Real-time adjustment method and system for distributed resources on the low-voltage side of distribution network

Abstract

The present invention provides a real-time adjustment method and system for distributed resources on the low-voltage side of a distribution network. The adjustment method comprises: obtaining historical operating data of distributed power equipment on the low-voltage side of the distribution network and preprocessing the data; determining the station-affiliation relationship of the power equipment and the topological structure between the power equipment; establishing a distributed optimization platform based on the topological structure, and calculating the voltage crisis coefficient on the low-voltage side of the distribution network; obtaining the first voltage crisis coefficient of the regulation node and the corresponding single adjustment cost of the photovoltaic power equipment and the energy storage power equipment; obtaining the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determining the second voltage crisis coefficient corresponding to the regulation node when the voltage exceeds the limit based on the cost limit boundary constraint, and determining the power equipment for dispatching the system voltage according to the ratio of the two voltage crisis coefficients; the adjustment method provided by the present application realizes dynamic scheduling and optimal control of distributed resources, thereby improving the operating efficiency and reliability of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for real-time adjustment of distributed resources on the low-voltage side of a distribution network. Background Art

[0002] With the development of new power systems, a large number of distributed resources, such as distributed photovoltaic power generation, energy storage equipment, and electric vehicles, have been connected to the low-voltage side of the distribution network. The integration of these distributed resources has made the operation of the distribution network more complex and changeable. Traditional sensing systems are unable to meet the requirements of real-time and accurate perception of the status of distributed resources.

[0003] As distributed resources such as photovoltaic power generation and energy storage equipment are connected to the power grid, the randomness and intermittency of distributed power sources will cause voltage and frequency fluctuations, affecting the stable operation of the power grid and having a negative impact on equipment safety and the economic operation of the power grid. How to accurately identify the voltage status in the distribution network and make perception judgments, and then adjust the power output of distributed power equipment, and work together to ensure power supply stability and reliability has always been a hot research issue. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method and system for real-time adjustment of distributed resources on the low-voltage side of a distribution network to solve the technical problems existing in the prior art.

[0005] The present invention proposes a real-time adjustment method for distributed resources on the low-voltage side of a distribution network, comprising:

[0006] Obtain historical operating data of distributed power equipment on the low-voltage side of the distribution network, and perform standardization and feature dimensionality reduction on the acquired historical operating data;

[0007] Relationship identification is performed on the historical operating data after dimensionality reduction to determine the station-related relationships of power equipment. Phase analysis is performed on power equipment with the same station-related relationships. Based on the phase analysis, the topology structure between the distributed power equipment on the low-voltage side of the power grid is determined;

[0008] A distributed optimization platform is established based on the topological structure. The voltage data of each power device is obtained in real time through the optimization platform. The voltage offset and voltage fluctuation in the voltage data are used as inputs of the fuzzy control system on the low-voltage side of the distribution network for fuzzy processing to obtain the voltage danger coefficient on the low-voltage side of the distribution network.

[0009] The nodes where the photovoltaic power equipment and the energy storage power equipment are located on the low-voltage side of the distribution network are defined as adjustment nodes, and the first voltage crisis coefficient of the adjustment node and the corresponding single adjustment costs of the photovoltaic power equipment and the energy storage power equipment are obtained;

[0010] Obtain the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determine the second voltage crisis coefficient corresponding to the adjustment node when the voltage exceeds the limit based on the cost limit boundary constraint, and determine the voltage control of the power equipment according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient.

[0011] Preferably, the steps of performing relationship identification on the historical operating data after dimensionality reduction processing to determine the station-related relationship of the power equipment, performing phase analysis on the power equipment with the same station-related relationship, and determining the topological structure between the distributed power equipment on the low-voltage side of the power grid according to the phase analysis include:

[0012] An adjacency matrix is constructed based on the historical operating data after dimensionality reduction processing, the sum of weights between all power devices is calculated based on the adjacency matrix to obtain a degree matrix, and a cluster matrix of the low-voltage side of the distribution network is obtained based on the adjacency matrix and the degree matrix;

[0013] Calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment;

[0014] Equivalently model the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it as a time series to obtain voltage data curve samples;

[0015] Obtaining turning points in the voltage data curve sample, marking their importance based on a hierarchical manner, time-segmenting the voltage data curve sample according to the importance marking results, and determining the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient;

[0016] The topology of the low-voltage side of the distribution network is determined according to the phase relationship between the upstream and downstream power equipment.

