New energy grid-connected power dispatching method and system

Abstract

The application discloses a new energy grid-connected power dispatching method and system, and belongs to the field of power dispatching.The method comprises the following steps: acquiring new energy station information of a target area; collecting target dispatching electric energy within a preset dispatching window; determining N station reliabilities; determining N station cumulative losses; generating N electric energy yields; performing dispatching iteration optimization based on the N station reliabilities, the N station cumulative losses and the target dispatching electric energy, and utilizing a first constraint condition and a second constraint condition to constrain the dispatching iteration optimization process, so as to generate a target power dispatching scheme; and performing grid-connected power dispatching on N new energy stations of the target area.The technical problems of low new energy grid-connected power dispatching reliability and poor dispatching effect in the prior art are solved.The technical effect of improving the fitting degree of power dispatching and actual operation and improving the dispatching quality is achieved.

Description

Technical Field

[0001] This application relates to the field of power dispatching, and in particular to methods and systems for dispatching grid-connected power from new energy sources. Background Technology

[0002] The stability and reliability of renewable energy grid connection are crucial for ensuring the safe operation of the power grid. Currently, the grid connection dispatching of renewable energy power plants is significantly out of sync with the actual operating conditions of each plant, failing to fully utilize plant resources. Existing technologies suffer from low reliability and poor dispatching efficiency in renewable energy grid connection dispatching. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for dispatching grid-connected new energy power, in order to solve the technical problems of low reliability and poor dispatching effect of grid-connected new energy power in the prior art.

[0004] In view of the above problems, this application provides a method and system for dispatching grid-connected power from new energy sources.

[0005] In a first aspect, this application provides a method for dispatching grid-connected renewable energy power, which is implemented through a renewable energy grid-connected power dispatching system, wherein the method includes:

[0006] Obtain information on new energy power stations in the target area, wherein the information on new energy power stations includes N new energy power stations and the location identifiers of N new energy power stations;

[0007] Collect the target dispatchable power energy within the preset dispatch window, and take the power dispatchable power energy that meets the target dispatchable power energy as the first constraint condition;

[0008] Collect operational data from the N new energy power stations within the historical scheduling window to determine the reliability of the N power stations;

[0009] Based on the location identifiers of N new energy power stations and the location of the load center in the target area, the cumulative losses of the N power stations are determined.

[0010] Collect the power output value of N new energy power plants within a preset scheduling window, generate N power output values, and use the power dispatched from N new energy power plants during grid-connected power dispatch to meet the N power output values ​​as a second constraint condition;

[0011] Based on the reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power, the scheduling iterative optimization is performed, and the scheduling iterative optimization process is constrained by the first constraint and the second constraint to generate the target power dispatch scheme.

[0012] The target power dispatch scheme is used to conduct grid-connected power dispatch for N new energy power plants in the target area.

[0013] On the other hand, this application also provides a new energy grid-connected power dispatching system for executing the new energy grid-connected power dispatching method as described in the first aspect, wherein the system includes:

[0014] The station information acquisition module is used to acquire new energy station information in the target area, wherein the new energy station information includes N new energy stations and N new energy station location identifiers;

[0015] The first constraint setting module is used to collect the target dispatch power energy within the preset dispatch window, and to set the power energy dispatched by the grid-connected power as the first constraint condition, which requires the power energy to meet the target dispatch power energy.

[0016] The power station reliability determination module is used to collect the operating data of the N new energy power stations within the historical scheduling window and determine the reliability of the N power stations;

[0017] The cumulative loss determination module is used to determine the cumulative loss of N power stations based on the location identifiers of N new energy power stations and the load center location of the target area.

[0018] The second constraint generation module is used to collect the power output value of N new energy power plants within a preset scheduling window, generate N power output values, and use the power dispatched from the N new energy power plants during grid-connected power dispatch to meet the N power output values ​​as the second constraint.

