Power grid reserve capacity adjustment method and system considering source-load correlation

Abstract

The invention discloses a method and system for adjusting the reserve capacity of a power grid considering the correlation between source and load, comprising: obtaining a predicted value of a new energy power generation and a predicted value of a power grid load in each time period to determine a predicted value of a net load in each time period; determining the net load of each typical scene in each time period according to the new energy power generation and the power grid load in each typical scene in each time period; when the weighted sum of the difference between the net load of each typical scene in each time period and the predicted value of the net load and the probability of each typical scene is greater than zero, taking the weighted sum as the adjusted reserve capacity of the power grid in each time period; taking the larger value of the reserve capacity of the power grid before and after the adjustment in each time period as the preferred value of the reserve capacity of the power grid in each time period; adjusting the reserve capacity of the power grid according to the preferred value of the reserve capacity of the power grid in each time period, so as to solve the technical problems such as the inaccurate and unreasonable arrangement of the reserve capacity of the power system due to the lack of consideration of the probability distribution of the new energy power generation and the system load.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid optimization and dispatching, and in particular, relates to a method and system for adjusting power grid reserve capacity taking into account source-load correlation. Background Art

[0002] At present, domestic power grid companies generally arrange for units to reserve spare capacity in advance at the power system operation and management level, which is mainly used to smooth the volatility of renewable energy power generation and power load. When calculating the reserved scale of spare capacity, it is generally necessary to consider the forecast error of renewable energy power generation and system load to prepare a reserve plan. Scientific and reasonable estimation of the forecast error of renewable energy power generation and system load is a prerequisite for reserving spare capacity, but there are great difficulties in accurately calculating the forecast error, which will affect the adequacy of the reserved spare capacity. Therefore, when preparing a reserve plan, it is necessary to consider various factors to check and adjust the existing reserve plan to ensure the accurate balance of power supply and demand. Reasonable and sufficient reserve capacity is crucial to improving the stability of the power system and promoting the consumption of new energy.

[0003] At present, in actual production applications, the forecast error of the day is mainly estimated empirically, and then the reserve capacity is reserved, without fully considering the probability of renewable energy generation and system load. This will result in the reserved reserve capacity being insufficient to support power balance in extreme scenarios. Therefore, if the reserve capacity cannot be adjusted by fully considering the probability of renewable energy generation and system load distribution, it may affect the safe and stable operation of the power system. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for adjusting the reserve capacity of a power grid taking into account the correlation between source and load, so as to solve the technical problems such as the inaccurate and unreasonable arrangement of the reserve capacity of the power system due to the lack of consideration of the probability distribution of renewable energy power generation and system load.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a method for adjusting the reserve capacity of a power grid taking into account the correlation between sources and loads, comprising:

[0007] Obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of the renewable energy power generation and the probability distribution function of the grid load in each time period;

[0008] Perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each period to obtain the source-load joint probability distribution function in each period;

[0009] Based on the source-load joint probability distribution function in each time period, multiple sampling scenes are generated; the source-load joint data in each sampling scene is clustered to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period;

[0010] Obtain the forecast value of renewable energy power generation and grid load in each time period to determine the forecast value of net load in each time period; determine the net load of each typical scenario in each time period according to the renewable energy power generation and grid load in each typical scenario in each time period; when the weighted sum of the difference between the net load of each typical scenario in each time period and the forecast value of net load and the probability of each typical scenario is greater than zero, use the weighted sum as the adjusted grid reserve capacity in each time period; use the larger value of the grid reserve capacity before and after adjustment in each time period as the preferred value of the grid reserve capacity in each time period; adjust the grid reserve capacity according to the preferred value of the grid reserve capacity in each time period.

[0011] Preferably, determining the probability distribution function of the renewable energy power generation and the probability distribution function of the grid load in each time period includes:

[0012] The Gaussian kernel density estimation method is used to perform probability density fitting based on the historical data of renewable energy power generation to obtain the probability density function of renewable energy power generation; the probability density function of renewable energy power generation is integrated to obtain the probability distribution function of renewable energy power generation;

[0013] The Gaussian kernel density estimation method is used to perform probability density fitting based on the historical data of the power grid load to obtain the probability density function of the power grid load; the probability density function of the power grid load is integrated to obtain the probability distribution function of the power grid load.

