Wind-solar power generation complementary allocation method for new energy power supply system

Abstract

The invention belongs to the field of power grid systems, and particularly relates to a wind-solar power generation complementary allocation method for a new energy power supply system. Comprising the following steps: collecting power data of each wind power plant and solar power plant to form a wind power generation power matrix and a solar power generation power matrix; and obtaining a predicted wind power generation power matrix and a predicted solar power generation power matrix through a future power estimation model. A complementarity index matrix is formed by complementarity indexes between the generated power of each wind power plant and the generated power of each solar power plant. And selecting an element with the minimum complementarity index from the complementarity index matrix, determining the corresponding wind power plant and solar power plant for deployment, updating the complementarity index matrix, repeating the step until the end, and outputting a final deployment scheme. The whole process aims at realizing complementary allocation of wind and light power generation and improving the stability and efficiency of a new energy power supply system.

Description

Technical Field

[0001] The present invention belongs to the field of power grid systems, and specifically relates to a wind-solar power generation complementary deployment method for a new energy power supply system. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, the development and utilization of renewable energy has become an important direction for the transformation of the global energy structure. In particular, wind energy and solar energy are regarded as the pillars of the future energy system due to their clean and renewable characteristics. However, both energy sources have significant intermittency and instability: wind energy is affected by wind speed and wind direction, and solar energy is affected by factors such as sunshine time and cloud cover, which leads to huge challenges in their access and scheduling in the power grid.

[0003] Traditional power grid systems rely on highly controllable energy sources such as thermal power and hydropower, while the instability of wind and solar energy requires that the power grid system must have higher flexibility and adaptability. To this end, researchers and engineers have developed a variety of technologies to meet this challenge, including but not limited to energy storage technology, intelligent scheduling systems, and demand-side management. Energy storage technology can temporarily store excess energy, intelligent scheduling systems can optimize the allocation of power generation resources based on real-time data, and demand-side management balances supply and demand by adjusting the electricity demand of the user end.

[0004] Although these technologies have alleviated the intermittent problem of wind and solar energy to a certain extent, how to further improve the complementarity of wind and solar power generation and the overall power supply efficiency is still a technical problem that needs to be solved urgently. Especially in the case of large-scale wind and solar power generation connected to the grid, how to achieve the optimal allocation of wind and solar power generation to minimize the impact on grid stability and maximize energy utilization efficiency is a hot topic in the current new energy field. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a method for complementary deployment of wind and solar power generation in a new energy power supply system, which optimizes complementarity, improves the stability and efficiency of the new energy system, reduces operating costs, and enhances the adaptability of the power grid through intelligent deployment of wind and solar power generation.

[0006] The technical solution of the present invention is as follows:

[0007] A wind-solar power generation complementary deployment method for a new energy power supply system, comprising:

[0008] Collect the power data of each wind farm and each solar power plant in the wind-solar power generation system, and use the collected power generation data of each power plant in the past t time periods to form the wind power generation power matrix and solar power generation power matrix of each power plant respectively;

[0009] Construct a future power estimation model prediction, input the weather forecast information for the next t time periods, and obtain the predicted wind power generation matrix and the predicted solar power generation matrix respectively;

[0010] The complementary index between the power generation of each wind farm and the power generation of each solar power plant in the future t time periods is calculated by predicting the wind power generation matrix and the solar power generation matrix, and the complementary index matrix in the future t time periods is obtained;

[0011] Select the element with the smallest complementary index from the complementary index matrix, obtain the corresponding wind farm and solar power plant with the smallest complementary index for corresponding deployment, delete the row and column of the matrix where the smallest element is located, form a new complementary index matrix, and repeat this step until the end.

[0012] Furthermore, the wind power generation matrix and solar power generation matrix of each power plant are the wind farm power generation data and solar power plant power generation data collected in the past t time periods arranged in chronological order;

[0013] Among them, the wind power generation power matrix is ​​expressed as:

[0014] P wind n =[P wind n-1 ,P wind n-2 ,...,P wind n-i ];

[0015] Among them, P wind n-i represents the power generation of the nth wind farm collected in the i-th time period, n=1,2,...,n; i=1,2,...,t;

[0016] The solar power generation matrix is ​​expressed as: P solar m =[P solar m-1 ,P solar m-2 ,...,P solar m-i ];

[0017] Among them, P solar m-i It represents the power generated by the m-th solar power plant collected in the i-th time period, m=1,2,...,m; i=1,2,...,t.

