A coordinated control method, device and system for an isolated DC microgrid

Abstract

The present application relates to the technical field of power supply systems, and specifically to a coordinated control method, device and system for an isolated DC microgrid, the method comprising: determining the power generation variation coefficient of each power supply in each interval based on the complexity and distribution of the power generation fluctuation value of each power supply in all time periods; determining the power generation oscillation coefficient of each power supply in each interval based on the difference in power generation fluctuation values ​​of each power supply in all time periods with other power supplies, and combining the power generation variation coefficient; determining the moment of inertia parameter value of each interval based on the intensity of all maxima on various fitting curves at the time intervals between adjacent inflection points, and regulating the isolated DC microgrid. The present application optimizes the moment of inertia parameters of the VSG in real time based on the balance coefficient of power supply and load, and improves the control efficiency of the isolated DC microgrid.

Description

Technical Field

[0001] The present application relates to the technical field of power supply systems, and in particular to a coordinated control method, device and system for an island DC microgrid. Background Art

[0002] An island DC microgrid is a small power system that operates independently of the main power grid and is self-sufficient without external power supply. This type of microgrid usually uses local renewable energy such as wind power, solar power, tidal power, etc., as well as energy storage systems to maintain power supply. Through reasonable coordinated control, the resources within the microgrid can be used more effectively to maintain the stable operation of the system.

[0003] Since distributed power sources, such as wind power, solar power, and tidal power generation, are intermittent and random, the output power is unstable. Virtual synchronous generator (VSG) technology can realize power exchange between DC power supply and system according to the characteristics of synchronous engine, and is often used in the coordinated control of DC microgrids. The moment of inertia is the core parameter in VSG control. The traditional method uses preset VSG parameters to control the microgrid. However, affected by the changes in power generation and system load, the VSG with constant moment of inertia cannot take into account the dynamic control performance of power and frequency in different modes of the microgrid, thereby reducing the efficiency of regulating the isolated DC microgrid. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides a coordinated control method for an island DC microgrid, the method comprising the following steps:

[0005] In the isolated DC microgrid, the power generation data of each power source and the load power data of each load point at all acquisition moments in each preset interval are obtained, and the sum of the load power data of all load points at each moment is recorded as the load energy consumption data at each moment;

[0006] Each interval is divided into multiple time periods. For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined.

[0007] Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, the power generation variation coefficient of each power source in each interval is determined; based on the difference in the power generation fluctuation values ​​of each power source in all time periods with other power sources, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined;

[0008] All load energy consumption data in each interval and all power generation data of each power supply are fitted respectively to obtain various fitting curves. Based on the intensity of all maximum values ​​on various fitting curves in the time intervals between adjacent inflection points and the power generation power oscillation coefficient, the power supply and load balance coefficient in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value to regulate the island DC microgrid.

[0009] Preferably, the method for determining the power generation fluctuation value of each power source in each time period within each interval is:

[0010] For each power source in each interval, analyze the cumulative sum of all adjacent data differences in each product function component in each time period, and calculate the mean of the cumulative sum of all product function components, which is recorded as the first mean value in each time period for each power source in each interval;

[0011] The expression of the power fluctuation value of the jth power supply in interval k in time period i is: ; In the formula, represents the first mean value of the jth power supply in interval k in time period i; represents the autocorrelation coefficient of the residual component of the jth power supply in interval k in time period i; Indicates a preset constant greater than 0.

[0012] Preferably, the coefficient of variation of the power generation of each power source in each interval is a result of merging the range of the power generation fluctuation value of each power source in all time periods in each interval with the fractal dimension.

[0013] Preferably, the method for determining the power supply difference value of each power supply in each interval is:

[0014] In each interval, the overall difference in power generation fluctuation values ​​between each power source and the other power sources in all time periods is analyzed, and the power supply difference value of each power source in each interval is the cumulative sum of the overall differences between each power source and all other power sources.

[0015] Preferably, the power generation oscillation coefficient of each power source in each interval is a result of integrating the power generation variation coefficient of each power source in each interval with the power supply difference value.