[0014] Preferably, when the Gaussian kernel density estimation method is used, the ratio of the frequency regulation mileage of renewable energy power generation to the grid load in each time period is used as the fitting sample, and the standard deviation of the fitting sample is used to determine the optimal bandwidth of the Gaussian kernel function according to the Silverman rule of thumb.

[0015] Preferably, based on the optimal bandwidth, the probability density function of the renewable energy power generation in each time period is obtained by the following relationship:

[0016] ,

[0017] In the formula, For the period of renewable energy power generation, For the period The probability density function of the renewable energy power generation is For the period The renewable energy power generation capacity samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples;

[0018] Integrate the probability density function of renewable energy power generation for 24 hours to obtain the probability distribution function of renewable energy power generation for each hour. ;

[0019] Based on the optimal bandwidth, the probability density function of the power grid load in each period is obtained by the following relationship:

[0020] ,

[0021] In the formula, For the period The grid load, For the period The probability density function of the power grid load is For the period The first samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples;

[0022] Integrate the probability density function of the power grid load for 24 hours to obtain the probability distribution function of the power grid load for each hour .

[0023] Preferably, the Monte Carlo method is used to generate multiple sampling scenarios through sampling based on the source-load joint probability distribution function in each time period; the source-load joint data of each time period in each sampling scenario is shown in the following formula:

[0024] ,

[0025] In the formula, , is the total number of sampled scenes, For the Sampling scene period of renewable energy power generation, For the Sampling scene period The grid load, ;

[0026] The source-load joint data in each sampling scenario is normalized by time period. The normalization formula is as follows:

[0027] ,

[0028] In the formula, For the Sampling scene period The source-load joint data normalization value, For the Sampling scene period Source-load joint data, , Respectively Sampling scene period Minimum and maximum values ​​in the source-load joint data.

[0029] Preferably, all the source-load joint data normalization values ​​are clustered to obtain the time period The next Class scene cluster; In the cluster of similar scenes, when the source load data of all time periods in all sampling scenes are When the sum of the Euclidean distances of the cluster centers of the scene clusters reaches the minimum, the time period Next Central value of renewable energy power generation of scenario cluster and time period Next The grid load center value of the scenario cluster As a period Next Source-load joint data of typical scenarios; and by time period Next The ratio between the number of sampled scenes in the scene cluster and the total number of sampled scenes is taken as the period Next The probability of a typical scenario;

[0030] Among them, any type of scene cluster obtained by clustering contains multiple sampling scenes, and the Euclidean distance between the source load data of all time periods in each sampling scene and the cluster center of the scene cluster satisfies the following relationship:

[0031] ,

[0032] In the formula, For the Class scene cluster The source load data of all time periods in the sampling scene is consistent with the The Euclidean distance between the cluster centers of the scene-like clusters, For the Class scene cluster Sampling scene period The normalized value of the renewable energy power generation power, For the Class scene cluster Sampling scene period The normalized value of the grid load is For the period Next The central value of renewable energy power generation of the scenario cluster, For the period Next The grid load center value of the scenario cluster, where: , is the total number of scene clusters.

[0033] Preferably, the predicted value of renewable energy power generation and the predicted value of grid load in each time period are obtained to determine the predicted value of net load in each time period, satisfying the following relationship:

[0034] ,

[0035] In the formula, Target period The predicted value of grid load, Target period The predicted value of new energy power generation, Target period The net load forecast value.

[0036] Preferably, the net load of each typical scenario in each time period is determined according to the renewable energy power generation and grid load in each typical scenario in each time period, satisfying the following relationship:

[0037] ,

[0038] In the formula, Target period Next The grid load in a typical scenario, Target period Next The power generation of new energy in a typical scenario, Target period Next The net load of a typical scenario.

[0039] Preferably, the preferred value of the grid reserve capacity in each time period satisfies the following relationship:

[0040] ,

[0041] In the formula, Target period The grid reserve capacity before adjustment is Target period The adjusted grid reserve capacity is Target period Next The difference between the net load of a typical scenario and the net load forecast value, For the The probability of a typical scenario, A collection of typical scenes.

