A communication power supply control method with multi - energy power supply

Abstract

The present invention relates to the field of multi - energy power supply control, and particularly to a communication power supply control method for multi - energy power supply. This method first obtains the power consumption data of each time period of each day in different cycles and the power generation data of different types of power generation facilities, and obtains the predicted meteorological data and actual meteorological data of different meteorological types every day. According to the change differences of the power consumption data of each time period between different days, the power consumption of each time period of the next day is predicted. According to the power generation data of each power generation facility at the same time period in the current day, and the differences between the predicted meteorological data and actual meteorological data of the meteorological types relied on by each type of power generation facility, the power generation of each time period of the next day is predicted. Furthermore, the power supply shortage time periods in the next day are extracted, and the power supply methods for the power supply shortage time periods are controlled. The present invention can effectively control the communication power supply of multi - energy power supply, ensure the power supply stability, and improve the effect of the final power supply control.

Description

Technical Field

[0001] The present invention relates to the field of multi - energy power supply control, and particularly to a communication power supply control method for multi - energy power supply. Background Art

[0002] With the rapid development of communication technology, the control of communication power supply with multi - energy power supply has become the mainstream trend in the industry. This control method effectively integrates various power sources such as commercial power, solar power generation, wind power generation, and energy storage batteries. It can not only improve the stability and reliability of the communication power supply system, but also significantly enhance the energy utilization efficiency, reduce the operation cost, and provide a strong guarantee for the sustainable development of the communication industry.

[0003] In related technologies, power supply services are mainly implemented by comprehensively scheduling and controlling various energy sources such as commercial power, solar power generation, and wind power generation, and relying on energy storage batteries. However, since renewable energy power generation methods such as solar power generation and wind power generation are easily affected by relevant weather and meteorology, the power supply stability is poor. At the same time, the frequent discharge of energy storage batteries easily causes attenuation problems of battery capacity and service life, resulting in the inability to effectively control the communication power supply with multi - energy power supply through existing methods, thereby reducing the effect of the final power supply control. Summary of the Invention

[0004] In order to solve the technical problem that the existing method cannot effectively control the communication power supply with multi - energy power supply, thereby reducing the effect of the final power supply control, the purpose of the present invention is to provide a communication power supply control method for multi - energy power supply, and the specific technical solution adopted is as follows:

[0005] The present invention proposes a communication power supply control method for multi - energy power supply, and the method includes:

[0006] Obtain the power consumption data of each time period of each day in different cycles within a preset historical time period and the power generation data of different types of power generation facilities, and at the same time obtain the predicted meteorological data and actual meteorological data of different meteorological types of each day within the preset historical time period, where each type of power generation facility depends on one meteorological type;

[0007] Take the last period as the current period, take the other periods except the current period as historical periods, take the last day as the current day, take the other days except the current day as historical days, and obtain the fluctuation similarity of each historical period according to the difference in the distribution of all the electricity consumption data between each historical period and the current period; based on the fluctuation similarity, screen out the matching historical periods of the current period from all the historical periods; obtain the deviation degree of each historical day in the matching historical periods according to the difference in the electricity consumption data of the same time periods between each historical day and the current day in the matching historical periods; based on the deviation degree, screen out the matching historical days of the current day from all the historical days in the matching historical periods; obtain the predicted electricity consumption of each time period of the next day of the current day according to the change in the electricity consumption data of each time period in the next historical day of the matching historical day, the electricity consumption data of the last time period of the current day, and the deviation degree of the matching historical day.

[0008] Obtain the meteorological deviation degree of each meteorological type according to the difference between the predicted meteorological data and the actual meteorological data of the same day within the preset historical time period for each meteorological type; obtain the predicted power generation of each time period of the next day of the current day according to the power generation data of each power generation facility at the same time periods of the current day and the meteorological deviation degree of the meteorological type relied on by each type of power generation facility.

[0009] Obtain the power supply shortage time periods of the next day of the current day according to the difference between the predicted electricity consumption and the predicted power generation at the same time periods of the next day of the current day; control the power supply methods for each power supply shortage time period according to the predicted electricity consumption of each time period in each power supply shortage time period and the number of all time periods.

[0010] Further, the obtaining of the fluctuation similarity of each historical period includes:

[0011] Take the current period or any one of the historical periods as the target period, and take the average value of the electricity consumption data of all time periods of all days in the target period as the overall electricity consumption of the target period;

[0012] Take the standard deviation of the electricity consumption data of all time periods of all days in the target period as the electricity consumption dispersion degree of the target period;

[0013] Obtain the fluctuation similarity of each historical period based on the calculation formula of the fluctuation similarity, and the calculation formula of the fluctuation similarity is:

[0014] ,

[0015] where represents the fluctuation similarity of the th historical period; represents the overall power consumption of the nth historical period; represents the overall power consumption of the current period; represents the power consumption dispersion of the nth historical period; represents the power consumption dispersion of the current period; represents the normalization function.

