Probabilistic power flow analysis method, device, electronic device, storage medium and program product for flexible interconnected distribution network under vehicle-grid interaction

Abstract

The present invention discloses a method, device, electronic device, storage medium and program product for probabilistic power flow analysis of a flexible interconnected distribution network under vehicle-grid interaction. The method comprises: obtaining uncertainty variable parameters of a distribution network system to be analyzed, and performing photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters; standardizing the above analysis results to obtain a unified power mismatch equation and a state correction function, and further obtaining a fuzzy linearization equation of the uncertainty variable; obtaining the series of output variables based on random variable estimation, processing the fuzzy linearization equation of the uncertainty variable, obtaining a characteristic vector of probability density and a quantile of the variable, and further constructing a probabilistic power flow equation; performing probabilistic power flow analysis based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result, thereby improving the accuracy of the probabilistic power flow analysis.

Description

Technical Field

[0001] The present disclosure relates to the technical field of distribution network, and in particular to a method, device, electronic device, storage medium and program product for probabilistic power flow analysis of a flexible interconnected distribution network under vehicle-grid interaction. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] The integration of distributed power sources has improved the overall operating efficiency of the distribution network system, but it has also posed challenges to the stable operation of the distribution network due to its intermittent and random characteristics. Especially in the context of vehicle-to-grid (V2G), electric vehicles, as flexible power loads and potential sources, have further exacerbated the uncertainty of the distribution network. Therefore, probabilistic power flow analysis of flexible distribution networks is particularly important.

[0004] However, the probabilistic power flow analysis scheme for flexible interconnected distribution networks in related technologies has large deviations in analysis results. Summary of the invention

[0005] In view of this, the purpose of the present disclosure is to propose a probabilistic power flow analysis method, device, electronic device, storage medium and program product for a flexible interconnected distribution network under vehicle-grid interaction, so as to at least solve one of the technical problems in the related technology to a certain extent.

[0006] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction, including:

[0007] Based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, the uncertainty variable parameters of the distribution network system to be analyzed are obtained, and based on the uncertainty variable parameters, the photovoltaic power generation output probability analysis, the wind power generation output probability analysis, the power load probability analysis and the electric vehicle charging demand probability analysis are respectively performed to obtain the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results;

[0008] Based on the reference operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result and the electric vehicle charging demand probability analysis result are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and based on the unified power mismatch equation and the state correction function, a fuzzy linearization equation of the uncertainty variable is obtained;

[0009] Based on the random variable estimation, the series of the output variable is obtained, the fuzzy linearized equation of the uncertain variable is processed to obtain the characteristic vector of the probability density and the quantile of the variable, and based on the input vector, the output vector and the quantile of the variable, the probabilistic power flow equation of the distribution network system to be analyzed is constructed;

[0010] Based on the probabilistic power flow equation, a probabilistic power flow analysis is performed on the distribution network system to be analyzed to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

[0011] Based on the same inventive concept, the second aspect of the exemplary embodiment of the present disclosure provides a probabilistic power flow analysis device for a flexible interconnected distribution network under vehicle-grid interaction, comprising:

[0012] The uncertainty variable parameter analysis module is configured to obtain the uncertainty variable parameters of the distribution network system to be analyzed based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, and to perform photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters, and obtain photovoltaic power generation output probability analysis results, wind power generation output probability analysis results, power load probability analysis results and electric vehicle charging demand probability analysis results;

[0013] A fuzzy linearization equation determination module is configured to perform standardization processing on the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results, and the electric vehicle charging demand probability analysis results based on the benchmark operating point of the distribution network system to be analyzed, so as to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and obtain a fuzzy linearization equation of an uncertain variable based on the unified power mismatch equation and the state correction function;

[0014] A probability power flow equation determination module is configured to obtain the series of output variables based on random variable estimation, process the fuzzy linearized equation of the uncertainty variable, obtain the characteristic vector of the probability density and the quantile of the variable, and construct the probability power flow equation of the distribution network system to be analyzed based on the input vector, the output vector and the quantile of the variable;

[0015] The probabilistic power flow analysis module is configured to perform probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

[0016] Based on the same inventive concept, a third aspect of the exemplary embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0017] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0018] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product, including computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method described in the first aspect.

[0019] From the above, it can be seen that the method, device, electronic device, storage medium and program product for probabilistic power flow analysis of a flexible interconnected distribution network under vehicle-grid interaction provided by the embodiments of the present disclosure include: based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, obtaining uncertainty variable parameters of the distribution network system to be analyzed, and performing photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters, respectively, to obtain photovoltaic power generation output probability analysis results, wind power generation output probability analysis results, power load probability analysis results and electric vehicle charging demand probability analysis results; based on the benchmark operating point of the distribution network system to be analyzed, performing the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results. The results of the probability analysis of the power load and the results of the probability analysis of the charging demand of the electric vehicle are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and a fuzzy linearization equation of the uncertainty variable is obtained based on the unified power mismatch equation and the state correction function; the series of the output variable is obtained based on the random variable estimation, the fuzzy linearization equation of the uncertainty variable is processed to obtain the characteristic vector of the probability density and the quantile of the variable, and the probabilistic power flow equation of the distribution network system to be analyzed is constructed based on the input vector, the output vector and the quantile of the variable; the probabilistic power flow analysis of the distribution network system to be analyzed is performed on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain the probabilistic power flow analysis result of the distribution network system to be analyzed, thereby improving the accuracy of the probabilistic power flow analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction provided by an exemplary embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of the division of the total battery capacity of an electric vehicle under vehicle-grid interaction provided by an exemplary embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of a smart grid system provided for an exemplary embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of simulation results provided for an exemplary embodiment of the present disclosure;

