A voltage control method and system based on smart grid

Abstract

The present application relates to the technical field of voltage control in distribution networks, and specifically to a voltage control method and system based on smart grids, the method comprising: determining a first power fluctuation value of each power supply side power generation node at each moment; determining a second power fluctuation value of each power supply side power generation node at each moment; determining the power concentration of each cluster at each moment; determining the power change index of each cluster at each moment; based on the power change index and the power concentration, determining the droop coefficient interval of each power supply side power generation node at each moment; determining the line loss degree of each power supply side power generation node at each moment, and controlling the voltage of the smart grid. The present application obtains the optimal droop control vector by analyzing the line loss degree of the smart grid, and controls the voltage of the smart grid in combination with the droop control method, thereby improving the stability of the operation of the smart grid.

Description

Technical Field

[0001] The present application relates to the technical field of voltage control in distribution networks, and in particular to a voltage control method and system based on smart grids. Background Art

[0002] Smart grid is regarded as the next generation of power grid. It combines information and communication technology with traditional power grid architecture and plays a positive role in improving the level of power grid voltage control management and power grid loss reduction planning. Through key technologies such as grid interconnection technology and energy management system, renewable energy systems are effectively integrated into smart grids, promoting the development of energy structure towards low-carbon and clean direction.

[0003] At present, smart grids use droop control to achieve automatic control of grid-connected voltages of different power supply side power generation nodes in the grid. However, traditional droop control only independently adjusts the node voltages of each power supply side power generation node connected to the smart grid, ignoring the impact of the correlation between different power supply side power generation nodes on the overall voltage distribution of the smart grid, resulting in increased line losses between different nodes, and in severe cases, causing power flow reversal in the grid, reducing the stability of smart grid operation. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a voltage control method and system based on a smart grid. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a voltage control method based on a smart grid, the method comprising the following steps:

[0006] At each power supply side power generation node of the smart grid, the output voltage and output power at each moment, as well as the historical input power at all acquisition moments within a preset time period before each moment, are obtained;

[0007] For each power supply side power generation node at each moment, the difference between all historical input powers and the fitting value on the fitting straight line obtained by fitting all historical input powers is analyzed to determine the first power fluctuation value of each power supply side power generation node at each moment; based on the distribution of all historical input powers of each power supply side power generation node at each moment and the inclination of the fitting straight line, the second power fluctuation value of each power supply side power generation node at each moment is determined;

[0008] The first and second power fluctuation values ​​of each power supply side power generation node at each moment are combined into a tuple, and the tuples of all power supply side power generation nodes are clustered to obtain multiple clusters, and the power concentration of each cluster at each moment is determined based on the degree of membership of each power supply side power generation node in each cluster and the cluster, and the second power fluctuation value, and the power change index of each cluster at each moment is determined in combination with the first power fluctuation value; based on the power change index and the power concentration, the droop coefficient interval of each power supply side power generation node at each moment is determined;

[0009] Based on the output voltage and output power of each power supply side power generation node at each moment and the droop coefficient interval, determine the voltage correction value of each power supply side power generation node at each moment, and determine the line loss degree of each power supply side power generation node at each moment in combination with the membership degree, so as to control the voltage of the smart grid;

[0010] The expression of the power concentration of each cluster at each time is: ; In the formula, represents the power concentration of cluster p at time i; represents the membership degree of the nth power supply side power generation node in cluster p at time i; represents the second power fluctuation value of the nth power supply side power generation node in cluster p at time i; represents the number of all power supply side power generation nodes in cluster p at time i; norm( ) represents the normalization function;

[0011] The expression of the power change index of each cluster at each moment is: ; In the formula, Represents the power change index of cluster p at time i; represents the first power fluctuation value of the nth power supply side power generation node in cluster p at time i;

[0012] The droop coefficient interval of each power supply side power generation node at each moment is an interval centered on the power concentration of the cluster to which each power supply side power generation node belongs at each moment, and composed of all values ​​of the power change index of the cluster to which the power supply side power generation node belongs whose distance from the center does not exceed.