[0016] Furthermore, the screening of the matching historical periods of the current period from all historical periods includes:

[0017] Taking the historical period corresponding to the maximum value of the fluctuation similarity as the matching historical period of the current period.

[0018] Furthermore, obtaining the deviation degree of each historical day in the matching historical period includes:

[0019] Taking any historical day in the matching historical period as the target historical day;

[0020] Taking the sequence formed by sorting the power consumption data of all time periods in the target historical day in chronological order as the first power consumption sequence of the target historical day, and taking the sequence formed by sorting the power consumption data of all time periods in the current day in chronological order as the second power consumption sequence of the current day;

[0021] Normalizing the mean square error between the first power consumption sequence and the second power consumption sequence as the deviation degree of the target historical day.

[0022] Furthermore, the screening of the matching historical day of the current day from all historical days in the matching historical period includes:

[0023] Taking the historical day corresponding to the maximum value of the deviation degree in the matching historical period as the matching historical day of the current day.

[0024] Furthermore, obtaining the predicted power consumption of each time period of the next day of the current day includes:

[0025] Taking the next historical day of the matching historical day as the reference historical day, taking any time period in the reference historical day as the target time period, and taking the difference between the power consumption data between the target time period and the adjacent previous time period as the power consumption change amount of the target time period;

[0026] Based on the calculation formula of the predicted power consumption, obtaining the predicted power consumption of each time period of the next day of the current day, and the calculation formula of the predicted power consumption is:

[0027] ,

[0028] ,

[0029] wherein, represents the predicted power consumption of the th time period of the day after the current day; represents the initial power consumption of the th time period of the day after the current day; represents the degree of deviation from the matching historical days; represents the power consumption data of the last time period of the current day; represents the th power consumption change of the reference historical day in the

[0030] Further, the obtaining of the meteorological deviation degree of each meteorological type includes:

[0031] Taking any one meteorological type as the target meteorological type, and taking the absolute value of the difference between the predicted meteorological data and the actual meteorological data of the target meteorological type on the same day within a preset historical time period as the initial deviation degree of the target meteorological type per day;

[0032] Normalizing the average value of the initial deviation degrees of the target meteorological type over all days to obtain the meteorological deviation degree of the target meteorological type.

[0033] Further, the obtaining of the predicted power generation of each time period of the day after the current day includes:

[0034] Based on the calculation formula of the predicted power generation, obtaining the predicted power generation of each time period of the day after the current day, and the calculation formula of the predicted power generation is:

[0035] ,

[0036] wherein, represents the predicted power generation of the th time period of the day after the current day; represents the power generation data of the th type of power generation facility in the th time period of the current day; represents the th meteorological deviation degree of the meteorological type relied on by the th type of power generation facility;

[0037] Further, the obtaining of the power supply shortage time period of the day after the current day includes:

[0038] On the day after the current day, the time period when the predicted power generation is less than the predicted power consumption is used as the time period to be analyzed, and the time period composed of consecutive time periods to be analyzed is used as the power supply shortage time period.

[0039] Further, the control of the power supply method for each power supply shortage time period includes:

[0040] Taking any power supply shortage time period in the day after the current day as the target power supply shortage time period, taking the sum value of the predicted power consumption of all time periods in the target power supply shortage time period as the numerator, taking the total power of the energy storage battery as the denominator, and taking the ratio as the power consumption ratio of the target power supply shortage time period;

[0041] After comprehensively processing the power consumption ratio of the target power supply shortage time period and the number of all time periods in the target power supply shortage time period and performing normalization processing, the power supply necessity of the energy storage battery in the target power supply shortage time period is obtained;

[0042] When the power supply necessity of the energy storage battery in the target power supply shortage time period is greater than the preset necessity threshold, then within the target power supply shortage time period, the power supply method is switched from the power generation facility to the energy storage battery, otherwise, within the target power supply shortage time period, the power supply method is switched from the power generation facility to the mains power supply.

[0043] The present invention has the following beneficial effects:

