Distributed photovoltaic power generation identification method and device

Abstract

The invention is suitable for the technical field of photovoltaic power generation detection, and provides a distributed photovoltaic power generation identification method and device, and the method comprises the steps: obtaining the actual load data and actual environment data of a to-be-detected region; the actual environment data comprises actual illumination intensity; performing time grouping on the actual load data according to geographical conditions to obtain multiple groups of actual load data; judging whether the possibility of distributed photovoltaic power generation exists in the corresponding time period or not according to the multiple groups of actual load data; if the possibility of distributed photovoltaic power generation exists, whether distributed photovoltaic power generation exists in the to-be-detected area in the corresponding time period or not is judged according to the relevance between the actual illumination intensity and the actual load data. The distributed photovoltaic power generation can be accurately identified according to the previous load characteristics in combination with the current load information and the illumination intensity, and the regulation and control performance and stability of the power grid are improved.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic power generation detection technology, and in particular, relates to a distributed photovoltaic power generation identification method and device. Background Art

[0002] In the context of the non-renewable energy crisis, the advantages of renewable energy have gradually become prominent, and my country has also begun to use renewable clean energy such as photovoltaics and wind power to replace the original non-renewable energy. Compared with non-renewable energy, the total reserves of photovoltaic energy are higher and will not be exhausted. There is no obvious pollution when using photovoltaic energy to generate electricity. In addition, there are fewer restrictions on the use of photovoltaic energy and lower cost consumption. Therefore, both at home and abroad, photovoltaic energy has been vigorously developed.

[0003] Research shows that my country's photovoltaic power generation capacity has exceeded 100 million kilowatts and continues to grow at a high speed. Photovoltaic power generation is greatly affected by the environment, climate, and light, and its power generation has a large volatility and is prone to power generation failures. In addition, small distributed photovoltaic power generation systems lack separate monitoring. Distributed photovoltaic power generation systems are large in number, widely distributed, and continue to increase. Relying on power companies to manually collect information on photovoltaic power generation systems requires a lot of costs and is difficult to ensure full reliability.

[0004] Therefore, it is urgent to identify the photovoltaic power generation system and analyze the status of photovoltaic power generation in order to maintain and regulate the power grid. Summary of the invention

[0005] In order to overcome the problems existing in the related technology, the embodiment of the present application provides a distributed photovoltaic power generation identification method and device, which can accurately identify distributed photovoltaic power generation based on previous load characteristics combined with current load information and light intensity, thereby improving the regulation performance and stability of the power grid.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a distributed photovoltaic power generation identification method, comprising:

[0008] Obtaining actual load data and actual environmental data of the area to be tested, where the actual environmental data includes actual light intensity;

[0009] The actual load data is grouped by time according to geographical conditions to obtain multiple groups of actual load data;

[0010] Determine whether there is a possibility of distributed photovoltaic power generation in the corresponding period based on multiple sets of actual load data;

[0011] If there is a possibility of distributed photovoltaic power generation, it is determined whether there is distributed photovoltaic power generation in the tested area during the corresponding period based on the correlation between the actual light intensity and the actual load data.

[0012] In one embodiment, judging whether there is a possibility of distributed photovoltaic power generation in a corresponding time period based on multiple sets of actual load data includes:

[0013] Obtain historical load data without photovoltaic power generation in the same period as the period corresponding to the multiple sets of actual load data;

[0014] The load value in the historical load data without photovoltaic power generation is used as the load reference value;

[0015] Calculate the average value of the load reference value for each preset time period;

[0016] Calculate the difference characteristic value between the average value of the load reference value per preset time period and the actual load;

[0017] The difference characteristic value is compared with a preset difference characteristic reference value. If the difference characteristic value is greater than the preset difference characteristic reference value, there is a possibility of distributed photovoltaic power generation in the corresponding period of the measured area.

[0018] In one embodiment, the calculation formula for the difference characteristic value between the average value of the load reference value per preset time period and the actual load is:

[0019]

[0020] Where N is the average value of the load reference value for the day, P(n) is the actual load, and P c (n) is the average value of the load reference value.

