A new energy confidence output information generation method, device and related equipment

Abstract

This application provides a method, apparatus, and related equipment for generating new energy confidence output information, specifically relating to the field of power system technology. The method includes: acquiring multiple output coefficients of a group of new energy equipment in the power grid during a historical period; sorting different output coefficients for different dates corresponding to the same time period within the same month in descending order to obtain multiple first sequences; determining a first coefficient in each first sequence based on a pre-set first confidence probability threshold; and generating multiple basic confidence output coefficients based on the multiple first coefficients and the multiple output coefficients, where each basic confidence output coefficient is the minimum value between the corresponding first coefficient and the corresponding output coefficient. This application can output more accurate new energy confidence output coefficients, improving the rationality and efficiency of high-proportion new energy power system power configuration during the planning process, and contributing to the early achievement of carbon peaking and carbon neutrality goals.

Description

Technical Field

[0001] This disclosure relates to the field of power system technology, specifically to a method, apparatus, and related equipment for generating new energy confidence output information. Background Technology

[0002] In recent years, new energy sources such as wind power and photovoltaics have developed rapidly, and their power generation output accounts for a gradually increasing proportion of the total grid load. However, they are highly volatile and random, and under the current technical conditions, they cannot form a stable power supply capacity. Moreover, they are greatly affected by weather factors.

[0003] In related technologies, when new energy sources participate in power balance calculations, only the reliable output of new energy sources during peak load periods or balance control periods is considered. The role of pumped storage and energy storage in transporting new energy power is not considered. That is, during low load periods, surplus power can be stored through energy storage to participate in power balance during peak load periods.

[0004] With the advancement of new power system construction, energy storage capacity is increasing. However, when considering the reliable output of new energy sources in power balance calculations, related technologies only consider the output during peak load periods or balance control periods. This is not conducive to fully leveraging the regulatory role of energy storage, resulting in a large error in the calculated reliable output ratio of new energy sources, which easily leads to an underestimation of the proportion of new energy sources participating in power balance. As the scale of new energy sources continues to expand, a low proportion of new energy sources participating in balance to ensure sufficient power supply security means that more conventional power sources may need to be configured. However, configuring more conventional power sources will lead to resource waste and poor economic efficiency of the power system. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, device, and related equipment for generating new energy confidence output information, which aims to fully consider the role of pumped storage and energy storage in regulating the power output of new energy, give full play to the ability of new energy to participate in the power balance at various times, and output a more accurate new energy confidence output ratio, so as to improve the rationality and efficiency of power system configuration with a high proportion of new energy in the power system planning process.

[0006] In a first aspect, embodiments of this disclosure provide a method for generating new energy confidence output information, the method comprising:

[0007] The power output coefficients of the new energy equipment group in the power grid are obtained in a historical period. Each power output coefficient corresponds to a date and a time period in the historical period. Different power output coefficients correspond to different dates and / or different time periods. The power output coefficient is the ratio of the power output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date.

[0008] Among the multiple output coefficients, the different output coefficients corresponding to different dates in the same month and time period are sorted in descending order to obtain multiple first sequences. Among the multiple first sequences, the different first sequences correspond to different months and / or the different first sequences correspond to different time periods.

[0009] According to a pre-set first confidence probability threshold, a first coefficient is determined in each of the first sequences to obtain a plurality of first coefficients corresponding one-to-one with the plurality of first sequences. The value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number. The first coefficient is the output coefficient in the corresponding first sequence that satisfies a first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number of the corresponding first sequence is greater than or equal to the first confidence probability threshold.

[0010] Based on the plurality of first coefficients and the plurality of output coefficients, confidence output information is generated, wherein the confidence output information includes a plurality of basic confidence output coefficients, the plurality of basic confidence output coefficients and the plurality of output coefficients are in one-to-one correspondence, and each of the basic confidence output coefficients is the minimum value of the corresponding first coefficient and the corresponding output coefficient.

[0011] Secondly, embodiments of this disclosure provide a new energy confidence output information generation device, the device comprising:

[0012] The acquisition module is used to acquire multiple output coefficients of the new energy equipment group in the power grid during a historical period. Each output coefficient corresponds to a date and a time period within the historical period. Different output coefficients correspond to different dates and / or different time periods. The output coefficient is the ratio of the output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date.

[0013] The first processing module is used to sort the different output coefficients of different dates corresponding to the same time period in the same month in descending order among the multiple output coefficients to obtain multiple first sequences, wherein the different first sequences correspond to different months and / or the different first sequences correspond to different time periods.

