Optimization method of power flow distribution considering multiple scenarios

Abstract

The present invention provides a method for optimizing the power flow distribution of a power grid considering multiple scenarios, including collecting user power demand data and power grid operation status data, obtaining factor data of power grid flow distribution; selecting factor data as fuzzy variables, performing fuzzification processing, forming a fuzzy variable table and a multi-scenario fuzzy rule table; using the Monte Carlo method to simulate multiple scenarios, generating a multi-scenario factor data sample set; constructing a power flow calculation model according to the power grid topology and equipment parameters, using the sample set data as input to perform power flow calculation, and obtaining power grid operation status information; based on the analysis of the operation status information, evaluating the power grid operation performance, and determining the optimization target and optimization method. The method comprehensively considers multiple factors, accurately calculates the operation status of the power grid in different scenarios through fuzzification processing and multi-scenario simulation, provides a reliable basis for the evaluation and optimization of the power grid operation performance, and helps to improve the stability and reliability of the power grid.

Description

Technical Field

[0001] The present invention belongs to the field of power grid system power flow distribution optimization, and specifically is a power grid power flow distribution optimization method considering multiple scenarios. Background Art

[0002] With the development of power systems and the growing demand for electricity from users, the operating environment of power grids has become increasingly complex. Power grid flow distribution is affected by many factors, including load changes, power output fluctuations, grid structure, operation control, and external environment. Accurately analyzing and optimizing power grid flow distribution is essential to ensure safe, stable, and efficient operation of the power grid.

[0003] Traditional power flow calculation methods are usually based on deterministic models and parameters, which make it difficult to take into account the various uncertainties that exist in actual power grids. For example, there are errors in load forecasting, power output is volatile, and external factors such as weather also have an impact on power grid operation. These uncertainties may cause power grid operation problems in certain scenarios, such as voltage exceeding the limit and current overload.

[0004] In order to better address these challenges, a grid power flow distribution optimization method that can consider multiple scenario factors is needed. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a method for optimizing power grid flow distribution by considering multiple scenarios. By collecting and analyzing user electricity demand data, power grid operation status data and various related factor data, a comprehensive evaluation and optimization of power grid flow distribution is carried out.

[0006] The technical solution of the present invention is as follows:

[0007] A method for optimizing power flow distribution in a multi-scenario power grid is characterized by comprising:

[0008] Collect user electricity demand data and grid operation status data, and obtain grid power flow distribution factor data based on the collected data;

[0009] The factor data of power grid flow distribution are selected as fuzzy variables, and the membership function is used to fuzzify the factor data to obtain the membership of each fuzzy variable, define the language value for the fuzzy variable, and form a fuzzy variable table and a multi-scenario fuzzy rule table;

[0010] The Monte Carlo method is used to perform multi-scenario simulation. In the simulation, based on the requirements of the multi-scenario fuzzy rule table and the membership degree of the fuzzy variable table, the factor data of each power grid flow distribution is randomly sampled to generate a multi-scenario factor data sample set;

[0011] The data in the multi-scenario factor data sample set is used as the input parameter of the power flow calculation. The power flow calculation model is used to perform power flow calculation for each scenario to obtain the operating status information of the power grid in each scenario.

[0012] Based on the analysis of the operating status information of the power grid in this scenario, the operating performance of the power grid in different scenarios is evaluated, and the optimization objectives and optimization methods are determined.

[0013] The factor data of power grid flow distribution include load factor data, power factor data, power grid structure factor data, operation control factor data and external factor data;

[0014] The load factor data specifically include load forecast value, load forecast error rate, load growth rate, and load peak-valley difference;

[0015] Among them, the power factor data specifically includes power output forecast value, power output volatility, and power installed capacity;

[0016] Among them, the specific data of power grid structure factors include line parameters, transformer parameters, node parameters, and network topology;

[0017] Among them, the operation control factor data include generator output, transformer tap position, line switching status, and relay protection parameters;

[0018] Among them, external factor data specifically include weather data and electricity price data.