[0021] In one embodiment, judging whether there is distributed photovoltaic power generation in the area to be tested in the corresponding period according to the correlation between the actual light intensity and the actual load data includes:

[0022] Calculate a first variation coefficient of the actual light intensity within a sampling period of the corresponding time period;

[0023] Calculate a second variation coefficient of the actual load data within a sampling period of the corresponding time period;

[0024] Calculate the actual correlation between the first variation coefficient and the second variation coefficient within a sampling period of the corresponding time period;

[0025] Calculate the reference correlation between the coefficient of variation of historical light intensity and the coefficient of variation of historical load data within a sampling period in the same period as the corresponding period; the same period as the corresponding period at least includes the photovoltaic power generation period;

[0026] Compare the actual correlation and the reference correlation within a sampling cycle of the corresponding time period. If the actual correlation is greater than or equal to the reference correlation, there is distributed photovoltaic power generation in the corresponding time period of the measured area.

[0027] In one embodiment, the first variation coefficient k of the actual light intensity is G The calculation formula is:

[0028]

[0029] Where T is the sampling period; G(t) is the actual light intensity at the current time t; G(t+1) is the actual light intensity at the next time t+1;

[0030] The second variation coefficient k of the actual load data P The calculation formula is:

[0031]

[0032] Among them, P(t) is the actual load at the current time t; P(t+1) is the actual load at the next time t+1.

[0033] In one embodiment, the calculation formula of the actual correlation λ between the first variation coefficient and the second variation coefficient is:

[0034]

[0035] Among them, k G is the first coefficient of variation, k P is the second coefficient of variation.

[0036] In one embodiment, the actual load data is grouped by time according to geographical conditions to obtain multiple groups of actual load data, including:

[0037] According to geographical conditions, the time of day is grouped according to the range of light intensity to obtain time groups;

[0038] The load data is divided into multiple groups of actual load data according to time grouping.

[0039] In one embodiment, it further includes:

[0040] If there is no possibility of distributed photovoltaic power generation, it is directly determined that there is no distributed photovoltaic power generation in the tested area during the corresponding period.

[0041] In one embodiment, judging whether there is a possibility of distributed photovoltaic power generation in a corresponding time period according to multiple sets of actual load data further includes:

[0042] If the difference characteristic value is less than or equal to the preset difference characteristic reference value, there is no possibility of distributed photovoltaic power generation in the corresponding period of the tested area.

[0043] In a second aspect, the present application provides a distributed photovoltaic power generation identification device, which applies the distributed photovoltaic power generation identification method of the first aspect, including:

[0044] A data acquisition module is used to obtain actual load data and actual environmental data of the area to be tested, where the actual environmental data includes actual light intensity;

[0045] A grouping module is used to group the actual load data by time according to geographical conditions to obtain multiple groups of actual load data;

[0046] The first identification module is used to determine whether there is a possibility of distributed photovoltaic power generation in a corresponding period according to multiple sets of actual load data;

[0047] The second identification module is used to determine whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data if there is the possibility of distributed photovoltaic power generation.

[0048] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0049] The embodiment of the present application, by comprehensively considering the actual load data and actual environmental data of the area to be tested, especially the actual light intensity, can more accurately determine whether there is distributed photovoltaic power generation in a specific period of time, which is more reliable than relying solely on load data or light data. The actual load data is also processed in a time grouping manner, which helps to identify the photovoltaic power generation situation in different time periods, and helps power grid operators or energy managers to better understand the distribution and change trends of distributed photovoltaic power generation, so as to make more scientific energy scheduling and management decisions. In addition, the time period in which distributed photovoltaic power generation may exist is preliminarily screened out through the actual load data, which can greatly narrow the scope of subsequent analysis and improve the overall judgment efficiency. Then, in the screened time period, the correlation between the actual light intensity and the load data is combined for further confirmation. This detailed analysis can ensure the accuracy of the judgment, avoid misjudgment or missed judgment, accurately identify distributed photovoltaic power generation, and improve the regulation performance and stability of the power grid.

[0050] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be 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 paying creative labor.

[0052] Figure 1 This is a schematic diagram of an application scenario of a distributed photovoltaic power generation identification method provided by an embodiment of the present application;

[0053] Figure 2 It is a flow chart of a distributed photovoltaic power generation identification method provided in an embodiment of the present application;

[0054] Figure 3 It is a structural schematic diagram of a distributed photovoltaic power generation identification device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0055] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0056] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0057] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0059] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0060] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0062] Reference Figure 1 , which is an application scenario diagram of the distributed photovoltaic power generation identification method of the present invention. The identification method runs in the distributed photovoltaic power generation identification 102. The distributed photovoltaic power generation identification device 102 obtains power load data from the power system 101, quickly distinguishes whether there is distributed photovoltaic power generation in the power system, and sends a signal to the control center 103 whether there is distributed photovoltaic power generation. The control center 103 plans the allocation of power resources in advance according to the signal of whether there is distributed photovoltaic power generation to ensure the normal operation of the power system.