[0018] Furthermore, the future power estimation model is trained and predicted based on the meteorological forecast information of the wind farm and the solar power plant in the past t time periods; the future power estimation model includes a wind future power estimation model and a solar future power estimation model;

[0019] Among them, the future wind power estimation model is expressed as:

[0020]

[0021] Among them, P pred_wind represents the predicted wind power, V represents wind speed, θ represents the angle between wind direction and the plane of wind turbine impeller, T represents temperature, H represents humidity, t represents time, a1, a2, a3, a4, b, c, d, e, ω1, It is a coefficient determined based on historical data and actual conditions;

[0022] Among them, the future solar power estimation model is expressed as:

[0023]

[0024] Among them, P pred_solar represents the predicted solar power generation power; I represents the light intensity; A represents the effective area of ​​the solar power plant; η represents the conversion efficiency of the solar power plant; a6, a7, a8, a9, f, ω2, It is a coefficient determined based on historical data and actual conditions.

[0025] Furthermore, the wind power future power estimation model and the solar power future power estimation model predict the predicted wind power generation power matrix P pred_wind n =[P pred_wind n-1 ,P pred_wind n -2,...,P pred_wind n-i ] and predicted solar power generation matrix P pred_solar m =[P pred_solar m-1 ,P pred_solar m-2 ,...,P pred_solar m-i ]; where P pred_wind n-i represents the power generation of the nth wind farm in the i-th time period in the future, P pred_wind n-i represents the power generation of the mth solar power plant in the i-th time period in the future, n=1,2,...,n; m=1,2,...,m; i=1,2,...,t.

[0026] Furthermore, the power generation complementarity index calculation step includes:

[0027] Calculate the mean and standard deviation of the power generated by each wind farm in the future t time periods, as well as the mean and standard deviation of the power generated by each solar power plant;

[0028] Calculate the covariance between the power generated by each wind farm and the power generated by each solar power plant in the future t time periods;

[0029] Calculate the power generation complementarity index based on the mean, standard deviation and covariance;

[0030] The calculation formula of the complementarity index is:

[0031]

[0032] Furthermore, the calculation formula for the average power generation value of the wind farm is:

[0033]

[0034] Among them, P pred_wind n (i) represents the power generated by the nth wind power plant in the i-th time period in the future;

[0035] The calculation formula for the standard deviation of the wind farm power generation is:

[0036]

[0037] The calculation formula for the average power generation of the solar power plant is:

[0038]

[0039] Among them, P pred_solar m (i) represents the power generated by the mth solar power plant in the i-th time period in the future;

[0040] The calculation formula for the standard deviation of the power generation of the solar power plant is:

[0041]

[0042] Covariance calculation formula:

[0043]

[0044] Complementarity index calculation formula:

[0045]

[0046] Furthermore, the complementarity index matrix is ​​expressed as:

[0047]

[0048] C nm It represents the complementarity index between the nth wind power plant and the mth solar power plant in the future tth time period.

[0049] The beneficial effects of the present invention are as follows: by collecting power data of wind and solar power plants, constructing a future power estimation model, calculating complementary indicators and making adjustments, the complementary characteristics of wind and solar energy can be fully utilized to improve energy utilization efficiency.

[0050] Reasonable deployment of wind and solar power generation can reduce the impact of fluctuations in a single energy source on the power supply system and enhance the stability of the system.

[0051] Using past weather forecast information to train models and predict future power generation can more accurately plan and allocate power resources.

[0052] By finding the combination with the smallest complementary index and making adjustments, we can achieve more optimized energy distribution and improve the economic efficiency of energy utilization.

[0053] Selecting appropriate time intervals for data collection based on actual conditions can adapt to different scenarios and needs and avoid excessive storage and processing pressure caused by too frequent data collection.

[0054] In summary, this technical solution helps to improve the reliability, stability and economy of the new energy power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 :Flow chart of complementary deployment of wind power generation and solar power generation;

[0056] Figure 2 : Schematic diagram of data collection steps;

[0057] Figure 3 : Schematic diagram of power prediction model;

[0058] Figure 4 : Complementarity index calculation flow chart;

[0059] Figure 5 : Schematic diagram of intelligent deployment steps; DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] See also Figure 1 , which shows a wind-solar power generation complementary deployment method for a new energy power supply system provided by an embodiment of the present invention, comprising:

[0062] S100: Collect power data of each wind farm and each solar power plant in the wind-solar power generation system, and use the collected power generation data of each power plant in the past t time periods to form a wind power generation matrix and a solar power generation matrix of each power plant.