[0013] Preferably, the first power fluctuation value of each power supply side power generation node at each moment is a result of merging the squares of the differences between all historical input powers of each power supply side power generation node at each moment and the corresponding fitting values ​​on the fitting straight line.

[0014] Preferably, the second power fluctuation value of each power supply side power generation node at each moment is the product of the range of all historical input powers of each power supply side power generation node at each moment and the slope of the fitting straight line.

[0015] Preferably, the method for determining the voltage correction value of each power supply side power generation node at each moment is:

[0016] At each moment, a random number is selected from different droop coefficient intervals to form a droop control vector. All droop control vectors are used as inputs of the particle swarm optimization algorithm to obtain the initial optimal droop control vector at each moment.

[0017] In each power supply side power generation node, the output power and output voltage at each moment are used as the input of the droop control method, wherein each element value in the initial optimal droop control vector is used as the droop coefficient of the corresponding power supply side power generation node, and the voltage correction value of each power supply side power generation node at each moment is output.

[0018] Preferably, the expression of the line loss degree of each power supply side power generation node at each moment is: ; In the formula, Indicates the line loss degree of power generation node n on the power supply side at time i; represents the membership degree of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node m at time i; represents the number of all power supply side power generation nodes in the smart grid at time i; ln( ) represents the logarithmic function with a natural constant as the base.

[0019] Preferably, the controlling the voltage of the smart grid includes:

[0020] The cumulative sum of the line loss levels of all power supply side power generation nodes at each moment is recorded as the overall line loss level of the smart grid at each moment;

[0021] All droop control vectors at each time are used as the input of the particle swarm optimization algorithm, the overall line loss degree is used as the population fitness of the particle swarm optimization algorithm, and the final optimal droop control vector is output;

[0022] The output voltage and output power of all power supply side power generation nodes at each moment are used as the input of the droop control method, wherein each element in the final optimal droop control vector is set as the droop coefficient of the corresponding power supply side power generation node, and the final voltage correction value of all power supply side power generation nodes at each moment is output.

[0023] In a second aspect, an embodiment of the present application further provides a voltage control system based on a smart grid, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned voltage control methods based on a smart grid are implemented.

[0024] This application has at least the following beneficial effects:

[0025] The present application determines the first power fluctuation value of each power supply side power generation node at each moment by analyzing the difference between all historical input powers and the fitting value on the fitting straight line obtained by fitting all historical input powers, and determines the second power fluctuation value of each power supply side power generation node at each moment in combination with the distribution of all historical input powers of each power supply side power generation node at each moment and the inclination of the fitting straight line. The beneficial effect is that more accurate fluctuation information of the inverter input power can be obtained; the present application determines the power fluctuation value of each power supply side power generation node at each moment based on the degree of membership of each power supply side power generation node and the clustering cluster in each clustering cluster, as well as the second power fluctuation value and the first power fluctuation value. The power change index of each clustering cluster is determined; based on the power change index and the power concentration, the droop coefficient interval of each power supply side power generation node at each moment is determined. The beneficial effect is to utilize the regional distribution of renewable energy to affect the power generation of different power supply side power generation nodes in the same time period, and divide the power supply side power generation nodes of the smart grid into clusters with different input power fluctuations to represent the power supply side power generation nodes in different regions, and calculate the range of droop coefficients according to the degree of input power fluctuation, so as to improve the accuracy of the overall regional droop control of the smart grid, so as to prevent the overall line loss between different nodes in the same region from expanding. The present application constructs a droop control vector according to the droop coefficient intervals of different clustering clusters, calculates the degree of grid line loss, and uses a particle swarm optimization algorithm to output the optimal droop control vector to control the node voltage of the power supply side power generation nodes in different clustering clusters. The beneficial effect is to improve the accuracy of the overall droop control of the smart grid voltage, reduce the overall line loss of the smart grid, and improve the stability of the operation of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flowchart of a voltage control method based on a smart grid provided in one embodiment of the present application;