[0025] Figure 5 A structural schematic diagram of a probabilistic power flow analysis device for a flexible interconnected distribution network under vehicle-grid interaction provided by an exemplary embodiment of the present disclosure;

[0026] Figure 6 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, usage scenarios, etc. of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0028] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0029] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0030] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of the present application. Other methods that meet the relevant laws and regulations may also be applied to the implementation method of the present application.

[0031] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0032] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0033] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0034] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0035] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0036] The integration of distributed power sources has improved the overall operating efficiency of the distribution network system, but it has also posed challenges to the stable operation of the distribution network due to its intermittent and random characteristics. Especially in the context of vehicle-to-grid (V2G), electric vehicles, as flexible power loads and potential sources, have further exacerbated the uncertainty of the distribution network. Therefore, probabilistic power flow analysis of flexible distribution networks is particularly important.

[0037] In a solution provided by the related art, a Gaussian function is used as the carrier of wind speed distribution information, and an improved reflection kernel density estimation method is used to analyze the digital characteristics of state variables such as node voltage and branch power, thereby completing the calculation of system probability flow. This method converts the constraints of the maximum entropy model from integral form to algebraic form, significantly improving the calculation efficiency.

[0038] However, the inventors of the present disclosure have discovered that this method may affect the accuracy of the calculation results when the data is insufficient or the data quality is low.

[0039] In another solution provided by the related art, the cumulative amount of the state variables of each node in the system is calculated, and the Beta distribution function is used to fit the probability distribution of the system state variables, thereby obtaining the power flow distribution of the system under different states. This method is not only applicable to AC / DC hybrid systems, but also to other complex power systems.

[0040] However, the inventors of the present disclosure have found that, in order to simplify the calculation process, the accumulation method may require some simplification of the system model, and these simplifications may affect the accuracy of the calculation results.

[0041] In a solution provided by the related art, based on the output probability model of distributed power sources, the chaos expansion theory of orthogonal polynomials is introduced to calculate the probability flow of wind power systems and perform statistical analysis on the results. This method reduces the complexity of calculation and improves the calculation efficiency by sparsely computing the polynomials.

[0042] However, the inventors of the present disclosure have found that this method is difficult to guarantee calculation accuracy for large-scale power systems or high-dimensional random spaces.

[0043] In a solution provided by the related art, a sample point set is generated using the untraceable transformation (UT) method, and a probability model is established to input the random variables into the internal network after equivalence, and the probability power flow calculation of the internal network is performed after equivalence. This method uses the probability equivalence technology to achieve dual dimensionality reduction in calculation.

[0044] However, the inventors of the present disclosure have discovered that the application effect of this method may be limited to certain types of power systems or under certain conditions.

[0045] In view of the above analysis, in order to improve the accuracy of probabilistic power flow calculation in flexible interconnected distribution networks, this paper analyzes this issue taking into account the factors of vehicle-grid interconnection, aiming to improve the ability of distribution networks to cope with uncertain factors, optimize resource allocation, and promote the efficient use of new energy.

[0046] In order to solve the above problems, the present disclosure provides a probabilistic power flow analysis scheme for a flexible interconnected distribution network under vehicle-grid interaction, which specifically includes: based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, obtaining the uncertainty variable parameters of the distribution network system to be analyzed, and performing the photovoltaic power generation output probability analysis, the wind power generation output probability analysis, the power load probability analysis and the electric vehicle charging demand probability analysis based on the uncertainty variable parameters, respectively, to obtain the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results; based on the benchmark operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results are analyzed. The charging demand probability analysis result and the electric vehicle charging demand probability analysis result are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and a fuzzy linearization equation of uncertainty variables is obtained based on the unified power mismatch equation and the state correction function; the series of output variables is obtained based on random variable estimation, the fuzzy linearization equation of uncertainty variables is processed to obtain a characteristic vector of probability density and a quantile of the variable, and a probabilistic power flow equation of the distribution network system to be analyzed is constructed based on the input vector, the output vector and the quantile of the variable; and a probabilistic power flow analysis is performed on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

[0047] The probabilistic power flow analysis scheme for the flexible interconnected distribution network under vehicle-grid interaction provided by the present invention not only fully considers the impact of electric vehicles as distributed power sources and flexible loads on the distribution network, but also accurately reflects the role of uncertain factors in the system through probabilistic calculation methods.

[0048] Furthermore, this scheme can not only provide important reference for the planning, operation and control of distribution networks, but also enrich the theoretical system of probabilistic analysis of power systems and provide new ideas and methods for research in related fields.

[0049] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0050] refer to Figure 1 , which is a flow chart of a probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction provided by an exemplary embodiment of the present disclosure.

[0051] The probabilistic power flow analysis method of the flexible interconnected distribution network under vehicle-grid interaction includes the following steps:

[0052] Step S110: Based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, the uncertainty variable parameters of the distribution network system to be analyzed are obtained; based on the uncertainty variable parameters, photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis are respectively performed to obtain photovoltaic power generation output probability analysis results, wind power generation output probability analysis results, power load probability analysis results and electric vehicle charging demand probability analysis results.