[0044] Considering that the existing methods cannot effectively control the communication power supply with multiple energy sources, reducing the effect of the final power supply control, this invention first obtains the power consumption data of each time period of each day in different cycles within a preset historical time period and the power generation data of different types of power generation facilities, and obtains the predicted meteorological data and actual meteorological data of different meteorological types every day. Since this invention needs to predict the future power consumption based on historical power consumption data and predict the future power generation based on historical power generation data, it is convenient to accurately predict the power supply shortage phenomenon of power generation facilities in the future and timely switch the power supply method to ensure power supply stability. At the same time, considering that the power consumption of users is affected by various factors such as environment and season, resulting in a certain similarity in power consumption in different cycles, therefore, multiple cycles are first divided into the current cycle and historical cycles, and multiple days are divided into the current day and historical days, and the fluctuation similarity is used to reflect the similarity degree of the fluctuation changes of power consumption data in time series between each historical cycle and the current cycle, so as to screen out the matching historical cycles with similar fluctuation change characteristics of power consumption data to the current cycle. Furthermore, the deviation degree is used to more detailedly reflect the difference in power consumption data of the same time period between each day in the matching historical cycle and the current day, and the historical days with the same power consumption data as each time period of the current day are screened out. Then, the predicted power consumption of each time period of the next day of the current day is obtained, realizing the accurate prediction of the power consumption of each time period of the next day. Since when predicting the power generation of power generation facilities, the meteorological type on which each type of power generation facility depends can directly affect the power generation of this power generation facility, the greater the difference between the predicted meteorological data and the actual meteorological data of the meteorological type, the greater the interference to the prediction of the power generation of the power generation facility depending on this meteorological type. Therefore, the meteorological deviation degree can be first used to reflect the deviation situation between the predicted meteorological data and the actual meteorological data of each meteorological type, and then the predicted power generation of each time period of the next day of the current day is obtained, realizing the accurate prediction of the power generation of each time period of the next day. Furthermore, the power supply shortage time period within the next day is accurately extracted, and the power supply method during the power supply shortage time period is timely switched and controlled to ensure power supply stability and reduce the frequent discharge of energy storage batteries, improving the effect of the final power supply control. Brief Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0046] Figure 1 It is a flowchart of a communication power supply control method with multiple energy sources provided by an embodiment of the present invention. Detailed Implementation Manner

[0047] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a communication power supply control method with multi-energy power supply according to the present invention, including its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0049] The following specifically describes the specific solution of a communication power supply control method with multi-energy power supply provided by the present invention in conjunction with the accompanying drawings.

[0050] Please refer to Figure 1 , which shows a flowchart of a communication power supply control method with multi-energy power supply provided by an embodiment of the present invention. The method includes:

[0051] Step S1: Obtain the power consumption data of each time period of each day in different cycles within a preset historical time period and the power generation data of different types of power generation facilities, and at the same time obtain the predicted meteorological data and actual meteorological data of different meteorological types of each day within the preset historical time period, where each type of power generation facility depends on one meteorological type.

[0052] In a multi-energy power supply system, the communication power supply is mainly composed of various types of renewable energy such as solar energy and wind energy, relying on energy storage batteries and commercial power. The usual power supply strategy is to give priority to using renewable energy for power supply. If the renewable energy cannot fully meet the power demand, then switch to using the energy storage battery for power supply, and finally consider commercial power as a supplement.

[0053] In the embodiment of the present invention, first, power monitoring devices such as smart meters in the multi-energy power supply system are used to collect the power consumption data of each time period of each day in different cycles within a preset historical time period by users, and collect the power generation data of different types of power generation facilities in each time period of each day in different cycles within the preset historical time period. The preset historical time period is set to three months, the length of each cycle is set to one week, that is, seven days, and the length of each time period is set to one hour, that is, one day is divided into 24 time periods. The power consumption data and power generation data of each time period respectively represent the electric energy consumed and generated in that time period, and the unit is kilowatt-hour.

[0054] Among them, the power generation facilities in the embodiments of the present invention specifically refer to power generation facilities that utilize renewable energy. Different types of power generation facilities include, for example, wind turbines that generate electricity using wind energy and solar panels that generate electricity using solar energy.

[0055] At the same time, since the power generation of power generation facilities is directly affected by weather and meteorology, the embodiments of the present invention also need to use a meteorological service API to obtain the predicted meteorological data and actual meteorological data of different meteorological types for each day within a preset historical time period, so as to take into account the weather and meteorological influence factors when predicting the power generation later, and improve the accuracy of power generation prediction. Among them, there is a certain deviation between the predicted meteorological data and the actual meteorological data of a certain meteorological type on a certain day. Different meteorological types include, for example, light radiation intensity and wind speed.

[0056] It should be noted that the meteorological types analyzed in the embodiments of the present invention are directly related to the power generation facilities. That is to say, each type of power generation facility depends on a meteorological type. For example, a wind turbine that generates electricity using wind energy depends on wind speed, while a solar panel that generates electricity using solar energy depends on light radiation intensity.

[0057] Step S2: Take the last cycle as the current cycle, take the other cycles except the current cycle as historical cycles, take the last day as the current day, take the other days except the current day as historical days, and obtain the fluctuation similarity of each historical cycle according to the difference in the distribution of all electricity consumption data between each historical cycle and the current cycle; based on the fluctuation similarity, screen out the matching historical cycle of the current cycle from all historical cycles; obtain the deviation degree of each historical day in the matching historical cycle according to the difference in the electricity consumption data at the same time period between each historical day and the current day in the matching historical cycle; based on the deviation degree, screen out the matching historical day of the current day from all historical days in the matching historical cycle; obtain the predicted electricity consumption for each time period of the next day of the current day according to the change in the electricity consumption data for each time period in the next historical day of the matching historical day, the electricity consumption data for the last time period of the current day, and the deviation degree of the matching historical day.

