obtain voltage overload detection results and branch overload detection results corresponding to each power flow calculation result. The voltage overload detection results include the voltage overload node number, voltage overload time point, and voltage overload amplitude. The branch overload detection results include the overloaded line number, line overload time point, line overload rate, overloaded transformer number, transformer overload time point, and transformer overload rate.

[0269] Based on the voltage over-limit detection results, the voltage over-limit index corresponding to each power flow calculation result is extracted; the voltage over-limit index includes the total number of over-limit nodes, the total number of over-limit occurrences, the maximum over-limit amplitude, and the percentage of over-limit time.

[0270] Based on the overload detection results of each branch, the equipment overload index corresponding to each power flow calculation result is extracted; the equipment overload index includes the total number of overloaded branches, the total number of overloads, and the maximum overload rate.

[0271] Based on the power flow calculation results, the network loss index corresponding to each power flow calculation result is extracted; the network loss index is the total active power loss value.

[0272] It should be noted that the total number of nodes exceeding the limit is the number of nodes in the system that exceed the limit at least once at a single moment, the total number of times exceeding the limit is the sum of the number of times exceeding the limit for all nodes at all time points, and the percentage of time exceeding the limit is the total number of times exceeding the limit divided by the product of the number of nodes and the number of time points; the total number of overloaded branches is the number of branches that experience at least one overload at a single moment, and the total number of overloads is the sum of the number of overloads for all nodes at all time points.

[0273] Therefore, through a systematic detection and extraction process, the original power flow calculation results are transformed into quantitative indicators that can be directly used for safety assessment. At the same time, by extracting multi-dimensional features such as location, time, and severity from voltage over-limit and branch overload events, and aggregating discrete over-limit events into statistical indicators such as total number of nodes, number of occurrences, and maximum amplitude, a refined characterization of the system's safety status is achieved. This effectively solves the problem of single-dimensional safety analysis and difficulty in quantitative comparison in traditional assessments, and provides key data support for accurately assessing the safety margin and risk level of the system under different operating scenarios.

[0274] In one embodiment, it also includes:

[0275] Based on the simulated operating fields, the photovoltaic access schemes, the power flow calculation results of each photovoltaic access scheme under each simulated operating scenario, the margin indicators, the maximum photovoltaic absorption capacity, the voltage over-limit indicators, the regional bottlenecks and time bottlenecks, the equipment overload indicators, the thermal bottlenecks, the network loss indicators, the economic efficiency, the robustness, the stable location of photovoltaic access, and the sensitive location of photovoltaic access, a multi-type actual operating scenario library is constructed.

[0276] Therefore, by integrating simulated operation scenarios, photovoltaic access schemes, power flow calculation results, and various safety and economic indicators, a complete multi-dimensional operation scenario library has been constructed. This transforms scattered assessment data into a systematic decision-making knowledge base, effectively solving the problems of data silos and information fragmentation in traditional assessments. At the same time, through correlation analysis of operation scenarios, system bottlenecks, economics, and safety margins, it provides panoramic decision support for photovoltaic planning of distribution networks, improving the systematicness and practicality of assessment results, and simultaneously meeting the decision-making needs of different stages such as planning, operation, and renovation.

[0277] In one embodiment, it also includes:

[0278] Determine the optimal grid configuration scheme based on the pre-generated optimal photovoltaic access scheme;

[0279] The optimal photovoltaic grid connection scheme is obtained as follows:

[0280] Determine the simulation scenarios;

[0281] The relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0282] Based on the aforementioned relation, a comprehensive objective function is constructed, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0283] Based on the constraints and the simulated operating scenarios, the comprehensive objective function is solved using the starfish optimization algorithm to obtain the optimal photovoltaic access scheme.

[0284] Therefore, by constructing a relationship between photovoltaic absorption rate, system power loss and equipment load rate, and combining it with a multi-scenario weighted evaluation mechanism, the problem of insufficient adaptability of the scheme caused by the single objective in traditional evaluation is effectively solved. At the same time, by dynamically adjusting the weight of each objective at different iteration stages and using the starfish optimization algorithm for global optimization, the optimal photovoltaic access scheme that performs well in multiple operating scenarios can be obtained under the premise of considering the randomness of photovoltaic output and load fluctuation. This improves the robustness and practicality of the photovoltaic access scheme and provides an optimized configuration scheme for distribution network photovoltaic planning that meets the absorption requirements and ensures the safe and stable operation of the system.