[0053] In this exemplary embodiment, based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, the uncertainty variable parameters of the distribution network system to be analyzed are obtained, and based on the uncertainty variable parameters, the photovoltaic power generation output probability analysis, the wind power generation output probability analysis, the power load probability analysis and the electric vehicle charging demand probability analysis are respectively performed to obtain the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results, including:

[0054] Obtaining the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient, and obtaining the radiation intensity of the sun based on the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient;

[0055] Obtaining the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency, and obtaining the photovoltaic power generation output probability analysis result based on the solar radiation intensity, the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency;

[0056] Obtaining the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and a first-order lag variable, and obtaining a probability density function of wind speed based on the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and the first-order lag variable;

[0057] Obtaining the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix, and obtaining the wind power generation output probability analysis result based on the probability density function of the wind speed, the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix;

[0058] Obtain random variables, load fluctuation factors and superimposed scaling functions, obtain active power based on the random variables, the load fluctuation factors and the superimposed scaling functions, obtain load density, dispersion coefficient and maximum likelihood function, obtain reactive power based on the load density, the dispersion coefficient and the maximum likelihood function, and obtain the power load probability analysis result based on the active power and the reactive power;

[0059] Obtaining the distance between the vehicle and the charging station, and obtaining the number of electric vehicles being charged based on the distance between the vehicle and the charging station;

[0060] The average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution are obtained, and based on the number of electric vehicles being charged, the average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution, the probability analysis result of the electric vehicle charging demand is obtained.

[0061] When implementing:

[0062] The uncertainty of various new energy sources in the distribution network mainly comes from natural factors such as wind speed and light intensity, as well as the randomness of electric vehicle charging and discharging. At the same time, the two-way interaction mode between the vehicle and the grid formed by the charging and discharging of electric vehicles has a significant impact on the power balance and stability of the distribution network. Therefore, it is necessary to accurately describe and analyze these uncertainty factors to reflect the probability distribution and correlation of uncertainty factors and provide input data for probabilistic power flow calculation.

[0063] The uncertainty random input variable analysis methods of flexible distribution networks considering vehicle-grid interaction mainly include photovoltaic output probability analysis, wind power output probability analysis, conventional load power probability analysis and electric vehicle charging demand probability analysis.

[0064] The actual output of the photovoltaic system mainly depends on the solar radiation intensity. Under certain time and space scale conditions, the solar radiation intensity obeys the normal distribution, and its calculation formula is:

[0065] (1)

[0066] In the above formula, It represents the angle between the direct sunlight and the normal of the lighting surface; represents the radiation flux on the sun's surface; represents the identity matrix; Represents the atmospheric transparency coefficient. According to the above formula, the actual output of the photovoltaic system also shows a normal distribution, and the expression is:

[0067] (2)

[0068] In the above formula, represents random variation increment; Indicates the number of photovoltaic panels; represents the normal distribution parameter; Represents the photoelectric conversion efficiency.

[0069] The output of wind power generation is directly related to wind speed, and wind speed also has large fluctuations due to factors such as seasons and regional natural conditions. The probability density function of wind speed can be expressed as:

[0070] (3)

[0071] In the above formula, Represents the number of original joint distribution training data points; represents the average annual temperature of the area where the wind farm is located; Represents the first-order lag variable. Further analysis of wind power output probability, namely:

[0072] (4)

[0073] In the above formula, Indicates the number of wind turbines; represents the standard deviation of wind speed; represents the shape parameter; Represents a calculation matrix.

[0074] Conventional load power is affected by many factors such as the user's electricity usage habits, electricity usage behavior, weather, etc. Its power can be expressed as:

[0075] (5)

[0076] In the above formula, represents a random variable; represents the load fluctuation factor; represents the superposition scaling function; Indicates load density; represents the coefficient of dispersion; represents the maximum likelihood function; Indicates active power; Indicates reactive power; Represents the probability of power load.

[0077] With the support of vehicle-to-grid (V2G) technology, the charging demand of electric vehicles is mainly determined by the battery capacity. For example, electric vehicles based on V2G technology can be Figure 2 Its battery capacity consists of three parts: V2G capacity s1, daily driving capacity s2 and standby driving capacity s3.

[0078] exist At this moment, the number of electric vehicles being charged can be expressed as:

[0079] (6)

[0080] In the above formula, Indicates The distance from a car to a charging station. Combined with the overall charging load of the car, the probability analysis result of the electric vehicle charging demand is obtained, and the expression is:

[0081] (7)

[0082] In the above formula, Indicates the average charging current; Indicates charging voltage; Indicates Charging time of a car; represents the scale parameter of the Weibull distribution.

[0083] Through the above exemplary embodiments, according to the vehicle-grid interaction characteristics and the operating characteristics of the flexible interconnected distribution network, the photovoltaic output, wind power output, power load and electric vehicle charging demand probability of the power grid are analyzed to clarify the random changes of various uncertain factors, providing a data basis for the subsequent analysis of the fuzzy correlation between the variables.

[0084] Step S120: Based on the benchmark operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed; based on the unified power mismatch equation and the state correction function, a fuzzy linearization equation of the uncertain variable is obtained.

