Power resource scheduling method and system based on virtual power plant, and medium

Abstract

The invention relates to the technical field of power resource scheduling of an intelligent power grid, in particular to a power resource scheduling method and system based on a virtual power plant and a medium, and the method comprises the steps: collecting three-phase current data of a power generation end of the intelligent power grid and power data of each load end; determining the abnormal degree of the current data of the power generation end in the time period based on the difference distribution of adjacent half-cycle curves between different phase current data in the same time period and the dispersion degree of the phase current data in harmonic waves; the droop coefficient in the droop control algorithm is optimized and adjusted by combining the abrupt change characteristic and the trend significance degree of the total power data of all the load ends at the same moment in each time period; and using the optimized droop coefficient as a droop coefficient used when data acquired next time is used for power resource scheduling operation. The invention aims to realize reasonable scheduling of power resources and reduce deviation existing in power resource scheduling based on the virtual power plant.

Description

Technical Field

[0001] The present application relates to the technical field of power resource scheduling of smart grids, and specifically to a power resource scheduling method, system and medium based on a virtual power plant. Background Art

[0002] In the current power system, a large number of distributed energy, energy storage devices and controllable loads are being rapidly deployed. As an important means to solve the problem of new energy consumption, virtual power plant technology plays an important role in the framework of the new smart grid system. The efficiency of power resource dispatch determines whether various new energy sources can be connected to the power grid stably and efficiently, which in turn becomes the key to solving the problem of new energy consumption.

[0003] Through its control center, a virtual power plant can monitor, predict, optimize and trade its aggregated power resources, balance the fluctuations of renewable energy, and then dispatch power resources. Among them, droop control technology is a commonly used control method that can realize power allocation between distributed power generation resources, reduce power fluctuations in the power grid and optimize the dispatch of power resources. In the process of optimizing the dispatch of power resources of virtual power plants using traditional droop control technology, the existing methods do not fully consider the influence of the power supply stability of distributed power sources in smart grids and sudden load changes, which makes it difficult for power generation units in the droop control process to respond well to changes in the power grid, resulting in large deviations in the dispatch of power resources based on virtual power plants. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a power resource scheduling method, system and medium based on a virtual power plant. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for scheduling power resources based on a virtual power plant, the method comprising the following steps:

[0006] Collect three-phase current data of the smart grid power generation end and power data of each load end;

[0007] Based on the data characteristics of the three-phase current data and the power data of each load end, the synchronous stability value of the power supply and the load is determined; specifically:

[0008] A1, in the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of the phase difference in each time period; obtain the average level of the discrete degree of all phase current data in the harmonics in each time period; forwardly fuse the abnormal index of the phase difference in the same time period with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period;

[0009] A2, based on the mutation characteristics and trend significance of the total power data of all load terminals at the same time in each time period, determine the significant value of the sharp change in power load power in each time period;

[0010] A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, to determine the synchronous stability value of power supply and load;

[0011] Based on the synchronous stability value of power supply and load, the droop coefficient in the droop control algorithm is optimized and adjusted; the optimized droop coefficient is used as the droop coefficient used when the next collected data is used for power resource scheduling operation.

[0012] Preferably, the two adjacent half-cycle curves are two half-cycle curves with the shortest distance between different phase current data within the time period.

[0013] Preferably, the half-cycle curve is: curve fitting is performed on each phase current data in each time period; and the curves between each adjacent maximum point and minimum point on the fitted curve are respectively used as each half-cycle curve.

[0014] Preferably, the method for determining the abnormality index is:

[0015] In the same time period, calculate the discrete degree of the time difference between all the same amplitudes in any two adjacent half-cycle curves between different phase current data;

[0016] The average level of the discreteness of the two half-cycle curves of all neighboring phase current data in the same time period is taken as the abnormal index of the phase difference in the time period.

[0017] Preferably, the mutation characteristics are further determined as:

[0018] The accumulated sum of the power data of all load terminals at the same time is taken as the total load power data at the same time;

[0019] A sliding t-test algorithm is used for all total load power data in each time period, and all mutation points and the t statistics corresponding to each total load power data are output;

[0020] The number of mutation points and the average value of all t statistics are reversely fused, and the reverse fusion result is used as the mutation feature.