[0016] Preferably, the method for determining the balancing coefficient of power supply and load in each interval is:

[0017] The various fitting curves include load power curves and power generation curves of each power source. In each interval, the time interval between each maximum value on the load power curve and its adjacent inflection points is calculated as the integral of the integral interval, and the cumulative sum of all integrals is recorded as the operating load intensity of the microgrid in each interval.

[0018] For the power generation curve of each power source in each interval, the power generation intensity of each power source in each interval is obtained according to the method for obtaining the operating load intensity;

[0019] The expression of the balance coefficient of power supply and load in interval k is: ; In the formula, represents the power oscillation coefficient of the jth power source in interval k; represents the operating load intensity of the microgrid in interval k; represents the power generation intensity of the jth power source in interval k; N represents the number of all power sources; Indicates a preset constant greater than 0.

[0020] Preferably, the expression of the moment of inertia parameter value of each interval is: ; In the formula, represents the moment of inertia parameter value in interval k; Indicates the balance coefficient of power supply and load in interval k; is the activation function; , These are preset values.

[0021] Preferably, regulating the isolated island DC microgrid includes:

[0022] The VSG technology is adopted, in which the moment of inertia parameter of the previous interval adjacent to the current interval is used as the moment of inertia value in the VSG in the current interval to regulate the power of the island DC microgrid in the current interval.

[0023] In a second aspect, an embodiment of the present application further provides a coordinated control device for an island DC microgrid, 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 a coordinated control method for an island DC microgrid as described above are implemented.

[0024] In a third aspect, an embodiment of the present application provides a coordinated control system for an island DC microgrid, the system comprising:

[0025] The microgrid data acquisition module is used to obtain the power generation data of each power source and the load power data of each load point at all acquisition moments in each preset interval in the isolated DC microgrid, and record the sum of the load power data of all load points at each moment as the load energy consumption data at each moment;

[0026] The microgrid data analysis module is used to divide each interval into multiple time periods. For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined.

[0027] Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, the power generation variation coefficient of each power source in each interval is determined; based on the difference in the power generation fluctuation values ​​of each power source in all time periods with other power sources, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined;

[0028] The microgrid control module is used to fit all load energy consumption data in each interval and all power generation data of each power supply to obtain various fitting curves. Based on the intensity of all maximum values ​​on various fitting curves at the time intervals between adjacent inflection points and the power generation oscillation coefficient, the balance coefficient of power supply and load in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value to control the isolated island DC microgrid.

[0029] It can be seen from the above embodiments that the coordinated control method of an isolated DC microgrid provided in the embodiments of the present application has at least the following beneficial effects:

[0030] The present application analyzes all power generation data in each time period and uses a local mean decomposition algorithm to obtain multiple product function components and a residual component. According to the differences in all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval is determined. The beneficial effect is that the fluctuation characteristics of new energy power generation can be analyzed more accurately, providing a basis for the setting of the moment of inertia of the VSG; the present application determines the power generation variation coefficient of each power source in each interval based on the complexity and distribution of the power generation fluctuation value of each power source in all time periods; based on the power generation fluctuation value of each power source in all time periods with other power sources The difference in the power generation fluctuation value is used to determine the power supply difference value of each power supply in each interval, and combined with the power generation change coefficient, the power generation oscillation coefficient of each power supply in each interval is determined, which has the beneficial effect of facilitating the adjustment of the control parameters in the VSG, suppressing the power fluctuation of the power supply, and improving the stability of the isolated DC microgrid; based on the intensity of the interval between the time intervals of all the maxima on various fitting curves between their adjacent inflection points, the balance coefficient of power supply and load in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value, and the isolated DC microgrid is regulated, which has the beneficial effect of adjusting the power generation resources to ensure the stable power generation state of the microgrid. This application optimizes the moment of inertia parameters of the VSG in real time based on the balance coefficient of power supply and load, thereby improving the control efficiency of the isolated DC microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] 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.

[0032] Figure 1 A flowchart of a coordinated control method for an island DC microgrid provided in one embodiment of the present application;

[0033] Figure 2 A schematic diagram of a process for extracting a power generation oscillation coefficient provided in one embodiment of the present application;

[0034] Figure 3 A schematic diagram of a process for extracting a power supply and load balancing coefficient provided in one embodiment of the present application;

[0035] Figure 4 A block diagram of a coordinated control system for an island DC microgrid provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the coordinated control method, device and system of an isolated DC microgrid proposed in the present application, its specific implementation method, structure, features and effects are described in detail as follows 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.