[0017] Preferably, the cluster matrix is expressed as:

[0018]

[0019] Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the The power equipment The weight of the power equipment;

[0020]

[0021] Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood;

[0022] The expression of the phase identification correlation coefficient is:

[0023]

[0024] Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

[0025] Preferably, the step of obtaining voltage data of each power device in real time through the optimization platform includes:

[0026] Obtaining historical voltage data and prediction credibility of the power equipment, and determining a first prediction model based on the historical voltage data and the prediction credibility;

[0027] Performing a first prediction on the voltage data of the power equipment according to the first prediction model, and exchanging a first predicted value of the line voltage according to the line connection relationship of the power equipment;

[0028] The first prediction value is processed according to the node voltage association prediction model to obtain voltage data corresponding to each power device.

[0029] Preferably, the expression of the first prediction model is:

[0030]

[0031] Where, represents the first predicted value, represents the hyperparameters of the first prediction model, represents the insensitive loss function, Indicates the The relaxation factor corresponding to the historical voltage data sample is: represents the number of samples, represents the prediction credibility of the first prediction model, represents the first-order norm;

[0032] The expression of the node voltage correlation prediction model is:

[0033]

[0034] Where, Represents voltage prediction data, 、 Respectively represent the voltage measurement value and reliability of the low-voltage side of the distribution network, 、 Respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the distribution network, They respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the user end.

[0035] Preferably, the expression of the single adjustment cost corresponding to the photovoltaic power equipment is:

[0036]

[0037] Where, Indicates the single adjustment cost corresponding to the photovoltaic power equipment, It represents the equivalent investment cost of a single adjustment of photovoltaic power equipment, which is related to the investment cost of photovoltaic power equipment and the number of adjustment lifespans. Indicates the photovoltaic unit active power compensation price, represents the photovoltaic curtailment penalty coefficient, represents the expected photovoltaic power generation;

[0038] The expression of the single adjustment cost corresponding to the energy storage power equipment is:

[0039]

[0040] Where, Indicates the single adjustment cost corresponding to the energy storage power equipment, It represents the equivalent investment cost of a single adjustment of the energy storage power equipment, which is related to the investment cost of the energy storage power equipment and the number of adjustment lifespans. represents the charge variation coefficient, Respectively represent the energy storage power equipment in Moment and The charge at the moment, represents the energy storage power reduction penalty coefficient, Indicates the active output power of energy storage.

[0041] Preferably, the expression of the cost limit boundary constraint is:

[0042]

[0043] The step of determining the voltage control of the power equipment according to the ratio of the first voltage critical coefficient to the second voltage critical coefficient includes:

[0044] If the first voltage criticality coefficient is greater than the second voltage criticality coefficient, selecting energy storage power equipment to perform system voltage control;

[0045] If the first voltage criticality coefficient is less than or equal to the second voltage criticality coefficient, photovoltaic power equipment is selected to perform system voltage control.

[0046] The present invention also proposes a real-time adjustment system for distributed resources on the low-voltage side of a distribution network, comprising:

[0047] A dimensionality reduction processing module is used to obtain historical operating data of distributed power equipment on the low-voltage side of the distribution network, and to perform standardization and feature dimensionality reduction processing on the acquired historical operating data;

[0048] The identification module is used to identify relationships in the historical operating data after dimensionality reduction processing to determine the station-related relationships of power equipment, perform phase analysis on power equipment with the same station-related relationships, and determine the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis;

[0049] A fuzzy processing module is used to establish a distributed optimization platform based on the topological structure, obtain voltage data of each power device in real time through the optimization platform, and use the voltage offset and voltage fluctuation in the voltage data as inputs of the fuzzy control system on the low-voltage side of the distribution network to perform fuzzy processing to obtain the voltage danger coefficient on the low-voltage side of the distribution network;

[0050] A calculation module is used to define the nodes where the photovoltaic power equipment and the energy storage power equipment on the low-voltage side of the distribution network are located as adjustment nodes, and obtain the first voltage crisis coefficient of the adjustment node and the corresponding single adjustment cost of the photovoltaic power equipment and the energy storage power equipment;

[0051] The scheduling module is used to obtain the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determine the second voltage crisis coefficient corresponding to the adjustment node when the voltage exceeds the limit based on the cost limit boundary constraint, and determine the voltage control of the power equipment according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient.

[0052] Preferably, the identification module includes:

[0053] A construction unit is configured to construct an adjacency matrix based on the historical operating data after dimensionality reduction processing, calculate the sum of weights between all power devices based on the adjacency matrix to obtain a degree matrix, and obtain a cluster matrix on the low-voltage side of the distribution network based on the adjacency matrix and the degree matrix;

[0054] A clustering unit is used to calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment;

[0055] The equivalent unit is used to perform equivalent modeling on the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it in time series to obtain voltage data curve samples;

[0056] a marking unit, configured to obtain turning points in the voltage data curve sample, mark the turning points based on their importance based on a hierarchical manner, time segment the voltage data curve sample based on the importance marking results, and determine the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient;

[0057] The determining unit is used to determine the topology of the low-voltage side of the distribution network according to the phase relationship between the upstream and downstream power equipment.