[0021] Preferably, the trend significance is determined by the probability that the output data of the adjustment function in the statsmode ls library is not stable when performing a stability test on all the total power data in the time period.

[0022] Preferably, the method for determining the synchronization stability value is:

[0023] Calculate the sum of the average value of the abnormal degree of the current data at the power generation end in all time periods and the average value of the significant value of the sharp change in the power load power of the power grid in all time periods;

[0024] The correlation and the sum value are reversely fused to obtain a synchronous stability value of power supply and load.

[0025] Preferably, the optimization and adjustment method is: the optimized droop coefficient is recorded as S, and the calculation formula is: S=Z×(R+0.5), where Z is the initial active droop coefficient in the droop control algorithm, and R is the normalized result of the synchronous stability value of power supply and load.

[0026] In a second aspect, an embodiment of the present application provides a power resource scheduling system based on a virtual power plant, the system comprising:

[0027] The power data acquisition module is used to collect the three-phase current data of the smart grid power generation end and the power data of each load end;

[0028] The power data feature extraction module is used to determine the synchronous stability value of power supply and load based on the data features of the three-phase current data and the power data of each load end; specifically:

[0029] A1, in the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of the phase difference in each time period; obtain the average level of the discrete degree of all phase current data in the harmonics in each time period; forwardly fuse the abnormal index of the phase difference in the same time period with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period;

[0030] A2, based on the mutation characteristics and trend significance of the total power data of all load terminals at the same time in each time period, determine the significant value of the sharp change in power load power in each time period;

[0031] A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, to determine the synchronous stability value of power supply and load;

[0032] The power resource scheduling operation module is used to optimize and adjust the droop coefficient in the droop control algorithm based on the synchronous stability value of power supply and load; and use the optimized droop coefficient as the droop coefficient used in the next power resource scheduling operation when the data collected is collected.

[0033] In the third aspect, an embodiment of the present application also provides a power resource scheduling medium based on a virtual power plant, the medium comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned power resource scheduling methods based on a virtual power plant when executing the computer program.

[0034] It can be seen from the above embodiments that the power resource scheduling method, system and medium based on a virtual power plant provided in the embodiments of the present application have at least the following beneficial effects:

[0035] This application analyzes the difference characteristics of the current phase output by the smart grid and the harmonic information contained therein, calculates the abnormal index of the current data at the power generation end including phase deviation and harmonic pollution, and then analyzes the mutation degree and trend significance of the load power, and calculates the significant value of the sharp change in the load power of the power grid. Its beneficial effect is that it can better highlight the abnormal or sharp change characteristics of the power generation end and the load end in the power grid, so that the subsequent judgment on the influence of the stability of power resource scheduling is more accurate. Combined with the matching characteristics of the fluctuation of the power generation end and the load end of the distributed power source in the power grid, the synchronous stability value of the power supply and the load is calculated. Its beneficial effect is that it can further optimize the droop coefficient by judging the stability of the operation of the current power grid system, thereby realizing the reasonable scheduling of power resources and reducing the deviation of power resource scheduling based on virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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.

[0037] Figure 1 A flowchart of a method for scheduling power resources based on a virtual power plant according to an embodiment of the present application;

[0038] Figure 2 A flow chart of a method for determining a synchronous stability value of power supply and load provided in one embodiment of the present application;

[0039] Figure 3 A flow chart of a method for determining the abnormality of current data at a power generation end provided in one embodiment of the present application;

[0040] Figure 4 A schematic diagram of the structure of a power resource scheduling system based on a virtual power plant is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of a power resource scheduling method, system and medium based on a virtual power plant proposed in the present application. 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.

[0042] Unless otherwise specified and limited, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0043] The following is a detailed description of a specific scheme of a power resource scheduling method, system and medium based on a virtual power plant provided by the present application in conjunction with the accompanying drawings.

[0044] See also Figure 1 , which shows a flowchart of a method for scheduling power resources based on a virtual power plant provided by an embodiment of the present application, the method comprising the following steps:

[0045] The first step is to collect the three-phase current data of the smart grid power generation end and the power data of each load end.