[0285] Therefore, the Monte Carlo method is used to fit the probability distributions of photovoltaic (PV) output and load, simulating the uncertainty, randomness, and load demand of PV power generation, generating various PV output and load scenarios. Based on these scenarios, simulated operating scenarios are constructed by random combination. The starfish optimization algorithm is used to optimize the PV access location and capacity in each scenario. Multiple starfish individuals are randomly generated to represent different PV access schemes, and their optimization spaces are determined. A hybrid five-dimensional and one-dimensional search mode is used to simulate the starfish's exploration behavior. The predation and regeneration strategy during the development phase is used to achieve efficient search for the global optimum. Fitness evaluation is performed by dynamically adjusting weights to further improve the solution accuracy. Finally, the optimal PV access schemes for various scenarios are obtained through multiple iterations. The PV absorption capacity under these schemes is evaluated and compared with actual grid access schemes to verify the model's effectiveness. Power flow analysis can also be performed on non-optimal solutions during the optimization process, establishing a database of actual operating scenarios under different access schemes, providing auxiliary decision-making for PV absorption capacity assessment.

[0286] Example 2

[0287] Based on the same inventive concept as the above embodiments, a second aspect of the present invention provides a distributed photovoltaic grid connection capacity assessment system, comprising:

[0288] The evaluation module is used to assess the distributed photovoltaic grid connection capacity based on a predetermined maximum photovoltaic grid connection capacity.

[0289] The method for determining the maximum photovoltaic absorption capacity includes:

[0290] Determine the simulation scenarios;

[0291] The relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0292] Based on the aforementioned relation, a comprehensive objective function is constructed, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0293] Based on the constraints and the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function, and each photovoltaic access scheme in the solution process and the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario are obtained.

[0294] Based on the power flow calculation results and the constraints, data features are extracted to determine the margin index corresponding to each power flow calculation result.

[0295] Based on the analysis of each photovoltaic grid connection scheme and the margin index corresponding to each photovoltaic grid connection scheme, the maximum photovoltaic grid absorption capacity is obtained.

[0296] Example 3

[0297] like Figure 3 As shown, in a third aspect, the present invention also provides an electronic device 100 for implementing the distributed photovoltaic absorption capacity assessment method provided in any of the above embodiments;

[0298] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0299] The memory 101 can be used to store the computer program 103. The processor 102 implements the distributed photovoltaic absorption capacity assessment method steps provided in any of the above embodiments by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0300] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0301] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0302] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for assessing the distributed photovoltaic power absorption capacity, and the processor 102 can execute multiple instructions to achieve the following:

[0303] Assess the distributed photovoltaic (PV) grid connection capacity based on a predetermined maximum grid connection capacity;

[0304] The method for determining the maximum photovoltaic absorption capacity includes:

[0305] Determine the simulation scenarios;

[0306] The relationship between photovoltaic absorption rate, system power loss, transformer load rate, line load rate and weighted calculated value is constructed; the weights of photovoltaic absorption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations.

[0307] Based on the aforementioned relation, a comprehensive objective function is constructed, and the constraints of the comprehensive objective function are determined; the comprehensive objective function aims to maximize the weighted average of the weighted calculated values ​​of each simulated running scenario.

[0308] Based on the constraints and the simulated operating scenarios, the starfish optimization algorithm is used to solve the comprehensive objective function, and each photovoltaic access scheme in the solution process and the power flow calculation results of each photovoltaic access scheme in each simulated operating scenario are obtained.

[0309] Based on the power flow calculation results and the constraints, data features are extracted to determine the margin index corresponding to each power flow calculation result.

[0310] Based on the analysis of each photovoltaic grid connection scheme and the margin index corresponding to each photovoltaic grid connection scheme, the maximum photovoltaic grid absorption capacity is obtained.

[0311] Example 4

[0312] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the distributed photovoltaic power absorption capacity assessment method provided in any of the above embodiments.

[0313] It should be noted that if the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0314] Therefore, the distributed photovoltaic grid absorption capacity assessment system, electronic device, and computer-readable storage medium provided by this invention also solve the problems mentioned in the background section, such as large deviations in absorption capacity assessment results, low local optimal optimization effects, and the instability of grid operation and unreasonable planning.