[0085] In this exemplary embodiment, based on the reference operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result and the electric vehicle charging demand probability analysis result are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed. Based on the unified power mismatch equation and the state correction function, a fuzzy linearization equation of the uncertainty variable is obtained, including:

[0086] Obtaining a fixed part and a random part of a random vector of an electric vehicle, and constructing a vehicle charging probability equation based on the fixed part and the random part;

[0087] Obtaining a nonlinear factor, at the reference operating point, based on the vehicle charging probability equation and the nonlinear factor, performing standardization processing on the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result, and the electric vehicle charging demand probability analysis result to obtain a system loss;

[0088] Acquire an initial value of a droop voltage, and construct the unified power mismatch equation for the reference operating point based on the system loss and the initial value of the droop voltage;

[0089] Acquire the number of system nodes, the regulated power of the distributed power source and the penalty factor, and construct the state correction function based on the unified power mismatch equation, the number of system nodes, the regulated power of the distributed power source and the penalty factor;

[0090] The power factor angle and the phase angle difference are obtained, and a fuzzy linearization equation of the uncertainty variable is constructed based on the state correction function, the power factor angle and the phase angle difference.

[0091] When implementing:

[0092] Since the fuzzy correlation between the above-obtained probability analysis results is a linear relationship, it is easy to analyze and calculate, and the input uncertainty variables are linearized to provide input parameters and boundary conditions for subsequent power flow calculations. Based on the application of vehicle-grid interaction technology, the energy storage resources of electric vehicles can be used as a buffer between the power grid and renewable energy. It consists of a fixed part and a randomly changing part. The formula is:

[0093] (8)

[0094] In the above formula, , denote the fixed and random parts of the EV random vector, respectively; Represents the vehicle charging probability equation. All probability analysis results are standardized at the distribution network system benchmark operating point, and the system loss is obtained as:

[0095] (9)

[0096] In the above formula, represents the nonlinear factor; , , , They represent the calculation results of photovoltaic output probability, wind power output probability, conventional load power probability and electric vehicle charging demand probability. The unified power mismatch equation of the benchmark operating point can be expressed as:

[0097] (10)

[0098] In the above formula, Represents the initial value of the droop voltage. The equation is iteratively solved using the Newton method, and the state correction function is obtained as follows:

[0099] (11)

[0100] In the above formula, Indicates the number of system nodes; Indicates the regulated power of distributed power sources; Represents the penalty factor. Combining the phase angle difference between the grid side and the distributed power side, the fuzzy linear representation of the uncertainty variable is obtained, namely:

[0101] (12)

[0102] In the above formula, Indicates the power factor angle; Indicates the phase angle difference.

[0103] Through the above exemplary embodiments, by standardizing the probability calculation results, a unified power mismatch equation and a state correction function of the power flow distribution are obtained, and then the uncertainty variables are fuzzily linearized, laying the foundation for the ultimate realization of the probabilistic power flow calculation of the distribution network.

[0104] Step S130: Based on the benchmark operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed; based on the unified power mismatch equation and the state correction function, a fuzzy linearization equation of the uncertain variable is obtained.

[0105] In this exemplary embodiment, the series of output variables obtained based on random variable estimation, the fuzzy linearized equation of the uncertainty variable is processed to obtain the characteristic vector of probability density and the quantile of the variable, and the probabilistic power flow equation of the distribution network system to be analyzed is constructed based on the input vector, the output vector and the quantile of the variable, including:

[0106] Acquire a conversion function, and perform conversion processing on the fuzzy linearization equation of the uncertainty variable based on the conversion function to obtain the position coefficient of the sampling point;

[0107] Obtaining the series of output variables based on random variable estimation, obtaining a system operation reference node, and obtaining an estimated power flow calculation result based on the location coefficient of the sampling point, the series of the output variables and the system operation reference node;

[0108] Obtaining a droop coefficient and a state matrix, and obtaining a characteristic vector of the probability density based on the estimated power flow calculation result, the droop coefficient and the state matrix;

[0109] Obtaining a cumulative distribution function, and obtaining the quantile of the variable based on the characteristic vector of the probability density and the cumulative distribution function;

[0110] An input vector and an output vector are constructed, and based on the input vector, the output vector and the quantile of the variable, a probabilistic power flow equation of the distribution network system to be analyzed is constructed.

[0111] When implementing:

[0112] The output variable series is estimated based on the random variable, and the eigenvector of the fuzzy linearized probability density is calculated. The probability flow of the distribution network is obtained by combining the semi-empirical formula. Assuming that the random variable exists sampling points, then its location coefficient can be expressed as:

[0113] (13)

[0114] In the above formula, Fuzzy linearized equations representing uncertain variables; Represents the conversion function. The reactances of the power grid system nodes are combined to obtain the estimated power flow calculation results, namely:

[0115] (14)

[0116] In the above formula, represents the series of the output variable; Indicates the system operation reference node. The eigenvector of the probability density is calculated using the droop control coefficient. The formula is:

[0117] (15)

[0118] In the above formula, represents the droop coefficient; Represents the state matrix. Referring to the origin moments of each order of the output variable, the quantile of the variable can be determined, that is:

[0119] (16)

[0120] In the above formula, Represents the cumulative distribution function. The semi-empirical formula is introduced to establish the probability flow equation of the distribution network, namely:

[0121] (17)

[0122] In the above formula, , Represent the input vector and output vector respectively.