[0019] The target power dispatch scheme generation module is used to perform iterative optimization of dispatch based on the reliability of N power stations, the cumulative loss of N power stations and the target dispatch power, and to constrain the iterative optimization process using the first constraint condition and the second constraint condition to generate the target power dispatch scheme.

[0020] The grid-connected power dispatching module is used to perform grid-connected power dispatching on N new energy power plants in the target area using the target power dispatching scheme.

[0021] Thirdly, this application also provides a new energy grid-connected power dispatching system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the first aspect above.

[0022] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects.

[0023] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0024] This application obtains information on new energy power stations in a target area, including N new energy power stations and their location identifiers. It then collects target dispatchable power within a preset dispatch window, using the requirement that grid-connected power dispatch must meet the target dispatchable power as a first constraint. Next, it collects operational data from the N new energy power stations within historical dispatch windows to determine their reliability. Based on the location identifiers of the N new energy power stations and the load center location of the target area, it determines the cumulative losses of the N power stations. It then collects the power output value of the N new energy power stations within the preset dispatch window, generating N power output values. The requirement that power dispatched from the N new energy power stations during grid-connected power dispatch must meet N power output values ​​is used as a second constraint. Based on the reliability of the N power stations, their cumulative losses, and the target dispatchable power, it performs iterative optimization of the dispatch process, constraining the process with the first and second constraints to generate a target power dispatch scheme. Finally, it uses this target power dispatch scheme to perform grid-connected power dispatch on the N new energy power stations in the target area. This has achieved the technical effect of improving the alignment between power dispatching and actual operation, and enhancing dispatching quality.

[0025] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating the new energy grid-connected power dispatching method of this application;

[0028] Figure 2 This is a flowchart illustrating the process of generating the reliability of N power stations in the new energy grid-connected power dispatching method of this application;

[0029] Figure 3 This is a flowchart illustrating the process of generating M candidate fitness values ​​in the new energy grid-connected power dispatching method of this application;

[0030] Figure 4 This is a schematic diagram of the structure of the new energy grid-connected power dispatching system of this application;

[0031] Figure 5 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0032] Explanation of reference numerals in the attached drawings: 11. Station information acquisition module; 12. First constraint setting module; 13. Station reliability determination module; 14. Cumulative loss determination module; 15. Second constraint generation module; 16. Target power dispatching scheme generation module; 17. Grid-connected power dispatching module; 300. Electronic device; 301. Memory; 302. Processor; 303. Communication interface; 304. Bus architecture. Detailed Implementation

[0033] This application provides a method and system for dispatching grid-connected new energy power, which solves the technical problems of low reliability and poor dispatching effect of grid-connected new energy power in the prior art.

[0034] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0035] The various non-limiting embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0036] Example 1

[0037] like Figure 1 As shown, this application provides a method for dispatching grid-connected power from new energy sources. The method is applied to a grid-connected power dispatching system for new energy sources, and specifically includes the following steps:

[0038] Step S100: Obtain information on new energy power stations in the target area, wherein the information on new energy power stations includes N new energy power stations and the location identifiers of N new energy power stations;

[0039] In one possible embodiment, the target area is any area requiring grid-connected power dispatch. The renewable energy power station information describes the status of renewable energy power stations established within the target area, including N renewable energy power stations and their location identifiers. The N renewable energy power stations describe the number of renewable energy power stations within the target area. The location identifiers describe the locations of the renewable energy power stations within the target area. Obtaining the renewable energy power station information for the target area provides a basis for subsequent grid-connected power dispatch.

[0040] Step S200: Collect the target dispatch power within the preset dispatch window, and take the power dispatch of grid-connected power that meets the target dispatch power as the first constraint condition;

[0041] In one embodiment, the preset scheduling window is a time period for grid-connected power dispatching set by those skilled in the art. The target dispatched power energy is determined based on the power operation status of the target area, and refers to the power energy dispatched from N renewable energy power plants. Furthermore, the requirement that the grid-connected power dispatched energy meet the target dispatched power energy is used as a first constraint condition, thus constraining power dispatching from the perspective of dispatching demand. This achieves the technical effect of improving the quality of power dispatching.

