A control method and system for new energy power balance

Abstract

The application provides a new energy power balance control method and system, which comprises the following steps: collecting historical output data sets of new energy in each region in a network and historical data sets of loads in each region in the network, calculating corresponding new energy output characteristic data sets of each region in the network, and calculating the sum of total new energy output; obtaining monthly and daily load peak values of each region, obtaining minimum values under a confidence level in combination with data in the typical data sets, calculating monthly power profit and loss values in combination with preset parameters, and controlling a power dispatching center to supply power to regional power grids according to the monthly power profit and loss values. The application can solve the problem of how to reduce the interval and time difference of new energy output characteristics to achieve power balance.

Description

Technical Field

[0001] This invention relates to the field of new energy power balancing, and in particular to a control method and system for new energy power balancing. Background Technology

[0002] With the construction of new power systems, the power grid is gradually transitioning to a system dominated by new energy sources. However, the randomness, volatility, and uncertainty of wind power and photovoltaic power output have a significant impact on the daily, monthly, and annual power balance.

[0003] Traditional power balancing methods only include renewable energy generation output in power balance calculations at an appropriate proportion. For example, during peak hours of regional and provincial power grids from 2013 to 2018, renewable energy output generally accounted for no less than 2% of installed capacity. In regions with a large renewable energy capacity, such as Northwest, Northeast, and North China, the peak contribution rate of wind power was generally no less than 2%, and the peak contribution rate of solar power was generally no less than 10% (midday peak). Based on this conclusion, if provincial power grids do not yet have the conditions for historical data analysis, as a transitional method, in Northwest, Northeast, and North China, wind power can be included in the balance at 2%, and solar power at 10% (midday peak). In other regions, renewable energy installed capacity can be uniformly included in the power balance at 2%. Traditional power balancing methods only use historical datasets of the total output of intermittent power sources from the previous year to predict the daily characteristic curve of the horizontal year, without utilizing datasets from the past few years or even the past decade. Given that this method has a small amount of predictable data and a limited number of foreseeable scenarios, its prediction accuracy is not high enough. Furthermore, it does not take into account the differences in new energy resources between different geographical regions, and the impact of future new energy deployment in different regions on the power output characteristic curve will vary. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by proposing a control method and system for new energy power balance, thereby solving the problem of how to reduce the interval and time differences in the output characteristics of new energy to achieve power balance.

[0005] To achieve the above objectives, this invention proposes a control method for new energy power balance, comprising: collecting historical power output datasets of new energy sources in each region of the network as a first dataset, and collecting historical load datasets of each region of the network as a second dataset;

[0006] Based on the first dataset and the new energy installed capacity of each region within the network, a new energy output characteristic dataset corresponding to each region within the network is calculated and used as the third dataset.

[0007] Based on the third dataset and the installed capacity of new energy in each region of the network, a typical dataset of the total output of new energy is calculated and used as the fourth dataset.

[0008] Based on the second dataset, the peak load for each region is obtained for each month and each day. Combined with the data in the fourth dataset, a fifth dataset is formed.

[0009] Based on the fifth dataset, the minimum value at the confidence level is obtained, and combined with preset parameters, the monthly electricity profit and loss value is calculated.

[0010] Based on the monthly power surplus or deficit values, the power dispatching center is controlled to replenish the regional power grid.

[0011] This invention provides a control method for new energy power balance. It employs a method of first predicting new energy in each region and then summing the predictions to obtain the network's new energy forecast. This method fully considers the differences in new energy resources between different geographical regions and the varying impacts of future new energy commissioning on power output characteristic curves in different regions. It utilizes a set of new energy data corresponding to peak load times from historical data and introduces the concept of confidence level. This proposes a probabilistic new energy output prediction method, improving the accuracy of new energy prediction and avoiding the problems of poor power balance caused by traditional methods that select regions based on the simultaneous rate of new energy.

[0012] Furthermore, the historical power output data of new energy sources in each region within the network is collected as the first dataset, and the historical load data of each region within the network is collected as the second dataset, specifically as follows:

[0013] Collect daily renewable energy output data for each month over several years in each region, divide the daily output data into several equidistant time intervals as first data, and combine the first data of the several equidistant time intervals according to the number of days in the month to form the first dataset of renewable energy output data for each region in each month.

[0014] Collect daily load data for each month over several years in each region, divide the daily load data into several equidistant time intervals, and combine these equidistant time intervals into a second dataset of load data for each region for each month according to the number of days in the month.