[0014] The second processing module is used to determine a first coefficient in each of the first sequences according to a preset first confidence probability threshold, thereby obtaining a plurality of first coefficients that correspond one-to-one with the plurality of first sequences. The value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number. The first coefficient is the output coefficient in the corresponding first sequence that satisfies a first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number of the corresponding first sequence is greater than or equal to the first confidence probability threshold.

[0015] The information generation module is used to generate confidence output information based on the plurality of first coefficients and the plurality of output coefficients, wherein the confidence output information includes a plurality of basic confidence output coefficients, the plurality of basic confidence output coefficients and the plurality of output coefficients are in one-to-one correspondence, and each of the basic confidence output coefficients is the minimum value of the corresponding first coefficient and the corresponding output coefficient.

[0016] Thirdly, this disclosure provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, performs the steps of the method described in the first aspect.

[0017] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0018] Fifthly, this disclosure provides a computer program product including computer instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0019] In this disclosure, multiple output coefficients are obtained by calculating the ratio of power output data to installed capacity of the new energy equipment group in each time period of historical periods. These output coefficients are then categorized and sorted by month and time period to form multiple first sequences. A first coefficient matching the first confidence probability threshold is determined in each first sequence, resulting in multiple first coefficients corresponding one-to-one with the multiple first sequences. A correspondence between the multiple first coefficients and the multiple output coefficients is established based on dimensions such as month, date, and time period, and the smallest value is taken to obtain multiple basic confidence output coefficients corresponding one-to-one with the multiple output coefficients. This provides a more accurate basic confidence output ratio for the new energy equipment group in each time period of each date. This ensures that pumped storage and energy storage, as regulating power sources, fully play their role in regulating new energy power, guaranteeing the full realization of the power balance effect of new energy and pumped storage / energy storage, and ensuring the rationality and efficiency of the power configuration of the high-proportion new energy power system. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for generating new energy confidence output information provided in an embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating another method for generating new energy confidence output information provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of a new energy confidence output information generation device provided in an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0025] Currently, many provinces in China have introduced policies mandating the allocation of energy storage to new energy sources. At the same time, with the advancement of the construction of new power systems, grid-side shared energy storage and user-side energy storage are being put into operation on a large scale. In addition, there are a large number of pumped storage power stations. Existing methods for new energy sources to participate in power balancing only consider the confidence output of new energy sources during peak load periods or balance control periods, without taking into account the role that a large amount of energy storage in the system can play.

[0026] Based on this, embodiments of this disclosure provide a method for generating new energy confidence output information, such as... Figure 1 As shown, the method includes:

[0027] Step 101: Obtain multiple output coefficients of the new energy equipment group in the power grid during historical periods.

[0028] Each output coefficient corresponds to a date and a time period within the historical period. Different output coefficients correspond to different dates and / or different time periods. The output coefficient is the ratio of the output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date.

[0029] A power grid can be understood as a network of power systems consisting of power generation, transmission, distribution, and consumption. It is used to transmit electrical energy from power plants to users, and its main function is to ensure a stable supply and safe operation of electricity.

[0030] A new energy equipment cluster can be understood as a cluster of power generation equipment connected to the power grid and corresponding to a target new energy source. For example, when the target new energy source is solar energy, the power generation equipment corresponding to the target new energy source is photovoltaic power generation equipment; and when the target new energy source is wind energy, the power generation equipment corresponding to the target new energy source is wind power generation equipment.

[0031] The aforementioned historical period can be understood as the entire year preceding the current year.

[0032] In application, historical periods can be divided into time periods based on actual needs. Without loss of generality, if a day is divided into 24 time periods, then the multiple output coefficients of the new energy equipment group in the historical period will be 365 × 24 output coefficients. It should be understood that the duration of a time period can also be set for adaptive adjustment, such as adjusting the duration to 30 minutes. In this case, a day will be divided into 48 time periods.

[0033] For the multiple output coefficients of the new energy equipment group in a historical period, the specific historical date and specific historical period corresponding to each output coefficient together constitute the unique identifier of that output coefficient.

[0034] The output data of the new energy equipment group at the corresponding time period on the corresponding date should be understood as: the actual power generation of the new energy equipment group at the corresponding time period on the corresponding date.

[0035] The installed capacity of the new energy equipment group at the corresponding time period on the corresponding date should be understood as: the maximum power generation that the new energy equipment group can theoretically produce at the corresponding time period on the corresponding date (which can be preset or estimated based on past actual power generation).