[0037] 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.

[0038] The specific scheme of a coordinated control method, device and system for an island DC microgrid provided by the present application is described in detail below with reference to the accompanying drawings.

[0039] See also Figure 1 , which shows a flowchart of a coordinated control method for an island DC microgrid provided by an embodiment of the present application, the method comprising the following steps:

[0040] Step S1: in the isolated DC microgrid, the power generation data of each power source and the load power of each load point at all acquisition times within each preset interval are obtained.

[0041] Compared with traditional power systems, distributed power sources in isolated microgrids are generally composed of inverters or a few small-capacity generators, that is, they are powered by various renewable energy sources. Various renewable energy sources in isolated microgrids are connected in parallel, and each power source is connected to a separate VSG.

[0042] A power meter is installed near the output end of each power supply to obtain the power generation data of each power supply at all collection times within each preset interval t; a power meter is installed at each load point in the microgrid to collect the load power data of each load point at all collection times within the same preset interval t, and the sum of the load power data of all load points at each time is recorded as the load energy consumption data at each time. The time interval for collecting the power generation data and the load power data is set to T.

[0043] It should be noted that the values ​​of the preset interval t and the time interval T are both manually set. In this embodiment, the value of the preset interval t is 5 minutes, and the value of the time interval T is 0.01 seconds. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0044] Step S2: For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the differences of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined.

[0045] Renewable energy is mainly used in isolated microgrids. Renewable energy generation usually uses power electronic inverters to convert direct current into alternating current and integrate it into the grid. Due to the volatility and uncertainty of renewable energy generation, VSG technology can provide stability and frequency modulation capabilities similar to traditional generators, allowing renewable energy generation to be integrated into the grid more smoothly and stably. When multiple machines are connected in parallel in a microgrid, power oscillation is prone to occur. Reasonable setting of the VSG moment of inertia can suppress power oscillation.

[0046] Specifically, in an island microgrid, a larger moment of inertia can provide stronger inertial support, which helps to smooth the power fluctuation caused by load changes, but may lead to slower system response and increased overshoot. On the contrary, a smaller moment of inertia will reduce the inertial support of the system, which may make the system more sensitive to disturbances, but can speed up the dynamic response of the system.

[0047] Therefore, the power data of the power supply in the isolated microgrid is analyzed. For each power supply in each interval, the local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power supply in each interval in each time period is determined, so as to more accurately analyze the fluctuation characteristics of the new energy power generation and provide a basis for the setting of the moment of inertia of the VSG. The specific determination process of the power generation fluctuation value of each power supply in each interval in each time period is as follows:

[0048] (1) Since solar energy, wind energy, tidal energy and other new energy sources are easily affected by natural conditions, intermittent and unstable, the corresponding power generation data may change rapidly and randomly. In order to more accurately analyze the change characteristics of power generation data in a short period of time, the preset interval is evenly divided into a preset number of time periods. The value of the preset number is artificially set. In this embodiment, the value of the preset number is 20. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0049] (2) Furthermore, for each power source in each interval, all power generation data in each time period are used as input of the local mean decomposition (LMD) algorithm, which outputs multiple product function (PF) components and a residual component.

[0050] Among them, the local mean decomposition algorithm is a well-known technology, and the specific process of performing modal decomposition on the signal is not described in detail.

[0051] (3) Furthermore, since each product function component contains the information of the power generation data at a specific frequency scale in the corresponding time period, the information of the power generation data at a specific frequency scale is analyzed based on the differences of all adjacent data in each product function component in each time period, specifically:

[0052] For each power source in each interval, analyze the cumulative sum of all adjacent data differences in each product function component in each time period, and calculate the mean of the cumulative sum of all product function components in each time period, which is recorded as the first mean value of each power source in each interval in each time period;

[0053] It should be noted that there are many methods for measuring the differences between data. In this embodiment, the absolute value of the difference between all adjacent data in each product function component is calculated to measure the difference between adjacent data within each product function component. 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.