[0046] Smart grids can effectively manage distributed energy based on real-time data monitoring and analysis, improve dispatch efficiency through information communication, and quickly locate and take appropriate recovery measures to ensure the stable operation of the power system.

[0047] The key technologies of virtual power plants include coordinated control technology, smart metering technology and information and communication technology. Among them, coordinated control technology can be used to aggregate distributed energy, energy storage and controllable loads in virtual power plants to achieve high-demand power output for smart grids.

[0048] However, the uncertainty of distributed power generation in the power grid and the sudden change of load will affect the accuracy of coordinated control, and thus affect the power supply quality in the process of power resource scheduling. Therefore, this embodiment collects and analyzes the data characteristics of the current data of the smart grid power generation end and the power data of each load end, so as to judge the stability of the operation of the current power grid system, and further optimizes the droop coefficient in the process of optimizing the scheduling of power resources of the virtual power plant using the droop control technology, thereby realizing the reasonable scheduling of power resources and reducing the deviation of power resource scheduling based on virtual power plants.

[0049] The present application collects the three-phase current data of the power generation end of the smart grid through the electric energy meter, collects the power data of each load end in the smart grid through the electric energy meter, and takes the cumulative sum of the power data collected from all load ends at the same time as the total load power of the grid.

[0050] In the embodiment of the present application, the time interval for collecting current and power data is set to 2 milliseconds, and the single data collection duration is set to 10 seconds. In other embodiments of the present application, the implementer can set it by himself.

[0051] At this point, the three-phase current data of the smart grid power generation end and the power data of each load end can be obtained through the above method.

[0052] The second step is to determine the synchronous stability value of power supply and load based on the data characteristics of the three-phase current data and the power data of each load end.

[0053] When applying droop control technology for optimized scheduling in the power resource scheduling process based on virtual power plants, it is easy to ignore the impact of the mismatch between power supply fluctuations and load fluctuations in the power grid on the response of the grid frequency or voltage changes, resulting in the set droop coefficient being difficult to effectively realize power resource scheduling.

[0054] Specifically, if the droop coefficient is too large, the power generation unit may respond too strongly to grid changes, thereby causing system oscillations; if the droop coefficient is too small, the power generation unit may not respond sufficiently and may be unable to adjust the output power in time to cope with grid changes.

[0055] Nowadays, more and more distributed power sources are connected to smart grids, and the load changes in the power grid may change dramatically at any time. The mismatch between the fluctuations of the two increases the difficulty of maintaining stable operation of the power grid. The uncertainty of distributed power generation in the power grid may cause the three-phase AC power provided by the power generation end to have phase differences and harmonic pollution problems, which in turn affects the efficiency of power resource scheduling. Accordingly, this application determines the synchronous stability value of power supply and load based on the data characteristics of three-phase current data and power data.

[0056] In the embodiment of the present application, a flow chart of a method for determining a synchronous stability value of power supply and load is shown in the attached Figure 2 As shown, specifically:

[0057] A1. In the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of phase difference in each time period; obtain the average level of discreteness of all phase current data in harmonics in each time period; forwardly fuse the abnormal index of phase difference in the same time period with the average level of discreteness to determine the abnormality of current data at the power generation end in this time period.

[0058] The relevant features of the three-phase alternating current at the power grid generation end are extracted. Since the current and power data may have large random fluctuations in the short term, the embodiment of the present application divides each acquisition time into 15 time periods of equal length, and the number of divided time periods can be set by the implementer.

[0059] Distributed power sources in smart grids may be connected to the grid at different locations and at different times, which may cause abnormal phase differences in the three-phase AC power and affect the balance of the grid. The abnormal phase difference is manifested as inconsistent time delays between currents in different phases.

[0060] To analyze this feature, the embodiment of the present application takes the i-th time period as an example, and uses a polynomial fitting technique to fit each phase current data in the three-phase current data to obtain a corresponding fitting curve, and the fitting curve is a sine waveform. Among them, the polynomial fitting technique is a well-known technique and will not be described in detail.

[0061] The maximum and minimum points of the fitting curve are calculated, and the fitting curve between adjacent maximum and minimum points is used as the half-cycle curve of the corresponding single-phase current, thereby dividing each single-phase current data into multiple half-cycles.