[0036] For example, if the output data and installed capacity of the new energy equipment group in the j-th time period of the i-th day are set to P... ij and G ij The output coefficient of the new energy equipment group in the j-th time period of the i-th day is K. ij , where K ij =P ij ÷G ij , where i and j are both positive integers.

[0037] Step 102: Among the multiple output coefficients, sort the different output coefficients of different dates corresponding to the same period in the same month in descending order to obtain multiple first sequences.

[0038] Among the plurality of first sequences, different first sequences correspond to different months, and / or different first sequences correspond to different time periods.

[0039] It should be understood that the number of the first sequence is the product of the number of months (usually 12) and the number of time periods included in each day (which can be 24, 48, etc.).

[0040] For example, a first sequence corresponding to the j-th time period of the l-th month in a historical period can be:

[0041]

[0042] In the above formula, X can be understood as the number of days contained in the Lth month.

[0043] Step 103: Based on a pre-set first confidence probability threshold, determine a first coefficient in each of the first sequences to obtain multiple first coefficients that correspond one-to-one with the multiple first sequences.

[0044] Wherein, the value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number, and the first coefficient is the output coefficient in the corresponding first sequence that satisfies the first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number in the corresponding first sequence is greater than or equal to the first confidence probability threshold.

[0045] For example, if the first confidence probability threshold is set to 0.95, and a first sequence contains 30 output coefficients, then the output coefficient with the sequence number 29 in the first sequence will be determined as the first coefficient corresponding to the first sequence.

[0046] The first confidence probability threshold ranges from [0.5, 1]. The specific value of the first confidence probability threshold can be adaptively selected according to actual needs. This application does not limit this. It should be understood that by sorting the values ​​and selecting the first coefficient in combination with the first confidence probability threshold, a more accurate confidence output coefficient can be conveniently determined. Therefore, the generated subsequent confidence output information can be more accurate (i.e., more accurately represent the actual ability of the new energy equipment group to participate in power balance). Furthermore, when the value of the first confidence probability threshold is less than 0.96, the interference of extreme thresholds on the overall dataset can be avoided, so as to further ensure the accuracy of the confidence output information.

[0047] Step 104: Generate confidence output information based on the plurality of first coefficients and the plurality of output coefficients.

[0048] The confidence output information includes multiple basic confidence output coefficients, and the multiple basic confidence output coefficients and the multiple output coefficients correspond one-to-one. Each basic confidence output coefficient is the minimum value between the corresponding first coefficient and the corresponding output coefficient.

[0049] For example, generating confidence output information based on the plurality of first coefficients and the plurality of output coefficients includes:

[0050] Based on the plurality of output coefficients and the plurality of first coefficients, generate a plurality of first data pairs that correspond one-to-one with the plurality of output coefficients, wherein each first data pair includes an output coefficient and a first coefficient indicating the same period of the same month as the included output coefficient;

[0051] The multiple sets of first data pairs are processed by a preset min function to output the minimum value of the two data points included in each set of first data pairs, thereby forming the aforementioned multiple basic confidence output coefficients.

[0052] In this disclosure, multiple output coefficients are obtained by calculating the ratio of power output data to installed capacity of the new energy equipment group in each time period of historical periods. These output coefficients are then categorized and sorted by month and time period to form multiple first sequences. A first coefficient matching the first confidence probability threshold is determined in each sequence, resulting in multiple first coefficients corresponding one-to-one with the multiple first sequences. A correspondence between the multiple first coefficients and the multiple output coefficients is established based on dimensions such as month, date, and time period, and the smallest value is taken to obtain multiple basic confidence output coefficients corresponding one-to-one with the multiple output coefficients. This provides a more accurate confidence output coefficient for the new energy equipment group in each time period of each date. This ensures that pumped storage and energy storage, as regulating power sources, fully play their role in regulating new energy power, guaranteeing the full realization of the power balance effect of new energy and pumped storage / energy storage, and ensuring the rationality and efficiency of the power configuration of a high-proportion new energy power system.