[0058] Preferably, in the identification module,

[0059] The expression of the cluster matrix is:

[0060]

[0061] Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the Power equipment for the first The weight of the power equipment;

[0062]

[0063] Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood;

[0064] The expression of the phase identification correlation coefficient is:

[0065]

[0066] Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows: the real-time adjustment method of distributed resources on the low-voltage side of the distribution network provided by the present application first obtains the historical operation data of the distributed power equipment on the low-voltage side of the distribution network, identifies the relationship between the historical operation data to determine the station-to-station relationship of the power equipment, performs phase analysis on the power equipment with the same station-to-station relationship, and determines the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis; the topological relationship between the power equipment is an important guarantee for carrying out the business of distribution line parameter identification, operation data analysis, station area operation planning, etc.; then, a distributed optimization platform is established based on the topological structure, and the voltage data of each power equipment is obtained in real time through the optimization platform, and the voltage offset and voltage in the voltage data are compared. The voltage fluctuation momentum is used as the input of the fuzzy control system on the low-voltage side of the distribution network for fuzzy processing to obtain the voltage crisis coefficient on the low-voltage side of the distribution network, and the single adjustment cost of the regulation node is obtained according to the voltage crisis coefficient, and the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit is obtained. The second voltage crisis coefficient corresponding to the regulation node when the voltage exceeds the limit is determined based on the cost limit boundary constraint, and the power equipment is determined to perform voltage control according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient; the real-time adjustment method for distributed resources on the low-voltage side of the distribution network disclosed in the present application can perceive the voltage situation on the low-voltage side of the distribution network in real time, and perform corresponding voltage scheduling to ensure the voltage stability of the system, optimize the operation of the distribution network, and improve the power supply reliability and power quality.

[0068] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flowchart of the method for real-time adjustment of distributed resources on the low-voltage side of a distribution network in the first embodiment of the present invention;

[0070] Figure 2 4 is a block diagram of the computer structure in the fourth embodiment of the present invention.

[0071] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0072] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0074] Example 1

[0075] See also Figure 1 , which shows a real-time adjustment method for distributed resources on the low-voltage side of a distribution network in a first embodiment of the present invention. Specifically, the real-time adjustment method for distributed resources on the low-voltage side of a distribution network includes steps S10 to S50:

[0076] S10, obtaining historical operating data of distributed power equipment on the low-voltage side of the distribution network, and performing standardization and feature dimensionality reduction processing on the obtained historical operating data;

[0077] Optionally, operating data can be collected through an automated data collection device. The collected operating data includes parameters such as voltage, current, power, and temperature. An overall analysis of the data will greatly increase the analysis time and computational complexity. Many similar data are reused, and useful data cannot be distinguished for focus. Therefore, the collected data first needs to be standardized and feature-reduced to remove duplicate data. Low-dimensional data can represent most of the features of the original data, and the desired results can be obtained by analyzing the low-dimensional data, thereby reducing the amount of data analysis.

[0078] S20, performing relationship identification on the historical operating data after dimensionality reduction processing to determine the station-related relationship of the power equipment, performing phase analysis on the power equipment with the same station-related relationship, and determining the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis;

[0079] The steps of performing relationship identification on the historical operation data after dimensionality reduction processing to determine the station-related relationship of the power equipment, performing phase analysis on the power equipment with the same station-related relationship, and determining the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis include:

[0080] An adjacency matrix is constructed based on the historical operating data after dimensionality reduction processing, the sum of weights between all power devices is calculated based on the adjacency matrix to obtain a degree matrix, and a cluster matrix of the low-voltage side of the distribution network is obtained based on the adjacency matrix and the degree matrix;

[0081] Calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment;

[0082] Equivalently model the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it as a time series to obtain voltage data curve samples;

[0083] Obtaining turning points in the voltage data curve sample, marking their importance based on a hierarchical manner, time-segmenting the voltage data curve sample according to the importance marking results, and determining the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient;

[0084] The topology of the low-voltage side of the distribution network is determined according to the phase relationship between the upstream and downstream power equipment.