[0019] The membership function uses trigonometric function to calculate the membership, and the calculation formula is:

[0020] ;

[0021] in, is the triangle membership function, a , b , c are the parameters of the triangle membership function, x is a fuzzy variable.

[0022] The method for defining the linguistic value of the fuzzy variable includes:

[0023] Based on statistical definition: Fuzzy variables are divided into different levels according to their data distribution;

[0024] Rule-based definition: Fuzzy variables are divided into different levels according to multi-scenario fuzzy rules.

[0025] The fuzzy variable table comprises:

[0026] The first column of the table lists all the fuzzy variables that need to be fuzzified;

[0027] The second column of the table lists the different linguistic values ​​of each fuzzy variable;

[0028] The third column of the table lists the membership interval corresponding to each language value.

[0029] The structure of the multi-scenario fuzzy rule table includes: each row represents a fuzzy rule; each column represents a fuzzy variable; and each element represents a fuzzy relationship.

[0030] When using the Monte Carlo method to simulate multiple scenarios, it is necessary to determine the number of Monte Carlo simulations to be performed. The method for determining the number of simulations is as follows:

[0031] According to the error analysis of the simulation results, determine the number of simulations required to achieve the predetermined accuracy;

[0032] Estimate the computational time required for a single simulation and determine the number of simulations based on available computing resources.

[0033] The Monte Carlo method is adopted to generate data samples of each factor by performing random sampling according to the membership of the fuzzy variables based on the fuzzy variables and the membership function and the multi-scenario fuzzy rule table.

[0034] The power flow calculation model includes a node power balance equation and a branch power equation;

[0035] Among them, the node power balance equation includes:

[0036] Active power balance equation:

[0037] ;

[0038] Reactive power balance equation:

[0039] ;

[0040] The branch power equations include:

[0041] Branch Road Active power:

[0042] ;

[0043] Branch Road Reactive power:

[0044] ;

[0045] in, and Node The injected active power and reactive power; and For Node and nodes The voltage amplitude, Node and nodes The phase angle difference, and Branch The conductance and susceptance of and For branch The active power and reactive power of is the total number of nodes.

[0046] The step of calculating the operating status information of the power grid in each scenario includes:

[0047] Obtain factor data for each scenario from a multi-scenario factor data sample set;

[0048] The active power and reactive power injected into the node are determined according to the factor data, and the active power and reactive power will be substituted into the node power balance equation;

[0049] Obtain grid structure parameters from a multi-scenario factor data sample set and substitute them into the formula;

[0050] For node voltage amplitude and phase angle, estimated values ​​are set at the initial stage and continuously updated through iterative calculation until the convergence condition is met;

[0051] Substitute the above acquired data into the node power balance equation and branch power equation to perform power flow calculation.

[0052] The beneficial effects of the present invention are as follows: it comprehensively considers data from multiple aspects such as load factors, power supply factors, power grid structure factors, operation control factors and external factors, and can more comprehensively reflect the influencing factors of power grid flow distribution.

[0053] The use of fuzzy variables and membership functions to fuzzify factor data can better describe the uncertainty and fuzziness in power grid operation and make the model closer to the actual situation.

[0054] Using the Monte Carlo method to perform multi-scenario simulations and generate multi-scenario factor data sample sets can more comprehensively consider the operation of the power grid under different scenarios and improve the accuracy and reliability of the analysis.

[0055] The power flow calculation model is constructed according to the topological structure and equipment parameters of the power grid, which can accurately calculate the operating status of the power grid in different scenarios, providing a reliable data basis for subsequent power grid operation performance evaluation and optimization.

[0056] Based on the analysis of the grid operation status information, it is possible to clearly evaluate the operation performance of the grid in different scenarios, determine specific optimization goals, such as improving the voltage qualification rate, reducing the network loss rate, improving the power supply reliability, etc., and determine the appropriate optimization method according to the optimization goal.

[0057] The entire method, from data collection, factor analysis, fuzzy processing, multi-scenario simulation, power flow calculation to operation performance evaluation and optimization, forms a complete system that can effectively optimize the power flow distribution of the power grid.