[0063] S200: construct a future power estimation model prediction, input weather forecast information for the next t time periods, and obtain a predicted wind power generation power matrix and a predicted solar power generation power matrix respectively.

[0064] S300: Calculate the power complementarity index between the power generation of each wind farm and the power generation of each solar power plant in the future t time periods by predicting the wind power generation matrix and the solar power generation matrix, and obtain the complementarity index matrix in the future t time periods.

[0065] S400: Selecting the element with the smallest complementary index from the complementary index matrix, obtaining the corresponding wind farm and solar power plant with the smallest complementary index for corresponding deployment, and deleting the row and column of the matrix where the smallest element is located to form a new complementary index matrix, and repeating this step until the end;

[0066] S100 is about collecting and organizing the power generation data of wind and solar power plants. Specifically, this step involves collecting the power data of each wind and solar power plant at a fixed time interval and arranging them in chronological order to form a wind power generation matrix P wind n And the solar power matrix P solar m , for further analysis and prediction.

[0067] In some possible embodiments, Figure 2 As shown, S100 specifically includes:

[0068] S110: In the past t time periods, power data of each wind farm and each solar power plant is collected at fixed time intervals.

[0069] In the past t time periods, a fixed time interval is set, such as every 1 hour or every 30 minutes, to collect the power generation data of each wind farm and each solar power plant. The selection of this time interval needs to be determined according to the actual situation and needs, ensuring that sufficiently detailed data can be obtained while avoiding excessive storage and processing pressure caused by too frequent data.

[0070] S120: Arrange the wind farm power generation data collected in the past t time periods in chronological order to form a wind power generation matrix P wind n .

[0071] The wind power generation power matrix is ​​expressed as:

[0072] P wind n =[P wind n-1 ,P wind n-2 ,...,P wind n-i ];

[0073] Among them, P wind n-i (n=1, 2, ..., n; i=1, 2, ..., t) represents the generated power of the nth wind farm collected in the i-th time period.

[0074] S130: Arrange the solar power plant power generation data collected in the past t time periods in chronological order to form a solar power generation power matrix P solar m ;

[0075] The solar power generation matrix is ​​expressed as:

[0076] P solar m =[P solar m-1 ,P solar m-2 ,...,P solar m-i ];

[0077] Among them, P solar m-i (m=1,2,...,m; i=1,2,...,t) represents the power generated by the mth solar power plant collected in the i-th time period.

[0078] S200 builds and applies a future power estimation model to predict the power generation of wind and solar power plants in a specific time period in the future. This model is based on meteorological forecast information in the past period, such as wind speed, light intensity and temperature, obtained through training, and is used to predict the wind power generation matrix P in the future t time periods pred_wind n And the solar power matrix P pred_solar m .

[0079] In some possible embodiments, Figure 3 As shown, S200 specifically includes:

[0080] S210: Establishing a future power estimation model. The future power estimation model is trained and predicted based on the weather forecast information of the wind farm and the solar power plant in the past t time periods, and the weather forecast information includes wind speed, light intensity, and temperature.

[0081] The future power estimation model includes a wind future power estimation model and a solar future power estimation model.

[0082] Among them, the future wind power estimation model is expressed as:

[0083]

[0084] Among them, P pred_wind represents the predicted wind power, V represents wind speed, θ represents the angle between wind direction and the plane of wind turbine impeller, T represents temperature, H represents humidity, t represents time, a1, a2, a3, a4, b, c, d, e, ω1, It is a coefficient determined based on historical data and actual conditions.

[0085] Among them, the future solar power estimation model is expressed as:

[0086]

[0087] Among them, P pred_solar represents the predicted solar power generation power; I represents the light intensity; A represents the effective area of ​​the solar power plant; η represents the conversion efficiency of the solar power plant; a6, a7, a8, a9, f, ω2 It is a coefficient determined based on historical data and actual conditions.