[0085] The expression of the cluster matrix is:

[0086]

[0087] Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the The power equipment The weight of the power equipment;

[0088]

[0089] Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood;

[0090] The expression of the phase identification correlation coefficient is:

[0091]

[0092] Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

[0093] Optionally, the data in the distributed power equipment is regarded as a data set, and the points in the data set are regarded as points in space. The weights between different power equipment and other power equipment are counted and an adjacency matrix is constructed. The elements of the adjacency matrix reflect the total weight relationship between each power equipment. The degree matrix is a skew matrix, and each element is the sum of the weights of the power equipment and the rest of the power equipment. Through cluster computing, the power equipment can be divided into substations, such as belonging to the same park, community or charging station; phase analysis is performed on the power equipment on the distribution line belonging to the same substation, and the impact of voltage fluctuations of upstream and downstream power equipment on the current power equipment is analyzed. Combined with the topological information of GIS, the topological relationship of the power equipment in the substation can be obtained; the topological relationship between power equipment is an important guarantee for carrying out distribution line parameter identification, operation data analysis, substation operation planning and other businesses.

[0094] S30, establishing a distributed optimization platform based on the topological structure, obtaining voltage data of each power device in real time through the optimization platform, and using the voltage offset and voltage fluctuation in the voltage data as inputs of the fuzzy control system on the low-voltage side of the distribution network for fuzzy processing to obtain a voltage danger coefficient on the low-voltage side of the distribution network;

[0095] The step of obtaining voltage data of each power device in real time through the optimization platform includes:

[0096] Obtaining historical voltage data and prediction credibility of the power equipment, and determining a first prediction model based on the historical voltage data and the prediction credibility;

[0097] Performing a first prediction on the voltage data of the power equipment according to the first prediction model, and exchanging a first predicted value of the line voltage according to the line connection relationship of the power equipment;

[0098] The first prediction value is processed according to the node voltage association prediction model to obtain voltage data corresponding to each power device.

[0099] The expression of the first prediction model is:

[0100]

[0101] Where, represents the first predicted value, represents the hyperparameters of the first prediction model, represents the insensitive loss function, Indicates the The relaxation factor corresponding to the historical voltage data sample is: represents the number of samples, represents the prediction credibility of the first prediction model, represents the first-order norm;

[0102] The expression of the node voltage correlation prediction model is:

[0103]

[0104] Where, Represents voltage prediction data, 、 Respectively represent the voltage measurement value and reliability of the low-voltage side of the distribution network, 、 Respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the distribution network, They respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the user end.

[0105] Optionally, building an optimization platform includes platform function analysis, hardware architecture, conditional constraints, software implementation, etc.; since the distributed power equipment on the low-voltage side of the distribution network may be in the upper and lower power grids at the same time, it may serve as both the load of the upper power grid and the power source of the lower power grid. Schematically, the distributed power equipment may serve as both the load of the distribution network and the power source on the user side; therefore, obtaining the voltage data of each power device requires considering the influence of the upper and lower power grid lines at the same time to improve the accuracy of voltage data acquisition. The node voltage correlation prediction model has the characteristics of both distribution and information interaction, and is suitable for application on a distributed optimization platform.

[0106] S40, defining a node where the photovoltaic power equipment and the energy storage power equipment on the low-voltage side of the distribution network are located as a regulation node, and obtaining a first voltage crisis coefficient of the regulation node and corresponding single regulation costs of the photovoltaic power equipment and the energy storage power equipment;

[0107] The expression of the single adjustment cost corresponding to photovoltaic power equipment is:

[0108]

[0109] Where, Indicates the single adjustment cost corresponding to the photovoltaic power equipment, It represents the equivalent investment cost of a single adjustment of photovoltaic power equipment, which is related to the investment cost of photovoltaic power equipment and the number of adjustment lifespans. Indicates the photovoltaic unit active power compensation price, represents the photovoltaic curtailment penalty coefficient, represents the expected photovoltaic power generation;

[0110] The expression of the single adjustment cost corresponding to the energy storage power equipment is:

[0111]

[0112] Where, Indicates the single adjustment cost corresponding to the energy storage power equipment, It represents the equivalent investment cost of a single adjustment of the energy storage power equipment, which is related to the investment cost of the energy storage power equipment and the number of adjustment lifespans. represents the charge variation coefficient, Respectively represent the energy storage power equipment in Moment and The charge at the moment, represents the energy storage power reduction penalty coefficient, Indicates the active output power of energy storage.