[0123] Through the above exemplary embodiments, based on the fuzzy linearization processing of uncertain variables, the probability flow equation of the distribution network is determined by combining the eigenvector of the probability density and the variable quantile to complete the probability flow analysis of the distribution network under vehicle-grid interaction.

[0124] Step S140: performing a probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

[0125] In this exemplary embodiment, the performing a probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed includes:

[0126] The current input vector of the distribution network system to be analyzed is obtained, and the current input vector is input into the probabilistic power flow equation to obtain a current output vector corresponding to the current input vector as the probabilistic power flow analysis result.

[0127] In this exemplary embodiment, after performing a probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation and obtaining a probabilistic power flow analysis result of the distribution network system to be analyzed, the method further includes:

[0128] Generating a probabilistic power flow chart of the distribution network system to be analyzed based on the probabilistic power flow analysis result;

[0129] The probabilistic power flow diagram is transmitted to a designated terminal device of the power distribution network system to be analyzed.

[0130] The present invention provides a probabilistic power flow analysis scheme for a flexible interconnected distribution network under vehicle-grid interaction, analyzes the probability of uncertain variables in the power grid, and uses the Newton-Raphael method to iteratively solve the unified power mismatch equation of the benchmark operating point to obtain a state correction function, and then fuzzy linearizes the uncertain variables. Based on this, the probabilistic power flow equation of the distribution network is determined by combining the eigenvector of the probability density and the variable quantile, and the probabilistic power flow calculation of the flexible interconnected distribution network is completed. The experimental results show that the probabilistic power flow analysis results of the distribution network obtained by the proposed method are closer to the reference value, and the analysis accuracy is high. It not only fully considers the impact of electric vehicles as distributed power sources and flexible loads on the distribution network, but also accurately reflects the role of uncertain factors in the system through the probabilistic calculation method.

[0131] Furthermore, this scheme can not only provide important reference for the planning, operation and control of distribution networks, but also enrich the theoretical system of probabilistic analysis of power systems and provide new ideas and methods for research in related fields.

[0132] In order to illustrate the technical effect of the probabilistic power flow analysis scheme for flexible interconnected distribution networks under vehicle-grid interaction provided by the present disclosure, the following example demonstration analysis is provided:

[0133] This test is mainly to verify and study the actual application effect of the method proposed in this disclosure, and to analyze the performance effectiveness of the method of this disclosure through comparative experiments.

[0134] refer to Figure 3 The vehicle-grid interactive flexible interconnected distribution network system is a smart grid system that integrates advanced power electronics technology, communication technology and new energy vehicle charging and discharging technology. The system consists of a large power grid, DC lines, AC lines and microgrids. The system adopts a modular layout, including a management platform, application terminals and third-party systems. The management platform is responsible for coordinating and arranging the operation of the entire system; the application terminals include the vehicle side, user side and business side, which are responsible for performing specific charging and discharging tasks; the third-party system provides energy support for the system.

[0135] The system adopts flexible interconnection technology, and realizes flexible, fast and efficient interconnection of various devices in the distribution network through advanced power electronic equipment and communication technology. At the same time, it supports vehicle-grid interaction technology, and new energy vehicles can access the power supply network through charging and swapping facilities to meet the power demand of electric vehicles.

[0136] This experiment uses the above distribution network system as an example model. The distributed power source and vehicle charging load of the system are shown in Table 1.

[0137] Table 1 Basic parameters of uncertainty variables of distribution network system

[0138]

[0139] Simulation software: Use PSS / E power system simulation software to build a distribution network simulation model, and select MATLAB / Simulink as the experimental platform. Based on the parameters in Table 1, set the voltage level of the distribution network to 10kV; set the interaction strategy between electric vehicles and the distribution network, including charging time of 12h, charging power of 30kW, and discharge power of 5kW; the simulation time is 24h, and the simulation step is 10s.

[0140] The experimental process is as follows: use MATLAB programming to simulate vehicle-grid interaction and call the probability flow calculation function in the power system toolbox; run the simulation program on the server to obtain the probability flow calculation results; use MATLAB's graphics drawing function to display the simulation results in the form of charts for easy observation and analysis.

[0141] Analysis of the effectiveness of the probability flow calculation of the distribution network: In order to verify the effectiveness of the method disclosed in this paper, a comparative experiment was conducted. In the experiment, method 1 and method 2 in the related technology were used as the comparison group, and the method disclosed in this paper was used as the experimental group, so as to more clearly demonstrate the advantages of the method proposed in this paper. This experiment took the output probability density of the photovoltaic system as the research object, and used three methods to analyze the probability flow of the distribution network system, and took the results obtained by running the Monte Carlo method 1000 times as a reference. The results are as follows: Figure 4 shown.

[0142] according to Figure 4 The displayed data show that the output probability density function of the photovoltaic system is closest to the result of the reference method when the method disclosed in this paper is used to analyze the probability flow distribution of the distribution network system, while there is a large deviation between the analysis result of the control group method and the reference result. This shows that the method disclosed in this paper can more accurately reflect the probability flow distribution of the distribution network, and the actual application effect is good.