[0028] Figure 2 A schematic diagram of a droop coefficient interval acquisition process provided by an embodiment of the present application;

[0029] Figure 3 A schematic diagram of a line loss degree extraction process provided by an embodiment of the present application;

[0030] Figure 4 A flow chart of the steps for determining a final voltage correction value provided by one embodiment of the present application;

[0031] Figure 5 A diagram showing a comparison of voltage distribution at a droop control node provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the voltage control method and system based on the smart grid proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The following is a detailed description of a specific scheme of a voltage control method and system based on a smart grid provided by the present application in conjunction with the accompanying drawings.

[0035] See also Figure 1 , which shows a flowchart of a voltage control method based on a smart grid provided by an embodiment of the present application, the method comprising the following steps:

[0036] Step S1: At each power supply side power generation node of the smart grid, the output voltage and output power at each moment, as well as the historical input power of all collection moments within a preset time period before each moment are obtained.

[0037] An inverter is installed at each power supply side power generation node in the smart grid, and a power meter is used to obtain the historical input power of the inverter input terminal at each power supply side power generation node in the smart grid at all collection moments within a preset time before each moment, as well as the output voltage and output power of the inverter output terminal at each power supply side power generation node at each moment, wherein the collection frequency f is set.

[0038] It should be noted that the values ​​of the preset duration and the acquisition frequency f are manually set. In this embodiment, the preset duration is 2s, and the acquisition frequency f is 10Hz. The implementer can set them according to the specific situation, and this embodiment does not impose any special restrictions.

[0039] Step S2: For each power supply side power generation node at each moment, analyze the difference between all historical input powers and the fitting value on the fitting straight line obtained by fitting all historical input powers, and determine the first power fluctuation value of each power supply side power generation node at each moment; based on the distribution of all historical input powers of each power supply side power generation node at each moment, and the inclination of the fitting straight line, determine the second power fluctuation value of each power supply side power generation node at each moment.

[0040] Due to the intermittent and unstable characteristics of renewable energy, the active power input from the power generation node on the power supply side of the smart grid to the inverter is highly random and volatile. The reduction and sudden increase of the inverter input power will cause voltage fluctuations at the smart grid access node and its nearby nodes, which will not only cause the line loss between nodes to increase or decrease, but also reduce the power supply quality and affect the stable operation of the distribution network.

[0041] Therefore, in order to ensure the stable and safe operation of the smart grid, the power fluctuation and the second power fluctuation value of each power supply side power generation node at each moment are determined by analyzing the fluctuation and change trend of the input power of each power supply side power generation node at each moment. Specifically:

[0042] S201: Determine a first power fluctuation value of each power supply side power generation node at each moment based on the difference between all historical input powers and the fitting value.

[0043] In each power supply side power generation node, all historical input powers of each power supply side power generation node at each moment are fitted to obtain a fitting straight line of each power supply side power generation node at each moment;

[0044] Furthermore, based on the difference between all historical input powers and fitting values, the first power fluctuation value of each power supply side power generation node at each moment is determined. The first power fluctuation value of each power supply side power generation node at each collection moment is the result of the square fusion of the difference between the historical input power of each power supply side power generation node at each collection moment and the corresponding fitting value on the fitting straight line.

[0045] It should be noted that there are many methods for measuring the difference between data. In this embodiment, the difference between the historical input power of the power generation node on the power supply side and the corresponding fitting value on the fitting line is calculated to measure the difference between the historical input power and its corresponding fitting value. The implementer may also use other methods for measuring the difference, such as ratio. This embodiment does not impose any special restrictions on the selection of the method for measuring the difference.

[0046] It should be understood that fusion refers to the result of combining two or more indicators through positive fusion, that is, combining two or more indicators by adding or multiplying them together to obtain a comprehensive indicator, so as to more comprehensively and accurately evaluate a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods, which can be selected by the implementer according to the specific situation, and this embodiment does not impose any special restrictions.