[0042] The present invention also proposes a power grid reserve capacity adjustment system considering source-load correlation, comprising:

[0043] The data distribution processing module is used to obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period; perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period to obtain the source-load joint probability distribution function in each time period;

[0044] The typical scene generation module is used to generate multiple sampling scenes based on the source-load joint probability distribution function in each time period; cluster the source-load joint data in each sampling scene to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period;

[0045] The grid reserve capacity optimization module is used to obtain the predicted value of renewable energy power generation and grid load in each time period to determine the predicted value of net load in each time period; determine the net load of each typical scene in each time period according to the renewable energy power generation and grid load in each typical scene in each time period; when the weighted sum of the difference between the net load of each typical scene in each time period and the predicted value of net load and the probability of each typical scene is greater than zero, use the weighted sum as the adjusted grid reserve capacity in each time period; use the larger value of the grid reserve capacity before and after adjustment in each time period as the preferred value of grid reserve capacity in each time period; adjust the grid reserve capacity according to the preferred value of grid reserve capacity in each time period.

[0046] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0047] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0048] The beneficial effects of the present invention are that, compared with the prior art, at least the method proposed in the present invention can improve the accuracy, rationality and adequacy of the determined grid reserve capacity, and when the power system is operated and produced based on the determined grid reserve capacity, the safe and stable operation of the grid can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for adjusting the reserve capacity of a power grid considering the source-load correlation proposed by the present invention;

[0050] Figure 2 is the source-load joint probability distribution function of time period 12 in the embodiment of the present invention, Figure 2 The vertical axis Copula is the source-load joint probability distribution function, and the horizontal axis is the photovoltaic power generation power and the grid load. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0052] The present invention provides a method for adjusting the reserve capacity of a power grid taking into account the correlation between source and load. Figure 1 As shown, including:

[0053] Step 1: Obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of the renewable energy power generation and the probability distribution function of the grid load in each time period.

[0054] Specifically, step 1 includes:

[0055] Step 1.1, obtaining historical data of renewable energy power generation and historical data of grid load;

[0056] In the embodiment, historical data within a period of time is counted, including wind power and photovoltaic power generation, grid load, etc. in each period. In the embodiment, the length of the period is 1 hour.

[0057] Step 1.2, using the Gaussian kernel density estimation method, perform probability density fitting based on the historical data of renewable energy power generation to obtain the probability density function of renewable energy power generation; integrate the probability density function of renewable energy power generation to obtain the probability distribution function of renewable energy power generation;

[0058] Step 1.3, using Gaussian kernel density estimation method, perform probability density fitting based on historical data of power grid load to obtain the probability density function of power grid load; integrating the probability density function of power grid load to obtain the probability distribution function of power grid load;

[0059] In the embodiment, using the Gaussian kernel density estimation method to perform probability density fitting based on the historical data of renewable energy power generation is a non-restrictive and preferred choice. Those skilled in the art can select different methods to perform probability density fitting on the wind power and photovoltaic power generation data by hour according to the requirements of calculation accuracy and calculation speed.

[0060] The choice of bandwidth is crucial to the accuracy of kernel density estimation. If the value is too large, it will lead to over-smoothing, loss of certain structural features (such as multi-peak characteristics), and large calculation deviation; if the value is too small, although the estimation deviation will be reduced, it will lead to under-smoothing, the density curve will show a spike characteristic, and a large estimation variance will appear. When using the Gaussian kernel density estimation method, according to the Silverman rule of thumb, the optimal bandwidth of the Gaussian kernel function is determined to satisfy the following relationship:

[0061] ,

[0062] In the formula, is the optimal bandwidth of the Gaussian kernel function, is the number of samples, is the standard deviation of the sample;

[0063] In the Silverman rule of thumb, when the distribution is not close to normal, an estimate of the generalized range is generated. Although in long-term planning, the renewable energy power generation and grid load are approximately normally distributed under normal operation, in ultra-short-term planning, especially every hour, the renewable energy power generation and grid load have non-normal distributions. In order to achieve accurate planning of grid reserve capacity under source-load correlation, the ratio of the frequency regulation mileage of renewable energy power generation to grid load in each time period is used as a fitting sample. The standard deviation of the fitting sample is used to determine the optimal bandwidth of the Gaussian kernel function according to the Silverman rule of thumb. The fitting sample is a quantitative indicator of source-load correlation. The optimal bandwidth determined based on the above method is a kind of correlation associated with source-load correlation.