[0143] Reliability analysis of the probability flow method of distribution network: When a large number of distributed power sources and loads are connected to the distribution network system, the random fluctuations caused directly affect the probability flow distribution, and then affect the change of node voltage amplitude. Therefore, based on the above experiments, in order to further test the feasibility of the actual application of the disclosed method, three methods are used to calculate the voltage amplitude of the system 1~10 nodes, and the Hill inequality coefficient is introduced. As an indicator to evaluate the performance of different methods. The value range of this indicator is between 0 and 1. The smaller the value, the closer the predicted value of the corresponding method is to the true value, that is, the more accurate the probabilistic flow analysis of the power grid system is. The comparison results are shown in Table 2.

[0144] Table 2 Analysis results of node voltage amplitude of distribution network system based on different methods

[0145]

[0146] It can be seen from the data in Table 2 that the Hill inequality coefficients obtained by calculating the voltage amplitudes of 10 nodes of the system using the disclosed method are Always kept below 0.03, while the maximum of method 1 and method 2 The values ​​are 3.338 and 2.334 respectively, indicating that these two methods are difficult to accurately calculate the voltage amplitude of the system nodes, and thus the probability flow analysis results of the system are inaccurate. The reliability of the disclosed method can be proved by comparing the experimental results.

[0147] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0148] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0149] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a probabilistic power flow analysis device for a flexible interconnected distribution network under vehicle-grid interaction.

[0150] refer to Figure 5 , which is a structural schematic diagram of a probabilistic power flow analysis device for a flexible interconnected distribution network under vehicle-grid interaction provided by an exemplary embodiment of the present disclosure.

[0151] The probabilistic power flow analysis device for flexible interconnected distribution network under vehicle-grid interaction includes the following modules:

[0152] The uncertainty variable parameter analysis module 510 is configured to obtain uncertainty variable parameters of the distribution network system to be analyzed based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, and perform photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters, and obtain photovoltaic power generation output probability analysis results, wind power generation output probability analysis results, power load probability analysis results and electric vehicle charging demand probability analysis results;

[0153] The fuzzy linearization equation determination module 520 is configured to perform standardization processing on the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results based on the reference operating point of the distribution network system to be analyzed, so as to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and obtain a fuzzy linearization equation of an uncertain variable based on the unified power mismatch equation and the state correction function;

[0154] The probability power flow equation determination module 530 is configured to obtain the series of output variables based on random variable estimation, process the fuzzy linearized equation of the uncertainty variable, obtain the characteristic vector of the probability density and the quantile of the variable, and construct the probability power flow equation of the distribution network system to be analyzed based on the input vector, the output vector and the quantile of the variable;

[0155] The probabilistic power flow analysis module 540 is configured to perform probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

[0156] In some exemplary embodiments, the uncertainty variable parameter analysis module 510 is specifically configured to:

[0157] Obtaining the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient, and obtaining the radiation intensity of the sun based on the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient;

[0158] Obtaining the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency, and obtaining the photovoltaic power generation output probability analysis result based on the solar radiation intensity, the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency;

[0159] Obtaining the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and a first-order lag variable, and obtaining a probability density function of wind speed based on the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and the first-order lag variable;

[0160] Obtaining the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix, and obtaining the wind power generation output probability analysis result based on the probability density function of the wind speed, the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix;

[0161] Obtain random variables, load fluctuation factors and superimposed scaling functions, obtain active power based on the random variables, the load fluctuation factors and the superimposed scaling functions, obtain load density, dispersion coefficient and maximum likelihood function, obtain reactive power based on the load density, the dispersion coefficient and the maximum likelihood function, and obtain the power load probability analysis result based on the active power and the reactive power;

[0162] Obtaining the distance between the vehicle and the charging station, and obtaining the number of electric vehicles being charged based on the distance between the vehicle and the charging station;

[0163] The average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution are obtained, and based on the number of electric vehicles being charged, the average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution, the probability analysis result of the electric vehicle charging demand is obtained.

[0164] In some exemplary embodiments, the fuzzy linearization equation determination module 520 is specifically configured as follows:

[0165] Obtaining a fixed part and a random part of a random vector of an electric vehicle, and constructing a vehicle charging probability equation based on the fixed part and the random part;

[0166] Obtaining a nonlinear factor, at the reference operating point, based on the vehicle charging probability equation and the nonlinear factor, performing standardization processing on the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result, and the electric vehicle charging demand probability analysis result to obtain a system loss;

[0167] Acquire an initial value of a droop voltage, and construct the unified power mismatch equation for the reference operating point based on the system loss and the initial value of the droop voltage;

[0168] Acquire the number of system nodes, the regulated power of the distributed power source and the penalty factor, and construct the state correction function based on the unified power mismatch equation, the number of system nodes, the regulated power of the distributed power source and the penalty factor;

[0169] The power factor angle and the phase angle difference are obtained, and a fuzzy linearization equation of the uncertainty variable is constructed based on the state correction function, the power factor angle and the phase angle difference.