[0042] Step S300: Collect the operating data of the N new energy power stations within the historical scheduling window to determine the reliability of the N power stations;

[0043] Furthermore, such as Figure 2 As shown, step S300 in this embodiment further includes:

[0044] Collect the power fluctuation variance of N new energy power plants during historical dispatch windows, and generate N first sub-reliability scores;

[0045] Analyze the power fault maintenance information of N new energy power stations to generate N second sub-reliability scores;

[0046] Collect the electricity output value of N new energy power plants per unit time and generate N electricity output values;

[0047] Each of the N energy output values ​​is divided by the sum of the N energy output values, and N third sub-reliability values ​​are generated based on the calculation results;

[0048] A comprehensive reliability analysis is performed based on N first sub-reliability values, N second sub-reliability values, and N third sub-reliability values ​​to generate N site reliability values.

[0049] Furthermore, step S300 in this embodiment of the application also includes:

[0050] Construct a second sub-reliability calculation formula, wherein the second sub-reliability calculation formula is:

[0051]

[0052] Where S(x) i Let k be the second sub-reliability of the i-th renewable energy power station. i Let be the total number of power equipment in the i-th renewable energy power station. For the i-th new energy power station, the j-th power equipment generates μ j The losses resulting from the subsequent failure. Let j be the design life of the j-th power equipment in the i-th renewable energy power station. Let j be the service life of the jth power equipment in the i-th new energy power station;

[0053] Collect the design life and service life of power equipment in N new energy power stations, combine the power fault maintenance information of N new energy power stations, and generate N second sub-reliability values ​​using the second sub-reliability calculation formula.

[0054] In the embodiments of this application, operational data of N renewable energy power stations within a historical dispatch window are collected and reliability analysis is performed to determine the operational reliability of the N renewable energy power stations, providing strong support for subsequent power dispatch. The historical dispatch window is the time period during which historical dispatch analysis is conducted in the target area. The reliability of the N power stations reflects the degree of operational reliability of the N renewable energy power stations. The higher the reliability of the power stations, the stronger their ability to achieve predetermined dispatch objectives during power dispatch.

[0055] Preferably, operational data from N renewable energy power plants within a historical scheduling window is collected to determine N power output datasets. Variance is calculated for each of the N power output datasets, and the results are used as N first-order sub-reliability scores. Preferably, power fault maintenance information for multiple devices within the N renewable energy power plants is collected, and N second-order sub-reliability scores are determined based on device reliability. Furthermore, the power output value of the N renewable energy power plants per unit time is collected to generate N power output values, determining the power plant's capacity. The sum of the N power output values ​​is then divided by the N power output values ​​to generate N third-order sub-reliability scores, providing a reliability analysis from the perspective of capacity strength. Finally, the N first-order, N second-order, and N third-order sub-reliability scores are weighted according to a preset weighting ratio to generate N power plant reliability scores. The preset weighting ratio is set by those skilled in the art.

[0056] In one embodiment, the second sub-reliability calculation formula is used to quantitatively calculate the equipment reliability of new energy power plants. The design life is the operating time of the power equipment under normal working conditions. The service life is the time the power equipment has been in use. Based on the design life and service life of the power equipment in N new energy power plants, and combined with the power fault maintenance information of the N new energy power plants, N second sub-reliability values ​​are generated using the second sub-reliability calculation formula.

[0057] Step S400: Based on the location identifiers of N new energy power stations and the load center location of the target area, determine the cumulative loss of N power stations;

[0058] Step S500: Collect the power output value of N new energy power plants within the preset scheduling window, generate N power output values, and use the power dispatched from the N new energy power plants during grid-connected power dispatch as a second constraint condition, which requires the power output value of the N power plants to be satisfied.