[0064] In the embodiment, the samples are respectively the renewable energy power generation and the grid load;

[0065] Based on the optimal bandwidth, the probability density function of the renewable energy power generation in each period is obtained by the following relationship:

[0066] ,

[0067] In the formula, For the period of renewable energy power generation, For the period The probability density function of the renewable energy power generation is For the period The renewable energy power generation capacity samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples;

[0068] Integrate the probability density function of renewable energy power generation for 24 hours to obtain the probability distribution function of renewable energy power generation for each hour. ;

[0069] Based on the optimal bandwidth, the probability density function of the power grid load in each period is obtained by the following relationship:

[0070] ,

[0071] In the formula, For the period The grid load, For the period The probability density function of the power grid load is For the period The first samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples;

[0072] Integrate the probability density function of the power grid load for 24 hours to obtain the probability distribution function of the power grid load for each hour .

[0073] Step 2: Perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period to obtain the source-load joint probability distribution function in each time period.

[0074] Specifically, the Frank Copula function is used to perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each period, and the source-load joint probability distribution function in each period is obtained, which satisfies the following relationship:

[0075] ,

[0076] In the formula, For the period The source-load joint probability distribution function is: For the period The probability distribution function of the renewable energy power generation is: For the period The probability distribution function of the grid load is is the Copula function, .

[0077] Use Frank Copula function to extract time periods The correlation between the probability distribution function of the renewable energy power generation and the probability distribution function of the grid load is further highlighted to facilitate use in subsequent steps.

[0078] In the embodiment, the source-load joint probability distribution function of time period 12 is as follows: Figure 2 As shown, Figure 2 The vertical axis Copula is the source-load joint probability distribution function, and the horizontal axis is the photovoltaic power generation power and the grid load.

[0079] Step 3, based on the source-load joint probability distribution function in each time period, generate multiple sampling scenes; cluster the source-load joint data in each sampling scene to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period.

[0080] Specifically, step 3 includes:

[0081] Step 3.1, using the Monte Carlo method, based on the source-load joint probability distribution function in each time period, multiple sampling scenarios are generated by sampling;

[0082] In the embodiment, the Monte Carlo method is used to sample according to the source-load joint probability distribution every hour. The renewable energy power generation and grid load of each hour correspond to each other according to the source-load joint probability distribution function of each hour. The samples corresponding to 24 hours constitute a scene, and a total of sampling scenarios, among which A large number such as 1000 or 10000 can be used to avoid accidental sampling errors. is 1000;

[0083] The source-load joint data for each period in each sampling scenario is shown in the following formula:

[0084] ,

[0085] In the formula, , is the total number of sampled scenes, For the Sampling scene period of renewable energy power generation, For the Sampling scene period The grid load, .

[0086] Step 3.2: Normalize the source-load joint data in each sampling scenario by time period. The normalization formula is as follows:

[0087] ,

[0088] In the formula, For the Sampling scene period The source-load joint data normalization value, For the Sampling scene period Source-load joint data, , Respectively Sampling scene period Minimum and maximum values ​​in the source-load joint data;

[0089] Step 3.3: Cluster all the source-load joint data normalization values ​​to obtain the time period The next Class scene cluster; In the cluster of similar scenes, when the source load data of all time periods in all sampling scenes are When the sum of the Euclidean distances of the cluster centers of the scene clusters reaches the minimum, the time period Next Central value of renewable energy power generation of scenario cluster and time period Next The grid load center value of the scenario cluster As a period Next Source-load joint data of typical scenarios; and by time period Next The ratio between the number of sampled scenes in the scene cluster and the total number of sampled scenes is taken as the period Next The probability of a typical scenario.

[0090] In the embodiment, a large amount of source-load scenario data (normalized data) is clustered using the K-means algorithm, and the clustering dimension is 48 (equal to the number of time periods × the number of data types, the number of time periods is 24 time periods, and the number of data types is two types: renewable energy power generation and grid load). A large number of source-load scenarios are clustered into K-type scenario clusters (K value is much smaller than , here we take 10 as an example), each scenario cluster has 1 cluster center (formally expressed as a 2×24 matrix, that is, the central value of renewable energy power generation and the central value of grid load every hour);

[0091] Among them, any type of scene cluster obtained by clustering contains multiple sampling scenes, and the Euclidean distance between the source load data of all time periods in each sampling scene and the cluster center of the scene cluster satisfies the following relationship:

[0092] ,

[0093] In the formula, For the Class scene cluster The source load data of all time periods in the sampling scene is consistent with the The Euclidean distance between the cluster centers of the scene-like clusters, For the Class scene cluster Sampling scene period The normalized value of the renewable energy power generation power, For the Class scene cluster Sampling scene period The normalized value of the grid load is For the period Next The central value of renewable energy power generation of the scenario cluster, For the period Next The grid load center value of the scenario cluster, where: , is the total number of scene clusters.