[0058] During the implementation process, according to different requirements and computing resources, we can flexibly select the appropriate sampling method, determine the number of simulations, etc., which has high flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The overall process of the power grid power flow distribution optimization method considering multiple scenarios is shown;

[0060] Figure 2 An exemplary embodiment flow chart of S100 is shown;

[0061] Figure 3 A flow chart of an exemplary embodiment of S200 is shown;

[0062] Figure 4 An exemplary embodiment flow chart of S300 is shown;

[0063] FIG. 5 shows a flow chart of an exemplary embodiment of S400 . DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] See also Figure 1 , which shows a method for optimizing power flow distribution in multiple scenarios provided by an embodiment of the present invention, including:

[0066] S100: Collect user power demand data and power grid operation status data, and obtain power grid power flow distribution factor data based on the collected data.

[0067] S200: Selecting factor data of power grid flow distribution as fuzzy variables, using membership function to perform fuzzification processing on the factor data, obtaining the membership of each fuzzy variable, defining language values ​​for the fuzzy variables, and forming a fuzzy variable table and a multi-scenario fuzzy rule table.

[0068] S300: A multi-scenario simulation is performed using the Monte Carlo method. In the simulation, based on the requirements of the multi-scenario fuzzy rule table and according to the membership degree of the fuzzy variable table, the factor data of each power grid flow distribution is randomly sampled to generate a multi-scenario factor data sample set.

[0069] S400: using the data in the multi-scenario factor data sample set as input parameters for power flow calculation, using the power flow calculation model, performing power flow calculation for each scenario, and obtaining the operation status information of the power grid in the scenario.

[0070] S500: Based on the analysis of the operating status information of the power grid in the scenario, the operating performance of the power grid in different scenarios is evaluated, and the optimization target and optimization method are determined.

[0071] S100 is the initial stage of the power grid flow distribution optimization method, which involves collecting user power demand data and power grid operation status data. The collected data includes historical power consumption data, real-time power consumption data, equipment status data, network topology data and load data. By collecting data, various factors affecting the power grid flow distribution can be obtained, providing basic information for subsequent power grid flow analysis and optimization.

[0072] Please refer to Figure 2 , which shows a flowchart of an exemplary embodiment of S100 of the present application. S100 specifically includes three steps, S110, S120 and S130. The three steps can fully understand the operation status of the power grid by collecting and analyzing user power consumption data and power grid operation data, and identify key factors affecting the power grid flow distribution, thereby providing a scientific basis for subsequent power grid optimization strategies.

[0073] S110: Collect user electricity consumption data in real time through smart meters and electricity consumption information collection systems, and collect grid operation data in real time through grid monitoring systems.

[0074] User electricity consumption data includes historical electricity consumption data and real-time electricity consumption data. Historical electricity consumption data is used to analyze electricity consumption patterns and predict future electricity demand. Real-time electricity consumption data is used to monitor electricity consumption in real time and adjust the grid operation strategy in a timely manner.

[0075] The grid operation data includes equipment status data, network topology data and load data. Equipment status data includes the operating status and parameters of generators, transformers, lines and other equipment. Network topology data is the structural information of the grid, including nodes, line connection relationships, etc. Load data is the load situation of each node in the grid.

[0076] S120: Determine factor data of power grid flow distribution.

[0077] The factors of power flow distribution in the power grid refer to various conditions and parameters that affect the flow and distribution of power in the power grid. Determining the factors of power flow distribution in the power grid requires comprehensive consideration of multiple aspects such as the physical characteristics, operating status, and external environment of the power grid.

[0078] Optionally, the factor data of power grid flow distribution includes load factor data, power supply factor data, power grid structure factor data, operation control factor data and external factor data.

[0079] The load factor data specifically include load forecast value, load forecast error rate, load growth rate, and load peak-valley difference. The power factor data specifically include power output forecast value, power output volatility, and power installed capacity. The grid structure factor data specifically include line parameters, transformer parameters, node parameters, and network topology. The operation control factor data include generator output, transformer tap position, line switching status, and relay protection parameters. The external factor data specifically include weather data and electricity price data.