[0088] S220: Input the weather forecast information for the next t time periods into the trained model, and use the future power estimation model to predict the power generation of wind farms and solar power plants in the next t time periods to obtain the predicted wind power generation power matrix P pred_wind n =[P pred_wind n-1 ,P pred_wind n -2,...,P pred_wind n-i ] and predicted solar power generation matrix P pred_solar m =[P pred_solar m-1 ,P pred_solar m-2 ,...,P pred_solar m-i ]. Among them, P pred_wind n-i (n=1,2,...,n;i=1,2,...,t) represents the power generation of the nth wind farm in the i-th time period in the future, P pred_wind n-i (m=1,2,...,m; i=1,2,...,t) represents the power generation of the mth solar power plant in the i-th time period in the future.

[0089] S300 involves calculating the complementarity index between the power generation of wind farms and solar power plants in the future t time periods. This includes calculating the mean and standard deviation of the power generation of each wind and solar power plant, as well as the covariance between them. Based on these statistical data, the complementary indexes of the power generation are further calculated, which measure the degree of mutual complementation of wind and solar power generation in different time periods. Finally, these indicators are combined into a complementary index matrix to provide a basis for subsequent optimization and deployment.

[0090] In some possible implementations, such as Figure 4 As shown, S300 specifically includes:

[0091] S310: Calculate the average value and standard deviation of the power generated by each wind farm in the future t time periods, and the average value and standard deviation of the power generated by each solar power plant.

[0092] The calculation formula for the average power generation of the nth wind farm is:

[0093]

[0094] Where P pred_wind n (i) represents the power generation of the nth wind power plant in the i-th time period in the future.

[0095] The calculation formula of the standard deviation of wind farm power generation is:

[0096]

[0097] The calculation formula for the average power generation of the mth solar power plant is:

[0098]

[0099] Among them, P pred_solar m (i) represents the power generated by the mth solar power plant in the i-th time period in the future.

[0100] The calculation formula of the standard deviation of the power generated by a solar power plant is:

[0101]

[0102] S320: Calculate the covariance between the power generation of each wind farm and the power generation of each solar power plant in the future t time periods.

[0103] Covariance calculation formula:

[0104]

[0105] S330: Calculate the power generation complementarity index according to the mean value, standard deviation and covariance.

[0106] Complementarity index calculation formula:

[0107] S340: Combining the calculated complementary indicators for the future t-th time period into a complementary indicator matrix, the complementary indicator matrix is ​​expressed as: C nm It represents the complementarity index between the nth wind power plant and the mth solar power plant in the future tth time period.

[0108] S400 identifies the best complementary pairing of wind farms and solar power plants by selecting the smallest element in the complementary index matrix, and then executes the corresponding power dispatch strategy. After each dispatch, the complementary index matrix is ​​updated, the rows and columns of the dispatched power plants are deleted, a new matrix is ​​formed, and this process is repeated until all power plants have completed the dispatch or reached the preset target, and finally the final dispatch plan is output.

[0109] In some possible implementations, such as Figure 5 As shown, S400 specifically includes:

[0110] S410: In the complementarity index matrix, find the element with the smallest complementarity index, which represents the best complementary pairing between the wind farm and the solar power plant. Once the smallest complementarity index is found, record the corresponding wind farm and solar power plant's identification, and mark these positions in the matrix so that they can be excluded in subsequent steps.

[0111] S420: Execute an actual power dispatch strategy according to the power plant corresponding to the minimum complementarity index. The power dispatch strategy includes adjusting the output of the wind farm and the solar power plant, or using energy storage equipment to balance power generation.

[0112] S430: After the deployment is executed, the complementary index matrix is ​​updated, the rows and columns of the deployed power plants are deleted, and a new complementary index matrix is ​​formed to prepare for the next round of deployment. If there are still undeployed power plants, steps S410 to S420 are repeated to continue to find and execute the deployment strategy corresponding to the minimum complementary index.

[0113] S440: When there are no more elements in the complementary indicator matrix, or the system reaches the preset allocation target, the allocation process is terminated, and after all allocations are completed, the final allocation plan is output.