[0053] In one embodiment, after obtaining multiple output coefficients of the new energy equipment group in the power grid over a historical period, the method further includes:

[0054] Among the multiple output coefficients, the different output coefficients corresponding to different time periods on the same date are accumulated to obtain multiple daily power coefficients;

[0055] Among the multiple daily electricity consumption coefficients, the different daily electricity consumption coefficients corresponding to different dates in the same month are sorted in descending order to obtain multiple second sequences, wherein the different second sequences correspond to different months;

[0056] According to a pre-set second confidence probability threshold, a second coefficient is determined in each second sequence to obtain multiple second coefficients that correspond one-to-one with the multiple second sequences. The value of the daily electricity consumption coefficient in the second sequence is negatively correlated with the corresponding sequence number. The second coefficient is the daily electricity consumption coefficient in the corresponding second sequence that satisfies the second preset condition and has the smallest sequence number. The second preset condition is that the ratio of the sequence number of the daily electricity consumption coefficient in the corresponding second sequence to the maximum sequence number of the corresponding second sequence is greater than or equal to the second confidence probability threshold.

[0057] After generating confidence output information based on the plurality of first coefficients and the plurality of output coefficients, the method further includes:

[0058] Based on the plurality of second coefficients, the plurality of basic confidence output coefficients are corrected to obtain a plurality of confidence output coefficients.

[0059] In this embodiment, in addition to determining the reference value of the output coefficient (i.e., the first coefficient) for each time period by dividing it into months and time periods, the output coefficient correction value (i.e., the second coefficient) for each date is further determined by dividing it into months. By combining the output coefficient correction value and the output coefficient reference value, the value of the basic confidence output coefficient is appropriately increased to reduce the adverse effects of extreme data and obtain a more accurate confidence output coefficient.

[0060] It should be noted that the process of determining multiple second coefficients based on a pre-set second confidence probability threshold can be found in the process of determining multiple first coefficients based on a first confidence probability threshold in the aforementioned embodiment. To avoid repetition, it will not be described again.

[0061] In one implementation, the plurality of confidence output coefficients can be obtained in the following manner:

[0062] Based on the plurality of second coefficients, a plurality of daily confidence output data corresponding one-to-one with the plurality of daily power consumption coefficients are determined, and each of the daily confidence output data is the minimum value between the corresponding second coefficient and the corresponding daily power consumption coefficient;

[0063] The multiple daily confidence output data are accumulated by month to obtain multiple first-month total outputs that correspond one-to-one with the multiple months; and the multiple basic confidence output coefficients are accumulated by month to obtain multiple second-month total outputs that correspond one-to-one with the multiple months.

[0064] Calculate the ratio of the total output of the first month to the total output of the second month for each month to obtain the adjustment parameters for each month.

[0065] Among the plurality of basic confidence output coefficients, each basic confidence output coefficient is multiplied by the monthly adjustment parameter corresponding to the month to obtain a plurality of confidence output coefficients that correspond one-to-one with the plurality of basic confidence output coefficients.

[0066] In this implementation, by calculating the difference between the cumulative total output over a period of time (i.e., the total output of the second month) and the cumulative total output over a date (i.e., the total output of the first month), the corresponding basic confidence output coefficient is adaptively adjusted, thereby obtaining a more accurate confidence output coefficient that reflects the output capacity of the new energy equipment group in the corresponding period.

[0067] In one embodiment, the step of correcting the plurality of basic confidence output coefficients based on the plurality of second coefficients to obtain the plurality of confidence output coefficients includes:

[0068] Based on the plurality of second coefficients, a plurality of daily confidence output data corresponding one-to-one with the plurality of daily power consumption coefficients are determined, and each of the daily confidence output data is the minimum value between the corresponding second coefficient and the corresponding daily power consumption coefficient;

[0069] The annual confidence power output is obtained by summing the multiple daily confidence power output data, and the annual basic confidence power output is obtained by summing the multiple basic confidence power output coefficients.

[0070] The ratio of the annual baseline confidence electricity volume to the annual confidence electricity volume is determined as the correction parameter;

[0071] The plurality of basic confidence output coefficients are corrected based on the correction parameters to obtain the plurality of confidence output coefficients.

[0072] Specifically, the step of correcting the plurality of basic confidence output coefficients based on the correction parameters to obtain the plurality of confidence output coefficients includes:

[0073] The correction parameter is multiplied by the plurality of basic confidence output coefficients to obtain the plurality of confidence output coefficients.

[0074] In one embodiment, the first confidence probability threshold and the second confidence probability threshold are the same.

[0075] In this embodiment, by setting the first confidence probability threshold and the second confidence probability threshold to be the same, the confidence levels of the first coefficient and the second coefficient are balanced, making the output confidence output coefficient more accurate.