[0080] S130: Obtaining or calculating the factor data of the power grid flow distribution through the collected data.

[0081] In some possible implementations, the load forecast value, load forecast error rate, load growth rate, and load peak-to-valley difference in the load factor data are obtained by:

[0082] The load forecast value is obtained through load forecasting model prediction.

[0083] The load forecast error rate is obtained through calculation, and the calculation formula is: load forecast error rate = (forecast load-actual load) / actual load*100%.

[0084] The load growth rate is obtained through calculation, and the calculation formula is: load growth rate = (current period load - base period load) / base period load * 100%.

[0085] The load peak-valley difference is obtained by calculating the difference between the peak load and the valley load.

[0086] In some possible implementations, the power output forecast value, power output volatility, and power installed capacity in the power factor data are obtained by:

[0087] The power output forecast value is obtained through prediction model.

[0088] The power output volatility is determined based on the standard deviation and coefficient of variation.

[0089] The installed capacity of power sources is obtained by counting the installed capacity of existing power sources.

[0090] In some possible implementations, the line parameters, transformer parameters, node parameters and network topology in the specific data of the grid structure factors are obtained through grid equipment records or design drawings.

[0091] In some possible implementations, the generator output, transformer tap position, line switching status and relay protection parameters in the operation control factor data are acquired in real time through a power grid monitoring system.

[0092] In some possible implementations, the weather data and electricity price data in the external factor data are obtained by:

[0093] Weather data is obtained from meteorological departments or meteorological websites.

[0094] Electricity price data is obtained through the electricity market trading platform or relevant departments.

[0095] Please refer to Figure 3 , which shows a flowchart of an exemplary embodiment of S200 of the present application. By fuzzifying the data of factors affecting the power grid flow distribution and combining them with multi-scenario fuzzy rules, it is possible to more comprehensively describe the uncertainty and ambiguity of power grid operation and generate a multi-scenario fuzzy rule table to provide a basis for subsequent Monte Carlo simulation, thereby improving the accuracy and reliability of power grid operation optimization.

[0096] S210: Determine the fuzzy variables and the membership function type, select factors suitable as fuzzy variables from the factor data of the power grid flow distribution, and calculate the membership through trigonometric functions.

[0097] According to the factors of power flow distribution determined in S120, the variables to be fuzzified are selected. The selected factors of power flow distribution include: load forecast value, load forecast error rate, load growth rate, load peak-to-valley difference, power output forecast value, power output volatility, power installed capacity, line parameters, transformer parameters, node parameters, network topology, generator output, transformer tap position, line switching state, relay protection parameters, weather data, and electricity price data.

[0098] The membership function is used to convert actual data into membership values ​​in a fuzzy set, indicating the degree of membership of the data in a fuzzy set. The triangular function can better describe the degree of membership of fuzzy variables.

[0099] S220: Determine the value range of each fuzzy variable, that is, its possible value interval, and determine the parameters of the membership function according to the actual situation of the fuzzy variable.

[0100] In some possible implementations, the triangle function needs to determine three parameters: a, b, c, i.e. the positions of the two endpoints of the base and the vertex. The selection of the three parameters will affect the shape of the membership function and the degree of membership of the fuzzy variables, and thus affect the results of the multi-scenario simulation.

[0101] S230: Calculate the membership degree of each fuzzy variable.

[0102] The membership calculation formula is:

[0103] ;

[0104] in, is the triangle membership function, a , b , c are the parameters of the triangle membership function, x is a fuzzy variable.

[0105] S240: Determine the language value of the fuzzy variable. For ease of understanding and description, a set of language values ​​is defined for each fuzzy variable.

[0106] The purpose of determining the linguistic value of fuzzy variables is to facilitate the understanding and description of the values ​​of fuzzy variables and to divide fuzzy variables into different levels.