[0063] Figure 2 is a flow chart of a distributed photovoltaic power generation identification method provided by an embodiment of the present application, referring to Figure 2 , the distributed photovoltaic power generation identification method is described in detail as follows:

[0064] The present application embodiment provides a distributed photovoltaic power generation identification method, including:

[0065] Step 201, obtaining actual load data and actual environment data of the area to be tested.

[0066] The actual environment data includes actual light intensity.

[0067] Obtain the actual load data and actual environmental data of the area to be tested, including the actual light intensity. Then compare and analyze these data with the preset standard data to determine whether the operating status of the area to be tested is normal. In addition, a database can be established based on these actual data to provide data support for subsequent regional planning, equipment upgrades, etc.

[0068] Step 202, grouping the actual load data by time according to geographical conditions to obtain multiple groups of actual load data.

[0069] We conduct detailed analysis on each set of data and study the characteristics of load data in different geographical areas in different time periods, such as peak value, low peak value, fluctuation range, etc. By deeply exploring these characteristics, we can find the differences in electricity demand in different regions, which will help the power sector to better allocate electricity. At the same time, we can also predict the future electricity demand trend in different geographical areas based on these data, providing a strong basis for power planning.

[0070] Step 203 , judging whether there is a possibility of distributed photovoltaic power generation in the corresponding time period according to the multiple sets of actual load data.

[0071] For example, the fluctuation characteristics of load data should be analyzed. If during the daytime, especially during the sunny period, the load shows a different pattern from the typical non-PV distribution, such as the load is significantly lower than expected around noon, this may indicate the possibility of distributed photovoltaic power generation. Because distributed photovoltaic power generation will inject electricity into the grid when the sun is good, it may reduce the amount of electricity obtained from the grid, resulting in a relatively low load value.

[0072] Secondly, it is also important to observe the seasonal changes in load data, local climate data, and especially the sunshine duration and intensity data, so the possibility of distributed photovoltaic power generation is needed for the corresponding time periods.

[0073] Step 204: If there is a possibility of distributed photovoltaic power generation, it is determined whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data.

[0074] For example, when the light intensity is high but the actual load data is low, this may indicate the presence of distributed photovoltaic power generation. Because in this case, there may be distributed photovoltaic power generation systems working under high light intensity, meeting part or all of the power demand, thereby reducing the actual load data. On the contrary, if the light intensity is very low and the actual load data is at a normal level or high, this is likely to indicate that there is no distributed photovoltaic power generation in the tested area during this period, because there is no additional photovoltaic power generation to reduce the power supply burden of the power grid.

[0075] Exemplarily, if there is no possibility of distributed photovoltaic power generation, it is directly determined that there is no distributed photovoltaic power generation in the test area during the corresponding period.

[0076] This embodiment can more accurately determine whether there is distributed photovoltaic power generation in a specific period of time by comprehensively considering the actual load data and actual environmental data of the area to be tested, especially the actual light intensity, which is more reliable than relying solely on load data or light data. The actual load data is also processed in a time grouping manner, which helps to identify the photovoltaic power generation situation in different time periods, and helps power grid operators or energy managers to better understand the distribution and change trends of distributed photovoltaic power generation, so as to make more scientific energy scheduling and management decisions. In addition, the time period in which distributed photovoltaic power generation may exist is preliminarily screened out through the actual load data, which can greatly narrow the scope of subsequent analysis and improve the overall judgment efficiency. Then, in the screened time period, the correlation between the actual light intensity and the load data is combined for further confirmation. This detailed analysis can ensure the accuracy of the judgment, avoid misjudgment or missed judgment, accurately identify distributed photovoltaic power generation, and improve the regulation performance and stability of the power grid.

[0077] In one embodiment, a process of grouping actual load data by time according to geographical conditions is introduced. Step 202 includes:

[0078] According to the geographical conditions, the daytime hours are grouped according to the range of light intensity to obtain time groups; and the load data are divided into multiple groups of actual load data according to the time groups.