[0113] S50, obtaining a charge state when the voltage on the low-voltage side of the distribution network exceeds a limit, determining a second voltage critical coefficient corresponding to the regulating node when the voltage exceeds a limit based on a cost limit boundary constraint, and determining voltage control of the power equipment based on a ratio of the first voltage critical coefficient to the second voltage critical coefficient;

[0114] The expression of the cost limit boundary constraint is:

[0115]

[0116] The step of determining the voltage control of the power equipment according to the ratio of the first voltage critical coefficient to the second voltage critical coefficient includes:

[0117] If the first voltage criticality coefficient is greater than the second voltage criticality coefficient, selecting energy storage power equipment to perform system voltage control;

[0118] If the first voltage criticality coefficient is less than or equal to the second voltage criticality coefficient, photovoltaic power equipment is selected to perform system voltage control.

[0119] Optionally, in this embodiment, the steps of performing system voltage control are:

[0120] Determine the objective function and mathematical model based on the optimal stability of the low-voltage distribution network, minimum voltage deviation, and minimum system power consumption, and determine the target constraints;

[0121] Introducing intermediate variables to process the target constraints, the problem of solving the objective function under the target constraints is converted into a linear dimension solution under the second-order cone model;

[0122] A relaxation transformation is performed on the second-order cone model to obtain a convex feasible operation domain corresponding to the second-order cone model. In the convex feasible operation domain, the solution of the second-order cone model is converted into a planning problem in multidimensional space, and then the control voltage corresponding to the output of the power equipment is obtained.

[0123] Optionally, the objective function is expressed as:

[0124]

[0125] Where, Represent weight factors, Indicates the preset voltage control time, represents the number of voltage nodes in the system, Represents the voltage nodes , voltage node exist The voltage at the moment, represents the reference voltage, Represents a voltage node and voltage nodes The phase angle difference between Indicates any preset time period.

[0126] The target constraints include at least economic benefit constraints, charge constraints, voltage response delay constraints, etc. The role of introducing intermediate variables is mainly to simplify the constraints and convert them into a mathematical solution model in linear dimension, improve the solution efficiency, and facilitate the analysis and calculation of the voltage adjustment effect. The role of relaxation transformation is mainly to convert the operation effect of the second-order cone model into a convex feasible operation domain, and then the conventional " -Relaxation" method for fast solution, Represents the slack variable. The smaller the slack variable, the higher the accuracy of the output voltage.

[0127] In summary, the real-time adjustment method of distributed resources on the low-voltage side of the distribution network provided by the present application first obtains the historical operation data of the distributed power equipment on the low-voltage side of the distribution network, identifies the relationship of the historical operation data to determine the station-to-station relationship of the power equipment, performs phase analysis on the power equipment with the same station-to-station relationship, and determines the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis; the topological relationship between the power equipment is an important guarantee for carrying out distribution line parameter identification, operation data analysis, substation operation planning and other businesses; then, a distributed optimization platform is established based on the topological structure, and the voltage data of each power equipment is obtained in real time through the optimization platform, and the voltage offset and voltage fluctuation in the voltage data are used as the distribution The input of the fuzzy control system on the low-voltage side of the power grid is fuzzy processed to obtain the voltage crisis coefficient on the low-voltage side of the distribution network, the single adjustment cost of the regulation node is obtained according to the voltage crisis coefficient, the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, the second voltage crisis coefficient corresponding to the regulation node when the voltage exceeds the limit is determined based on the cost limit boundary constraint, and the power equipment is determined to perform voltage control according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient; the real-time adjustment method for distributed resources on the low-voltage side of the distribution network disclosed in the present application can perceive the voltage situation on the low-voltage side of the distribution network in real time, and perform corresponding voltage scheduling to ensure the voltage stability of the system, optimize the operation of the distribution network, and improve the power supply reliability and power quality.

[0128] Example 2

[0129] This embodiment provides a real-time adjustment system for distributed resources on the low-voltage side of a distribution network, including:

[0130] A dimensionality reduction processing module is used to obtain historical operating data of distributed power equipment on the low-voltage side of the distribution network, and to perform standardization and feature dimensionality reduction processing on the acquired historical operating data;

[0131] The identification module is used to identify relationships in the historical operating data after dimensionality reduction processing to determine the station-related relationships of power equipment, perform phase analysis on power equipment with the same station-related relationships, and determine the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis;

[0132] A fuzzy processing module is used to establish a distributed optimization platform based on the topological structure, obtain voltage data of each power device in real time through the optimization platform, and use the voltage offset and voltage fluctuation in the voltage data as inputs of the fuzzy control system on the low-voltage side of the distribution network to perform fuzzy processing to obtain the voltage danger coefficient on the low-voltage side of the distribution network;

[0133] A calculation module is used to define the nodes where the photovoltaic power equipment and the energy storage power equipment on the low-voltage side of the distribution network are located as adjustment nodes, and obtain the first voltage crisis coefficient of the adjustment node and the corresponding single adjustment cost of the photovoltaic power equipment and the energy storage power equipment;

[0134] The scheduling module is used to obtain the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determine the second voltage crisis coefficient corresponding to the adjustment node when the voltage exceeds the limit based on the cost limit boundary constraint, and determine the voltage control of the power equipment according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient.