[0015] This invention collects historical renewable energy output and load data from different regions, fully considers the differences in renewable energy resources between regions, and improves the efficiency of subsequent peak value finding by equally segmenting load data, thereby increasing the system's calculation speed and accuracy and reducing the interval and time differences in renewable energy power control.

[0016] Furthermore, based on the first dataset and the installed capacity of new energy in each region of the network, a dataset of new energy output characteristics corresponding to each region of the network is calculated as the third dataset. Based on the third dataset and the installed capacity of new energy in each region of the network, a typical dataset of the sum of total new energy output is calculated as the fourth dataset. Specifically:

[0017] Based on the new energy installed capacity of each region and each month corresponding to the first dataset, the new energy output characteristic data of each region in the network are calculated and formed into the third dataset.

[0018] Based on the third dataset, and combined with the installed capacity of new energy in each region in each month of the horizontal year, the new energy output data of each region in each month of the horizontal year are calculated, and a typical dataset of the sum of the total new energy output is obtained to form the fourth dataset.

[0019] Furthermore, based on the second dataset, the peak load for each day of each month is obtained, and combined with the data in the fourth dataset to form the fifth dataset, specifically:

[0020] Based on the second dataset, the peak load data for each day of each month is obtained, and the data in the fourth dataset corresponding to the equidistant moments before and after the peak load data is selected. All data sets obtained by traversing the entire month are combined to form the fifth dataset.

[0021] Furthermore, based on the fifth dataset, the minimum value at the confidence level is obtained, and combined with preset parameters, the monthly electricity profit and loss value is calculated, specifically as follows:

[0022] Based on the fifth dataset, at a preset confidence level, obtain the minimum value of the fifth dataset;

[0023] Based on the minimum value in the fifth dataset, combined with the peak load value for each month of the preset horizontal year, the daily power generation capacity value of traditional power sources, and the reserve rate, the monthly power surplus / deficit value is calculated.

[0024] Furthermore, the step of controlling the power dispatching center to replenish the regional power grid based on the monthly power surplus / deficit values ​​specifically involves:

[0025] Based on the monthly power surplus / deficit values, the location of power shortages in each region is determined, and the power dispatch center is controlled to supply power to the corresponding shortage locations in each region to make up for the power shortage.

[0026] This invention also proposes a control system for new energy power balance, comprising:

[0027] The system includes a data acquisition module, a first-level computing module, a second-level computing module, a third-level computing module, and a power control module.

[0028] The data acquisition module is used to collect historical power output data of new energy sources in each region of the network as the first dataset, and to collect historical load data of each region of the network as the second dataset.

[0029] The first-level computing module is used to calculate the new energy output characteristic dataset corresponding to each region in the network based on the first dataset and the new energy installed capacity of each region in the network, as the third dataset; and to calculate the typical dataset of the sum of the total new energy output based on the third dataset and the new energy installed capacity of each region in the network, as the fourth dataset.

[0030] The secondary computing module is used to obtain the peak load of each region for each month and day based on the second dataset, and combine it with the data in the fourth dataset to form a fifth dataset;

[0031] The third-level calculation module is used to obtain the minimum value under the confidence level based on the fifth dataset, and calculate the monthly power surplus or deficit value in combination with preset parameters.

[0032] The power control module is used to control the power dispatching station to replenish the regional power grid based on the monthly power surplus or deficit values.

[0033] This invention provides a control system for new energy power balance. By classifying and partitioning the collected data, the data transmission becomes more stable. The calculation module performs hierarchical operations, improving the accuracy of calculations. By improving the overall performance of the system, the system reduces the time and interval differences in the output data of new energy resources, and also improves the accuracy of data output calculations, thereby enhancing the stability of power control.

[0034] Furthermore, the data acquisition module is used to collect historical power output datasets of new energy sources in various regions within the network as the first dataset, and to collect historical load datasets of various regions within the network as the second dataset, including:

[0035] First acquisition unit and second acquisition unit;

[0036] The first acquisition unit is used to collect daily new energy output data for each month of each region over several years of history, divide the daily output data into several equidistant time periods, and combine the several equidistant time periods into a first dataset of new energy output data for each region for each month according to the number of days in the month.