[0047] Preferably, in the present embodiment, the first power fluctuation value of each power supply side power generation node at each collection moment is the cumulative sum of the squares of the differences between the historical input power of each power supply side power generation node at each collection moment and the corresponding fitting value on the fitting straight line; in actual application, as other implementation methods, the first power fluctuation value of each power supply side power generation node at each collection moment is an exponential function value of the independent variable, with a natural constant as the base and the sum of the squares of the differences between the historical input power of each power supply side power generation node at each collection moment and the corresponding fitting value on the fitting straight line.

[0048] Furthermore, according to the first power fluctuation value of each power supply side power generation node at each collection moment, it can be understood that when the difference between the historical input power and the fitting value is greater, the first power fluctuation value of the power supply side power generation node is greater, indicating that the line loss of the power supply side power generation node is greater, and the power supply quality at the corresponding power supply side power generation node is lower; conversely, when the difference between the historical input power and the fitting value is greater, the first power fluctuation value of the power supply side power generation node is smaller, indicating that the line loss of the power supply side power generation node is smaller, and the power supply quality at the corresponding power supply side power generation node is higher.

[0049] S202: Determine a second power fluctuation value of each power supply side power generation node at each moment based on the distribution of all historical input powers of each power supply side power generation node at each moment and the inclination of the fitting straight line.

[0050] The second power fluctuation value of each power supply side power generation node at each moment is the product of the range of all historical input powers of each power supply side power generation node at each moment and the slope of the fitting straight line.

[0051] Furthermore, according to the second power fluctuation value of each power supply side power generation node at each moment, it can be understood that if the range of the historical input power of the power supply side power generation node is larger, and the slope of the fitting straight line is greater than 0, then the second power fluctuation value of the power supply side power generation node is larger, indicating that the power provided by the power supply side power generation is stronger at this time; conversely, if the range of the historical input power of the power supply side power generation node is larger, and the slope of the fitting straight line is less than or equal to 0, then the second power fluctuation value of the power supply side power generation is smaller, conversely, the smaller the range of the historical input power of the power supply side power generation node and the smaller the absolute value of the slope of the fitting straight line, the larger the second power fluctuation value of the power supply side power generation node, indicating that the meteorological factors have a greater impact on the power supply side power generation, and the power provided by the power supply side power generation is weaker at this time.

[0052] Step S3: The first and second power fluctuation values ​​of each power supply side power generation node are combined into a tuple, and the tuples of all power supply side power generation nodes at each moment are clustered to obtain multiple clusters. Based on the degree of membership of each power supply side power generation node in each cluster and the cluster cluster, and the second power fluctuation value, the power concentration of each cluster cluster at each moment is determined, and combined with the first power fluctuation value, the power change index of each cluster cluster at each moment is determined; based on the power change index and the power concentration, the droop coefficient interval of each power supply side power generation node at each moment is determined.

[0053] The distribution of various renewable energy sources such as wind power and hydropower is highly regional, that is, the power generation changes of power supply side nodes in the same region are highly similar. Therefore, in the same time period, the closer the power supply side power generation nodes are, the stronger the correlation is, and the greater the similarity of the input power fluctuation information of their inverters is.

[0054] Therefore, the second power fluctuation value and the first power fluctuation value of each power supply side power generation node at each moment are combined into a tuple of each power supply side power generation node at each moment; all power supply side power generation nodes at each moment are clustered to obtain multiple cluster clusters at each moment, and the degree of membership of the cluster center to which each power supply side power generation node belongs at each moment is obtained.

[0055] It should be understood that there are many commonly used clustering methods. In this embodiment, the fuzzy C-means clustering algorithm (FCM) is used to cluster the binary groups of all power supply side power generation nodes at each moment, and multiple cluster clusters and the membership degree of the cluster center to which each power supply side power generation node belongs at each moment are obtained; in actual application, the implementer may also adopt other clustering algorithms such as the fuzzy pattern recognition algorithm (GFCM), and this embodiment does not impose any special restrictions on the selection of clustering algorithms.