[0054] (4) Further, based on the first mean value and the autocorrelation coefficient, the power generation fluctuation value of each power source in each time period in each interval is determined, specifically:

[0055] The expression of the power fluctuation value of the jth power supply in interval k in time period i is: ; In the formula, represents the first mean value of the jth power supply in interval k in time period i; represents the autocorrelation coefficient of the residual component of the jth power supply in interval k in time period i; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set manually. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation results, implementers can set it according to specific circumstances. The setting of the numerical value is not particularly limited in this embodiment.

[0056] The calculation process of the autocorrelation coefficient is a well-known technique, and the specific calculation process will not be described in detail.

[0057] Furthermore, according to the power generation fluctuation value of each power source in each time period in each interval, it can be understood that each product function component contains the information of the power generation data in the corresponding time period on a specific frequency scale. The greater the difference between all adjacent data in the product function component, the larger the first mean, indicating that the local difference of the power generation data at different frequencies is larger, and the more obvious the rapid fluctuation in the time period is; the residual component contains the low-frequency information of the power generation data in the corresponding time period. If the residual component shows nonlinear or non-stationary characteristics, it indicates that the power generation data has a complex dynamic trend. The smaller the autocorrelation coefficient of the residual component, the more unstable the power generation data is, and there are trend changes to varying degrees. Therefore, if the first mean is larger, the residual component The smaller the autocorrelation coefficient is, the greater the power generation fluctuation value of the power supply is, which means that the power generation data of the power supply is more unstable, there is a complex dynamic change trend, and the corresponding power generation capacity is more affected by natural conditions; conversely, the smaller the difference between all adjacent data in the product function component, the smaller the first mean, indicating that the local difference of the power generation data at different frequencies is smaller, and the rapid fluctuation within the time period is less obvious; the larger the autocorrelation coefficient of the residual component is, the more stable the power generation data is, and there is the same degree of trend change. Therefore, if the first mean is smaller and the autocorrelation coefficient of the residual component is larger, the power generation fluctuation value of the power supply is smaller, which means that the power generation data of the power supply is more stable, and the corresponding power generation capacity is less affected by natural conditions.

[0058] Step S3: Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, determine the power generation variation coefficient of each power source in each interval; based on the difference in power generation fluctuation values ​​between each power source and other power sources in all time periods, determine the power supply difference value of each power source in each interval, and combine the power generation variation coefficient to determine the power generation oscillation coefficient of each power source in each interval.

[0059] The power generation status of different power sources in an isolated microgrid may be different, which may cause the output power of the power source to oscillate to varying degrees. The more unstable the power generation capacity of each power source is and the more inconsistent the power generation status between the power sources is, the more obvious the oscillation of its output power will be.

[0060] Therefore, in order to quantify the stability of power generation and understand the interaction and coordination between different types of power sources, in each interval, based on the complexity and distribution of the power generation fluctuation value of each power source in all time periods, the power generation variation coefficient of each power source in each interval is determined; based on the difference in power generation fluctuation values ​​between each power source and other power sources in all time periods, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined, so as to facilitate the adjustment of the control parameters in the VSG, suppress the power fluctuation of certain power sources, and improve the stability of the island DC microgrid, specifically:

[0061] (1) Determine the fractal dimension of the power generation fluctuation value of each power source in all time periods;

[0062] It should be noted that there are many methods for obtaining fractal dimension. In this embodiment, the power generation fluctuation value of each power source in all time periods is used as the input of the power generation variation coefficient of each power source in the Higuchi algorithm, and the output is the fractal dimension of each power source. In actual application, the implementer may also adopt other methods such as Hurst exponent method and box counting method. Regarding the choice of method for obtaining fractal dimension, this embodiment does not make any special restrictions.

[0063] The process of obtaining the fractal dimension and the Higuch algorithm are both well-known technologies, and the specific process of obtaining the fractal dimension using the Higuch algorithm will not be described in detail.

[0064] (2) Further, the results of the fusion of the range of power generation fluctuation values ​​of each power source in all time periods within each interval and the fractal dimension are analyzed and recorded as the power generation variation coefficient of each power source in each interval.