[0114] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0115] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0116] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0117] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0118] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for complementary deployment of wind and solar power generation in a new energy power supply system, characterized in that: include: Collect the power data of each wind farm and each solar power plant in the wind-solar power generation system, and use the collected power generation data of each power plant in the past t time periods to form the wind power generation power matrix and solar power generation power matrix of each power plant respectively; Construct a future power estimation model prediction, input the weather forecast information for the next t time periods, and obtain the predicted wind power generation matrix and the predicted solar power generation matrix respectively; The complementary index between the power generation of each wind farm and the power generation of each solar power plant in the future t time periods is calculated by predicting the wind power generation matrix and the solar power generation matrix, and the complementary index matrix in the future t time periods is obtained; Select the element with the smallest complementary index from the complementary index matrix, obtain the corresponding wind farm and solar power plant with the smallest complementary index for corresponding deployment, delete the row and column of the matrix where the smallest element is located, form a new complementary index matrix, and repeat this step until the end.

2. According to the method for complementary deployment of wind and solar power generation in a new energy power supply system of claim 1, it is characterized in that: The wind power generation matrix and solar power generation matrix of each power plant are the wind farm power generation data and solar power plant power generation data collected in the past t time periods arranged in chronological order; Among them, the wind power generation power matrix is ​​expressed as: P wind n =[P wind n-1 ,P wind n-2 ,...,P wind n-i ]; Among them, P windn-i represents the power generation of the nth wind farm collected in the i-th time period, n=1,2,...,n; i=1,2,...,t; The solar power generation matrix is ​​expressed as: P solarm =[P solarm-1 ,P solarm-2 ,...,P solarm-i ]; Among them, P solarm-i It represents the power generated by the m-th solar power plant collected in the i-th time period, m=1,2,...,m; i=1,2,...,t.

3. According to the method for complementary deployment of wind and solar power generation in a new energy power supply system of claim 1, it is characterized in that: The future power estimation model is based on the training and prediction of the meteorological forecast information of the wind farm and the solar power plant in the past t time periods; the future power estimation model includes a wind future power estimation model and a solar future power estimation model; Among them, the future wind power estimation model is expressed as: Among them, P pred_wind represents the predicted wind power, V represents wind speed, θ represents the angle between wind direction and the plane of wind turbine impeller, T represents temperature, H represents humidity, t represents time, a1, a2, a3, a4, b, c, d, e, ω1, It is a coefficient determined based on historical data and actual conditions; Among them, the future solar power estimation model is expressed as: Among them, P pred_solar represents the predicted solar power generation power; I represents the light intensity; A represents the effective area of ​​the solar power plant; η represents the conversion efficiency of the solar power plant; a6, a7, a8, a9, f, ω2, It is a coefficient determined based on historical data and actual conditions.

4. According to claim 3, a wind-solar power generation complementary deployment method for a new energy power supply system is characterized in that: The wind power future power estimation model and the solar power future power estimation model predict the predicted wind power generation power matrix P pred_windn =[P pred_windn-1 ,P pred_windn -2,...,P pred_windn-i ] and predicted solar power generation matrix P pred_solarm =[P pred_solarm-1 ,P pred_solarm-2 ,...,P pred_solarm-i ]; where P pred_windn-i represents the power generation of the nth wind farm in the i-th time period in the future, P pred_windn-i represents the power generation of the mth solar power plant in the i-th time period in the future, n=1,2,...,n; m=1,2,...,m; i=1,2,...,t.

5. According to the method for complementary deployment of wind and solar power generation in a new energy power supply system of claim 1, it is characterized in that: The power generation complementarity index calculation step comprises: Calculate the mean and standard deviation of the power generated by each wind farm in the future t time periods, as well as the mean and standard deviation of the power generated by each solar power plant; Calculate the covariance between the power generated by each wind farm and the power generated by each solar power plant in the future t time periods; Calculate the power generation complementarity index based on the mean, standard deviation and covariance; The calculation formula of the complementarity index is:

6. According to the method for complementary deployment of wind and solar power generation in a new energy power supply system of claim 5, it is characterized in that: The calculation formula for the average power generation of the wind farm is: Among them, P pred_windn (i) represents the power generated by the nth wind power plant in the i-th time period in the future; The calculation formula for the standard deviation of the wind farm power generation is: The calculation formula for the average power generation of the solar power plant is: Among them, P pred_solarm (i) represents the power generated by the mth solar power plant in the i-th time period in the future; The calculation formula for the standard deviation of the power generation of the solar power plant is: Covariance calculation formula: Complementarity index calculation formula:

7. According to claim 5, a wind-solar power generation complementary deployment method for a new energy power supply system is characterized in that: The complementary indicator matrix is ​​expressed as: C nm It represents the complementarity index between the nth wind power plant and the mth solar power plant in the future tth time period.

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