[0076] In one embodiment, after correcting the plurality of basic confidence output coefficients based on the plurality of second coefficients to obtain the plurality of confidence output coefficients, the method further includes:

[0077] Historical weather data of meteorological indicators associated with the new energy equipment group during the historical period are obtained, as well as simulated weather data of the meteorological indicators during the production simulation period are obtained.

[0078] Based on the differences between the historical weather data and the simulated weather data, the multiple confidence output coefficients are corrected to obtain multiple target data.

[0079] The production of the power grid during the production simulation period is simulated based on the multiple target data to obtain the production simulation results.

[0080] The meteorological indicators associated with the new energy equipment group can be understood as key weather indicators that affect the power output capacity of the new energy equipment group. For example, when the new energy equipment group is a photovoltaic power generation equipment group, the meteorological indicators associated with the new energy equipment group are sunshine duration and / or sunshine intensity, while when the new energy equipment group is a wind power generation equipment group, the meteorological indicators associated with the new energy equipment group are wind duration and / or wind intensity.

[0081] In this embodiment, considering that the output capacity of the new energy equipment group is greatly affected by the weather, historical weather data and simulated weather data during the production simulation period are obtained, and the differences between the two are compared to form a difference correction parameter. Based on this, the confidence output coefficient is corrected to obtain more accurate target data.

[0082] Specifically, based on the differences between the historical weather data and the simulated weather data, the multiple confidence output coefficients are corrected to obtain multiple target data, including:

[0083] The ratio of the simulated weather data to the historical weather data is determined as the difference correction parameter, and the difference correction parameter is multiplied by the plurality of confidence output coefficients to obtain a plurality of target data corresponding one-to-one with the plurality of confidence output coefficients.

[0084] For ease of understanding, see Figure 2 Taking wind power and solar power as examples, the following examples illustrate the points:

[0085] (1) Obtain historical data on the power output of new energy across the entire network and the installed capacity of new energy during the same period, and calculate the actual power output coefficient of new energy.

[0086] Specifically, wind power output P w ={P w11 P w12 …P wij …};where P w11 P represents the power output of the wind power system at the first moment of the first day. wij This represents the output data for the j-th wind power unit on day i.

[0087] Photovoltaic power output P s ={P s11 P s12 …P sij …};where P s11 The power output data for the first moment of the first day of photovoltaic power generation, P sij This represents the power output data for the photovoltaic system on the i-th day and the j-th day.

[0088] Wind power installed capacity G w ={G w1 G w2 …G wi …};where G w1 For the first day of wind power installations, G wi Let be the installed capacity of wind power on day i.

[0089] Photovoltaic installations G s ={G s1 G s2 …G si …};where Gs1 For the first day of photovoltaic installations, G si Let be the installed capacity of photovoltaic power on day i.

[0090] Wind power output coefficient K w ={k w11 k w12 …k wij …}; k wij This represents the actual output coefficient of the wind power at time j on day i.

[0091] Photovoltaic power output coefficient K s ={k s11 k s12 …k sij …}; k sij Let be the actual output coefficient of the photovoltaic system on the i-th day and the j-th day.

[0092] (2) Add up the power output coefficients of new energy sources at different times on the same day to calculate the daily power generation coefficient of new energy sources.

[0093]

[0094] Among them, K wi K represents the daily power generation coefficient for wind power on day i. si Let n be the daily power generation coefficient of photovoltaic power on day i, and n be the number of sampling points for new energy output per day (which can also be understood as the number of time periods mentioned above).

[0095] (3) Calculate the confidence daily electricity consumption coefficient of new energy sources.

[0096] Specifically, first calculate the basic confidence daily electricity consumption coefficient for new energy sources for each month. Then, set a pre-set confidence probability level α (which can be understood as the aforementioned second confidence probability threshold) to ensure that m satisfies... Where m is a natural number and X is the total number of days in the month of calculation. The daily renewable energy power coefficients for each month are sorted, with wind power... in The coefficient for the largest daily electricity consumption in the first month for wind power; among which photovoltaic power... in This represents the coefficient of the largest daily electricity consumption for photovoltaic power in the l-th month. Therefore, under a pre-set confidence probability α, the base confidence daily electricity consumption coefficients for wind power and photovoltaic power in each month are obtained. (This can be understood as the aforementioned second coefficient), and then the actual daily wind power and solar power coefficients are compared with the corresponding monthly wind power and solar power confidence coefficients. By comparing the values, the smaller value is used to obtain the confidence daily power generation coefficients for wind power and solar power.