[0065] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating 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. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0066] Preferably, in the present embodiment, the power generation variation coefficient of each power supply in each interval is the product of the range of the power generation fluctuation values ​​of all time periods of each power supply in each interval and the fractal dimension; in actual application, as other implementation methods, the power generation variation coefficient of each power supply in each interval is an exponential function value with a natural constant as the base and the sum of the range of the power generation fluctuation values ​​of all time periods of each power supply in each interval and the fractal dimension as the independent variable.

[0067] (3) Furthermore, within each interval, the overall difference in power generation fluctuation values ​​between each power source and the other power sources in all time periods is analyzed, and the cumulative sum of the overall differences between each power source and all the other power sources is calculated and recorded as the power supply difference value of each power source in each interval.

[0068] It should be noted that there are many methods to measure the overall difference in power generation fluctuation values ​​between each power source and the other power sources in all time periods. In this embodiment, the DTW distance between the sequence composed of the power generation fluctuation values ​​of all time periods in each power source and the sequence composed of the linear fluctuation differences of all time periods between the other power sources is calculated to measure the difference in power generation fluctuation values ​​between different power sources. The implementer may also use other methods such as Euclidean distance, Manhattan distance, etc. to measure the overall difference in power generation fluctuation values ​​of all time periods between each power source and the other power sources according to the specific circumstances. This embodiment does not impose any special restrictions.

[0069] The calculation process of the DYW distance is a well-known technology, and the specific calculation process will not be repeated here.

[0070] (4) Further, based on the power generation variation coefficient and the power supply difference value, the power generation oscillation coefficient of each power source in each interval is determined, specifically:

[0071] The power generation oscillation coefficient of each power source in each interval is the result of integrating the power generation variation coefficient of each power source in each interval with the power supply difference value.

[0072] Preferably, in this embodiment, the expression of the power generation oscillation coefficient of each power supply in each interval is: The expression of the power generation oscillation coefficient of each power supply is: ; In the formula, represents the power oscillation coefficient of the jth power source in interval k; represents the power generation coefficient of the jth power source in interval k; Indicates the power supply difference value of the jth power supply in interval k. In actual application, as another implementation method, the power generation oscillation coefficient of each power supply in each interval is an exponential function value with a natural constant as the base and the sum of the power generation change coefficient of each power supply in each interval and the power supply difference value as the independent variable.

[0073] Further, according to the power generation oscillation coefficient of each power supply in each interval, it can be understood that the greater the complexity of the nonlinear fluctuation value of the power supply in all time periods, the more complex the distribution, the greater the random variation coefficient of the power supply, indicating that the random variation characteristics of the power generation fluctuation value sequence are more obvious, and the power generation capacity of the power supply is more unstable; the greater the difference in the nonlinear fluctuation values ​​between different power supplies, the greater the power supply difference value of the power supply, indicating that the difference in the power generation state of different power supplies is greater; therefore, if the power generation power variation coefficient of the power supply is larger, the greater the power supply difference value, the greater the power generation power oscillation coefficient of the power supply, indicating that the power oscillation phenomenon in the power generation process of the power supply is more significant; conversely, the smaller the complexity of the nonlinear fluctuation value of the power supply in all time periods, the smaller the random variation coefficient of the power supply, indicating that the random variation characteristics of the power generation fluctuation value sequence are not more obvious, and the power generation capacity of the power supply is more stable; the smaller the difference in the nonlinear fluctuation values ​​between different power supplies, the smaller the power supply difference value of the power supply, indicating that the difference in the power generation state of different power supplies is smaller; therefore, if the power generation power variation coefficient of the power supply is smaller, the smaller the power supply difference value, the smaller the power generation power oscillation coefficient of the power supply, indicating that the power in the power generation process of the power supply is more stable.

[0074] Preferably, the schematic diagram of the power generation oscillation coefficient extraction process provided in this embodiment is as follows: Figure 2 shown.

[0075] Step S4: Based on the strength of all the maximum values ​​on various fitting curves in the time intervals between adjacent inflection points and the power generation power oscillation coefficient, the power supply and load balancing coefficient in each interval is determined.

[0076] When the VSG operates in the isolated microgrid mode, the microgrid system loses the support of the large power grid, the system inertia will be reduced, and when there is a large load fluctuation in the microgrid, the system stability will also be greatly affected. Furthermore, combined with the relationship between the load power change characteristics and the power generation power change characteristics in the power grid system, the stability of the microgrid operation within the current acquisition time is judged. Specifically, when the microgrid is in a stable operating state, the power generation power at this time has no obvious oscillation, and a relative balance is maintained between the power generation power and the load power.