[0107] For example, the fuzzy variable of load forecast value is divided into three language values ​​of "high", "medium" and "low", which correspond to different load forecast value intervals. When the expression "load forecast value is high" is used, its specific meaning can be clearly understood and compared with other language values.

[0108] In a possible implementation, a method for determining a language value includes:

[0109] Based on statistics: According to the data distribution of fuzzy variables, they are divided into different levels. For example, the load forecast value is divided into three levels: "high", "medium" and "low", which correspond to the upper quartile, median and lower quartile of the load forecast value respectively.

[0110] Rule-based: According to the multi-scenario fuzzy rules, the fuzzy variables are divided into different levels. For example, if the rule is "if the load forecast value is high, the power output forecast value is high", the load forecast value is divided into three levels: "high", "medium" and "low", corresponding to different intervals of the power output forecast value.

[0111] S250: Arrange the language value and the corresponding membership interval of each fuzzy variable into a table to form a fuzzy variable table.

[0112] The fuzzy variable table organizes the linguistic values ​​of fuzzy variables and the corresponding membership intervals into a table for subsequent multi-scenario simulation and optimization. The fuzzy variable table clearly shows the different linguistic values ​​of each fuzzy variable and its corresponding membership distribution.

[0113] In some possible implementations, the fuzzy variable table is constructed as follows:

[0114] Fuzzy variables: The first column of the table lists all the variables that need to be fuzzified, such as load forecast value, load forecast error rate, load growth rate, load peak-to-valley difference, power output forecast value, power output volatility, power installed capacity, line parameters, transformer parameters, node parameters, network topology, generator output, transformer tap position, line switching status, relay protection parameters, weather data, electricity price data, etc.

[0115] Linguistic Values: The second column of the table lists the different linguistic values ​​for each fuzzy variable, such as "high", "medium", "low".

[0116] Membership interval: The third column of the table lists the membership interval corresponding to each language value, for example [0.8,1], [0.4,0.8).

[0117] For example, the load forecast value is fuzzified and divided into three language values ​​of "high", "medium" and "low". According to the triangle membership function, the fuzzy variable table shown in Table 1 is obtained:

[0118] Table 1

[0119] Fuzzy variables Language Value Membership interval Load forecast value high [0.8,1] Load forecast value middle [0.4,0.8) Load forecast value Low [0,0.4)

[0120] In Table 1, when the load forecast value is equal to 0.8, its membership to the “high” language value is 1, its membership to the “medium” language value is 0, and its membership to the “low” language value is 0.

[0121] When the load forecast value is equal to 0.6, its membership to the "high" language value is 0, its membership to the "medium" language value is 1, and its membership to the "low" language value is 0.

[0122] When the load forecast value is equal to 0.4, its membership to the "high" language value is 0, its membership to the "medium" language value is 0, and its membership to the "low" language value is 1.

[0123] S260: According to the multi-scenario fuzzy rules, the language values ​​of the fuzzy variables are combined to form a multi-scenario fuzzy rule table. The fuzzy rules are converted into a table form to facilitate fuzzy logic operations and reasoning.

[0124] In some possible implementations, the multi-scenario fuzzy rule table is composed of:

[0125] Row: Each row represents a fuzzy rule, describing the relationship between multiple premises and conclusions.

[0126] Column: Each column represents a fuzzy variable, which can be a premise or a conclusion.

[0127] Element: Each element represents a fuzzy relationship and can be the linguistic value of a fuzzy variable or a fuzzy logic operator.

[0128] Exemplarily, there are two multi-scenario fuzzy rules:

[0129] If the load forecast value is high and the power output forecast value is high, the generator output is high.

[0130] If the load forecast value is low, or the power output forecast value is low, the generator output is low.

[0131] According to these two rules, the multi-scenario fuzzy rule table shown in Table 2 is obtained.

[0132] Table 2

[0133] Load forecast value Power output forecast value Generator output Rule 1 high high high Rule 2 high Low Low

[0134] In Table 2, the first row represents Rule 1, which means that when both the load forecast value and the power output forecast value are high, the generator output is also high.