[0097]

[0098] (4) Calculate the basic confidence output coefficient of new energy sources at each time point.

[0099] Similar to the calculation method of the new energy confidence daily power coefficient, the new energy confidence output coefficient is first calculated at each time of each month.

[0100] Specifically, the wind power and solar power output coefficients for each day at time j in month l are sorted from largest to smallest. Then, the wind power and solar power output coefficients for each time period in each month are calculated under a pre-set confidence probability α (which can be understood as the first confidence probability threshold). (This can be understood as the aforementioned first coefficient). Then, the actual values ​​at each moment are compared with the confidence output coefficients at the corresponding moments of the corresponding month, and the smaller value is taken to obtain the confidence output coefficients k′ of wind power and photovoltaic foundations at each moment. wij 、k′ sij .

[0101] Similarly, set a pre-set confidence level α, so that m satisfies Where m is a natural number and X is the total number of days in the month.

[0102] in It represents the first maximum output coefficient of wind power at time j in the i-th month.

[0103] in It is the output coefficient of the photovoltaic system at the j-th moment of the i-th month.

[0104] Therefore, under the pre-set confidence probability α, the wind power and solar power confidence output coefficients at each time point in each month are...

[0105]

[0106] (5) Correct the basic confidence output coefficient with the new energy confidence power coefficient to obtain the new energy confidence output coefficient at each time.

[0107] The new energy confidence daily electricity coefficients obtained in step (3) are summed to calculate the new energy annual confidence electricity, K′. w K′ s The annual confidence amounts of wind power and solar power are respectively:

[0108]

[0109] By summing the basic confidence output coefficients of new energy sources at each time point obtained in step (4), the basic confidence electricity volume of new energy sources for the whole year, K″, is calculated. w K″ sThese are the basic confidence amounts of wind power and solar power for the whole year:

[0110]

[0111] The renewable energy confidence output coefficient at each time point is obtained by correcting the annual renewable energy confidence power volume / annual renewable energy basic confidence power volume.

[0112]

[0113] These represent the confidence output coefficients of wind power and photovoltaic power at time j on day i, respectively.

[0114] By implementing the above scheme, the daily power generation of new energy sources and the power output at various times can be calculated more accurately, ensuring that the regulating power sources such as pumped storage and energy storage play a full role in regulating the power generation of new energy sources, ensuring that the effects of new energy sources and pumped storage and energy storage participating in power balance are fully realized, ensuring the rationality and efficiency of the power source configuration of the high-proportion new energy power system, providing guidance for the planning and operation of new power systems, and improving the reliability and economy of power system operation.

[0115] See Figure 3 , Figure 3 This disclosure provides a new energy confidence output information generation device, such as... Figure 3 As shown, the new energy confidence output information generation device 300 includes:

[0116] The acquisition module 301 is used to acquire multiple output coefficients of the new energy equipment group in the power grid during a historical period. Each output coefficient corresponds to a date and a time period within the historical period. Different output coefficients correspond to different dates and / or different time periods. The output coefficient is the ratio of the output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date.

[0117] The first processing module 302 is used to sort the different output coefficients of different dates corresponding to the same time period in the same month in descending order among the multiple output coefficients to obtain multiple first sequences, wherein the different first sequences correspond to different months and / or the different first sequences correspond to different time periods.

[0118] The second processing module 303 is used to determine a first coefficient in each of the first sequences according to a preset first confidence probability threshold, and obtain a plurality of first coefficients corresponding one-to-one with the plurality of first sequences, wherein the value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number, and the first coefficient is the output coefficient in the corresponding first sequence that satisfies a first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number of the corresponding first sequence is greater than or equal to the first confidence probability threshold.

[0119] The information generation module 304 is used to generate confidence output information based on the plurality of first coefficients and the plurality of output coefficients, wherein the confidence output information includes a plurality of basic confidence output coefficients, the plurality of basic confidence output coefficients and the plurality of output coefficients are in one-to-one correspondence, and each of the basic confidence output coefficients is the minimum value of the corresponding first coefficient and the corresponding output coefficient.

[0120] In one embodiment, the new energy confidence output information generation device 300 further includes:

[0121] The third processing module is used to accumulate the different output coefficients corresponding to different time periods on the same date among the multiple output coefficients to obtain multiple daily power coefficients;

[0122] The fourth processing module is used to sort the different daily electricity consumption coefficients corresponding to different dates in the same month in descending order among the multiple daily electricity consumption coefficients to obtain multiple second sequences, wherein the different second sequences correspond to different months.