[0062] In the three-phase current data, each phase current data is arranged alternately, and under normal circumstances, there is a constant angle phase difference between the three phases of the three-phase alternating current. This means that the time difference between the current of each phase and the current of the next phase at the same amplitude is constant.

[0063] In the embodiment of the present application, the flow chart of the method for determining the abnormality degree of the current data at the power generation end is as shown in the attached Figure 3 As shown, specifically:

[0064] (1) In the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, the abnormal index of the phase difference in each time period is determined.

[0065] Preferably, in an embodiment of the present application, within the same time period, the degree of discreteness of the time difference between all the same amplitudes in any two adjacent half-cycle curves between different phase current data is calculated; the average level of the degree of discreteness of all adjacent two half-cycle curves between all phase current data within the same time period is taken as the abnormality index of the phase difference within the time period.

[0066] The two adjacent half-cycle curves are two half-cycle curves with the shortest distance between different phase current data within the time period.

[0067] In one implementation of the embodiment of the present application, in a certain half-cycle curve, each moment corresponds to an amplitude, and the absolute value of the difference between the half-cycle curve and the adjacent half-cycle curve in the next phase current data at the same amplitude at the corresponding moment is calculated respectively, and the standard deviation of all the absolute values ​​is calculated, and the average value of the standard deviation calculated for all two adjacent half-cycle curves between all adjacent phase current data in the i-th time period is taken as the abnormal index of the phase difference in the time period, which is recorded as D i .

[0068] It should be understood that the obtained D i The larger the value is, the more likely it is that there is a phase anomaly in the current data at the power generation end during this time period.

[0069] Since the first and last parts of the single-phase current data within the time period may contain data parts that are less than half a cycle length, these data parts that are less than half a cycle length are not included in the calculation.

[0070] (2) Obtain the average level of dispersion of all phase current data in harmonics in each time period.

[0071] When distributed power sources are connected to the grid, they may introduce harmonics due to some power electronic equipment, thus affecting the quality of the grid.

[0072] In order to analyze the harmonic pollution of the current at the power generation end, the present application adopts a harmonic detection method based on the Mallat algorithm to obtain the harmonic data of each phase current data. Among them, the harmonic detection method based on the Mallat algorithm is a well-known technology and will not be described in detail.

[0073] Preferably, in the embodiment of the present application, the average level of the discreteness of the harmonic data of all phase current data in each time period is used as the harmonic distortion value of the time period.

[0074] In one implementation of the embodiment of the present application, taking the i-th time period as an example, the average value of the coefficient of variation of the harmonic data of all phase current data in the i-th time period is taken as the harmonic distortion value of the time period, denoted as E i .

[0075] It should be understood that the obtained E i The larger it is, the greater the coefficient of variation of the harmonic data of all phase current data in this time period, that is, the greater the discreteness of the harmonic data, indicating that the greater the harmonic pollution of the power generation end current in this time period.

[0076] (3) The abnormal index of the phase difference in the same time period is forwardly integrated with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period.

[0077] It can be understood that forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0078] Preferably, in the embodiment of the present application, taking the i-th time period as an example, the abnormality degree F of the current data at the power generation end in the time period is calculated. i The expression is: F i =D i ×E i Where, F i Indicates the abnormality of the current data at the power generation end in the i-th time period, D i represents the abnormal index of the phase difference in the i-th time period, E i Indicates the harmonic distortion value of the i-th time period.

[0079] In other embodiments of the present application, the expression of the abnormality degree of the current data at the power generation end during the time period can also be set as: i =D i +E i .

[0080] It should be understood that the obtained F i The larger it is, the more serious the phase difference and harmonic pollution problems of the power supply current of the power grid during this period of time are.

[0081] A2, based on the mutation characteristics and trend significance of the total power data of all load ends at the same time in each time period, determine the significant value of the sharp change in power load power in each time period.

[0082] In addition to the impact of the power generation end on the power resource scheduling, the rapid change of load power in the smart grid may also cause the fluctuation of the grid frequency, affecting the stability of the grid and the efficiency of power resource scheduling. Therefore, it is necessary to further extract the relevant features of load power.