[0059] Preferably, the transmission distances of the N new energy power stations are determined based on their location markers and the load center location of the target area, thus obtaining the transmission distances of the N power stations. Then, the ratio of each transmission distance of the N power stations to the sum of their transmission distances is taken as the cumulative loss of the N power stations. The cumulative loss of the N power stations reflects the cumulative losses incurred by the N new energy power stations during transmission.

[0060] Preferably, the power output values ​​of N renewable energy power plants within a preset dispatch window are collected to generate N power output values. These N power output values ​​reflect the planned power production of the N renewable energy power plants within the preset dispatch window. Furthermore, the power dispatched from the N renewable energy power plants during grid-connected power dispatch must meet these N power output values ​​as a second constraint. This constrains the generation of the power dispatch scheme from the perspective of the dispatched party.

[0061] Step S600: Based on the reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power, perform scheduling iterative optimization, and use the first constraint condition and the second constraint condition to constrain the scheduling iterative optimization process to generate the target power dispatch scheme;

[0062] Step S700: Use the target power dispatch scheme to perform grid-connected power dispatch for N new energy power plants in the target area.

[0063] Furthermore, such as Figure 3 As shown, step S600 in this embodiment further includes:

[0064] The reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power are input into the grid-connected power dispatch decision layer for analysis, generating Q initial power dispatch schemes;

[0065] Traverse the Q initial power dispatch schemes and filter them using the first and second constraints to generate M candidate power dispatch schemes.

[0066] Perform fitness analysis on M candidate power dispatch schemes to generate M candidate fitnesss.

[0067] Furthermore, after generating M candidate fitnesss, step S600 in this embodiment of the application further includes:

[0068] The reciprocal of the ratio of the fitness of each of the M candidate fitnesss to the sum of the fitness of the M candidate fitnesss is taken as the M adjustment coefficients;

[0069] The preset adjustment range is updated using M adjustment coefficients to generate M candidate adjustment ranges;

[0070] The M candidate power dispatch schemes are adjusted multiple times using M candidate adjustment ranges to generate a set of M candidate power dispatch schemes.

[0071] Based on a set of M candidate power dispatch schemes, iterative optimization is performed to generate the target power dispatch scheme.

[0072] Furthermore, based on the set of M candidate power dispatch schemes, iterative optimization is performed to generate the target power dispatch scheme. In this embodiment, step S600 further includes:

[0073] The union of the M candidate power dispatch schemes is used to generate an iterative power dispatch scheme set.

[0074] Without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the first iterative power dispatch scheme, and the first iterative fitness is generated.

[0075] Again, without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the second iterative power dispatch scheme, and the fitness of the second iteration is generated.

[0076] Determine whether the fitness of the first iteration is greater than that of the second iteration. If not, then the second iteration power dispatch scheme is adopted as the stage power dispatch scheme.

[0077] If so, generate a random number and determine whether the random number is greater than the preset value. If so, use the second iteration power dispatch scheme as the stage power dispatch scheme; otherwise, use the first iteration power dispatch scheme as the stage power dispatch scheme.

[0078] After multiple iterations, the stage power dispatch scheme corresponding to the maximum fitness value during the iteration process is taken as the target power dispatch scheme.

[0079] Furthermore, step S600 in this embodiment of the application also includes:

[0080] Multiple historical power station reliability, multiple historical power station cumulative loss, multiple historical target dispatch power, and multiple historical initial power dispatch schemes are obtained as training data.

[0081] The training data is divided into multiple groups, and the network layers constructed by the convolutional neural network are trained in a supervised manner until the output converges, thereby generating the grid-connected power dispatch decision layer that has been trained.

[0082] Preferably, the scheduling iterative optimization is performed based on the reliability of N power stations, the cumulative loss of N power stations, and the target dispatchable power. The scheduling iterative optimization process is constrained by a first constraint and a second constraint to generate a target power dispatch scheme. This target power dispatch scheme is the optimal scheme for grid-connected power dispatch of new energy sources within the target area. Using this target power dispatch scheme to perform grid-connected power dispatch for the N new energy power stations in the target area achieves the technical effect of improving the alignment between power dispatch and the operational status of the power stations in the target area.