[0123] The fifth processing module is used to determine a second coefficient in each second sequence according to a preset second confidence probability threshold, so as to obtain a plurality of second coefficients corresponding one-to-one with the plurality of second sequences. The value of the daily electricity consumption coefficient in the second sequence is negatively correlated with the corresponding sequence number. The second coefficient is the daily electricity consumption coefficient in the corresponding second sequence that satisfies the second preset condition and has the smallest sequence number. The second preset condition is that the ratio of the sequence number of the daily electricity consumption coefficient in the corresponding second sequence to the maximum sequence number of the corresponding second sequence is greater than or equal to the second confidence probability threshold.

[0124] The correction module is used to correct the plurality of basic confidence output coefficients based on the plurality of second coefficients to obtain a plurality of confidence output coefficients.

[0125] In one embodiment, the correction module is specifically used for:

[0126] Based on the plurality of second coefficients, a plurality of daily confidence output data corresponding one-to-one with the plurality of daily power consumption coefficients are determined, and each of the daily confidence output data is the minimum value between the corresponding second coefficient and the corresponding daily power consumption coefficient;

[0127] The annual confidence power output is obtained by summing the multiple daily confidence power output data, and the annual basic confidence power output is obtained by summing the multiple basic confidence power output coefficients.

[0128] The ratio of the annual baseline confidence electricity volume to the annual confidence electricity volume is determined as the correction parameter;

[0129] The plurality of basic confidence output coefficients are corrected based on the correction parameters to obtain the plurality of confidence output coefficients.

[0130] In one embodiment, the step of correcting the plurality of basic confidence output coefficients based on the correction parameters to obtain the plurality of confidence output coefficients includes:

[0131] The correction parameter is multiplied by the plurality of basic confidence output coefficients to obtain the plurality of confidence output coefficients.

[0132] In one embodiment, the first confidence probability threshold and the second confidence probability threshold are the same.

[0133] In one embodiment, the new energy confidence output information generation device 300 further includes a weather adjustment module, which is used for:

[0134] Historical weather data of the weather indicators associated with the new energy equipment group during the historical period are obtained, as well as simulated weather data of the weather indicators during the production simulation period are obtained.

[0135] Based on the differences between the historical weather data and the simulated weather data, the multiple confidence output coefficients are corrected to obtain multiple target data.

[0136] The production of the power grid during the production simulation period is simulated based on the multiple target data to obtain the production simulation results.

[0137] The new energy confidence output information generation device 300 provided in this embodiment can realize the various processes in the above-described new energy confidence output information generation method embodiment. To avoid repetition, it will not be described again here.

[0138] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0139] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0140] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 402 or a computer program loaded from storage unit 408 into random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0141] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0142] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the new energy confidence output information generation method. For example, in some embodiments, the new energy confidence output information generation method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the new energy confidence output information generation method described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform a new energy confidence output information generation method by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0146] As used herein, the term "machine-readable medium" refers to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0148] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0149] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0150] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 or Figure 2The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0151] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0152] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for generating new energy confidence output information, characterized in that, The method includes: The power output coefficients of the new energy equipment group in the power grid are obtained in a historical period. Each power output coefficient corresponds to a date and a time period in the historical period. Different power output coefficients correspond to different dates and / or different time periods. The power output coefficient is the ratio of the power output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date. Among the multiple output coefficients, the different output coefficients corresponding to different dates in the same month and time period are sorted in descending order to obtain multiple first sequences. Among the multiple first sequences, the different first sequences correspond to different months and / or the different first sequences correspond to different time periods. According to a pre-set first confidence probability threshold, a first coefficient is determined in each of the first sequences to obtain a plurality of first coefficients corresponding one-to-one with the plurality of first sequences. The value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number. The first coefficient is the output coefficient in the corresponding first sequence that satisfies a first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number of the corresponding first sequence is greater than or equal to the first confidence probability threshold. Based on the plurality of first coefficients and the plurality of output coefficients, confidence output information is generated, wherein the confidence output information includes a plurality of basic confidence output coefficients, the plurality of basic confidence output coefficients and the plurality of output coefficients are in one-to-one correspondence, and each of the basic confidence output coefficients is the minimum value of the corresponding first coefficient and the corresponding output coefficient.