[0170] In some exemplary embodiments, the probabilistic power flow equation determination module 530 is specifically configured to:

[0171] Acquire a conversion function, and perform conversion processing on the fuzzy linearization equation of the uncertainty variable based on the conversion function to obtain the position coefficient of the sampling point;

[0172] Obtaining the series of output variables based on random variable estimation, obtaining a system operation reference node, and obtaining an estimated power flow calculation result based on the location coefficient of the sampling point, the series of the output variables and the system operation reference node;

[0173] Obtaining a droop coefficient and a state matrix, and obtaining a characteristic vector of the probability density based on the estimated power flow calculation result, the droop coefficient and the state matrix;

[0174] Obtaining a cumulative distribution function, and obtaining the quantile of the variable based on the characteristic vector of the probability density and the cumulative distribution function;

[0175] An input vector and an output vector are constructed, and based on the input vector, the output vector and the quantile of the variable, a probabilistic power flow equation of the distribution network system to be analyzed is constructed.

[0176] In some exemplary embodiments, the probabilistic power flow analysis module 540 is specifically configured to:

[0177] The current input vector of the distribution network system to be analyzed is obtained, and the current input vector is input into the probabilistic power flow equation to obtain a current output vector corresponding to the current input vector as the probabilistic power flow analysis result.

[0178] In some exemplary embodiments, the probabilistic power flow analysis module 540 is further configured to:

[0179] Generating a probabilistic power flow chart of the distribution network system to be analyzed based on the probabilistic power flow analysis result;

[0180] The probabilistic power flow diagram is transmitted to a designated terminal device of the power distribution network system to be analyzed.

[0181] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0182] The device of the above embodiment is used to implement the corresponding probabilistic power flow analysis method of the flexible interconnected distribution network under vehicle-grid interaction in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0183] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction described in any of the above embodiments is implemented.

[0184] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0185] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0186] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0187] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0188] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0189] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0190] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0191] The electronic device of the above embodiment is used to implement the corresponding probabilistic power flow analysis method of the flexible interconnected distribution network under vehicle-grid interaction in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0192] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the probabilistic power flow analysis method of a flexible interconnected distribution network under vehicle-grid interaction as described in any of the above embodiments.

[0193] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0194] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0195] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the probabilistic power flow analysis method of the flexible interconnected distribution network under vehicle-grid interaction as described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0196] Based on the same inventive concept, corresponding to the probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction. Corresponding to the execution subject corresponding to each step in each embodiment of the probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0197] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the probabilistic power flow analysis method of the flexible interconnected distribution network under vehicle-grid interaction as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0198] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." In addition, in some embodiments, the present disclosure may also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0199] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive examples) of computer-readable storage media may include, for example: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device.

[0200] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0201] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0202] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0203] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified in the boxes in the flowchart and / or block diagram.

[0204] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable medium produce a product that includes an instruction device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0205] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable device to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0206] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0207] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0208] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0209] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0210] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0211] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0212] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0213] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A probabilistic power flow analysis method for a flexible interconnected distribution network under vehicle-grid interaction, characterized in that: include: Based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, the uncertainty variable parameters of the distribution network system to be analyzed are obtained, and based on the uncertainty variable parameters, the photovoltaic power generation output probability analysis, the wind power generation output probability analysis, the power load probability analysis and the electric vehicle charging demand probability analysis are respectively performed to obtain the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results; Based on the reference operating point of the distribution network system to be analyzed, the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results are standardized to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed. Based on the unified power mismatch equation and the state correction function, a fuzzy linearization equation of uncertainty variables is obtained, which specifically includes: obtaining a fixed part and a random part of a random vector of an electric vehicle, and constructing a vehicle charging probability equation based on the fixed part and the random part; obtaining a nonlinear factor, and at the reference operating point, constructing a fuzzy linearization equation of the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results. The output probability analysis result, the wind power output probability analysis result, the power load probability analysis result and the electric vehicle charging demand probability analysis result are standardized to obtain the system loss; the initial value of the droop voltage is obtained, and based on the system loss and the initial value of the droop voltage, the unified power mismatch equation of the reference operating point is constructed; the number of system nodes, the regulated power and the penalty factor of the distributed power source are obtained, and based on the unified power mismatch equation, the number of system nodes, the regulated power of the distributed power source and the penalty factor, the state correction function is constructed; the power factor angle and the phase angle difference are obtained, and based on the state correction function, the power factor angle and the phase angle difference, the fuzzy linearization equation of the uncertainty variable is constructed; Based on the random variable estimation, the series of the output variable is obtained, the fuzzy linearized equation of the uncertain variable is processed to obtain the characteristic vector of the probability density and the quantile of the variable, and based on the input vector, the output vector and the quantile of the variable, the probabilistic power flow equation of the distribution network system to be analyzed is constructed; Based on the probabilistic power flow equation, a probabilistic power flow analysis is performed on the distribution network system to be analyzed to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