[0037] The second acquisition unit is used to collect daily load data for each month of each region over several historical years, divide the daily load data into several equidistant time intervals, and combine the several equidistant time intervals into a second dataset of load data for each region for each month according to the number of days in the month.

[0038] Furthermore, the first-level computing module is used to calculate, based on the first dataset and the installed capacity of new energy in each region of the network, a new energy output characteristic dataset corresponding to each region of the network, as the third dataset; and based on the third dataset and the installed capacity of new energy in each region of the network, to calculate a typical dataset of the sum of total new energy output, as the fourth dataset, including:

[0039] Primary data processing module and secondary data processing module;

[0040] The first-level data processing module is used to calculate the new energy output characteristic data corresponding to each region in the network based on the new energy installed capacity of each region in each month corresponding to the first dataset, and form the third dataset.

[0041] The secondary data processing module is used to calculate the new energy output data of each region in each month of the horizontal year based on the third dataset and the new energy installed capacity of each region in each month of the horizontal year, and to obtain a typical dataset of the sum of the total new energy output to form the fourth dataset.

[0042] Furthermore, the secondary computing module is used to obtain the peak load for each region on each month and day based on the second dataset, and combine it with the data in the fourth dataset to form a fifth dataset, including:

[0043] Data peak traversal unit and data peak segmentation processing unit;

[0044] The data peak tracing unit is used to obtain the daily load peak data for each month based on the second dataset;

[0045] The data peak segmentation processing unit is used to select data from the fourth dataset corresponding to the equidistant moments before and after the load peak data, and to form a fifth dataset by traversing all data sets obtained throughout the month.

[0046] Furthermore, the third-level calculation module is used to obtain the minimum value at the confidence level based on the fifth dataset, and calculate the monthly electricity surplus / deficit value in combination with preset parameters, including:

[0047] Level 3 data processing module and Level 4 data processing module;

[0048] The third-level data processing module is used to obtain the minimum value of the fifth dataset at a preset confidence level.

[0049] The fourth-level data processing module is used to calculate the monthly power surplus / deficit value based on the minimum value in the fifth dataset, combined with the preset peak load value, daily power generation capacity value of traditional power sources, and reserve rate for each month of the horizontal year.

[0050] Furthermore, the power control module is used to control the power dispatching station to replenish power according to the monthly power surplus / deficit values, including:

[0051] Power shortage assessment module and power transmission control module;

[0052] The power shortage determination module is used to determine the location of the power shortage in each region based on the power surplus or deficit value of each month.

[0053] The power transmission and control module is used to control the power dispatching station to supply power to the gap locations in each region to make up for the power shortage. Attached Figure Description

[0054] Figure 1 A flowchart illustrating the steps of a new energy power balance control method provided in an embodiment of the present invention.

[0055] Figure 2 This is a control system module diagram for a new energy power balance provided in an embodiment of the present invention.

[0056] Figure 3 This is a diagram of a data acquisition module for a new energy power balance control system provided in an embodiment of the present invention.

[0057] Figure 4 This is a diagram of the first-level operation module of a new energy power balance control system provided in an embodiment of the present invention.

[0058] Figure 5 This is a diagram of a secondary operation module of a new energy power balance control system provided in an embodiment of the present invention.

[0059] Figure 6 This is a diagram of a three-level operation module for a new energy power balance control system provided in an embodiment of the present invention.

[0060] Figure 7 This is a power control module diagram of a new energy power balance control system provided in an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] See Figure 1 , Figure 1 This is a flowchart illustrating the steps of a new energy power balance control method provided in an embodiment of the present invention. Figure 1 As shown, this invention proposes a control method for the balance of new energy power, including steps 101 to 106, each step of which is as follows:

[0064] Step 101: Collect historical power output data of new energy sources in each region of the network as the first dataset, and collect historical load data of each region of the network as the second dataset.

[0065] As an example of this embodiment, daily renewable energy output data for each month over several historical years are collected for each region. The Let the historical data of each month of the nth region over the previous y years be represented, where, Where m is the number of days in the month. The daily output data is divided into several equally spaced time intervals as the first data set. These first data sets are then grouped according to the number of days in the month to form the first dataset of new energy output data for each region and month, as follows:

[0066] ,

[0067] in, This is the data for the m-th day of the month at time t. Each time interval is 15 minutes long, and a day contains a total of 96 time intervals.