[0094] The present invention adjusts the reserve capacity level of the power system by analyzing historical data, performing probability density fitting and probability distribution calculation, performing joint distribution fitting, probabilistic sampling and clustering, while taking into account the joint probability distribution of renewable energy power generation and system load.

[0095] Compared with traditional methods, such as preparing a backup plan by only considering the forecast error of renewable energy power generation and system load for a single day, the method proposed in the present invention can adjust the existing backup plan based on a large amount of historical data and considering the joint probability distribution of renewable energy power generation and system load, which greatly reduces the risks caused by unreasonable backup plans and makes the backup scheduling decision more reasonable.

[0096] Step 4, obtain the predicted value of renewable energy power generation and the predicted value of grid load in each time period to determine the predicted value of net load in each time period; determine the net load of each typical scenario in each time period according to the renewable energy power generation and grid load in each typical scenario in each time period; when the weighted sum of the difference between the net load of each typical scenario in each time period and the predicted value of net load and the probability of each typical scenario is greater than zero, use the weighted sum as the adjusted grid reserve capacity in each time period; use the larger value of the grid reserve capacity before and after adjustment in each time period as the preferred value of the grid reserve capacity in each time period; adjust the grid reserve capacity according to the preferred value of the grid reserve capacity in each time period.

[0097] Specifically, step 4 includes:

[0098] Step 4.1, obtain the predicted value of renewable energy power generation and the predicted value of grid load in each time period to determine the predicted value of net load in each time period, satisfying the following relationship:

[0099] ,

[0100] In the formula, Target period The predicted value of grid load, Target period The predicted value of new energy power generation, Target period The net load forecast value;

[0101] Step 4.2, according to the renewable energy power generation and grid load in each typical scenario in each time period, determine the net load of each typical scenario in each time period, satisfying the following relationship:

[0102] ,

[0103] In the formula, Target period Next The grid load in a typical scenario, Target period Next The power generation of new energy in a typical scenario, Target period Next The net load of a typical scenario;

[0104] In an embodiment, the probability of each typical scenario is fixed.

[0105] Step 4.3, when the weighted sum of the difference between the net load of each typical scenario in each time period and the predicted net load value and the probability of each typical scenario is greater than zero, the weighted sum is used as the adjusted grid reserve capacity in each time period;

[0106] Step 4.4, taking the larger value of the power grid reserve capacity before and after adjustment in each time period as the preferred value of the power grid reserve capacity in each time period;

[0107] Specifically, the difference between the net load of each typical scenario and the net load forecast value in different time periods may be greater than 0 or less than 0. Therefore, when the weighted sum of the difference and the probability of each typical scenario is greater than 0,

[0108] The optimal value of the grid reserve capacity in each period satisfies the following relationship:

[0109] ,

[0110] In the formula, Target period The grid reserve capacity before adjustment is Target period The adjusted grid reserve capacity is Target period Next The difference between the net load of a typical scenario and the net load forecast value, For the The probability of a typical scenario, A collection of typical scenes.

[0111] Step 4.5, adjusting the grid reserve capacity according to the preferred value of the grid reserve capacity in each time period.

[0112] The present invention can ensure the safe and stable operation of the power grid when the power system is operated and produced based on the determined power grid reserve capacity. For example, when the power generation of renewable energy drops suddenly or the power load increases suddenly, since the probability distribution of extreme situations is considered in advance, the reserved reserve capacity will be sufficient to cope with the fluctuation of power supply and demand, thereby supporting the safe and stable operation of the power system.