[0135] The second row represents Rule 2, which means that when at least one of the load forecast value and the power output forecast value is low, the generator output is also low.

[0136] Please refer to Figure 4 , which shows a flowchart of an exemplary embodiment of S300 of the present application, S300 uses the Monte Carlo method to perform multi-scenario simulation to generate a multi-scenario factor data sample set. Each generated sample contains all the factor data that need to be sampled to form a complete scenario, and the multi-scenario factor data jointly determine the operating state of the power grid in the scenario. Through multiple samplings, a multi-scenario factor data sample set is finally generated to prepare for subsequent power flow calculation and analysis.

[0137] The Monte Carlo method is a numerical simulation method based on random sampling. In the optimization of power grid power distribution, the Monte Carlo method is used to simulate the impact of various factors on the power grid power distribution, so as to evaluate the reliability and safety of power grid operation and formulate optimization strategies.

[0138] S310: Determine the number of Monte Carlo simulations to be performed based on demand and computing resources.

[0139] Determining the number of simulations is one of the key parameters of Monte Carlo simulation, which directly affects the accuracy of the simulation results and the calculation time.

[0140] In some possible implementations, the number of simulations is determined as follows:

[0141] Based on the error analysis of the simulation results, determine the number of simulations required to achieve the desired accuracy.

[0142] Estimate the computational time required for a single simulation and determine the number of simulations based on available computing resources.

[0143] S320: According to the fuzzy variables and membership functions determined in S200, and the multi-scenario fuzzy rule table, random sampling is performed according to the membership of the fuzzy variables to generate data samples of each factor.

[0144] Analyze the multi-scenario fuzzy rule table to determine the fuzzy variables that need to be randomly sampled. For example, if the rule table contains "load forecast value" and "power output forecast value", both variables need to be sampled.

[0145] In some possible implementations, if there is a correlation between variables, it is necessary to consider the impact of the correlation and use a joint probability distribution for sampling.

[0146] In some possible implementations, selectable sampling methods include uniform sampling, normal sampling, or a sampling method selected based on the distribution of a variable.

[0147] In Monte Carlo simulation, each generated sample contains all the factor data that need to be sampled to form a complete scenario. The factor data can be load forecast values, power output forecast values, grid structure parameters, equipment status, etc. The factor data jointly determine the operating status of the grid in this scenario.

[0148] For example, there are two fuzzy variables: load forecast value and power output forecast value. The language values ​​of load forecast value are "high", "medium", and "low", and the corresponding membership intervals are [0.8, 1], [0.4, 0.8), [0, 0.4) respectively; the language values ​​of power output forecast value are "strong", "medium", and "weak", and the corresponding membership intervals are [0.7, 1], [0.3, 0.7), [0, 0.3) respectively.

[0149] The multi-scenario fuzzy rules are: if the load forecast value is high and the power output forecast value is strong, the grid is in good condition; if the load forecast value is high and the power output forecast value is strong, the grid is in good condition. If the load forecast value is high and the power output forecast value is medium, the grid is in good condition. If the load forecast value is high and the power output forecast value is weak, the grid is in general condition. If the load forecast value is medium and the power output forecast value is strong, the grid is in good condition. If the load forecast value is medium and the power output forecast value is medium, the grid is in general condition. If the load forecast value is medium and the power output forecast value is weak, the grid is in poor condition. If the load forecast value is low and the power output forecast value is strong, the grid is in general condition. If the load forecast value is low and the power output forecast value is medium, the grid is in poor condition. If the load forecast value is low and the power output forecast value is weak, the grid is in very poor condition.

[0150] When performing this step, proceed as follows:

[0151] For the load forecast value, random sampling is performed according to its membership interval. A random number is generated. If the value of the random number in the interval [0,1] is greater than or equal to 0.8, the load forecast value is considered to be "high"; if it is in the interval [0.4,0.8), the load forecast value is considered to be "medium"; if it is in the interval [0,0.4), the load forecast value is considered to be "low".