[0083] For all the total load power data in the i-th time period, this application uses a sliding t-test algorithm to obtain its mutation characteristics. The input of the algorithm is all the total load power data in the time period, the significance level is set to 0.05, and the output is the t statistic corresponding to each total load power data point and all mutation points. Among them, the sliding t-test algorithm is a well-known technology and will not be repeated.

[0084] Preferably, in the embodiment of the present application, the number of mutation points and the average value of all t statistics are reversely fused, and the reverse fusion result is used as the mutation feature.

[0085] In one implementation of the embodiment of the present application, the expression for calculating the abnormal mutation value of the total load power in the time period is: In the formula, G i represents the abnormal mutation value of the total load power in the i-th time period, X represents the number of mutation points of the total load power data in this time period, and Y represents the average value of the t statistic of all total load power data in this time period.

[0086] It should be understood that the obtained G i That is, it indicates the mutation degree of the total load power data within the time period. The more mutation points there are and the smaller the average value of the t statistic is, the more likely it is that there is a mutation in a set of sequences, that is, G i The larger the value is, the greater the mutation degree of load power is. The numerator is added with 1 to avoid G i When the value is zero, the data has certain fluctuations. At this time, the size of Y can reflect the degree of fluctuation.

[0087] In addition, the rapid change of load power may be manifested as a rapid change with an obvious trend, in which there is no mutation. Therefore, the embodiment of the present application uses the addfuller function in the statsmode ls library to test the stability of load power. The input is the total load power data in the i-th time period, and the output data is the probability of non-stationary, which is recorded as the trend significance degree P of the total load power in the i-th time period. i The process of load power stability test by the addfuller function in the statsmode ls library is a well-known technique and will not be described in detail. It should be understood that the obtained P i The larger it is, the more unstable the total load power in the power grid is and the more obvious the corresponding trend characteristics are.

[0088] In other implementations of the embodiments of the present application, a moving average algorithm, an exponential smoothing method, etc. may also be used to calculate the trend significance of the total load power data in the i-th time period.

[0089] Preferably, in an embodiment of the present application, the method for calculating the significant value of the grid charge power with a sharp change characteristic is: forwardly fuse the mutation characteristics and trend significance of the total power data of all load ends at the same time in each time period, and use the result of the forward fusion as the significant value of the sharp change in the grid load power in the time period.

[0090] In one implementation of the present application, the expression for calculating the significant value of the rapid change in power grid load power is: i =G i ×P i Where H i Indicates the significant value of the sharp change in power load in the i-th time period, G i represents the abnormal mutation value of the total load power in the i-th time period, P i Indicates the trend significance of the total load power in the i-th time period.

[0091] In other implementations of the embodiments of the present application, the calculation expression for the significant value of the sharp change in the power load of the power grid during the time period may also be set to: i =G i +P i .

[0092] It should be understood that the obtained H i The larger it is, the faster the load of the power grid changes during this period, and the more likely it is to affect the stability of power resource scheduling.

[0093] A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, determines the synchronous stability value of power supply and load.

[0094] Furthermore, the fluctuation of distributed power generation in the smart grid does not match the fluctuation of load, which may lead to the activation of power generation energy, affect its absorption rate, and thus reduce the efficiency of power resource scheduling.

[0095] Through the above analysis, the fluctuation of distributed power generation and load can be respectively expressed by the abnormal degree F of the current data at the generating end in the i-th time period and i , the significant value H of the sharp change of power load in the i-th time period i reflects the size.

[0096] Therefore, the sequence obtained by arranging the abnormality degree of the current data at the power generation end in all time periods of a single collection time in ascending time order is recorded as the abnormality degree sequence M, and the sequence obtained by arranging the significant values ​​of the sharp change in the power grid load power in all time periods in ascending time order is recorded as the significant value sequence N.

[0097] The correlation between the sequences M and N is calculated and recorded as T. The larger the obtained T is, the more the fluctuation state of the power generation end matches the fluctuation state of the load end. In this embodiment, the absolute value of the correlation coefficient between the sequences M and N is calculated using the Pearson correlation coefficient as T. The Pearson correlation coefficient is a well-known technology and will not be described in detail.

[0098] Then calculate the mean of all elements in sequence M and the mean of all elements in sequence N, and record the sum of the two means as U. The smaller the obtained U is, the smaller the current anomaly at the power generation end and the sharp change at the load end are.