[0068] Collect daily load data for each month of several historical years in each region. ,in, Where m is the number of days in the month. The daily load data is divided into several equally spaced second data points, and these second data points are then grouped according to the number of days in the month to form the second dataset of load data for each region for each month, as follows:

[0069] ,

[0070] in, This is the data for the m-th day of the month at time t. Each time interval is 15 minutes long, and a day contains a total of 96 time intervals.

[0071] This invention collects historical renewable energy output and load data from different regions, fully considers the differences in renewable energy resources between regions, and improves the efficiency of subsequent peak value finding by equally segmenting load data, thereby increasing the system's calculation speed and accuracy and reducing the interval and time differences in renewable energy power control.

[0072] Step 102: Based on the first dataset and the installed capacity of new energy in each region of the network, calculate the new energy output characteristic dataset corresponding to each region of the network, and use it as the third dataset.

[0073] As an example of this embodiment, the new energy installed capacity of each region and each month is based on the first dataset. The renewable energy output characteristic data for each region within the network were calculated respectively. The third dataset is composed as follows:

[0074] ,

[0075] in, For the dataset of day m of this month, This refers to the renewable energy output characteristics data for the region at time t on day m of the month.

[0076] Step 103: Based on the third dataset and the installed capacity of new energy in each region of the network, calculate a typical dataset of the total output of new energy, which is used as the fourth dataset.

[0077] As an example of this embodiment, based on the third dataset Combined with the installed capacity of new energy in each region in each month of the horizontal year Calculate the renewable energy output data for each region in each month of the horizontal year. And calculate a typical dataset of the total output of new energy sources. The fourth dataset is composed as follows:

[0078] ,

[0079] in, This is a typical dataset representing the sum of total new energy output in each month of a horizontal year (y).

[0080] Step 104: Based on the second dataset, obtain the peak load for each region on each day and month, and combine it with the data in the fourth dataset to form the fifth dataset.

[0081] As an example of this embodiment, based on the second dataset Obtain daily peak load data for each month. And select data from the fourth dataset corresponding to equidistant moments before and after the load peak data. This will iterate through all the data sets obtained throughout the month. The fifth set of numbers is formed as follows:

[0082] .

[0083] Step 105: Based on the fifth dataset, obtain the minimum value at the confidence level, and calculate the monthly electricity profit and loss value by combining it with preset parameters.

[0084] As an example of this embodiment, based on the fifth dataset At the preset confidence level Next, obtain the minimum value of the fifth dataset. ,Right now ,in, for The data inside, To satisfy the probability that the expression within the parentheses is true, The value is usually taken as 0.95.

[0085] Based on the minimum value in the fifth dataset Combined with the preset peak load values ​​for each month of the horizontal year Daily power generation capacity of traditional power sources With a reserve ratio of 5%, the monthly electricity profit and loss value is calculated. ,in, = .

[0086] Step 106: Based on the monthly power surplus / deficit values, control the power dispatching center to replenish the regional power grid.

[0087] As an example of this embodiment, if we calculate the electricity surplus or deficit... A positive number indicates a power surplus, with larger numbers representing a greater surplus and a larger margin for grid operation. This is used to calculate the power surplus / deficit. A negative value indicates a power shortage, with larger values ​​indicating a greater power shortage. Based on these results, the power dispatch center can replenish power to various regions to address the power shortage and prevent the waste of power resources and imbalances in power supply caused by the shortage.

[0088] This invention provides a control method for new energy power balance. It employs a method of first predicting new energy in each region and then summing the predictions to obtain the network's new energy forecast. This method fully considers the differences in new energy resources between different geographical regions and the varying impacts of future new energy commissioning on power output characteristic curves in different regions. It utilizes a set of new energy data corresponding to peak load times from historical data and introduces the concept of confidence level. This proposes a probabilistic new energy output prediction method, improving the accuracy of new energy prediction and avoiding the problems of poor power balance caused by traditional methods that select regions based on the simultaneous rate of new energy.

[0089] Example 2

[0090] See Figure 2 , Figure 2 This is a block diagram of a new energy power balance control system provided in an embodiment of the present invention. (See diagram below.) Figure 2 As shown, this invention also proposes a control system for new energy power balance, specifically including:

[0091] Data acquisition module 201, first-level calculation module 202, second-level calculation module 203, third-level calculation module 204, and power control module 205;

[0092] The data acquisition module 201 is used to collect historical power output data of new energy sources in each region of the network as the first dataset, and to collect historical load data of each region of the network as the second dataset.