[0152] For the power output forecast value, random sampling is also performed according to its membership interval. Another random number is generated. If the value of the random number in the interval [0,1] is greater than or equal to 0.7, the power output forecast value is considered to be "strong"; if it is in the interval [0.3,0.7), the power output forecast value is considered to be "medium"; if it is in the interval [0,0.3), the power output forecast value is considered to be "weak".

[0153] The sampled load forecast value and power output forecast value are combined into a multi-scenario factor data sample. For example, if the load forecast value obtained from a certain sampling is "medium" and the power output forecast value is "strong", then the sample is (load forecast value: medium, power output forecast value: strong).

[0154] The above process is repeated multiple times to generate multiple multi-scenario factor data samples and form a multi-scenario factor data sample set.

[0155] Through S320, random sampling is performed according to the membership of fuzzy variables, and a multi-scenario factor data sample set is generated, preparing for subsequent power flow calculation and analysis.

[0156] Please refer to Figure 5, which shows a flowchart of an exemplary embodiment of S400 of the present application. S400 constructs a power flow calculation model based on the topological structure and equipment parameters of the power grid, takes the data in the multi-scenario factor data sample set as input parameters, and performs power flow calculation for each scenario, thereby obtaining the operating status of the power grid in the scenario, including node voltage, branch power and other information. S400 can fully consider the impact of multi-scenario factors on the power grid power distribution, generate a multi-scenario factor sample set through Monte Carlo simulation, and use the power flow calculation formula to accurately calculate the operating status of the power grid in different scenarios, providing a reliable data basis for subsequent power grid operation performance evaluation and optimization, and helping to improve the stability and reliability of the power grid.

[0157] S410: Construct a power flow calculation model according to the topological structure and equipment parameters of the power grid.

[0158] The node power balance equation includes:

[0159] Active power balance equation:

[0160] ;

[0161] Reactive power balance equation:

[0162] ;

[0163] The branch power equations include:

[0164] Branch Road Active power:

[0165] ;

[0166] Branch Road Reactive power:

[0167] ;

[0168] in, and Node The injected active power and reactive power; and For Node and nodes The voltage amplitude, Node and nodes The phase angle difference, and Branch The conductance and susceptance of and For branch The active power and reactive power of is the total number of nodes.

[0169] S420: Using the data in the multi-scenario factor data sample set as input parameters of the power flow calculation model, performing power flow calculation for each scenario, and obtaining operation status information of the power grid in the scenario, such as node voltage and branch power.

[0170] In some possible implementations, when performing power flow calculation, the relevant data in the multi-scenario factor data sample set is substituted into the above formula to calculate the operating state of the power grid in each scenario. The specific steps are as follows:

[0171] S421: Obtain factor data for each scenario from the multi-scenario factor data sample set.

[0172] S422: Determine the active power and reactive power injected into the node according to the factor data. The active power and reactive power will be substituted into the node power balance equation.

[0173] S423: Grid structure parameters, such as branch conductance and susceptance, are also obtained from the multi-scenario factor data sample set and substituted into the formula.

[0174] S424: For the node voltage amplitude and phase angle, estimated values ​​are set initially and continuously updated through iterative calculation until the convergence condition is met.

[0175] S425: Substitute the above acquired data into the node power balance equation and the branch power equation to perform power flow calculation.

[0176] S500 conducts a detailed analysis of the operating status data of the power grid in each scenario obtained in S400, including node voltage, branch current, power loss, etc. Check whether these data are within a reasonable range, whether there are problems such as voltage over-limit and current overload, and evaluate the operating performance of the power grid in different scenarios. Determine the optimization target based on the power grid operation requirements and goals. For example, possible optimization targets include improving the voltage qualification rate, reducing the network loss rate, and improving power supply reliability. Determine the appropriate optimization method based on the analysis of the optimization target and influencing factors. This may include adjusting the power output, optimizing the power grid structure, and adding reactive power compensation equipment.