[0099] Preferably, in an embodiment of the present application, the method for calculating the synchronous stability value of power supply and load is: reversely fusing the correlation T with the sum value U to obtain the synchronous stability value of power supply and load.

[0100] It can be understood that reverse fusion is a fusion method such as subtraction and division between data. The specific reverse fusion method is determined by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0101] In one implementation of the embodiment of the present application, the expression for calculating the synchronous stability value W of the power supply and the load is: Where W represents the synchronous stability value of power supply and load, T represents the correlation between sequences M and N, and U represents the sum of the mean of all elements in sequence M and the mean of all elements in sequence N.

[0102] It should be understood that the larger the T is, the more it indicates that the sequences M and N have the same abnormal degree change trend, and the smaller the U is, the less significant the drastic changes in the two sequences are. That is, the larger the synchronous stability value W is, the higher the stability of the power supply and load of the power grid is.

[0103] The third step is to optimize and adjust the droop coefficient in the droop control algorithm based on the synchronous stability value of power supply and load; and use the optimized droop coefficient as the droop coefficient used in the next power resource scheduling operation when the data collected is collected.

[0104] The embodiment of the present application calculates the synchronous stability value W of the power supply and load by analyzing the abnormal characteristics of the output current of the distributed power supply in the power grid and the rapid change characteristics of the load, and combining the matching degree of the fluctuations of the two.

[0105] It should be understood that this value reflects the degree of synchronization stability of the power supply and load of the power grid during the acquisition period. If W is smaller, it means that the stability of power generation and load in the power grid is smaller. In the process of power resource scheduling, in order to improve the stability of the system, it is necessary to increase the droop coefficient to improve the rapid response to changes in the power grid system. If W is larger, the droop coefficient can be reduced in the process of power resource scheduling to avoid excessive oscillation of the power grid system.

[0106] The present application optimizes and adjusts the initial active droop coefficient in the droop control algorithm. The specific calculation formula of the optimized droop coefficient S is: S=Z×(R+0.5), where Z is the initial active droop coefficient in the droop control algorithm, and R is the normalized result of the synchronous stability value of the power supply and the load. The present application uses the tanh activation function for normalization. The tanh activation function is a well-known technology and will not be repeated here.

[0107] For the first collection duration, the initial active power droop control technology is used for adjustment. Subsequently, the S value calculated in the previous collection duration is used as the optimized droop coefficient value for the current collection duration. In this way, power resources are dispatched according to the actual operation of the power grid, making the allocation of power resources in the smart grid more efficient and flexible.

[0108] See also Figure 4 , Figure 4 is a structural diagram of a power resource scheduling system based on a virtual power plant provided in an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in an embodiment corresponding to a power resource scheduling method based on a virtual power plant. Figure 4 , the system comprising:

[0109] The power data acquisition module is used to collect the three-phase current data of the smart grid power generation end and the power data of each load end;

[0110] The power data feature extraction module is used to determine the synchronous stability value of power supply and load based on the data features of the three-phase current data and the power data of each load end; specifically:

[0111] A1, in the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of the phase difference in each time period; obtain the average level of the discrete degree of all phase current data in the harmonics in each time period; forwardly fuse the abnormal index of the phase difference in the same time period with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period;

[0112] A2, based on the mutation characteristics and trend significance of the total power data of all load terminals at the same time in each time period, determine the significant value of the sharp change in power load power in each time period;

[0113] A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, to determine the synchronous stability value of power supply and load;

[0114] The power resource scheduling operation module is used to optimize and adjust the droop coefficient in the droop control algorithm based on the synchronous stability value of power supply and load; and use the optimized droop coefficient as the droop coefficient used in the next power resource scheduling operation when the data collected is collected.

[0115] Based on the same inventive concept as the above method, an embodiment of the present application also provides a power resource scheduling medium based on a virtual power plant, 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 power resource scheduling methods based on a virtual power plant.

[0116] The various embodiments in the present application 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.