[0315] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0316] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0317] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0318] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0319] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0320] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the grid absorption capacity of distributed photovoltaic power, characterized in that, The application relates to a method for determining a maximum photovoltaic (PV) consumption capacity. The method comprises the following steps: determining a PV maximum consumption capacity based on a predetermined PV maximum consumption capacity evaluation distributed PV consumption capacity; wherein the method for determining the PV maximum consumption capacity comprises the following steps: determining each simulation operation scenario; constructing a relationship formula of a PV consumption rate, system power loss, transformer load rate, line load rate and a weighted calculation value; the weight of the PV consumption rate, the weight of the system power loss, the weight of the transformer load rate and the weight of the line load rate are dynamically adjusted according to the number of iterations; constructing a comprehensive objective function based on the relationship formula and determining a constraint condition of the comprehensive objective function; the comprehensive objective function aims to maximize the weighted average value of the weighted calculation value of each simulation operation scenario; based on the constraint condition and each simulation operation scenario, a sea star optimization algorithm is used to solve the comprehensive objective function, so that each PV access scheme in the solving process and a power flow calculation result of each PV access scheme under each simulation operation scenario are obtained; based on each power flow calculation result and the constraint condition, data feature extraction is performed, and a margin index corresponding to each power flow calculation result is determined; based on each PV access scheme and each margin index corresponding to each PV access scheme, the PV maximum consumption capacity is obtained; the method for obtaining the PV maximum consumption capacity based on each PV access scheme and each margin index corresponding to each PV access scheme comprises the following steps: based on each PV access scheme, a total PV access capacity corresponding to each PV access scheme is determined; based on each total PV access capacity interval and each total PV access capacity, each PV access scheme is grouped, and a PV access scheme set corresponding to each total PV access capacity interval is determined; based on each PV access scheme set and each margin index corresponding to each PV access scheme, a minimum margin index corresponding to each PV access scheme set is determined; 2. The distributed photovoltaic power consumption capacity evaluation method according to claim 1, characterized in that, based on a pre-set margin safety threshold and each minimum margin index, the PV maximum consumption capacity is determined.

3. The distributed photovoltaic power consumption capacity evaluation method according to claim 1, characterized in that, The constraint condition comprises a node voltage constraint, a line power flow constraint, a transformer capacity constraint, a PV access capacity constraint and a line power flow balance constraint, and the margin index comprises a node voltage margin and a branch load rate margin. The application further comprises the following steps: based on each voltage out-of-limit index corresponding to each PV access scheme, regional bottlenecks and time bottlenecks of each PV access scheme under each simulation operation scenario are analyzed; based on each device overload index corresponding to each PV access scheme, thermal bottlenecks of each PV access scheme under each simulation operation scenario are analyzed; based on each net loss index corresponding to each PV access scheme, the economic efficiency of each PV access scheme under each simulation operation scenario is analyzed; based on each margin index corresponding to each PV access scheme, the robustness of each PV access scheme under each simulation operation scenario is analyzed. The photovoltaic access stability position and the photovoltaic access sensitive position of each photovoltaic access scheme under each simulation operation scene are analyzed based on each photovoltaic access scheme and each voltage out-of-limit index, each equipment overload index, each network loss index and each margin index corresponding to each photovoltaic access scheme.

4. The distributed photovoltaic power consumption capacity evaluation method according to claim 3, characterized in that, The determination method of each voltage out-of-limit index, each equipment overload index and each network loss index comprises: Out-of-limit overload detection is performed based on each power flow calculation result and the constraint condition, to obtain a voltage out-of-limit detection result and a branch overload detection result corresponding to each power flow calculation result; the voltage out-of-limit detection result comprises a voltage out-of-limit node number, a voltage out-of-limit time point and a voltage out-of-limit amplitude, and the branch overload detection result comprises an overload line number, a line overload time point, a line overload rate, an overload transformer number, a transformer overload time point and a transformer overload rate; A voltage out-of-limit index corresponding to each power flow calculation result is extracted based on each voltage out-of-limit detection result; the voltage out-of-limit index comprises a total number of out-of-limit nodes, a total number of out-of-limit times, a maximum out-of-limit amplitude and an out-of-limit time proportion; An equipment overload index corresponding to each power flow calculation result is extracted based on each branch overload detection result; the equipment overload index comprises a total number of overload branches, a total number of overload times and a maximum overload rate; A network loss index corresponding to each power flow calculation result is extracted based on each power flow calculation result; the network loss index is a total active network loss value.

5. The distributed photovoltaic power consumption capacity evaluation method according to claim 1, characterized in that, The determination method of each simulation operation scene comprises: Historical photovoltaic power data and historical power grid load data are obtained; The historical photovoltaic power data are calculated by using a Monte Carlo method, to generate each photovoltaic output scene; The historical power grid load data are calculated by using a Monte Carlo method, to generate each load scene; Each simulation operation scene is determined based on each photovoltaic output scene and each load scene.