[0135] Preferably, the steps of performing relationship identification on the historical operating data after dimensionality reduction processing to determine the station-related relationship of the power equipment, performing phase analysis on the power equipment with the same station-related relationship, and determining the topological structure between the distributed power equipment on the low-voltage side of the power grid according to the phase analysis include:

[0136] An adjacency matrix is constructed based on the historical operating data after dimensionality reduction processing, the sum of weights between all power devices is calculated based on the adjacency matrix to obtain a degree matrix, and a cluster matrix of the low-voltage side of the distribution network is obtained based on the adjacency matrix and the degree matrix;

[0137] Calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment;

[0138] Equivalently model the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it as a time series to obtain voltage data curve samples;

[0139] Obtaining turning points in the voltage data curve sample, marking their importance based on a hierarchical manner, time-segmenting the voltage data curve sample according to the importance marking results, and determining the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient;

[0140] The topology of the low-voltage side of the distribution network is determined according to the phase relationship between the upstream and downstream power equipment.

[0141] Preferably, the cluster matrix is expressed as:

[0142]

[0143] Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the Power equipment for the first The weight of the power equipment;

[0144]

[0145] Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood;

[0146] The expression of the phase identification correlation coefficient is:

[0147]

[0148] Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

[0149] Preferably, the step of obtaining voltage data of each power device in real time through the optimization platform includes:

[0150] Obtaining historical voltage data and prediction credibility of the power equipment, and determining a first prediction model based on the historical voltage data and the prediction credibility;

[0151] Performing a first prediction on the voltage data of the power equipment according to the first prediction model, and exchanging a first predicted value of the line voltage according to the line connection relationship of the power equipment;

[0152] The first prediction value is processed according to the node voltage association prediction model to obtain voltage data corresponding to each power device.

[0153] Preferably, the expression of the first prediction model is:

[0154]

[0155] Where, represents the first predicted value, represents the hyperparameters of the first prediction model, represents the insensitive loss function, Indicates the The relaxation factor corresponding to the historical voltage data sample is: represents the number of samples, represents the prediction credibility of the first prediction model, represents the first-order norm;

[0156] The expression of the node voltage correlation prediction model is:

[0157]

[0158] Where, Represents voltage prediction data, 、 Respectively represent the voltage measurement value and reliability of the low-voltage side of the distribution network, 、 Respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the distribution network, They respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the user end.

[0159] Preferably, the expression of the single adjustment cost corresponding to the photovoltaic power equipment is:

[0160]

[0161] Where, Indicates the single adjustment cost corresponding to the photovoltaic power equipment, It represents the equivalent investment cost of a single adjustment of photovoltaic power equipment, which is related to the investment cost of photovoltaic power equipment and the number of adjustment lifespans. Indicates the photovoltaic unit active power compensation price, represents the photovoltaic curtailment penalty coefficient, represents the expected photovoltaic power generation;

[0162] The expression of the single adjustment cost corresponding to the energy storage power equipment is:

[0163]

[0164] Where, Indicates the single adjustment cost corresponding to the energy storage power equipment, It represents the equivalent investment cost of a single adjustment of the energy storage power equipment, which is related to the investment cost of the energy storage power equipment and the number of adjustment lifespans. represents the charge variation coefficient, Respectively represent the energy storage power equipment in Moment and The charge at the moment, represents the energy storage power reduction penalty coefficient, Indicates the active output power of energy storage.

[0165] Preferably, the expression of the cost limit boundary constraint is:

[0166]

[0167] The step of determining the voltage control of the power equipment according to the ratio of the first voltage critical coefficient to the second voltage critical coefficient includes:

[0168] If the first voltage criticality coefficient is greater than the second voltage criticality coefficient, selecting energy storage power equipment to perform system voltage control;

[0169] If the first voltage criticality coefficient is less than or equal to the second voltage criticality coefficient, photovoltaic power equipment is selected to perform system voltage control.

[0170] Example 3

[0171] This embodiment provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned method for real-time adjustment of distributed resources on the low-voltage side of a distribution network is implemented.

[0172] Example 4

[0173] The present invention also provides a computer, see Figure 2 , shown is a computer in an embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned method for real-time adjustment of distributed resources on the low-voltage side of the distribution network is implemented.