[0113] The present invention also proposes a power grid reserve capacity adjustment system considering source-load correlation, comprising:

[0114] The data distribution processing module is used to obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period; perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period to obtain the source-load joint probability distribution function in each time period;

[0115] The typical scene generation module is used to generate multiple sampling scenes based on the source-load joint probability distribution function in each time period; cluster the source-load joint data in each sampling scene to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period;

[0116] The grid reserve capacity optimization module is used to obtain the predicted value of renewable energy power generation and grid load in each time period to determine the predicted value of net load in each time period; determine the net load of each typical scene in each time period according to the renewable energy power generation and grid load in each typical scene in each time period; when the weighted sum of the difference between the net load of each typical scene in each time period and the predicted value of net load and the probability of each typical scene is greater than zero, use the weighted sum as the adjusted grid reserve capacity in each time period; use the larger value of the grid reserve capacity before and after adjustment in each time period as the preferred value of grid reserve capacity in each time period; adjust the grid reserve capacity according to the preferred value of grid reserve capacity in each time period.

[0117] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0118] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0119] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0120] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for adjusting the reserve capacity of a power grid considering the correlation between source and load, characterized in that: include: Obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of the renewable energy power generation and the probability distribution function of the grid load in each time period; Perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each period to obtain the source-load joint probability distribution function in each period; Based on the source-load joint probability distribution function in each time period, multiple sampling scenes are generated; the source-load joint data in each sampling scene is clustered to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period; Obtain the predicted value of renewable energy power generation and the predicted value of grid load in each time period to determine the predicted value of net load in each time period; According to the renewable energy power generation and grid load in each typical scenario in each time period, the net load of each typical scenario in each time period is determined; when the weighted sum of the difference between the net load of each typical scenario in each time period and the predicted net load value and the probability of each typical scenario is greater than zero, the weighted sum is used as the adjusted grid reserve capacity in each time period; The larger value of the power grid reserve capacity before and after adjustment in each time period is taken as the preferred value of the power grid reserve capacity in each time period; the power grid reserve capacity is adjusted according to the preferred value of the power grid reserve capacity in each time period.

2. The power grid backup capacity adjustment method considering source-load correlation according to claim 1, characterized in that, Determine the probability distribution function of renewable energy power generation and grid load in each time period, including: The Gaussian kernel density estimation method is used to perform probability density fitting based on the historical data of renewable energy power generation to obtain the probability density function of renewable energy power generation; the probability density function of renewable energy power generation is integrated to obtain the probability distribution function of renewable energy power generation; The Gaussian kernel density estimation method is used to perform probability density fitting based on the historical data of the power grid load to obtain the probability density function of the power grid load; the probability density function of the power grid load is integrated to obtain the probability distribution function of the power grid load.

3. The method for adjusting the power grid reserve capacity considering source-load correlation according to claim 2, characterized in that: When using the Gaussian kernel density estimation method, the ratio of the frequency regulation mileage of renewable energy power generation to the grid load in each time period is used as the fitting sample. The standard deviation of the fitting sample is used to determine the optimal bandwidth of the Gaussian kernel function according to the Silverman rule of thumb.

4. The method for adjusting the power grid reserve capacity considering source-load correlation according to claim 3, characterized in that: Based on the optimal bandwidth, the probability density function of the renewable energy power generation in each period is obtained by the following relationship: ， In the formula, For the period of renewable energy power generation, For the period The probability density function of the renewable energy power generation is For the period The renewable energy power generation capacity samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples; Integrate the probability density function of renewable energy power generation for 24 hours to obtain the probability distribution function of renewable energy power generation for each hour. ; Based on the optimal bandwidth, the probability density function of the power grid load in each period is obtained by the following relationship: ， In the formula, For the period The grid load, For the period The probability density function of the power grid load is For the period The first samples, is the optimal bandwidth of the Gaussian kernel function, is the Gaussian kernel function, , is the number of samples; Integrate the probability density function of the power grid load for 24 hours to obtain the probability distribution function of the power grid load for each hour .

5. The method for adjusting the power grid reserve capacity considering the source-load correlation according to claim 1, characterized in that: The Monte Carlo method is used to generate multiple sampling scenarios based on the source-load joint probability distribution function in each time period through sampling; the source-load joint data of each time period in each sampling scenario is shown as follows: ， In the formula, , is the total number of sampled scenes, For the Sampling scene period of renewable energy power generation, For the Sampling scene period The grid load, ; The source-load joint data in each sampling scenario is normalized by time period. The normalization formula is as follows: ， In the formula, For the Sampling scene period The source-load joint data normalization value, For the Sampling scene period Source-load joint data, , Respectively Sampling scene period Minimum and maximum values ​​in the source-load joint data.