[0093] As an example of this embodiment, the data acquisition module 201 includes: a first acquisition unit 301 and a second acquisition unit 302;

[0094] The first acquisition unit 301 is used to acquire daily new energy output data for each month of each region over several years of history, divide the daily output data into several equidistant time periods, and combine the several equidistant time periods into a first dataset of new energy output data for each region in each month according to the number of days in the month.

[0095] The second acquisition unit 302 is used to acquire daily load data for each month of each region over several years of history, divide the daily load data into several equidistant time intervals, and combine the several equidistant time intervals into a second dataset of load data for each region for each month according to the number of days in the month.

[0096] The first-level computing module 202 is used to calculate the new energy output characteristic dataset corresponding to each region in the network based on the first dataset and the new energy installed capacity of each region in the network, as the third dataset; and to calculate the typical dataset of the sum of the total new energy output based on the third dataset and the new energy installed capacity of each region in the network, as the fourth dataset.

[0097] As an example of this embodiment, the first-level calculation module 202 includes: a first-level data processing module 401 and a second-level data processing module 402;

[0098] The first-level data processing module 401 is used to calculate the new energy output characteristic data corresponding to each region in the network based on the new energy installed capacity of each region in each month corresponding to the first dataset, and form a third dataset.

[0099] The secondary data processing module 402 is used to calculate the new energy output data of each region in each month of the horizontal year based on the third dataset and the new energy installed capacity of each region in each month of the horizontal year, and to obtain a typical dataset of the sum of the total new energy output to form the fourth dataset.

[0100] The secondary computing module 203 is used to obtain the peak load of each region for each month and day based on the second dataset, and combine it with the data in the fourth dataset to form a fifth dataset;

[0101] As an example of this embodiment, the secondary operation module 203 includes: a data peak traversal unit 501 and a data peak segmentation processing unit 502;

[0102] The data peak tracing unit 501 is used to obtain the daily load peak data for each month based on the second dataset;

[0103] The data peak segmentation processing unit 502 is used to select data from the fourth dataset corresponding to the equidistant moments before and after the load peak data, and to form a fifth dataset by traversing all data sets obtained throughout the month.

[0104] The third-level calculation module 204 is used to obtain the minimum value under the confidence level based on the fifth dataset, and calculate the monthly power surplus or deficit value in combination with preset parameters.

[0105] As an example of this embodiment, the three-level operation module 204 includes: a three-level data processing module 601 and a four-level data processing module 602;

[0106] The third-level data processing module 601 is used to obtain the minimum value of the fifth dataset at a preset confidence level based on the fifth dataset.

[0107] The fourth-level data processing module 602 is used to calculate the monthly power surplus / deficit value based on the minimum value in the fifth dataset, combined with the preset peak load value, daily power generation capacity value of traditional power sources, and reserve rate for each month of the horizontal year.

[0108] The power control module 205 is used to control the power dispatching station to replenish the regional power grid according to the monthly power surplus or deficit values.

[0109] As an example of this embodiment, the power control module 205 includes: a power shortage judgment module 701 and a power transmission regulation module 702;

[0110] The power shortage judgment module 701 is used to determine the location of the power shortage in each region based on the power surplus or deficit value of each month;

[0111] The power transmission and control module 702 is used to control the power dispatching station to supply power to the gap location in each area to make up for the power shortage.

[0112] This invention provides a control system for new energy power balance. By classifying and partitioning the collected data, the data transmission becomes more stable. The calculation module performs hierarchical operations, improving the accuracy of calculations. By improving the overall performance of the system, the system reduces the time and interval differences in the output data of new energy resources, and also improves the accuracy of data output calculations, thereby enhancing the stability of power control.

[0113] Example 3

[0114] This embodiment uses data from regions A and B in July 2018 as examples to predict the power balance control in July 2021, and details the process according to the steps in Embodiment 1. Region A had an installed capacity of 3564MW in July 2018, and Region B had an installed capacity of 1948MW in July 2018. The predicted load for July 2021 is 142000MW. According to power planning, the installed capacity of wind farms in Region A will increase to 5000MW, and the installed capacity of wind farms in Region B will increase to 4000MW. The daily power generation capacity of traditional power sources is 118530MW. In practical applications, the monthly data for the years preceding the target prediction year should be processed according to the steps in Embodiment 1.