[0117] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0118] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0119] It will be appreciated that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for dispatching electric power resources based on a virtual power plant, characterized in that: The method comprises the following steps: Collect three-phase current data of the smart grid power generation end and power data of each load end; Based on the data characteristics of the three-phase current data and the power data of each load end, the synchronous stability value of the power supply and the load is determined; specifically: A1, in the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of the phase difference in each time period; obtain the average level of the discrete degree of all phase current data in the harmonics in each time period; forwardly fuse the abnormal index of the phase difference in the same time period with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period; A2, based on the mutation characteristics and trend significance of the total power data of all load terminals at the same time in each time period, determine the significant value of the sharp change in power load power in each time period; A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, to determine the synchronous stability value of power supply and load; Based on the synchronous stability value of power supply and load, the droop coefficient in the droop control algorithm is optimized and adjusted; the optimized droop coefficient is used as the droop coefficient used when the next collected data is used for power resource scheduling operation.

2. A method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The two adjacent half-cycle curves are two half-cycle curves with the shortest distance between the current data of different phases within the time period.

3. A method for dispatching electric power resources based on a virtual power plant as claimed in claim 2, characterized in that: The half-cycle curve is: curve fitting is performed on each phase current data in each time period; and the curves between each adjacent maximum point and minimum point on the fitted curve are respectively used as each half-cycle curve.

4. The method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The method for determining the abnormality index is: In the same time period, calculate the discrete degree of the time difference between all the same amplitudes in any two adjacent half-cycle curves between different phase current data; The average level of the discreteness of the two half-cycle curves of all neighboring phase current data in the same time period is taken as the abnormal index of the phase difference in the time period.

5. The method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The mutation characteristics are further determined as: The accumulated sum of the power data of all load terminals at the same time is taken as the total load power data at the same time; A sliding t-test algorithm is used for all total load power data in each time period, and all mutation points and the t statistics corresponding to each total load power data are output; The number of mutation points and the average value of all t statistics are reversely fused, and the reverse fusion result is used as the mutation feature.

6. The method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The trend significance is determined by the probability that the output data of the adjuster function in the statsmodels library is not stable when performing a stationarity test on all the total power data in the time period.

7. The method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The method for determining the synchronization stability value is: Calculate the sum of the average value of the abnormal degree of the current data at the power generation end in all time periods and the average value of the significant value of the sharp change in the power load power of the power grid in all time periods; The correlation and the sum value are reversely fused to obtain a synchronous stability value of power supply and load.

8. The method for dispatching electric power resources based on a virtual power plant according to claim 1, characterized in that: The optimization adjustment method is: the optimized droop coefficient is recorded as S, and the calculation formula is: S=Z×(R+0.5), where Z is the initial active droop coefficient in the droop control algorithm, and R is the normalized result of the synchronous stability value of power supply and load.

9. A power resource dispatching system based on a virtual power plant, implementing a power resource dispatching method based on a virtual power plant as described in claims 1-8, characterized in that: The system comprises: The power data acquisition module is used to collect the three-phase current data of the smart grid power generation end and the power data of each load end; The power data feature extraction module is used to determine the synchronous stability value of power supply and load based on the data features of the three-phase current data and the power data of each load end; specifically: A1, in the same time period, based on the difference distribution of all two adjacent half-cycle curves between different phase current data, determine the abnormal index of the phase difference in each time period; obtain the average level of the discrete degree of all phase current data in the harmonics in each time period; forwardly fuse the abnormal index of the phase difference in the same time period with the average level of the discrete degree to determine the abnormal degree of the current data at the power generation end in the time period; A2, based on the mutation characteristics and trend significance of the total power data of all load terminals at the same time in each time period, determine the significant value of the sharp change in power load power in each time period; A3, combining the correlation and numerical value between the abnormal degree of the current data at the power generation end in all time periods and the significant value of the sharp change in the power load power of the power grid in all time periods, to determine the synchronous stability value of power supply and load; The power resource scheduling operation module is used to optimize and adjust the droop coefficient in the droop control algorithm based on the synchronous stability value of power supply and load; and use the optimized droop coefficient as the droop coefficient used in the next power resource scheduling operation when the data collected is collected.

10. A power resource scheduling medium based on a virtual power plant, 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 processor implements the steps of a power resource scheduling method based on a virtual power plant as described in any one of claims 1-8.

Images