[0177] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0178] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0179] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0180] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0181] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing power flow distribution in multiple scenarios, characterized in that: include: Collect user electricity demand data and grid operation status data, and obtain grid power flow distribution factor data based on the collected data; The factor data of power grid flow distribution are selected as fuzzy variables, and the membership function is used to fuzzify the factor data to obtain the membership of each fuzzy variable, define the language value for the fuzzy variable, and form a fuzzy variable table and a multi-scenario fuzzy rule table; The Monte Carlo method is used to perform multi-scenario simulation. In the simulation, based on the requirements of the multi-scenario fuzzy rule table and the membership degree of the fuzzy variable table, the factor data rows of each power grid flow distribution are randomly sampled to generate a multi-scenario factor data sample set. The data in the multi-scenario factor data sample set is used as the input parameter of the power flow calculation. The power flow calculation model is used to perform power flow calculation for each scenario to obtain the operating status information of the power grid in each scenario. Based on the analysis of the operating status information of the power grid in this scenario, the operating performance of the power grid in different scenarios is evaluated, and the optimization target and optimization method are determined; The factor data of power grid flow distribution includes load factor data, power factor data, power grid structure factor data, operation control factor data and external factor data; The load factor data specifically include load forecast value, load forecast error rate, load growth rate, and load peak-valley difference; Among them, the power factor data specifically includes power output forecast value, power output volatility, and power installed capacity; Among them, the specific data of power grid structure factors include line parameters, transformer parameters, node parameters, and network topology; Among them, the operation control factor data include generator output, transformer tap position, line switching status, and relay protection parameters; Among them, external factor data specifically include weather data and electricity price data; The membership function uses trigonometric function to calculate the membership, and the calculation formula is: ； in, a , b , c are the parameters of the triangle membership function, x is a fuzzy variable; The method for defining the linguistic value of the fuzzy variable includes: Based on statistical definition: Fuzzy variables are divided into different levels according to their data distribution; Rule-based definition: Fuzzy variables are divided into different levels according to multi-scenario fuzzy rules; The fuzzy variable table comprises: The first column of the table lists all the fuzzy variables that need to be fuzzified; The second column of the table lists the different linguistic values ​​of each fuzzy variable; The third column of the table lists the membership interval corresponding to each language value; The multi-scenario fuzzy rule table is composed of: each row represents a fuzzy rule; each column represents a fuzzy variable; each element represents a fuzzy relationship; The step of calculating the operating status information of the power grid in each scenario includes: Obtain factor data for each scenario from a multi-scenario factor data sample set; The active power and reactive power injected into the node are determined according to the factor data, and the active power and reactive power will be substituted into the node power balance equation; Obtain grid structure parameters from a multi-scenario factor data sample set and substitute them into the formula; For node voltage amplitude and phase angle, estimated values ​​are set at the initial stage and continuously updated through iterative calculation until the convergence condition is met; Substitute the above acquired data into the node power balance equation and branch power equation to perform power flow calculation.

2. The method for optimizing power flow distribution in multiple scenarios according to claim 1 is characterized in that: When using the Monte Carlo method to simulate multiple scenarios, it is necessary to determine the number of Monte Carlo simulations to be performed. The method for determining the number of simulations is as follows: According to the error analysis of the simulation results, determine the number of simulations required to achieve the predetermined accuracy; Estimate the computational time required for a single simulation and determine the number of simulations based on available computing resources.

3. The method for optimizing power flow distribution in multiple scenarios according to claim 2 is characterized in that: The Monte Carlo method is adopted to generate data samples of each factor by performing random sampling according to the membership of the fuzzy variables based on the fuzzy variables and the membership function and the multi-scenario fuzzy rule table.

4. The method for optimizing power flow distribution in multiple scenarios according to claim 1 is characterized in that: The power flow calculation model includes a node power balance equation and a branch power equation; Among them, the node power balance equation includes: Active power balance equation: ； Reactive power balance equation: ； The branch power equations include: Branch Road Active power: ； Branch Road Reactive power: ； in, and Node The injected active power and reactive power; and For Node and nodes The voltage amplitude, Node and nodes The phase angle difference, and Branch The conductance and susceptance of is the total number of nodes.

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