[0077] Therefore, in order to evaluate the stability of the isolated DC microgrid, the balance coefficient of power supply and load in each interval is determined based on the intensity of all the maximum values ​​on various fitting curves in the time interval between their adjacent inflection points, so as to facilitate the adjustment of power generation resources and ensure the stable power generation state of the microgrid. Specifically:

[0078] (1) Since the load power is usually in a fluctuating state, when there is a large load fluctuation, the instantaneous power consumption in the short term is greater. Therefore, in order to better analyze the load and power fluctuations, all load energy consumption data in each interval are fitted to obtain various fitting curves, among which the various fitting curves include load power fitting curves and power generation fitting curves of each power supply.

[0079] (2) Further, in each interval, the time interval between two adjacent inflection points of each maximum value on the load power fitting curve is calculated as the integral of the integral interval, and the cumulative sum of all the integrals is used as the operating load intensity of the microgrid in each interval.

[0080] Similarly, for the power generation power fitting curve of each power source in each interval, the power generation power intensity of each power source in each interval is obtained according to the method for obtaining the operating load intensity of the microgrid.

[0081] (3) Further, based on the power generation oscillation coefficient, the operating load intensity and the power generation intensity, a power supply and load balancing coefficient in each interval is determined, specifically:

[0082] The expression of the balance coefficient of power supply and load in interval k is: ; In the formula, represents the power oscillation coefficient of the jth power source in interval k; represents the operating load intensity of the microgrid in interval k; represents the power generation intensity of the jth power source in interval k; N represents the number of all power sources; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set manually. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation results, implementers can set it according to specific circumstances. The setting of the numerical value is not particularly limited in this embodiment.

[0083] Furthermore, according to the balancing coefficient of power supply and load in each interval, it can be understood that the smaller the operating load intensity of the microgrid, the smaller the short-term instantaneous power consumption of the microgrid; when the oscillation degree of the power generation power of the power source is lower, the smaller the power generation power oscillation coefficient of the power source is, indicating that the impact of the power generation state difference of the power source on the microgrid system is smaller; the smaller the difference between the operating load intensity of the microgrid and the power generation power intensity of all power sources is, the stronger the potential balance relationship between the power generation capacity and load consumption in the microgrid is; therefore, if the difference between the operating load intensity and the power generation power intensity is smaller, the power generation power oscillation coefficient is smaller, and the balancing coefficient of power supply and load is larger, indicating that the microgrid at this time The more stable the power changes of the system; conversely, the greater the operating load intensity of the microgrid, the greater the short-term instantaneous power consumption of the microgrid; when the oscillation degree of the power generation power of the power source is higher, the greater the power generation power oscillation coefficient of the power source, indicating that the greater the impact of the power generation state difference of the power source on the microgrid system; the greater the difference between the operating load intensity of the microgrid and the power generation power intensity of all power sources, the weaker the potential balance relationship between the power generation capacity and load consumption in the microgrid; therefore, if the difference between the operating load intensity and the power generation power intensity is greater, the power generation power oscillation coefficient is greater, and the balance coefficient between power supply and load is smaller, indicating that the power changes of the microgrid system at this time are more unstable.

[0084] Preferably, the schematic diagram of the process of extracting the balance coefficient of power supply and load provided in this embodiment is as follows: Figure 3 shown.

[0085] Step S5: Based on the balance coefficient of power supply and load in each interval, and in combination with a preset value, the moment of inertia parameter value of each interval is determined to regulate the island DC microgrid.

[0086] The balance coefficient of power supply and load reflects the stability of the current isolated microgrid system. The moment of inertia parameters of the VSG are optimized in real time according to the size of the obtained balance coefficient. Specifically, the calculation process of the moment of inertia parameters is as follows:

[0087] The expression of the moment of inertia parameter value in each interval is: ; In the formula, represents the moment of inertia parameter value in interval k; Indicates the balance coefficient of power supply and load in interval k; is the activation function; , These are preset values.