[0079] For example, taking Beijing time in a certain place as an example, Beijing time 7:00-9:00, Beijing time 9:00-11:00, Beijing time 11:00-14:00 and Beijing time 14:00-17:00 are in different light intensity ranges, and the extracted daytime actual load data are divided into the first group of load information, the second group of load information, the third group of load information, and the fourth group of load information according to the light intensity range. Then the first group of load information corresponds to the time period: Beijing time 7:00-9:00, the second group of load information corresponds to the time period: Beijing time 9:00-11:00, the third group of load information corresponds to the time period: Beijing time 11:00-14:00, and the fourth group of load information corresponds to the time period: Beijing time 14:00-17:00.

[0080] This embodiment can more accurately reflect the characteristics of load changes in different time periods by grouping the time according to the light intensity under geographical conditions. For example, people's electricity consumption behavior may be significantly different during periods of sufficient light and periods of weak light, and this grouping method helps capture these subtle changes.

[0081] In one embodiment, a process of determining whether there is a possibility of distributed photovoltaic power generation in a corresponding time period based on multiple sets of actual load data is introduced. Step 203 includes:

[0082] Step 2031, obtaining historical load data without photovoltaic power generation in the same time period as the time period corresponding to the multiple groups of actual load data.

[0083] Step 2032: Use the load value in the historical load data without photovoltaic power generation as a load reference value.

[0084] Exemplarily, historical load values ​​without photovoltaic power generation in time periods corresponding to the first group of load information, the second group of load information, the third group of load information, and the fourth group of load information in the historical load data are extracted as load reference values.

[0085] Step 2033, calculating the average value of the load reference value for each preset time period.

[0086] Exemplarily, for example, the preset time period is 10 minutes, and the average value of the load reference value every 10 minutes in the corresponding time period is calculated.

[0087] Step 2034, calculating the difference characteristic value between the average value of the load reference value for each preset time period and the actual load.

[0088] For example, the difference characteristic value η between the average value of the load reference value per preset time period and the actual load P The calculation formula is:

[0089]

[0090] Where N is the average value of the load reference value for the day, P(n) is the actual load, and P c (n) is the average value of the load reference value, that is, the average value of the above load reference value per preset time period.

[0091] Step 2035, comparing the difference characteristic value with a preset difference characteristic reference value. If the difference characteristic value is greater than the preset difference characteristic reference value, there is a possibility of distributed photovoltaic power generation in the corresponding period of the measured area.

[0092] Exemplarily, the difference characteristic reference value corresponding to the time period of the measured area is selected according to the real-time light intensity, and the difference characteristic reference value η P,z The selection formula is:

[0093]

[0094] Among them, G is the real-time light intensity, G c,st is the calibration value of real-time light intensity, P c,st It is the calibrated value of the average value of the load reference value per preset time period.

[0095] If the difference characteristic value is greater than the preset difference characteristic reference value, the difference characteristic value is large, the actual load is small, and the difference with the historical load situation without photovoltaic power generation is large, indicating that distributed photovoltaic power generation may inject electrical energy into the power grid at this time.

[0096] Exemplarily, judging whether there is a possibility of distributed photovoltaic power generation in a corresponding time period according to multiple sets of actual load data also includes:

[0097] If the difference characteristic value is less than or equal to the preset difference characteristic reference value, there is no possibility of distributed photovoltaic power generation in the corresponding period of the tested area.

[0098] If the difference characteristic value is less than the preset difference characteristic reference value, the difference characteristic value is small and the actual load is large, indicating that the distributed photovoltaic power generation has not injected electric energy into the grid at this time; if the difference characteristic value is equal to the preset difference characteristic reference value, the load situation at this time is the same as the historical load situation without photovoltaic power generation, indicating that the distributed photovoltaic power generation has not injected electric energy into the grid at this time.

[0099] In one embodiment, in step 204, after initially determining the possibility of the existence of distributed photovoltaic power generation, a second accurate determination of the existence of distributed photovoltaic power generation is made. Determining whether there is distributed photovoltaic power generation in the test area in the corresponding period of time based on the correlation between the actual light intensity and the actual load data includes:

[0100] Step 2041, calculating a first variation coefficient of actual light intensity within a sampling period of a corresponding time period.