[0058] Since the present invention needs to predict future power consumption based on historical power consumption data, and predict future power generation based on historical power generation data, so as to facilitate subsequent accurate prediction of power supply shortage phenomena of power generation facilities, so as to timely switch the power supply mode to ensure power supply stability. At the same time, considering that the power consumption of users is affected by various factors such as environment and season, resulting in certain similarities in power consumption in different cycles. Therefore, in order to accurately predict future power consumption in the subsequent process, the embodiments of the present invention first take the last cycle as the current cycle, take other cycles except the current cycle as historical cycles, take the last day as the current day, and take other days except the current day as historical days. Then, it is necessary to initially screen out historical cycles similar to the fluctuation change characteristics of the power consumption data in the current cycle from all historical cycles, so as to accurately predict power consumption in the subsequent process. Therefore, the distribution differences of all power consumption data between each historical cycle and the current cycle can be analyzed first, and the obtained fluctuation similarity reflects the similarity degree of the fluctuation changes of the power consumption data between each historical cycle and the current cycle in time series. Subsequently, based on the fluctuation similarity, the historical cycle with the most similar fluctuation change characteristics to the power consumption data of the current cycle can be accurately screened out.

[0059] Preferably, in an embodiment of the present invention, the method for obtaining the fluctuation similarity of each historical cycle specifically includes:

[0060] Take the current cycle or any historical cycle as the target cycle, and take the average value of the power consumption data of all time periods of all days in the target cycle as the overall power consumption of the target cycle, and reflect the overall level of the power consumption data of all time periods of all days in the target cycle through the overall power consumption.

[0061] Take the standard deviation of the power consumption data of all time periods of all days in the target cycle as the power consumption dispersion degree of the target cycle, and reflect the dispersion degree of the power consumption data of all time periods of all days in the target cycle through the power consumption dispersion degree.

[0062] Through the above same method, the overall power consumption and the power consumption dispersion degree of the current cycle and any historical cycle can be obtained, and then based on the calculation formula of the fluctuation similarity, the fluctuation similarity of each historical cycle can be obtained. The calculation formula of the fluctuation similarity is:

[0063] ,

[0064] Wherein, represents the fluctuation similarity of the th historical cycle; represents the overall power consumption of the th historical cycle; represents the overall power consumption of the current cycle; represents the power consumption dispersion of the nth historical period; represents the power consumption dispersion of the current period; represents the normalization function.

[0065] Among them, is used to reflect the difference in the overall power consumption between the nth historical period and the current period. The smaller it is, the closer the overall distribution level of the power consumption data between the nth historical period and the current period, and further indicates that the fluctuation characteristics of the two are more similar. is used to reflect the difference in the power consumption dispersion between the nth historical period and the current period. The smaller it is, the more similar the discrete characteristics of the power consumption data between the nth historical period and the current period, and further indicates that the fluctuation characteristics of the two are more similar. Then, is subjected to negative correlation normalization processing, and the calculation result is limited to the range.

[0066] In an embodiment of the present invention, the normalization processing can be specifically, for example, the maximum-minimum normalization processing. And the normalization in subsequent steps can all adopt the maximum-minimum normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, which will not be elaborated here.

[0067] The greater the fluctuation similarity of each historical period, the more similar the fluctuation change characteristics of the power consumption data of this historical period and the power consumption data of the current period. Therefore, based on the fluctuation similarity, the historical period with the most similar fluctuation change characteristics to the power consumption data of the current period can be preliminarily screened out from all historical periods, that is, the matching historical period of the current period, which is convenient for subsequent screening of the historical days with the same power consumption data as each time period of the current day among all historical days in the matching historical period, improving the accuracy of the subsequent power consumption prediction for each time period of the next day, and further improving the control effect of the communication power supply for multi-energy power supply.

[0068] Preferably, in an embodiment of the present invention, the historical period corresponding to the maximum value of the fluctuation similarity is used as the matching historical period of the current period.

[0069] In the subsequent steps of the embodiments of the present invention, it is necessary to predict the electricity consumption of the day after the current day in the matching historical period based on the electricity consumption data of the day after the historical day that is most consistent with the electricity consumption data of the current day, so as to improve the accuracy of electricity consumption prediction. The smaller the difference in the electricity consumption data of the same time period between a certain historical day and the current day in the matching historical period, the more consistent the electricity consumption data between the historical day and the current day in terms of time series. Therefore, the difference in the electricity consumption data of the same time period between each historical day and the current day in the matching historical period can be analyzed first, and the deviation degree obtained can more detailedly reflect the difference in the electricity consumption data of the same time period between each day in the matching historical period and the current day. Subsequently, based on the deviation degree, the historical day that is most consistent with the electricity consumption data of each time period of the current day can be accurately selected from all the historical days in the matching historical period, so as to improve the accuracy of predicting the electricity consumption of each time period of the day after the current day.