[0083] In one embodiment, the reliability of N power stations, the cumulative losses of N power stations, and the target dispatchable power input to the grid-connected power dispatch decision layer are analyzed to generate Q initial power dispatch schemes. These Q initial power dispatch schemes are then traversed, and filtered using a first constraint and a second constraint to generate M candidate power dispatch schemes. The M candidate power dispatch schemes are those that simultaneously satisfy both the first and second constraints. Fitness analysis is then performed on the M candidate power dispatch schemes, which involves weighted calculation of the reciprocals of the corresponding power station reliability and cumulative losses to obtain the M candidate fitness schemes.

[0084] Preferably, the grid-connected power dispatch decision layer is used for intelligent analysis of grid-connected power dispatch. It acquires multiple historical power station reliability data, multiple historical power station cumulative losses, multiple historical target dispatch power volumes, and multiple historical initial power dispatch schemes as training data. Then, the training data is divided into multiple groups, and the network layers constructed using the convolutional neural network are sequentially subjected to supervised training until the output converges, thereby generating the trained grid-connected power dispatch decision layer.

[0085] In one embodiment, the fitness of each of the M candidate power dispatch schemes is divided by the reciprocal of the sum of the fitness of the M candidate power dispatch schemes, which are then used as M adjustment coefficients. These M adjustment coefficients reflect the degree to which the set of M candidate power dispatch schemes can be optimized; a higher fitness indicates a smaller degree of optimization required. The M adjustment coefficients are then multiplied by a preset adjustment range to generate M candidate adjustment ranges. These preset adjustment ranges are pre-set to determine the appropriate optimization range. The M candidate power dispatch schemes are then adjusted multiple times using these M candidate adjustment ranges to generate a set of M candidate power dispatch schemes. This achieves the technical effect of expanding the selection of optimal schemes.

[0086] Preferably, the set of M candidate power dispatch schemes is unioned to generate an iterative power dispatch scheme set, eliminating overlapping terms and avoiding repeated optimization. Then, without replacement, a candidate power dispatch scheme is randomly selected from the iterative power dispatch scheme set as the first iterative power dispatch scheme, and a first iterative fitness is generated, obtained using the same calculation method as the candidate fitness. Again, without replacement, a candidate power dispatch scheme is randomly selected from the iterative power dispatch scheme set as the second iterative power dispatch scheme, and a second iterative fitness is generated.

[0087] Preferably, it is determined whether the fitness of the first iteration is greater than that of the second iteration. If not, the second iteration power dispatch scheme is used as the stage power dispatch scheme. If so, a random number is generated, and it is determined whether the random number is greater than a preset value. If so, the second iteration power dispatch scheme is used as the stage power dispatch scheme; otherwise, the first iteration power dispatch scheme is used as the stage power dispatch scheme. Optionally, a random number between (0,1) is randomly generated using the RAND function. The preset value is set by those skilled in the art. After multiple iterations, the stage power dispatch scheme corresponding to the maximum fitness value during the iteration process is used as the target power dispatch scheme.

[0088] In summary, the new energy grid-connected power dispatching method provided in this application has the following technical effects:

[0089] This application acquires information on renewable energy power plants in a target area, then collects target dispatchable power within a preset dispatch window. The requirement that grid-connected power dispatch must meet the target dispatchable power is used as the first constraint. Next, operational data from N renewable energy power plants within historical dispatch windows is collected to determine the reliability of each plant. Based on the location identifiers of the N renewable energy power plants and the load center location in the target area, the cumulative losses of the N plants are determined, generating N power output values. The requirement that power dispatched from the N renewable energy power plants during grid-connected power dispatch must meet N power output values ​​is used as the second constraint. Iterative optimization of dispatch is performed based on the reliability of the N plants, their cumulative losses, and the target dispatchable power. The first and second constraints constrain the iterative optimization process, improving its accuracy. Finally, the target power dispatch scheme is used to dispatch grid-connected power to the N renewable energy power plants in the target area. This achieves the technical effect of improving the alignment between power dispatch and actual operation, and enhancing dispatch quality.