[0056] It should be noted that the fuzzy C-means clustering algorithm and the method for determining the membership degree of the power supply side power generation node are both well-known technologies, and the specific process will not be repeated here.

[0057] Furthermore, since the selection of the droop coefficient needs to be limited within an appropriate range during the droop control process to ensure that the output voltage of the power supply side power generation node is adjusted in time, in order to avoid the node voltage exceeding the limit, the power concentration of each clustering cluster at each moment is determined according to the degree of membership of each power supply side power generation node in each clustering cluster and the second power fluctuation value, and the power change index of each clustering cluster at each moment is determined in combination with the first power fluctuation value; based on the power change index and the power concentration, the droop coefficient interval of each power supply side power generation node at each moment is determined, specifically:

[0058] (1) Based on the membership degree of each power supply side power generation node in each cluster and the cluster, and the second power fluctuation value, determine the power concentration of each cluster at each time.

[0059] The power concentration of cluster p at time i The expression is: ; In the formula, represents the membership degree of the nth power supply side power generation node in cluster p at time i; represents the second power fluctuation value of the nth power supply side power generation node in cluster p at time i; represents the number of all power supply side power generation nodes in cluster p at time i; norm( ) represents the normalization function.

[0060] According to the power concentration of each cluster at each moment, it can be understood that the larger the second power fluctuation value of the power supply side power generation node in the cluster, the greater the probability that its node voltage exceeds the limit, and setting a larger droop coefficient increases the node voltage drop amplitude, so the calculated droop coefficient interval has a larger interval center value. In addition, the greater the membership of the power supply side power generation node to its cluster center, the closer it is to the overall change trend of the inverter input power in the cluster. Considering the overall droop control of the power supply side power generation node voltage in the same area, the power supply side power generation node with a larger membership has a greater weight in calculating the interval center value of the cluster cluster droop coefficient interval.

[0061] (2) Based on the power concentration of each cluster at each moment and in combination with the first power fluctuation value, determine the power change index of each cluster at each moment.

[0062] The power change index of cluster p at time i The expression is: ; In the formula, It represents the first power fluctuation value of the nth power supply side power generation node in cluster p at time i.

[0063] According to the power change index of each cluster at each moment, it can be understood that the more violent the instantaneous fluctuation of the historical input power number, the larger the first power fluctuation value, which means that a larger droop coefficient variation range needs to be set to ensure accurate control of each power supply side power generation node in the cluster, and the greater the membership of the power supply side power generation node and the greater the power concentration, the larger the power change index of the cluster, which means that a larger interval radius needs to be set to ensure accurate control of the voltage of each power supply side power generation node in the cluster; conversely, if the first power fluctuation value is smaller and the power concentration is smaller, a smaller weight should be given to the power supply side power generation node with a smaller membership, and the resulting power change index of the cluster is smaller, which means that the range of the droop coefficient interval can be appropriately narrowed.

[0064] (3) Based on the power variation index and the power concentration, determine the droop coefficient interval of each power supply side power generation node at each moment.

[0065] The droop coefficient interval of each power supply side power generation node at each moment is an interval centered on the power concentration of the cluster to which each power supply side power generation node belongs at each moment, and consisting of all values ​​of the power change index of the cluster to which the power supply side power generation node belongs whose distance from the center does not exceed.

[0066] Preferably, the schematic diagram of the droop coefficient interval acquisition process provided in this embodiment is as follows: Figure 2 shown.

[0067] Step S4: Based on the output voltage and output power of each power supply side power generation node at each moment and the droop coefficient interval, determine the voltage correction value of each power supply side power generation node at each moment, and combine the membership degree to determine the line loss degree of each power supply side power generation node at each moment to control the voltage of the smart grid.