2. The method according to claim 1, characterized in that Based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, the uncertainty variable parameters of the distribution network system to be analyzed are obtained, and based on the uncertainty variable parameters, the photovoltaic power generation output probability analysis, the wind power generation output probability analysis, the power load probability analysis and the electric vehicle charging demand probability analysis are respectively performed to obtain the photovoltaic power generation output probability analysis results, the wind power generation output probability analysis results, the power load probability analysis results and the electric vehicle charging demand probability analysis results, including: Obtaining the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient, and obtaining the radiation intensity of the sun based on the angle between the direct sunlight and the normal of the lighting surface, the radiation flux of the solar surface, the unit matrix and the atmospheric transparency coefficient; Obtaining the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency, and obtaining the photovoltaic power generation output probability analysis result based on the solar radiation intensity, the random variation increment, the number of photovoltaic panels, the normal distribution parameter and the photoelectric conversion efficiency; Obtaining the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and a first-order lag variable, and obtaining a probability density function of wind speed based on the number of original joint distribution training data points, the annual average temperature of the region where the wind farm is located, and the first-order lag variable; Obtaining the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix, and obtaining the wind power generation output probability analysis result based on the probability density function of the wind speed, the number of wind turbines, the standard deviation of wind speed, the shape parameters and the calculation matrix; Obtain random variables, load fluctuation factors and superimposed scaling functions, obtain active power based on the random variables, the load fluctuation factors and the superimposed scaling functions, obtain load density, dispersion coefficient and maximum likelihood function, obtain reactive power based on the load density, the dispersion coefficient and the maximum likelihood function, and obtain the power load probability analysis result based on the active power and the reactive power; Obtaining the distance between the vehicle and the charging station, and obtaining the number of electric vehicles being charged based on the distance between the vehicle and the charging station; The average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution are obtained, and based on the number of electric vehicles being charged, the average charging current, the charging voltage, the charging time of the vehicle and the scale parameter of the Weibull distribution, the probability analysis result of the electric vehicle charging demand is obtained.

3. The method according to claim 1, characterized in that The output variable series is obtained based on random variable estimation, the fuzzy linearized equation of the uncertainty variable is processed to obtain the characteristic vector of probability density and the quantile of the variable, and the probabilistic power flow equation of the distribution network system to be analyzed is constructed based on the input vector, the output vector and the quantile of the variable, including: Acquire a conversion function, and perform conversion processing on the fuzzy linearization equation of the uncertainty variable based on the conversion function to obtain the position coefficient of the sampling point; Obtaining the series of output variables based on random variable estimation, obtaining a system operation reference node, and obtaining an estimated power flow calculation result based on the location coefficient of the sampling point, the series of the output variables and the system operation reference node; Obtaining a droop coefficient and a state matrix, and obtaining a characteristic vector of the probability density based on the estimated power flow calculation result, the droop coefficient and the state matrix; Obtaining a cumulative distribution function, and obtaining a quantile of the variable based on a characteristic vector of the probability density and the cumulative distribution function; An input vector and an output vector are constructed, and based on the input vector, the output vector and the quantile of the variable, a probabilistic power flow equation of the distribution network system to be analyzed is constructed.

4. The method according to claim 1, characterized in that The performing a probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed includes: The current input vector of the distribution network system to be analyzed is obtained, and the current input vector is input into the probabilistic power flow equation to obtain a current output vector corresponding to the current input vector as the probabilistic power flow analysis result.

5. The method according to claim 1, characterized in that After performing a probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation and obtaining a probabilistic power flow analysis result of the distribution network system to be analyzed, the method further includes: Generating a probabilistic power flow chart of the distribution network system to be analyzed based on the probabilistic power flow analysis result; The probabilistic power flow diagram is transmitted to a designated terminal device of the power distribution network system to be analyzed.

6. A probabilistic power flow analysis device for a flexible interconnected distribution network under vehicle-grid interaction, characterized in that: include: The uncertainty variable parameter analysis module is configured to obtain the uncertainty variable parameters of the distribution network system to be analyzed based on the data of vehicle-grid interaction and the operation data of the flexible interconnected distribution network, and to perform photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters, and obtain photovoltaic power generation output probability analysis results, wind power generation output probability analysis results, power load probability analysis results and electric vehicle charging demand probability analysis results; The fuzzy linearization equation determination module is configured to perform standardization processing on the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result and the electric vehicle charging demand probability analysis result based on the reference operating point of the distribution network system to be analyzed, so as to obtain a unified power mismatch equation and a state correction function of the distribution network system to be analyzed, and obtain a fuzzy linearization equation of uncertainty variables based on the unified power mismatch equation and the state correction function, and is specifically configured to: obtain a fixed part and a random part of the electric vehicle random vector, and construct a vehicle charging probability equation based on the fixed part and the random part; obtain a nonlinear factor, and at the reference operating point, based on the vehicle charging probability equation and the nonlinear factor, perform standardization processing on the photovoltaic power generation output probability analysis result, the wind power generation output probability analysis result, the power load probability analysis result and the electric vehicle charging demand probability analysis result to obtain system loss; obtain an initial value of a droop voltage, and construct the unified power mismatch equation of the reference operating point based on the system loss and the initial value of the droop voltage; Obtain the number of system nodes, the regulated power and penalty factor of the distributed power source, and construct the state correction function based on the unified power mismatch equation, the number of system nodes, the regulated power of the distributed power source and the penalty factor; obtain the power factor angle and the phase angle difference, and construct the fuzzy linearization equation of the uncertainty variable based on the state correction function, the power factor angle and the phase angle difference; A probability power flow equation determination module is configured to obtain the series of output variables based on random variable estimation, process the fuzzy linearized equation of the uncertainty variable, obtain the characteristic vector of the probability density and the quantile of the variable, and construct the probability power flow equation of the distribution network system to be analyzed based on the input vector, the output vector and the quantile of the variable; The probabilistic power flow analysis module is configured to perform probabilistic power flow analysis on the distribution network system to be analyzed based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result of the distribution network system to be analyzed.

7. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 5.

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