6. The distributed photovoltaic power consumption capacity evaluation method according to claim 5, characterized in that, The calculation of the historical photovoltaic power data by using the Monte Carlo method to generate each photovoltaic output scene comprises: A historical photovoltaic output proportion is determined based on the historical photovoltaic power data and a preset photovoltaic rated capacity; A beta distribution probability density function of the historical photovoltaic output proportion is determined by fitting a shape parameter of the beta distribution based on the historical photovoltaic output proportion; Each photovoltaic output proportion sample is generated by using an inverse transform sampling method based on the beta distribution probability density function of the historical photovoltaic output proportion; Each photovoltaic actual output sample is calculated based on each photovoltaic output proportion sample and the preset photovoltaic rated capacity; Each photovoltaic output curve is determined by dynamically correcting each photovoltaic actual output sample based on a preset photovoltaic panel temperature coefficient; Each smoothed photovoltaic output curve is determined by smoothing each photovoltaic output curve by using a moving average filtering method; Each photovoltaic output scene is generated based on each smoothed photovoltaic output curve.

7. The distributed photovoltaic power consumption capacity evaluation method according to claim 5, characterized in that, The calculation of the historical power grid load data by using the Monte Carlo method to generate each load scene comprises: Determine each statistical feature based on the historical power grid load data, wherein the statistical features include mean, maximum, minimum, standard deviation, median, quantile; Determine the optimal distribution and corresponding optimal distribution parameters based on the historical power grid load data and each statistical feature; Generate each load scenario by using inverse transform sampling method based on the optimal distribution and corresponding optimal distribution parameters.

8. The distributed photovoltaic power consumption capacity evaluation method according to claim 7, characterized in that, The method for determining the optimal distribution and corresponding optimal distribution parameters based on the historical power grid load data and each statistical feature comprises: Fit the distribution parameters of each distribution by using maximum likelihood estimation method based on the historical power grid load data and each statistical feature, to obtain the distribution parameters of each distribution, wherein the distributions include normal distribution, gamma distribution and Weibull distribution; Determine the empirical distribution function and corresponding theoretical cumulative distribution function of each distribution based on the distribution parameters of each distribution; Determine the optimal distribution and corresponding optimal distribution parameters by using Kolmogorov-Smirnov method based on the empirical distribution function and corresponding theoretical cumulative distribution function of each distribution. 9.A distributed photovoltaic (PV) accommodation capacity evaluation system, characterized in that, The method comprises: The evaluation module is configured to evaluate the distributed photovoltaic consumption capacity based on the predetermined maximum photovoltaic consumption capacity. The method for determining the maximum photovoltaic consumption capacity comprises: Determine each simulation operation scenario; Construct a relationship between the photovoltaic consumption rate, system power loss, transformer load rate, line load rate and weighted calculation value; the weights of the photovoltaic consumption rate, system power loss, transformer load rate and line load rate are dynamically adjusted according to the number of iterations; Construct a comprehensive objective function based on the relationship, and determine the constraint conditions of the comprehensive objective function; the comprehensive objective function aims to maximize the weighted average of the weighted calculation values of each simulation operation scenario; Solve the comprehensive objective function by using the sea star optimization algorithm based on the constraint conditions and each simulation operation scenario, to obtain each photovoltaic access scheme in the solving process and the power flow calculation result of each photovoltaic access scheme under each simulation operation scenario; Extract data features based on each power flow calculation result and the constraint conditions, to determine the margin index corresponding to each power flow calculation result; Analyze each photovoltaic access scheme and each margin index corresponding to each photovoltaic access scheme, to obtain the maximum photovoltaic consumption capacity. The method for obtaining the maximum photovoltaic consumption capacity by analyzing each photovoltaic access scheme and each margin index corresponding to each photovoltaic access scheme comprises: Determine the total photovoltaic access capacity corresponding to each photovoltaic access scheme based on each photovoltaic access scheme; Group each photovoltaic access scheme based on each total photovoltaic access capacity interval and each photovoltaic access scheme, to determine the photovoltaic access scheme set corresponding to each total photovoltaic access capacity interval; Determine the minimum margin index corresponding to each photovoltaic access scheme set based on each photovoltaic access scheme set and each margin index corresponding to each photovoltaic access scheme. The preset margin safety threshold and each minimum margin index are compared to determine the maximum consumption capacity of the photovoltaic.

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