[0068] There are differences in node voltage distribution between power supply side power generation nodes in different regions in the smart grid, which leads to line losses when the power grid transmits power. At the same time, the output voltage changes of power supply side power generation nodes in different regions when they are incorporated into the smart grid are relatively random. In order to reduce the line losses of power supply side power generation nodes in different regions, it is necessary to adjust the droop control of all power supply side power generation nodes in the smart grid as a whole. Specifically:

[0069] (1) At each time point, a random number is selected from different droop coefficient intervals to form a droop control vector. All droop control vectors are used as the input of the particle swarm optimization algorithm to obtain the initial optimal droop control vector at each time point.

[0070] Among them, the particle swarm optimization algorithm is a well-known technology, and the specific process of finding the optimal strategy will not be described in detail.

[0071] (2) Furthermore, in each power supply side power generation node, the output power and output voltage at each moment are used as inputs of the droop control method, wherein each element in the initial optimal droop control vector is used as the droop coefficient of the corresponding power supply side power generation node, and the voltage correction value of each power supply side power generation node at each moment is output.

[0072] Among them, the droop control method is a well-known technology, and its specific control principle is not repeated here.

[0073] (3) Further, based on the voltage correction value of each power supply side power generation node at each moment and in combination with the membership degree, the line loss degree of each power supply side power generation node at each moment is determined, specifically:

[0074] Line loss degree of power supply side power generation node n at time i The expression is: ; In the formula, represents the membership degree of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node m at time i; represents the number of all power supply side power generation nodes in the smart grid at time i; ln( ) represents the logarithmic function with the natural constant e as the base.

[0075] Preferably, the schematic diagram of the line loss degree extraction process provided in this embodiment is as follows: Figure 3 shown.

[0076] (4) Furthermore, the cumulative sum of the line loss levels of all power supply side power generation nodes at each moment is recorded as the overall line loss level of the smart grid at each moment.

[0077] (5) Furthermore, all droop control vectors at each time are used as the input of the particle swarm optimization algorithm, and the overall line loss degree is used as the population fitness of the particle swarm optimization algorithm to output the final optimal droop control vector.

[0078] (6) Furthermore, the output voltage and output power of all power supply side power generation nodes at each moment are used as inputs of the droop control method, wherein each element in the final optimal droop control vector is set as the droop coefficient of the corresponding power supply side power generation node, and the final voltage correction value of all power supply side power generation nodes at each moment is output.

[0079] Preferably, the final voltage correction value determination step flow chart provided in this embodiment is as follows: Figure 4 shown.

[0080] Preferably, the droop control node voltage distribution comparison effect diagram provided in this embodiment is as follows Figure 5 As shown, Figure 5 The horizontal axis represents the node number, the vertical axis represents the node voltage, and the unit is pu, which means dimensionless. The dotted line represents the node voltage distribution of traditional droop control, the solid line represents the node voltage distribution of droop control of this scheme, the dots represent the node voltage data points of traditional droop control, and the triangles represent the node voltage data points of droop control of this scheme.

[0081] So far, this embodiment calculates the overall line loss degree of the smart grid by analyzing the correlation characteristics between different power supply side power generation nodes and the difference in the degree of membership between the power supply side power generation nodes and different cluster center nodes, and uses this as the fitness function of the droop control vector to improve the accuracy of the overall droop control of the smart grid voltage, reduce the overall line loss degree of the smart grid, and improve the stability of the smart grid operation.

[0082] Based on the same inventive concept as the above method, an embodiment of the present application also provides a voltage control system based on a smart grid, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned voltage control methods based on a smart grid.

[0083] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. 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.