[0115] Step 101: Collect historical power output data of new energy sources in each region of the network as the first dataset, and collect historical load data of each region of the network as the second dataset.

[0116] Specifically: Collect daily renewable energy output data for each month of each region over several historical years. The Let the historical data of each month of the nth region over the previous y years be represented, where, Where m is the number of days in the month. The daily output data is divided into several equally spaced time intervals as the first data set. These first data sets are then grouped according to the number of days in the month to form the first dataset of new energy output data for each region and month, as follows:

[0117] ,

[0118] in, This is the data for the m-th day of the month at time t. Each time interval is 15 minutes long, and a day contains a total of 96 time intervals.

[0119] Collect daily load data for each month of several historical years in each region. ,in, Where m is the number of days in the month. The daily load data is divided into several equally spaced second data points, and these second data points are then grouped according to the number of days in the month to form the second dataset of load data for each region for each month, as follows:

[0120] ,

[0121] in, This is the data for the m-th day of the month at time t. Each time interval is 15 minutes long, and a day contains a total of 96 time intervals.

[0122] Step 102: Based on the first dataset and the installed capacity of new energy in each region of the network, calculate the new energy output characteristic dataset corresponding to each region of the network, and use it as the third dataset.

[0123] Specifically: Based on the first dataset, the installed capacity of new energy vehicles in each region for each month. The renewable energy output characteristic data for each region within the network were calculated respectively. The third dataset is composed as follows:

[0124] ,

[0125] in, For the dataset of day m of this month, This refers to the renewable energy output characteristics data for the region at time t on day m of the month.

[0126] As an example of this embodiment, according to steps 101 and 102, the wind power output data set for region A in July 2018 (data length 31*24) is collected, and based on the installed capacity of region A in July (3564MW), the output characteristic coefficient for July is obtained as follows: As shown in Table 1.

[0127] Table 1. Wind power output and output characteristic coefficients in Area A in July 2018 (15 minutes per moment)

[0128]

[0129] Similarly, the wind power output and output characteristic coefficient of region B in July 2018 are obtained, as shown in Table 2.

[0130] Table 2 Wind power output and output characteristic coefficients in Region B, July 2018 (15 minutes per moment)

[0131]

[0132] Step 103: Based on the third dataset and the installed capacity of new energy in each region of the network, calculate a typical dataset of the total output of new energy, which is used as the fourth dataset.

[0133] Specifically: based on the third dataset Combined with the installed capacity of new energy in each region in each month of the horizontal year Calculate the renewable energy output data for each region in each month of the horizontal year. And calculate a typical dataset of the total output of new energy sources. The fourth dataset is composed as follows:

[0134] ,

[0135] As an example of this embodiment, the renewable energy output data for each region in each month of the horizontal year are calculated according to step 103. , And calculate a typical dataset of the sum of total power output from new energy sources. As shown in Table 3, similarly, the following can be calculated: and etc.

[0136] Table 3. Projected Total Power Output of New Energy Sources in July 2021 (Horizontal Year)

[0137]

[0138] Step 104: Based on the second dataset, obtain the peak load for each region on each day and month, and combine it with the data in the fourth dataset to form the fifth dataset.

[0139] Specifically: based on the second dataset Obtain daily peak load data for each month. And select data from the fourth dataset corresponding to equidistant moments before and after the load peak data. This will iterate through all the data sets obtained throughout the month. The fifth set of numbers is formed as follows:

[0140] .

[0141] As an example of this embodiment, the second dataset collected according to steps 101 and 104 is shown in Table 4.

[0142] Table 4 Regional load values ​​in July 2018 (15 minutes per moment)

[0143]

[0144] Acquired peak load data And select data from the fourth dataset corresponding to equidistant moments before and after the load peak data. This will iterate through all the data sets obtained throughout the month. As shown in Table 5.

[0145] Table 5 Data set of new energy power output corresponding to peak hours

[0146]

[0147] Similarly, , , , And form the fifth dataset. As shown in Table 6.

[0148] Table 6 Data sets of new energy power output corresponding to peak hours

[0149]

[0150] Step 105: Based on the fifth dataset, obtain the minimum value at the confidence level, and calculate the monthly electricity profit and loss value by combining it with preset parameters.

[0151] Specifically, based on the fifth dataset... At the preset confidence level Next, obtain the minimum value of the fifth dataset. ,Right now ,in, for The data inside, To satisfy the probability that the expression within the parentheses is true, The value is usually taken as 0.95.