[0174] The memory 10 includes at least one type of storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as the computer's hard disk. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 10 may include both an internal storage unit of the computer and an external storage device. The memory 10 can be used not only to store application software installed in the computer and various types of data, but also to temporarily store data that has been output or is about to be output.

[0175] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs.

[0176] It should be pointed out that Figure 2 The structure shown does not constitute a limitation of the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0177] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0178] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0179] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0180] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for real-time adjustment of distributed resources on the low-voltage side of a distribution network, characterized in that: include: Obtain historical operating data of distributed power equipment on the low-voltage side of the distribution network, and perform standardization and feature dimensionality reduction on the acquired historical operating data; Relationship identification is performed on the historical operating data after dimensionality reduction to determine the station-related relationships of power equipment. Phase analysis is performed on power equipment with the same station-related relationships. Based on the phase analysis, the topology structure between the distributed power equipment on the low-voltage side of the power grid is determined; A distributed optimization platform is established based on the topological structure. The voltage data of each power device is obtained in real time through the optimization platform. The voltage offset and voltage fluctuation in the voltage data are used as inputs of the fuzzy control system on the low-voltage side of the distribution network for fuzzy processing to obtain the voltage danger coefficient on the low-voltage side of the distribution network. The nodes where the photovoltaic power equipment and the energy storage power equipment are located on the low-voltage side of the distribution network are defined as adjustment nodes, and the first voltage crisis coefficient of the adjustment node and the corresponding single adjustment costs of the photovoltaic power equipment and the energy storage power equipment are obtained; Obtain the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determine the second voltage crisis coefficient corresponding to the adjustment node when the voltage exceeds the limit based on the cost limit boundary constraint, and determine the voltage control of the power equipment according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient.

2. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 1, characterized in that: The steps of performing relationship identification on the historical operation data after dimensionality reduction processing to determine the station-related relationship of the power equipment, performing phase analysis on the power equipment with the same station-related relationship, and determining the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis include: An adjacency matrix is constructed based on the historical operating data after dimensionality reduction processing, the sum of weights between all power devices is calculated based on the adjacency matrix to obtain a degree matrix, and a cluster matrix of the low-voltage side of the distribution network is obtained based on the adjacency matrix and the degree matrix; Calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment; Equivalently model the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it as a time series to obtain voltage data curve samples; Obtaining turning points in the voltage data curve sample, marking their importance based on a hierarchical manner, time-segmenting the voltage data curve sample according to the importance marking results, and determining the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient; The topology of the low-voltage side of the distribution network is determined according to the phase relationship between the upstream and downstream power equipment.

3. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 2, characterized in that: The expression of the cluster matrix is: Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the The power equipment The weight of the power equipment; Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood; The expression of the phase identification correlation coefficient is: Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

4. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 1, characterized in that: The step of obtaining voltage data of each power device in real time through the optimization platform includes: Obtaining historical voltage data and prediction credibility of the power equipment, and determining a first prediction model based on the historical voltage data and the prediction credibility; Performing a first prediction on the voltage data of the power equipment according to the first prediction model, and exchanging a first predicted value of the line voltage according to the line connection relationship of the power equipment; The first prediction value is processed according to the node voltage association prediction model to obtain voltage data corresponding to each power device.

5. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 4, characterized in that: The expression of the first prediction model is: Where, represents the first predicted value, represents the hyperparameters of the first prediction model, represents the insensitive loss function, Indicates the The relaxation factor corresponding to the historical voltage data sample is: represents the number of samples, represents the prediction credibility of the first prediction model, represents the first-order norm; The expression of the node voltage correlation prediction model is: Where, Represents voltage prediction data, 、 Respectively represent the voltage measurement value and reliability of the low-voltage side of the distribution network, 、 Respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the distribution network, They respectively represent the voltage measurement value and reliability of the line layer connecting the power equipment and the user end.

6. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 1, characterized in that: The expression of the single adjustment cost corresponding to photovoltaic power equipment is: Where, Indicates the single adjustment cost corresponding to the photovoltaic power equipment, It represents the equivalent investment cost of a single adjustment of photovoltaic power equipment, which is related to the investment cost of photovoltaic power equipment and the number of adjustment lifespans. Indicates the photovoltaic unit active power compensation price, represents the photovoltaic curtailment penalty coefficient, represents the expected photovoltaic power generation; The expression of the single adjustment cost corresponding to the energy storage power equipment is: Where, Indicates the single adjustment cost corresponding to the energy storage power equipment, It represents the equivalent investment cost of a single adjustment of the energy storage power equipment, which is related to the investment cost of the energy storage power equipment and the number of adjustment lifespans. represents the charge variation coefficient, Respectively represent the energy storage power equipment in Moment and The charge at the moment, represents the energy storage power reduction penalty coefficient, Indicates the active output power of energy storage.