[0070] Preferably, in an embodiment of the present invention, the method for obtaining the deviation degree of each historical day in the matching historical period specifically includes:

[0071] Take any historical day in the matching historical period as the target historical day, and the sequence formed by sorting the electricity consumption data of all time periods in the target historical day in chronological order is used as the first electricity consumption sequence of the target historical day, and the sequence formed by sorting the electricity consumption data of all time periods in the current day in chronological order is used as the second electricity consumption sequence of the current day.

[0072] Normalize the mean square error between the first electricity consumption sequence and the second electricity consumption sequence as the deviation degree of the target historical day.

[0073] As an example, in an embodiment of the present invention, the expression of the deviation degree of the target historical day can be specifically, for example:

[0074] ,

[0075] where, represents the deviation degree of the target historical day; represents the first electricity consumption sequence of the target historical day; represents the second electricity consumption sequence of the current day; represents the mean square error between the first electricity consumption sequence and the second electricity consumption sequence; represents the normalization function.

[0076] where, The larger, the greater the difference in the electricity consumption data of the same time period between the target historical day and the current day, and further it shows that the electricity consumption data of each time period of the target historical day is more deviated from the electricity consumption data of each time period of the current day, and for Perform normalization processing to limit the calculation result within the range.

[0077] By the same method as described above, the deviation degree of each historical day in the matching historical period can be obtained. The smaller the deviation degree of a certain historical day in the matching historical period, the more consistent the electricity consumption data of that historical day and the electricity consumption data of the current day are in time series. Therefore, based on the deviation degree, the historical day with the most consistent electricity consumption data for each time period of the current day can be selected from all historical days in the matching historical period, that is, the matching historical day of the current day. Subsequently, based on the electricity consumption data of each time period in the next day of the matching historical day, the electricity consumption of each time period in the next day of the current day can be accurately predicted.

[0078] Preferably, in an embodiment of the present invention, the historical day corresponding to the maximum value of the deviation degree in the matching historical period is used as the matching historical day of the current day.

[0079] Since the fluctuation change characteristics of the electricity consumption data between the matching historical day and the current day are the most similar, and at the same time, the electricity consumption data will not change significantly in a short period of time, the change characteristics of the electricity consumption data between the next day of the matching historical day and the next day of the current day are also similar. Furthermore, based on the change of the electricity consumption data of each time period in the next historical day of the matching historical day and the electricity consumption data of the last time period of the current day, the electricity consumption data of each time period in the next day of the current day can be predicted. When predicting, the deviation degree of the matching historical day is considered to improve the fault tolerance of the analysis of the power supply shortage situation of subsequent power generation facilities.

[0080] Preferably, in an embodiment of the present invention, the method for obtaining the predicted electricity consumption of each time period in the next day of the current day specifically includes:

[0081] Take the next historical day of the matching historical day as the reference historical day, take any time period in the reference historical day as the target time period, and take the difference between the electricity consumption data between the target time period and the adjacent previous time period as the electricity consumption change amount of the target time period. The electricity consumption change amount is used to reflect the change amount of the electricity consumption data of the target time period relative to the electricity consumption data of the adjacent previous time period, providing a data basis for subsequent predicted electricity consumption.

[0082] It should be noted that the previous time period adjacent to the first time period of the reference historical day is the previous day of the reference historical day, that is, the last time period of the matching historical day.

[0083] Then, based on the calculation formula of the predicted electricity consumption, obtain the predicted electricity consumption of each time period in the next day of the current day. The calculation formula of the predicted electricity consumption is:

[0084] ,

[0085] ,

[0086] Among them, represents the predicted power consumption of the th time period of the day after the current day; represents the initial power consumption of the th time period of the day after the current day; represents the deviation degree of the matching historical day; represents the power consumption data of the last time period of the current day; represents the th power consumption change of the reference historical day.

[0087] Among them, reflects the cumulative change of the power consumption data of the last time period of the current day from the last time period of the current day to the th time period of the day after the current day, and and and The sum value of is initially used as the predicted result of the power consumption of the th time period of the day after the current day. At the same time, in order to improve the fault tolerance of detecting the power supply shortage period of the power generation facilities in the subsequent stage and ensure the power supply stability, the deviation degree of the matching historical day is used, and the initial power consumption of the th time period of the day after the current day is appropriately increased , and The larger it is, the more inconsistent the power consumption data between the matching historical day and the current day, and the higher the degree of adjustment.

[0088] Step S3: Obtain the meteorological deviation degree of each meteorological type according to the difference between the predicted meteorological data and the actual meteorological data of the same day in the preset historical time period for each meteorological type; obtain the predicted power generation of each time period of the day after the current day according to the power generation data of each power generation facility in the same time period of the current day and the meteorological deviation degree of the meteorological type relied on by each type of power generation facility.