[0152] As an example of this embodiment, take The value is 0.95, which gives... It is 471.51MW.

[0153] Based on the minimum value in the fifth dataset Combined with the preset peak load values ​​for each month of the horizontal year Daily power generation capacity of traditional power sources With a reserve ratio of 5%, the monthly electricity profit and loss value is calculated. ,in, = .

[0154] As an example of this embodiment, the daily power generation capacity of a conventional power source... It is 118,530 MW. It is 142,000 MW. The value is 471.51 MW, calculated from the above parameters:

[0155] = 0098MW.

[0156] Step 106: Based on the monthly power surplus / deficit values, control the power dispatching center to replenish the regional power grid.

[0157] As an example of this embodiment, due to the calculated... The negative value of 0098MW indicates that there is a power shortage in regions A and B. Therefore, the power dispatching station is controlled to transmit and replenish power to regions A and B in the months when there is a power shortage, so as to achieve a balance of power supply and demand.

[0158] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0159] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0160] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for the balance of new energy power, characterized in that, include: Collect historical power output data of new energy sources in each region within the network as the first dataset, and collect historical load data of each region within the network as the second dataset; Based on the first dataset and the new energy installed capacity of each region within the network, a new energy output characteristic dataset corresponding to each region within the network is calculated and used as the third dataset. Based on the third dataset and the installed capacity of new energy in each region of the network, a typical dataset of the total output of new energy is calculated and used as the fourth dataset. Based on the second dataset, the peak load for each region is obtained for each month and each day. Combined with the data in the fourth dataset, a fifth dataset is formed. Based on the fifth dataset, the minimum value at the confidence level is obtained, and combined with preset parameters, the monthly electricity profit and loss value is calculated. Specifically, based on the fifth dataset, the minimum value of the fifth dataset is obtained at the preset confidence level. Based on the minimum value in the fifth dataset, combined with the peak load value, daily power generation capacity of traditional power sources, and reserve rate for each month of the preset horizontal year, the monthly power profit and loss value is calculated; wherein, the minimum value under the confidence level is specifically: In the formula, for The data inside, To satisfy the probability that the expression within the parentheses is true, For confidence level, This is the fifth dataset; Based on the monthly power surplus or deficit values, the power dispatching center is controlled to replenish the regional power grid.

2. The control method for new energy power balance as described in claim 1, characterized in that, The process involves collecting historical power output data from various regions within the network, which serves as the first dataset, and collecting historical load data from various regions within the network, which serves as the second dataset. Specifically: Collect daily renewable energy output data for each month over several years in each region, divide the daily output data into several equidistant time intervals as first data, and combine the first data of the several equidistant time intervals according to the number of days in the month to form the first dataset of renewable energy output data for each region in each month. Collect daily load data for each month over several years in each region, divide the daily load data into several equidistant time intervals, and combine these equidistant time intervals into a second dataset of load data for each region for each month according to the number of days in the month.

3. The control method for new energy power balance as described in claim 2, characterized in that, The process involves calculating the renewable energy output characteristic dataset for each region within the network based on the first dataset and the renewable energy installed capacity of each region within the network, which serves as the third dataset. Then, based on the third dataset and the renewable energy installed capacity of each region within the network, a typical dataset representing the sum of total renewable energy output is calculated, which serves as the fourth dataset. Specifically: Based on the new energy installed capacity of each region and each month corresponding to the first dataset, the new energy output characteristic data of each region in the network are calculated and formed into the third dataset. Based on the third dataset, and combined with the installed capacity of new energy in each region in each month of the horizontal year, the new energy output data of each region in each month of the horizontal year are calculated, and a typical dataset of the sum of the total new energy output is obtained to form the fourth dataset.

4. The control method for new energy power balance as described in claim 3, characterized in that, The step involves obtaining the peak load for each day of each month based on the second dataset, and combining this data with the data in the fourth dataset to form the fifth dataset. Specifically: Based on the second dataset, the peak load data for each day of each month is obtained, and the data in the fourth dataset corresponding to the equidistant moments before and after the peak load data is selected. All data sets obtained throughout the month are combined to form the fifth dataset. Specifically, the equidistant moments before and after the peak load data are 1 hour before and after the peak period.