[0084] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0085] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A voltage control method based on smart grid, characterized in that: The method comprises the following steps: At each power supply side power generation node of the smart grid, the output voltage and output power at each moment, as well as the historical input power at all acquisition moments within a preset time period before each moment, are obtained; For each power supply side power generation node at each moment, the difference between all historical input powers and the fitting value on the fitting straight line obtained by fitting all historical input powers is analyzed to determine the first power fluctuation value of each power supply side power generation node at each moment; based on the distribution of all historical input powers of each power supply side power generation node at each moment and the inclination of the fitting straight line, the second power fluctuation value of each power supply side power generation node at each moment is determined; The first and second power fluctuation values ​​of each power supply side power generation node at each moment are combined into a tuple, and the tuples of all power supply side power generation nodes are clustered to obtain multiple clusters, and the power concentration of each cluster at each moment is determined based on the degree of membership of each power supply side power generation node in each cluster and the cluster, and the second power fluctuation value, and the power change index of each cluster at each moment is determined in combination with the first power fluctuation value; based on the power change index and the power concentration, the droop coefficient interval of each power supply side power generation node at each moment is determined; Based on the output voltage and output power of each power supply side power generation node at each moment and the droop coefficient interval, determine the voltage correction value of each power supply side power generation node at each moment, and determine the line loss degree of each power supply side power generation node at each moment in combination with the membership degree, so as to control the voltage of the smart grid; The expression of the power concentration of each cluster at each time is: ; In the formula, represents the power concentration of cluster p at time i; represents the membership degree of the nth power supply side power generation node in cluster p at time i; represents the second power fluctuation value of the nth power supply side power generation node in cluster p at time i; represents the number of all power supply side power generation nodes in cluster p at time i; norm( ) represents the normalization function; The expression of the power change index of each cluster at each moment is: ; In the formula, Represents the power change index of cluster p at time i; represents the first power fluctuation value of the nth power supply side power generation node in cluster p at time i; The droop coefficient interval of each power supply side power generation node at each moment is an interval consisting of all values ​​of the power change index of the cluster to which each power supply side power generation node belongs, with the power concentration of the cluster to which each power supply side power generation node belongs at each moment as the center and the distance from the center not exceeding the power concentration of the cluster to which the power supply side power generation node belongs; The expression of the line loss degree of each power supply side power generation node at each moment is: ; In the formula, Indicates the line loss degree of power generation node n on the power supply side at time i; represents the membership degree of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node n at time i; represents the voltage correction value of the power supply side power generation node m at time i; represents the number of all power supply side power generation nodes in the smart grid at time i; ln( ) represents a logarithmic function with a natural constant as the base; The controlling of the voltage of the smart grid includes: The cumulative sum of the line loss levels of all power supply side power generation nodes at each moment is recorded as the overall line loss level of the smart grid at each moment; All droop control vectors at each time are used as the input of the particle swarm optimization algorithm, the overall line loss degree is used as the population fitness of the particle swarm optimization algorithm, and the final optimal droop control vector is output; The output voltage and output power of all power supply side power generation nodes at each moment are used as the input of the droop control method, wherein each element in the final optimal droop control vector is set as the droop coefficient of the corresponding power supply side power generation node, and the final voltage correction value of all power supply side power generation nodes at each moment is output.

2. A voltage control method based on smart grid as claimed in claim 1, characterized in that: The first power fluctuation value of each power supply side power generation node at each moment is a result of integrating the square of the difference between all historical input powers of each power supply side power generation node at each moment and the corresponding fitting value on the fitting straight line.

3. A voltage control method based on smart grid as claimed in claim 1, characterized in that: The second power fluctuation value of each power supply side power generation node at each moment is the product of the range of all historical input powers of each power supply side power generation node at each moment and the slope of the fitting straight line.

4. A voltage control method based on smart grid as claimed in claim 1, characterized in that: The method for determining the voltage correction value of each power supply side power generation node at each moment is as follows: At each moment, a random number is selected from different droop coefficient intervals to form a droop control vector. All droop control vectors are used as inputs of the particle swarm optimization algorithm to obtain the initial optimal droop control vector at each moment. In each power supply side power generation node, the output power and output voltage at each moment are used as the input of the droop control method, wherein each element value in the initial optimal droop control vector is used as the droop coefficient of the corresponding power supply side power generation node, and the voltage correction value of each power supply side power generation node at each moment is output.

5. A voltage control system based on a smart grid, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the voltage control method based on the smart grid as described in any one of claims 1 to 4 are implemented.

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