[0089] The above process realizes the prediction of the power consumption in each time period of the day after the current day. In order to accurately predict the power shortage periods of the power generation facilities on the day after the current day in the subsequent process, it is also necessary to predict the power generation in each time period of the day after the current day. Since the power generation facilities in the embodiments of the present invention all generate electricity using renewable energy such as wind energy and solar energy, the power generation effect of these power generation facilities is directly affected by weather and meteorology. Moreover, the greater the difference between the predicted meteorological data and the actual meteorological data of a certain meteorological type, the greater the impact on the prediction of the power generation of the power generation facilities relying on this meteorological type. Therefore, the difference between the predicted meteorological data and the actual meteorological data of the same day of each meteorological type in a preset historical time period can be analyzed, and the deviation between the predicted meteorological data and the actual meteorological data of each meteorological type is reflected by the obtained meteorological deviation degree. Subsequently, based on the meteorological deviation degree, the prediction result of the power generation of the power generation facilities relying on a certain meteorological type can be adjusted, the fault tolerance of the detection of the power shortage periods of the power generation facilities in the subsequent process can be improved, the stability of the power supply can be ensured, and the effect of the final power supply control can be improved.

[0090] Preferably, in an embodiment of the present invention, the method for obtaining the meteorological deviation degree of each meteorological type specifically includes:

[0091] First, take any one meteorological type as the target meteorological type, and take the absolute value of the difference between the predicted meteorological data and the actual meteorological data of the target meteorological type on the same day in the preset historical time period as the initial deviation degree of the target meteorological type every day. The greater the initial deviation degree, the greater the deviation between the predicted meteorological data and the actual meteorological data of the target meteorological type on the same day.

[0092] Furthermore, the average value of the initial deviation degrees of the target meteorological type on all days can be normalized, and the calculation result is limited to within the range, so as to obtain the meteorological deviation degree of the target meteorological type.

[0093] As an example, in an embodiment of the present invention, the expression formula of the meteorological deviation degree of the target meteorological type can be specifically, for example:

[0094] ,

[0095] where, represents the meteorological deviation degree of the target meteorological type; represents the predicted meteorological data of the target meteorological type on the th day in the preset historical time period; represents the actual meteorological data of the target meteorological type on the th day in the preset historical time period; represents the number of days in the preset historical time period; represents on the Initial deviation degree of the day; Denote the normalization function.

[0096] Then, according to the power generation data of each power generation facility at the same time period in the current day, and the meteorological deviation degree of the meteorological type relied on by each type of power generation facility, the predicted power generation of each time period of the next day of the current day can be obtained, realizing the prediction of the power generation of each time period of the next day of the current day.

[0097] Preferably, in an embodiment of the present invention, the method for obtaining the predicted power generation of each time period of the next day of the current day specifically includes:

[0098] Based on the calculation formula of the predicted power generation, obtain the predicted power generation of each time period of the next day of the current day. The calculation formula of the predicted power generation is:

[0099] ,

[0100] Wherein, Denote the predicted power generation of the th time period of the next day of the current day; Denote the power generation data of the th type of power generation facility in the th time period in the current day; Denote the meteorological deviation degree of the meteorological type relied on by the th type of power generation facility; Denote the number of types of power generation facilities.

[0101] Wherein, in the embodiment of the present invention, the power generation data of all types of power generation facilities in the same time period in the current day is comprehensively considered , to predict the power generation of the corresponding time period of the next day of the current day. At the same time, in order to improve the fault tolerance of detecting the power supply shortage period of the power generation facility in the subsequent stage and ensure the stability of the power supply, is used to appropriately reduce the result of the power generation prediction of the th time period of the next day of the current day, and The larger it is, it indicates that the meteorological type relied on by the th type of power generation facility has a greater impact on the power generation of this type of power generation facility. Then, is adjusted down to a greater extent, and the prediction result of the power generation is reduced to a greater extent.

[0102] So far, through the above steps, the prediction of the power consumption and power generation of each time period of the next day of the current day is realized.

[0103] Step S4: Obtain the power shortage time period for the next day of the current day according to the difference between the predicted power consumption and the predicted power generation at the same time period of the next day of the current day; control the power supply mode for each power shortage time period according to the predicted power consumption of each time period in each power shortage time period and the number of all time periods.

[0104] Since power generation facilities using renewable energy are easily affected by weather and meteorology, resulting in insufficient power generation to meet power consumption, in order to ensure stable power supply at this time, it is necessary to switch the power supply mode. Therefore, the power shortage time period for the next day of the current day can be first obtained according to the difference between the predicted power consumption and the predicted power generation at the same time period of the next day of the current day, realizing the prediction of the power shortage situation for the next day of the current day, facilitating the subsequent timely switching of the power supply mode and ensuring stable power supply.