5. The control method for new energy power balance as described in claim 1, characterized in that, The process of controlling the power dispatching center to replenish the regional power grid based on the monthly power surplus / deficit values ​​specifically involves: Based on the monthly power surplus / deficit values, the location of power shortages in each region is determined, and the power dispatch center is controlled to supply power to the corresponding shortage locations in each region to make up for the power shortage.

6. A control system for balancing new energy power, characterized in that, include: The system includes a data acquisition module, a first-level computing module, a second-level computing module, a third-level computing module, and a power control module. The data acquisition module is used to collect historical power output data of new energy sources in each region of the network as the first dataset, and to collect historical load data of each region of the network as the second dataset. The first-level computing module is used to calculate the new energy output characteristic dataset corresponding to each region in the network based on the first dataset and the new energy installed capacity of each region in the network, as the third dataset; and to calculate the typical dataset of the sum of the total new energy output based on the third dataset and the new energy installed capacity of each region in the network, as the fourth dataset. The secondary computing module is used to obtain the peak load of each region for each month and day based on the second dataset, and combine it with the data in the fourth dataset to form a fifth dataset; The third-level calculation module is used to obtain the minimum value under the confidence level based on the fifth dataset, and calculate the monthly power profit and loss value by combining it with preset parameters. It includes a third-level data processing module and a fourth-level data processing module. The third-level data processing module is used to obtain the minimum value of the fifth dataset under the preset confidence level. The fourth-level data processing module is used to calculate the monthly power profit and loss value based on the minimum value in the fifth dataset, combined with the peak load value, daily power generation capacity of traditional power sources, and reserve rate for each month of the preset horizontal year. Specifically, the minimum value under the confidence level is: In the formula, for The data inside, To satisfy the probability that the expression within the parentheses is true, For confidence level, This is the fifth dataset; The power control module is used to control the power dispatching station to replenish the regional power grid based on the monthly power surplus or deficit values.

7. A control system for new energy power balance as described in claim 6, characterized in that, The data acquisition module is used to collect historical power output data of new energy sources in various regions within the network as the first dataset, and to collect historical load data of various regions within the network as the second dataset, including: First acquisition unit and second acquisition unit; The first acquisition unit is used to collect daily new energy output data for each month of each region over several years of history, divide the daily output data into several equidistant time periods, and combine the several equidistant time periods into a first dataset of new energy output data for each region for each month according to the number of days in the month. The second acquisition unit is used to collect daily load data for each month of each region over several historical years, divide the daily load data into several equidistant time intervals, and combine the several equidistant time intervals into a second dataset of load data for each region for each month according to the number of days in the month.

8. A control system for new energy power balance as described in claim 6, characterized in that, The first-level computing module is used to calculate, based on the first dataset and the installed capacity of new energy in each region of the network, a new energy output characteristic dataset corresponding to each region of the network, as the third dataset; and to calculate, based on the third dataset and the installed capacity of new energy in each region of the network, a typical dataset of the sum of total new energy output, as the fourth dataset, including: Primary data processing module and secondary data processing module; The first-level data processing module is used to calculate the new energy output characteristic data corresponding to each region in the network based on the new energy installed capacity of each region in each month corresponding to the first dataset, and form the third dataset. The secondary data processing module is used to calculate the new energy output data of each region in each month of the horizontal year based on the third dataset and the new energy installed capacity of each region in each month of the horizontal year, and to calculate a typical dataset of the sum of the total new energy output to form the fourth dataset.

9. A control system for new energy power balance as described in claim 6, characterized in that, The secondary processing module is used to obtain the peak load for each region on each day and month based on the second dataset, and combine it with the data in the fourth dataset to form a fifth dataset, including: Data peak traversal unit and data peak segmentation processing unit; The data peak tracing unit is used to obtain the daily load peak data for each month based on the second dataset; The data peak segmentation processing unit is used to select data from the fourth dataset corresponding to the equidistant moments before and after the load peak data, and to form a fifth dataset by traversing all data sets obtained throughout the month. Specifically, the equidistant moments before and after the load peak data are 1 hour before and after the peak period.

10. A control system for new energy power balance as described in claim 6, characterized in that, The power control module is used to control the power dispatching station to replenish power according to the monthly power surplus or deficit values, including: Power shortage assessment module and power transmission control module; The power shortage judgment module is used to determine the location of the power shortage in each region based on the monthly power surplus / deficit values. The power transmission and control module is used to control the power dispatching station to supply power to the gap locations in each region to make up for the power shortage.

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