6. The method for adjusting the power grid reserve capacity considering the source-load correlation according to claim 5, characterized in that: Cluster all the source-load joint data normalization values ​​to get the time period The next Class scene cluster; In the cluster of similar scenes, when the source load data of all time periods in all sampling scenes are When the sum of the Euclidean distances of the cluster centers of the scene clusters reaches the minimum, the time period Next Central value of renewable energy power generation of scenario cluster and time period Next The grid load center value of the scenario cluster As a period Next Source-load joint data of typical scenarios; and by time period Next The ratio between the number of sampled scenes in the scene cluster and the total number of sampled scenes is taken as the period Next The probability of a typical scenario; Among them, any type of scene cluster obtained by clustering contains multiple sampling scenes, and the Euclidean distance between the source load data of all time periods in each sampling scene and the cluster center of the scene cluster satisfies the following relationship: ， In the formula, For the Class scene cluster The source load data of all time periods in the sampling scene is consistent with the The Euclidean distance of the cluster center of the scene cluster, For the Class scene cluster Sampling scene period The normalized value of the renewable energy power generation power is For the Class scene cluster Sampling scene period The normalized value of the grid load is For the period Next The central value of renewable energy power generation of the scenario cluster, For the period Next The grid load center value of the scenario cluster, where: , is the total number of scene clusters.

7. The method for adjusting the power grid reserve capacity considering source-load correlation according to claim 1, characterized in that: Obtain the predicted value of renewable energy power generation and grid load in each period to determine the predicted value of net load in each period, satisfying the following relationship: ， In the formula, Target period The predicted value of grid load, Target period The predicted value of new energy power generation, Target period The net load forecast value.

8. The method for adjusting the power grid reserve capacity considering source-load correlation according to claim 7, characterized in that: According to the renewable energy power generation and grid load in each typical scenario in each time period, the net load of each typical scenario in each time period is determined to satisfy the following relationship: ， In the formula, Target period Next The grid load in a typical scenario, Target period Next The power generation of new energy in a typical scenario, Target period Next The net load of a typical scenario.

9. The method for adjusting the power grid reserve capacity considering source-load correlation according to claim 8, characterized in that: The optimal value of the grid reserve capacity in each period satisfies the following relationship: ， In the formula, Target period The grid reserve capacity before adjustment is Target period The adjusted grid reserve capacity is Target period Next The difference between the net load of a typical scenario and the net load forecast value, For the The probability of a typical scenario, A collection of typical scenes.

10. A power grid reserve capacity adjustment system considering source-load correlation, characterized in that: include: The data distribution processing module is used to obtain the renewable energy power generation and grid load in each time period, and determine the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period; perform joint distribution fitting on the probability distribution function of renewable energy power generation and the probability distribution function of grid load in each time period to obtain the source-load joint probability distribution function in each time period; The typical scene generation module is used to generate multiple sampling scenes based on the source-load joint probability distribution function in each time period; cluster the source-load joint data in each sampling scene to obtain the scene cluster in each time period; when the sum of the Euclidean distances between the source-load data in all sampling scenes and the cluster center of the scene cluster in each time period reaches the minimum, the cluster center of the scene cluster in each time period is used as the source-load joint data of the typical scene in each time period; and the ratio between the number of sampling scenes in the scene cluster in each time period and the total number of sampling scenes is used as the probability of the typical scene in each time period; The grid reserve capacity optimization module is used to obtain the predicted value of renewable energy power generation and grid load in each time period to determine the predicted value of net load in each time period; According to the renewable energy power generation and grid load in each typical scenario in each time period, the net load of each typical scenario in each time period is determined; when the weighted sum of the difference between the net load of each typical scenario in each time period and the predicted net load value and the probability of each typical scenario is greater than zero, the weighted sum is used as the adjusted grid reserve capacity in each time period; The larger value of the power grid reserve capacity before and after adjustment in each time period is taken as the preferred value of the power grid reserve capacity in each time period; the power grid reserve capacity is adjusted according to the preferred value of the power grid reserve capacity in each time period.

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