7. The method for real-time adjustment of distributed resources on the low-voltage side of a distribution network according to claim 6, characterized in that: The expression of the cost limit boundary constraint is: The step of determining the voltage control of the power equipment according to the ratio of the first voltage critical coefficient to the second voltage critical coefficient includes: If the first voltage criticality coefficient is greater than the second voltage criticality coefficient, selecting energy storage power equipment to perform system voltage control; If the first voltage criticality coefficient is less than or equal to the second voltage criticality coefficient, photovoltaic power equipment is selected to perform system voltage control.

8. A real-time adjustment system for distributed resources on the low-voltage side of a distribution network, characterized in that: include; A dimensionality reduction processing module is used to obtain historical operating data of distributed power equipment on the low-voltage side of the distribution network, and to perform standardization and feature dimensionality reduction processing on the acquired historical operating data; The identification module is used to identify relationships in the historical operating data after dimensionality reduction processing to determine the station-related relationships of power equipment, perform phase analysis on power equipment with the same station-related relationships, and determine the topological structure between the distributed power equipment on the low-voltage side of the power grid based on the phase analysis; A fuzzy processing module is used to establish a distributed optimization platform based on the topological structure, obtain voltage data of each power device in real time through the optimization platform, and use the voltage offset and voltage fluctuation in the voltage data as inputs of the fuzzy control system on the low-voltage side of the distribution network to perform fuzzy processing to obtain the voltage danger coefficient on the low-voltage side of the distribution network; A calculation module is used to define the nodes where the photovoltaic power equipment and the energy storage power equipment on the low-voltage side of the distribution network are located as adjustment nodes, and obtain the first voltage crisis coefficient of the adjustment node and the corresponding single adjustment cost of the photovoltaic power equipment and the energy storage power equipment; The scheduling module is used to obtain the charge state when the voltage on the low-voltage side of the distribution network exceeds the limit, determine the second voltage crisis coefficient corresponding to the adjustment node when the voltage exceeds the limit based on the cost limit boundary constraint, and determine the voltage control of the power equipment according to the ratio of the first voltage crisis coefficient to the second voltage crisis coefficient.

9. The real-time adjustment system for distributed resources on the low-voltage side of the distribution network according to claim 8, characterized in that: The identification module includes: A construction unit is configured to construct an adjacency matrix based on the historical operating data after dimensionality reduction processing, calculate the sum of weights between all power devices based on the adjacency matrix to obtain a degree matrix, and obtain a cluster matrix on the low-voltage side of the distribution network based on the adjacency matrix and the degree matrix; A clustering unit is used to calculate and sort the eigenvalues of the cluster matrix, select vectors corresponding to preset eigenvalues to construct a vector matrix, cluster the row elements of the vector matrix, and obtain the corresponding station-affiliated relationship of the distributed power equipment; The equivalent unit is used to perform equivalent modeling on the power grid lines corresponding to the power equipment of the same type, obtain the voltage data of each electronic device at different times in the power grid line, and express it in time series to obtain voltage data curve samples; a marking unit, configured to obtain turning points in the voltage data curve sample, mark the turning points based on their importance based on a hierarchical manner, time segment the voltage data curve sample based on the importance marking results, and determine the phase relationship between upstream and downstream power equipment based on a phase identification correlation coefficient; The determining unit is used to determine the topology of the low-voltage side of the distribution network according to the phase relationship between the upstream and downstream power equipment.

10. The real-time adjustment system for distributed resources on the low-voltage side of a power distribution network according to claim 9, characterized in that: In the identification module, The expression of the cluster matrix is: Where, represents the cluster matrix, represents the degree matrix, , indicating the The degree of power equipment, represents the adjacency matrix, Indicates the Power equipment for the first The weight of the power equipment; Where, and Respectively represent Power equipment and Power equipment, and The weight values between satisfy the Gaussian distribution. represents the second-order norm, Indicates the standard deviation of the Gaussian distribution that the weight value satisfies, Respectively Neighborhood and Neighborhood; The expression of the phase identification correlation coefficient is: Where, Indicates the The voltage data matrix corresponding to each power device, Indicates the The voltage data matrix corresponding to each power device, Indicates the The first power equipment and The covariance between the voltage matrices of the power equipment, Respectively represent The first power equipment and The standard deviation corresponding to each piece of power equipment.

Images