[0105] Preferably, in an embodiment of the present invention, the method for obtaining the power shortage time period for the next day of the current day specifically includes:

[0106] In the next day of the current day, the time periods when the predicted power generation is less than the predicted power consumption are used as the time periods to be analyzed. The time periods to be analyzed are the power shortage time periods. Then, the time period composed of consecutive time periods to be analyzed is used as the power shortage time period.

[0107] It should be noted that for a single time period to be analyzed, the time periods on both adjacent sides are non-time periods to be analyzed. At this time, this time period to be analyzed is also considered as a power shortage time period.

[0108] The greater the predicted power consumption of each time period in a certain power shortage time period and the greater the length of the power shortage time period, it indicates that the power shortage time period requires more power supply replenishment from energy storage batteries to ensure stable power supply during the power shortage time period. Therefore, the power supply mode for each power shortage time period can be controlled according to the predicted power consumption of each time period in each power shortage time period and the number of all time periods to improve the final power supply control effect.

[0109] Preferably, in an embodiment of the present invention, the method for obtaining the control of the power supply mode for each power shortage time period specifically includes:

[0110] First, take any power shortage time period in the next day of the current day as the target power shortage time period. Take the sum value of the predicted power consumption of all time periods in the target power shortage time period as the numerator, and take the total power of the energy storage battery as the denominator. Take the ratio as the power consumption ratio of the target power shortage time period, where the total power of the energy storage battery is a known value.

[0111] Then, the power consumption ratio during the target power supply shortage period and the number of all time periods in the target power supply shortage period are combined and normalized to obtain the power supply necessity of the energy storage battery during the target power supply shortage period. The greater the power supply necessity, the longer the power supply shortage time in the target power supply shortage period and the more power is required. Furthermore, it indicates that it is more necessary to use the energy storage battery for power supply replenishment to ensure stable power supply.

[0112] In an embodiment of the present invention, the combination of the power consumption ratio during the target power supply shortage period and the number of all time periods in the target power supply shortage period can be achieved by calculating their sum value or product value, and no limitation is made here.

[0113] As an example, in an embodiment of the present invention, the expression of the power supply necessity of the energy storage battery during the target power supply shortage period can be specifically, for example:

[0114] ,

[0115] where, represents the power supply necessity of the energy storage battery during the target power supply shortage period; represents the number of all time periods in the target power supply shortage period; represents the predicted power consumption of the th time period in the target power supply shortage period; represents the total power of the energy storage battery; represents the power consumption ratio during the target power supply shortage period; represents the normalization function.

[0116] At the same time, in order to reduce the frequent discharge of the energy storage battery and avoid the attenuation problems of its battery capacity and service life, when the power supply necessity of the energy storage battery during the target power supply shortage period is greater than the preset necessity threshold, then within the target power supply shortage period, the power supply method is switched from the power generation facility power supply to the energy storage battery power supply; otherwise, within the target power supply shortage period, the power supply method is switched from the power generation facility power supply to the mains power supply. Among them, the preset necessity threshold is set to 0.4, and the specific value of the preset necessity threshold can also be set by the implementer according to the specific implementation scenario, and no limitation is made here.

[0117] By the same method as above, the power supply method for each power supply shortage period on the next day of the current day can be controlled.

[0118] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A communication power supply control method for multi-energy power supply, characterized in that: The method comprises: Obtaining electricity consumption data for each period of each day in different cycles within a preset historical time period and power generation data for different types of power generation facilities, and obtaining forecast meteorological data and actual meteorological data for different meteorological types of each day within the preset historical time period, wherein each power generation facility relies on one meteorological type; The last cycle is taken as the current cycle, the cycles other than the current cycle are taken as historical cycles, the last day is taken as the current day, the days other than the current day are taken as historical days, and the fluctuation similarity of each historical cycle is obtained according to the difference in the distribution of all the power consumption data between each historical cycle and the current cycle; based on the fluctuation similarity, the matching historical cycle of the current cycle is screened out from all historical cycles; the degree of deviation of each historical day in the matching historical cycle is obtained according to the difference in the power consumption data of the same time period between each historical day and the current day in the matching historical cycle; based on the degree of deviation, the matching historical day of the current day is screened out from all historical days in the matching historical cycle; according to the change in the power consumption data of each time period in the next historical day of the matching historical day, the power consumption data of the last time period of the current day, and the degree of deviation of the matching historical day, the predicted power consumption of each time period of the next day of the current day is obtained; According to the difference between the predicted meteorological data and the actual meteorological data of each meteorological type on the same day within the preset historical time period, the meteorological deviation degree of each meteorological type is obtained; according to the power generation data of each power generation facility in the same time period of the current day, and the meteorological deviation degree of the meteorological type on which each type of power generation facility depends, the predicted power generation in each time period of the next day of the current day is obtained; According to the difference between the predicted power consumption and the predicted power generation in the same time period on the day after the current day, the power shortage time period on the day after the current day is obtained; according to the predicted power consumption in each time period in each power shortage time period and the number of all time periods, the power supply mode of each power shortage time period is controlled.