2. The method according to claim 1, characterized in that, After obtaining multiple output coefficients of the new energy equipment group in the power grid over a historical period, the method further includes: Among the multiple output coefficients, the different output coefficients corresponding to different time periods on the same date are accumulated to obtain multiple daily power coefficients; Among the multiple daily electricity consumption coefficients, the different daily electricity consumption coefficients corresponding to different dates in the same month are sorted in descending order to obtain multiple second sequences, wherein the different second sequences correspond to different months; According to a pre-set second confidence probability threshold, a second coefficient is determined in each second sequence to obtain multiple second coefficients that correspond one-to-one with the multiple second sequences. The value of the daily electricity consumption coefficient in the second sequence is negatively correlated with the corresponding sequence number. The second coefficient is the daily electricity consumption coefficient in the corresponding second sequence that satisfies the second preset condition and has the smallest sequence number. The second preset condition is that the ratio of the sequence number of the daily electricity consumption coefficient in the corresponding second sequence to the maximum sequence number of the corresponding second sequence is greater than or equal to the second confidence probability threshold. After generating confidence output information based on the plurality of first coefficients and the plurality of output coefficients, the method further includes: Based on the plurality of second coefficients, the plurality of basic confidence output coefficients are corrected to obtain a plurality of confidence output coefficients.

3. The method according to claim 2, characterized in that, The step of correcting the plurality of basic confidence output coefficients based on the plurality of second coefficients to obtain the plurality of confidence output coefficients includes: Based on the plurality of second coefficients, a plurality of daily confidence output data corresponding one-to-one with the plurality of daily power consumption coefficients are determined, and each of the daily confidence output data is the minimum value between the corresponding second coefficient and the corresponding daily power consumption coefficient; The annual confidence power output is obtained by summing the multiple daily confidence power output data, and the annual basic confidence power output is obtained by summing the multiple basic confidence power output coefficients. The ratio of the annual baseline confidence electricity volume to the annual confidence electricity volume is determined as the correction parameter; The plurality of basic confidence output coefficients are corrected based on the correction parameters to obtain the plurality of confidence output coefficients.

4. The method according to claim 3, characterized in that, The step of correcting the plurality of basic confidence output coefficients based on the correction parameters to obtain the plurality of confidence output coefficients includes: The correction parameter is multiplied by the plurality of basic confidence output coefficients to obtain the plurality of confidence output coefficients.

5. The method according to claim 2, characterized in that, The first confidence probability threshold and the second confidence probability threshold are the same.

6. The method according to claim 2, characterized in that, After correcting the plurality of basic confidence output coefficients based on the plurality of second coefficients to obtain the plurality of confidence output coefficients, the method further includes: Historical weather data of meteorological indicators associated with the new energy equipment group during the historical period are obtained, as well as simulated weather data of the meteorological indicators during the production simulation period are obtained. Based on the differences between the historical weather data and the simulated weather data, the multiple confidence output coefficients are corrected to obtain multiple target data. The production of the power grid during the production simulation period is simulated based on the multiple target data to obtain the production simulation results.

7. A new energy confidence output information generation device, characterized in that, The device includes: The acquisition module is used to acquire multiple output coefficients of the new energy equipment group in the power grid during a historical period. Each output coefficient corresponds to a date and a time period within the historical period. Different output coefficients correspond to different dates and / or different time periods. The output coefficient is the ratio of the output data and installed capacity of the new energy equipment group in the corresponding time period of the corresponding date. The first processing module is used to sort the different output coefficients of different dates corresponding to the same time period in the same month in descending order among the multiple output coefficients to obtain multiple first sequences, wherein the different first sequences correspond to different months and / or the different first sequences correspond to different time periods. The second processing module is used to determine a first coefficient in each of the first sequences according to a preset first confidence probability threshold, thereby obtaining a plurality of first coefficients that correspond one-to-one with the plurality of first sequences. The value of the output coefficient in the first sequence is negatively correlated with the corresponding sequence number. The first coefficient is the output coefficient in the corresponding first sequence that satisfies a first preset condition and has the smallest sequence number. The first preset condition is that the ratio of the sequence number of the output coefficient in the corresponding first sequence to the maximum sequence number of the corresponding first sequence is greater than or equal to the first confidence probability threshold. The information generation module is used to generate confidence output information based on the plurality of first coefficients and the plurality of output coefficients, wherein the confidence output information includes a plurality of basic confidence output coefficients, the plurality of basic confidence output coefficients and the plurality of output coefficients are in one-to-one correspondence, and each of the basic confidence output coefficients is the minimum value of the corresponding first coefficient and the corresponding output coefficient.

8. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

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