[0101] Exemplarily, the first variation coefficient k of the actual light intensity is G The calculation formula is:

[0102]

[0103] Wherein, T is the sampling period; G(t) is the actual light intensity at the current time t; G(t+1) is the actual light intensity at the next time t+1.

[0104] Step 2042, calculating a second variation coefficient of the actual load data within a sampling period of the corresponding time period.

[0105] For example, the second variation coefficient k of the actual load data is P The calculation formula is:

[0106]

[0107] Among them, P(t) is the actual load at the current time t; P(t+1) is the actual load at the next time t+1.

[0108] Step 2043, calculating the actual correlation between the first variation coefficient and the second variation coefficient within a sampling period of the corresponding time period.

[0109] Exemplarily, the calculation formula for the actual correlation λ between the first variation coefficient and the second variation coefficient is:

[0110]

[0111] Among them, k G is the first coefficient of variation, k P is the second coefficient of variation.

[0112] Step 2044, calculate the reference correlation between the coefficient of variation of the historical light intensity and the coefficient of variation of the historical load data within a sampling period of the same period as the corresponding period.

[0113] The corresponding time period and the same time period at least include the photovoltaic power generation time period. Of course, it can also include the photovoltaic power generation time period and the non-photovoltaic power generation time period.

[0114] For example, the sampling period in which the average light intensity of a sampling cycle is the same as the current actual light intensity is obtained, and the correlation between the variation coefficient of the historical light intensity of the sampling period and the variation coefficient of the historical load data is used as the reference correlation of the corresponding period. The reference correlation λ of the corresponding period is 1 The specific calculation formula is:

[0115]

[0116] Among them, k G1 k is the coefficient of variation of the light intensity during the same sampling period when the average value of the historical light intensity of a sampling period is the same as the current actual light intensity. P1 It is the coefficient of variation of the historical load data in the sampling period when the average value of the historical light intensity in a sampling period is the same as the current actual light intensity.

[0117]

[0118] Among them, G 1 (t) is the light intensity during the sampling period when the average value of the historical light intensity of a sampling period is the same as the current actual light intensity.

[0119]

[0120] Among them, P 1 (t) is the load information of the sampling period in which the average value of the historical light intensity of a sampling period is the same as the current actual light intensity.

[0121] Step 2045, compare the actual correlation and the reference correlation within a sampling period of the corresponding time period. If the actual correlation is greater than or equal to the reference correlation, distributed photovoltaic power generation exists in the corresponding time period of the measured area.

[0122] This embodiment can more accurately capture the dynamic correlation between the light intensity and the load data by calculating the coefficient of variation of the actual light intensity and the actual load data in the corresponding time period and comparing the actual correlation between them. Compared with the comparison of a single data point, this method can better reflect the overall change trend and correlation of the system. Further, by introducing historical data as a reference, the rationality of the actual correlation can be further verified, thereby improving the accuracy and reliability of the judgment. By comparing the actual correlation and the reference correlation, it is possible to effectively identify whether there is distributed photovoltaic power generation in the area to be tested during the corresponding time period. This method is not only applicable to time periods with sufficient sunlight during the day, but can also be extended to other time periods where photovoltaic power generation may exist through analysis of historical data.

[0123] This method does not rely on specific hardware or equipment, and only needs to collect actual light intensity and load data, and perform corresponding calculations and analysis. This makes the system highly flexible and adaptable, and can adapt to distributed photovoltaic power generation systems of different sizes and types.

[0124] The distributed photovoltaic power generation identification method of the embodiment of the present application can more accurately determine whether there is distributed photovoltaic power generation in a specific period of time by comprehensively considering the actual load data and actual environmental data of the area to be tested, especially the actual light intensity, which is more reliable than relying solely on load data or light data. It also processes the actual load data in a time grouping manner, which helps to identify the photovoltaic power generation situation in different time periods, and helps power grid operators or energy managers to better understand the distribution and change trends of distributed photovoltaic power generation, so as to make more scientific energy scheduling and management decisions. In addition, the time period in which distributed photovoltaic power generation may exist is preliminarily screened out through the actual load data, which can greatly narrow the scope of subsequent analysis and improve the overall judgment efficiency. Then, in the screened time period, the correlation between the actual light intensity and the load data is further confirmed. This detailed analysis can ensure the accuracy of the judgment, avoid misjudgment or missed judgment, accurately identify distributed photovoltaic power generation, and improve the regulation performance and stability of the power grid.