[0088] It should be noted that if the moment of inertia is too large, the dynamic response of the system will be slow, which is not conducive to rapid adjustment. If the moment of inertia is too small, the system may overreact, resulting in system instability. Therefore, the usual value range of the moment of inertia is [0.13, 0.84]. In order to ensure that the moment of inertia parameter value is in [0.13, 0.84], in this embodiment , The values ​​are 0.13, 0.71, , The value of has no difference, but the range of the moment of inertia parameter value must be in [0.13, 0.84]. The implementer can also set it according to the specific situation. , The value of .

[0089] Furthermore, the initial values ​​of the moment of inertia and damping coefficient of VSG are set to a and b respectively, and the moment of inertia parameter value in the previous interval adjacent to the current interval is used as the moment of inertia value in VSG in the current interval. The damping coefficient remains unchanged, and the power of the island DC microgrid in the current interval is regulated.

[0090] It should be noted that the initial value a of the moment of inertia and the initial value b of the damping coefficient are both manually set. In this embodiment, the initial values ​​of the moment of inertia and the damping coefficient are set to 0.48 and 15, respectively. The initial value of the moment of inertia must be guaranteed to be within the range of [0.13, 0.84]. The implementer can set it according to the specific situation.

[0091] So far, this embodiment aims at the problem that the VSG with constant moment of inertia cannot take into account the dynamic regulation performance of power and frequency in different modes of the microgrid, which leads to low efficiency of coordinated control of the microgrid. By analyzing the rapid fluctuation and nonlinear characteristics of each power generation data in the isolated DC microgrid, the nonlinear fluctuation difference of the power generation data is calculated, so as to accurately obtain the short-term variation characteristics of the power generation of each power source; further, by analyzing the random variation characteristics of the nonlinear fluctuation difference at different times, and the difference characteristics of the power generation state between different power sources, the power generation power oscillation coefficient is determined, and combined with the potential balance relationship between the power generation power and the load power, the balance coefficient of power supply and load is obtained, so as to optimize the moment of inertia parameters of the VSG in real time, realize the coordinated control of the isolated DC microgrid, and improve the control efficiency of the isolated microgrid.

[0092] Based on the same inventive concept as the above method, an embodiment of the present application also provides a coordinated control device for an island DC microgrid, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned coordinated control methods for an island DC microgrid when executing the computer program.

[0093] Based on the same inventive concept as the above method, the embodiment of the present application also provides a coordinated control system for an island DC microgrid, including:

[0094] The microgrid data acquisition module is used to obtain the power generation data of each power source and the load power data of each load point at all acquisition moments in each preset interval in the isolated DC microgrid, and record the sum of the load power data of all load points at each moment as the load energy consumption data at each moment;

[0095] The microgrid data analysis module is used to divide each interval into multiple time periods. For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined.

[0096] Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, the power generation variation coefficient of each power source in each interval is determined; based on the difference in the power generation fluctuation values ​​of each power source in all time periods with other power sources, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined;

[0097] The microgrid control module is used to fit all load energy consumption data in each interval and all power generation data of each power supply to obtain various fitting curves. Based on the intensity of all maximum values ​​on various fitting curves at the time intervals between adjacent inflection points and the power generation oscillation coefficient, the balance coefficient of power supply and load in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value to control the isolated island DC microgrid.

[0098] The block diagram of a coordinated control system of an isolated DC microgrid provided in an embodiment of the present application is as follows: Figure 4 shown.

[0099] 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.

[0100] 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.

[0101] 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 coordinated control method for an isolated DC microgrid, characterized in that: The method comprises the following steps: In the isolated DC microgrid, the power generation data of each power source and the load power data of each load point at all acquisition moments in each preset interval are obtained, and the sum of the load power data of all load points at each moment is recorded as the load energy consumption data at each moment; Each interval is divided into multiple time periods. For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined. Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, the power generation variation coefficient of each power source in each interval is determined; based on the difference in the power generation fluctuation values ​​of each power source in all time periods with other power sources, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined; All load energy consumption data in each interval and all power generation data of each power supply are fitted respectively to obtain various fitting curves. Based on the intensity of all maximum values ​​on various fitting curves in the time intervals between adjacent inflection points and the power generation power oscillation coefficient, the power supply and load balance coefficient in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value to regulate the island DC microgrid.

2. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The method for determining the power generation fluctuation value of each power source in each time period within each interval is as follows: For each power source in each interval, analyze the cumulative sum of all adjacent data differences in each product function component in each time period, and calculate the mean of the cumulative sum of all product function components, which is recorded as the first mean value in each time period for each power source in each interval; The expression of the power fluctuation value of the jth power supply in interval k in time period i is: ; In the formula, represents the first mean value of the jth power supply in interval k in time period i; represents the autocorrelation coefficient of the residual component of the jth power supply in interval k in time period i; Indicates a preset constant greater than 0.

3. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The power generation power variation coefficient of each power source in each interval is a result of the fusion of the range of the power generation power fluctuation value of each power source in all time periods in each interval and the fractal dimension.

4. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The method for determining the power supply difference value of each power supply in each interval is: In each interval, the overall difference in power generation fluctuation values ​​between each power source and the other power sources in all time periods is analyzed, and the power supply difference value of each power source in each interval is the cumulative sum of the overall differences between each power source and all other power sources.

5. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The power generation oscillation coefficient of each power source in each interval is a result of integrating the power generation variation coefficient of each power source in each interval with the power supply difference value.

6. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The method for determining the balance coefficient of power supply and load in each interval is: The various fitting curves include load power curves and power generation curves of each power source. In each interval, the time interval between each maximum value on the load power curve and its adjacent inflection points is calculated as the integral of the integral interval, and the cumulative sum of all integrals is recorded as the operating load intensity of the microgrid in each interval. For the power generation curve of each power source in each interval, the power generation intensity of each power source in each interval is obtained according to the method for obtaining the operating load intensity; The expression of the balance coefficient of power supply and load in interval k is: ; In the formula, represents the power oscillation coefficient of the jth power source in interval k; represents the operating load intensity of the microgrid in interval k; represents the power generation intensity of the jth power source in interval k; N represents the number of all power sources; Indicates a preset constant greater than 0.

7. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The expression of the moment of inertia parameter value of each interval is: ; In the formula, represents the moment of inertia parameter value in interval k; Indicates the balance coefficient of power supply and load in interval k; is the activation function; , These are preset values.

8. The coordinated control method of an isolated island DC microgrid according to claim 1, characterized in that: The regulating and controlling of the isolated island DC microgrid includes: The VSG technology is adopted, in which the moment of inertia parameter of the previous interval adjacent to the current interval is used as the moment of inertia value in the VSG in the current interval to regulate the power of the island DC microgrid in the current interval.

9. A coordinated control device for an isolated DC microgrid, 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 coordinated control method of an island DC microgrid as described in any one of claims 1 to 8 are implemented.

10. A coordinated control system for an isolated DC microgrid, implementing a coordinated control method for an isolated DC microgrid as claimed in claim 1, characterized in that: The system comprises: The microgrid data acquisition module is used to obtain the power generation data of each power source and the load power data of each load point at all acquisition moments in each preset interval in the isolated DC microgrid, and record the sum of the load power data of all load points at each moment as the load energy consumption data at each moment; The microgrid data analysis module is used to divide each interval into multiple time periods. For each power source in each interval, a local mean decomposition algorithm is used based on all the power generation data in each time period to obtain multiple product function components and a residual component. According to the difference of all adjacent data in each product function component in each time period and the autocorrelation of the residual component, the power generation fluctuation value of each power source in each interval in each time period is determined. Based on the complexity and distribution of the power generation fluctuation values ​​of each power source in all time periods in each interval, the power generation variation coefficient of each power source in each interval is determined; based on the difference in the power generation fluctuation values ​​of each power source in all time periods with other power sources, the power supply difference value of each power source in each interval is determined, and combined with the power generation variation coefficient, the power generation oscillation coefficient of each power source in each interval is determined; The microgrid control module is used to fit all load energy consumption data in each interval and all power generation data of each power supply to obtain various fitting curves. Based on the intensity of all maximum values ​​on various fitting curves at the time intervals between adjacent inflection points and the power generation oscillation coefficient, the balance coefficient of power supply and load in each interval is determined, and the moment of inertia parameter value of each interval is determined in combination with the preset value to control the isolated island DC microgrid.

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