2. A communication power supply control method for multi-energy power supply according to claim 1, characterized in that: The method of obtaining the fluctuation similarity of each historical period includes: The current cycle or any historical cycle is taken as the target cycle, and the average value of the power consumption data in all time periods on all days in the target cycle is taken as the overall power consumption of the target cycle; The standard deviation of the power consumption data in all time periods of all days in the target cycle is used as the power consumption dispersion of the target cycle; Based on the calculation formula of fluctuation similarity, the fluctuation similarity of each historical period is obtained. The calculation formula of fluctuation similarity is: ， in, Indicates Similarity of fluctuations in historical cycles; Indicates The overall electricity consumption in the historical period; Indicates the overall power consumption of the current cycle; Indicates The dispersion of electricity consumption in a historical period; Indicates the power consumption dispersion of the current cycle; Represents the normalization function.

3. A communication power supply control method for multi-energy power supply according to claim 1, characterized in that: The matching historical periods of the current period are selected from all historical periods, including: The historical period corresponding to the maximum value of fluctuation similarity is taken as the matching historical period of the current period.

4. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The obtaining of the deviation degree of each historical day in the matching historical period includes: Match any historical day in the historical period as the target historical day; The sequence formed by sorting the power consumption data of all time periods in the target historical day in chronological order is used as the first power consumption sequence of the target historical day, and the sequence formed by sorting the power consumption data of all time periods in the current day in chronological order is used as the second power consumption sequence of the current day; The mean square error between the first electricity consumption sequence and the second electricity consumption sequence is normalized and used as the degree of deviation of the target historical day.

5. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The matching historical days of the current day are selected from all the historical days in the matching historical period, including: The historical day corresponding to the maximum value of the deviation degree in the matching historical period is used as the matching historical day of the current day.

6. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The method of obtaining the predicted power consumption for each period of the next day of the current day includes: The next historical day of the matching historical day is used as a reference historical day, any time period in the reference historical day is used as a target time period, and the difference between the power consumption data of the target time period and the adjacent previous time period is used as the power consumption change of the target time period; Based on the calculation formula for predicted power consumption, the predicted power consumption for each period of the next day of the current day is obtained. The calculation formula for predicted power consumption is: ， ， in, Indicates the day after the current day. Forecasted electricity consumption for each period; Indicates the day after the current day. The initial power consumption of each period; Indicates the degree of deviation from matching historical days; Indicates the electricity consumption data for the last period of the current day; Indicates the reference historical day The change in electricity consumption during a period of time.

7. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The obtaining of the meteorological deviation degree of each meteorological type comprises: Taking any meteorological type as the target meteorological type, taking the absolute value of the difference between the predicted meteorological data and the actual meteorological data of the target meteorological type on the same day within a preset historical time period as the initial deviation degree of the target meteorological type on each day; The average value of the initial deviations of the target meteorological type on all days is normalized to obtain the meteorological deviation of the target meteorological type.

8. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The method of obtaining the predicted power generation in each time period of the next day of the current day includes: Based on the calculation formula for predicted power generation, the predicted power generation for each period of the next day of the current day is obtained. The calculation formula for predicted power generation is: ， in, Indicates the day after the current day. Forecasted power generation for each period; Indicates the current day The first Electricity generation data for various types of power generation facilities; Indicates The degree of meteorological deviation of the meteorological type on which the type of power generation facility depends; The number representing the type of power generation facility.

9. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The obtaining of the power shortage time period of the next day of the current day includes: In the next day after the current day, the time period when the predicted power generation is less than the predicted power consumption is used as the time period to be analyzed, and the time period formed by the continuous time periods to be analyzed is used as the insufficient power supply time period.

10. The method for controlling a communication power supply using multiple energy sources according to claim 1, characterized in that: The controlling of the power supply mode in each power shortage time period includes: Taking any power shortage time period in the next day after the current day as the target power shortage time period, taking the sum of the predicted power consumption of all time periods in the target power shortage time period as the numerator, taking the total power of the energy storage battery as the denominator, and taking the ratio as the power consumption proportion of the target power shortage time period; The power consumption proportion in the target power shortage time period and the number of all time periods in the target power shortage time period are integrated and normalized to obtain the power supply necessity of the energy storage battery in the target power shortage time period; When the power supply necessity of the energy storage battery during the target power supply shortage time period is greater than the preset necessity threshold, the power supply mode is switched from power supply by the power generation facility to power supply by the energy storage battery during the target power supply shortage time period; otherwise, the power supply mode is switched from power supply by the power generation facility to power supply by the AC power supply during the target power supply shortage time period.

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