[0125] See also Figure 3 The embodiment of the present application provides a distributed photovoltaic power generation identification device, which applies the distributed photovoltaic power generation identification method of the above embodiment, including a data acquisition module 301, a grouping module 302, a first identification module 303 and a second identification module 304.

[0126] The data acquisition module 301 is used to acquire actual load data and actual environmental data of the area to be tested, where the actual environmental data includes actual light intensity.

[0127] The grouping module 302 is used to group the actual load data by time according to geographical conditions to obtain multiple groups of actual load data.

[0128] The first identification module 303 is used to determine whether there is a possibility of distributed photovoltaic power generation in a corresponding period according to multiple groups of actual load data.

[0129] The second identification module 304 is used to determine whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data if there is a possibility of distributed photovoltaic power generation.

[0130] Exemplarily, the first identification module 303 is specifically used for:

[0131] Obtain historical load data without photovoltaic power generation in the same period as the period corresponding to the multiple sets of actual load data;

[0132] The load value in the historical load data without photovoltaic power generation is used as the load reference value;

[0133] Calculate the average value of the load reference value for each preset time period;

[0134] Calculate the difference characteristic value between the average value of the load reference value per preset time period and the actual load;

[0135] The difference characteristic value is compared with a preset difference characteristic reference value. If the difference characteristic value is greater than the preset difference characteristic reference value, there is a possibility of distributed photovoltaic power generation in the corresponding period of the measured area.

[0136] Exemplarily, the calculation formula for the difference characteristic value between the average value of the load reference value per preset time period and the actual load is:

[0137]

[0138] Where N is the average value of the load reference value for the day, P(n) is the actual load, and P c (n) is the average value of the load reference value.

[0139] Exemplarily, in step 304, judging whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data includes:

[0140] Calculate a first variation coefficient of the actual light intensity within a sampling period of the corresponding time period;

[0141] Calculate a second variation coefficient of the actual load data within a sampling period of the corresponding time period;

[0142] Calculate the actual correlation between the first variation coefficient and the second variation coefficient within a sampling period of the corresponding time period;

[0143] Calculate the reference correlation between the coefficient of variation of historical light intensity and the coefficient of variation of historical load data within a sampling period in the same period as the corresponding period; the same period as the corresponding period at least includes the photovoltaic power generation period;

[0144] Compare the actual correlation and the reference correlation within a sampling cycle of the corresponding time period. If the actual correlation is greater than or equal to the reference correlation, there is distributed photovoltaic power generation in the corresponding time period of the measured area.

[0145] Exemplarily, the first variation coefficient k of the actual light intensity is G The calculation formula is:

[0146]

[0147] Where T is the sampling period; G(t) is the actual light intensity at the current time t; G(t+1) is the actual light intensity at the next time t+1;

[0148] The second variation coefficient k of the actual load data P The calculation formula is:

[0149]

[0150] Among them, P(t) is the actual load at the current time t; P(t+1) is the actual load at the next time t+1.

[0151] Exemplarily, the calculation formula for the actual correlation λ between the first variation coefficient and the second variation coefficient is:

[0152]

[0153] Among them, k G is the first coefficient of variation, k P is the second coefficient of variation.

[0154] Exemplarily, step 302 specifically includes:

[0155] According to geographical conditions, the time of day is grouped according to the range of light intensity to obtain time groups;

[0156] The load data is divided into multiple groups of actual load data according to time grouping.

[0157] Exemplarily, the second identification module 304 is further used for:

[0158] If there is no possibility of distributed photovoltaic power generation, it is directly determined that there is no distributed photovoltaic power generation in the tested area during the corresponding period.

[0159] Exemplarily, in the first identification module 303, judging whether there is a possibility of distributed photovoltaic power generation in the corresponding time period according to multiple sets of actual load data also includes:

[0160] If the difference characteristic value is less than or equal to the preset difference characteristic reference value, there is no possibility of distributed photovoltaic power generation in the corresponding period of the tested area.

[0161] For the beneficial effects of a distributed photovoltaic power generation identification device, please refer to the beneficial effects of a distributed photovoltaic power generation identification method.

[0162] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0163] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic power generation identification method, characterized in that: include: Obtain actual load data and actual environmental data of the area to be tested; The actual environment data includes actual light intensity; The actual load data is grouped by time according to geographical conditions to obtain multiple groups of actual load data; Determine whether there is a possibility of distributed photovoltaic power generation in the corresponding period based on multiple sets of actual load data; If there is a possibility of distributed photovoltaic power generation, it is determined whether there is distributed photovoltaic power generation in the tested area in the corresponding period according to the correlation between the actual light intensity and the actual load data.

2. The distributed photovoltaic power generation identification method according to claim 1, characterized in that: The determining whether there is a possibility of distributed photovoltaic power generation in a corresponding time period according to multiple sets of actual load data includes: Acquire historical load data without photovoltaic power generation in the same period as the period corresponding to the multiple groups of actual load data; Using the load value in the historical load data without photovoltaic power generation as a load reference value; Calculate the average value of the load reference value for each preset time period; Calculate the difference characteristic value between the average value of the load reference value per preset time period and the actual load; The difference characteristic value is compared with a preset difference characteristic reference value. If the difference characteristic value is greater than the preset difference characteristic reference value, there is a possibility of distributed photovoltaic power generation in the corresponding period of the measured area.

3. The distributed photovoltaic power generation identification method according to claim 2, characterized in that: The difference characteristic value η between the average value of the load reference value per preset time length and the actual load P The calculation formula is: The load reference value average value.

4. The distributed photovoltaic power generation identification method according to claim 1, characterized in that: The determining whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data includes: Calculate a first variation coefficient of the actual light intensity within a sampling period of a corresponding time period; Calculate a second variation coefficient of the actual load data within a sampling period of a corresponding time period; Calculate the actual correlation between the first variation coefficient and the second variation coefficient within a sampling period of the corresponding time period; Calculate the reference correlation between the coefficient of variation of historical light intensity and the coefficient of variation of historical load data within a sampling period in the same period as the corresponding period; the same period as the corresponding period at least includes the photovoltaic power generation period; The actual correlation and the reference correlation are compared within a sampling period of a corresponding time period. If the actual correlation is greater than or equal to the reference correlation, distributed photovoltaic power generation exists in the corresponding time period of the measured area.

5. The distributed photovoltaic power generation identification method according to claim 4, characterized in that: The first variation coefficient k of the actual light intensity G The calculation formula is: Where T is the sampling period; G(t) is the actual light intensity at the current time t; G(t+1) is the actual light intensity at the next time t+1; The second variation coefficient k of the actual load data P The calculation formula is: Among them, P(t) is the actual load at the current time t; P(t+1) is the actual load at the next time t+1.

6. The distributed photovoltaic power generation identification method according to claim 4, characterized in that: The calculation formula of the actual correlation λ between the first variation coefficient and the second variation coefficient is: Among them, k G is the first variation coefficient, k P is the second variation coefficient.

7. The distributed photovoltaic power generation identification method according to claim 1, characterized in that: The actual load data is grouped by time according to geographical conditions to obtain multiple groups of actual load data, including: According to geographical conditions, the time of day is grouped according to the range of light intensity to obtain time groups; The load data is divided into a plurality of groups of actual load data according to the time grouping.

8. The distributed photovoltaic power generation identification method according to any one of claims 1 to 7, characterized in that: Also includes: If there is no possibility of distributed photovoltaic power generation, it is directly determined that there is no distributed photovoltaic power generation in the tested area during the corresponding period.

9. The distributed photovoltaic power generation identification method according to claim 2, characterized in that: The determining whether there is a possibility of distributed photovoltaic power generation in a corresponding time period according to the multiple sets of actual load data also includes: If the difference characteristic value is less than or equal to the preset difference characteristic reference value, there is no possibility of distributed photovoltaic power generation in the corresponding period of the tested area.

10. A distributed photovoltaic power generation identification device, characterized in that: The distributed photovoltaic power generation identification method according to any one of claims 1 to 9 is applied, comprising: A data acquisition module, used to acquire actual load data and actual environmental data of the area to be tested, wherein the actual environmental data includes actual light intensity; A grouping module, used for grouping the actual load data by time according to geographical conditions to obtain multiple groups of actual load data; The first identification module is used to determine whether there is a possibility of distributed photovoltaic power generation in a corresponding period according to multiple sets of actual load data; The second identification module is used to determine whether there is distributed photovoltaic power generation in the test area in the corresponding period according to the correlation between the actual light intensity and the actual load data if there is the possibility of distributed photovoltaic power generation.

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