[0090] Example 2

[0091] Based on the same inventive concept as the new energy grid-connected power dispatching method in the foregoing embodiments, such as Figure 4 As shown, this application also provides a new energy grid-connected power dispatching system, the system comprising:

[0092] The station information acquisition module 11 is used to acquire new energy station information in the target area, wherein the new energy station information includes N new energy stations and N new energy station location identifiers;

[0093] The first constraint setting module 12 is used to collect the target dispatch power within the preset dispatch window and set the power dispatch power of grid-connected power as the first constraint condition, which requires the power to meet the target dispatch power.

[0094] The power station reliability determination module 13 is used to collect the operating data of the N new energy power stations within the historical scheduling window and determine the reliability of the N power stations;

[0095] The cumulative loss determination module 14 is used to determine the cumulative loss of N power stations based on the location identifiers of N new energy power stations and the load center location of the target area.

[0096] The second constraint generation module 15 is used to collect the power output value of N new energy power plants within a preset scheduling window, generate N power output values, and use the power dispatched from the N new energy power plants during grid-connected power dispatch to meet the N power output values ​​as the second constraint condition.

[0097] The target power dispatch scheme generation module 16 is used to perform dispatch iterative optimization based on the reliability of N power stations, the cumulative loss of N power stations and the target dispatch power, and to constrain the dispatch iterative optimization process using the first constraint condition and the second constraint condition to generate the target power dispatch scheme.

[0098] The grid-connected power dispatching module 17 is used to perform grid-connected power dispatching on N new energy power plants in the target area using the target power dispatching scheme.

[0099] Furthermore, the site reliability determination module 13 is used to perform the following steps:

[0100] Collect the power fluctuation variance of N new energy power plants during historical dispatch windows, and generate N first sub-reliability scores;

[0101] Analyze the power fault maintenance information of N new energy power stations to generate N second sub-reliability scores;

[0102] Collect the electricity output value of N new energy power plants per unit time and generate N electricity output values;

[0103] Each of the N energy output values ​​is divided by the sum of the N energy output values, and N third sub-reliability values ​​are generated based on the calculation results;

[0104] A comprehensive reliability analysis is performed based on N first sub-reliability values, N second sub-reliability values, and N third sub-reliability values ​​to generate N site reliability values.

[0105] Furthermore, the site reliability determination module 13 is used to perform the following steps:

[0106] Construct a second sub-reliability calculation formula, wherein the second sub-reliability calculation formula is:

[0107]

[0108] Where S(x) i Let k be the second sub-reliability of the i-th renewable energy power station. i Let be the total number of power equipment in the i-th renewable energy power station. For the i-th new energy power station, the j-th power equipment generates μ j The losses resulting from the subsequent failure. Let j be the design life of the j-th power equipment in the i-th renewable energy power station. Let j be the service life of the jth power equipment in the i-th new energy power station;

[0109] Collect the design life and service life of power equipment in N new energy power stations, combine the power fault maintenance information of N new energy power stations, and generate N second sub-reliability values ​​using the second sub-reliability calculation formula.

[0110] Furthermore, the target power dispatch scheme generation module 16 is used to perform the following steps:

[0111] The reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power are input into the grid-connected power dispatch decision layer for analysis, generating Q initial power dispatch schemes;

[0112] Traverse the Q initial power dispatch schemes and filter them using the first and second constraints to generate M candidate power dispatch schemes.

[0113] Perform fitness analysis on M candidate power dispatch schemes to generate M candidate fitnesss.

[0114] Furthermore, the target power dispatch scheme generation module 16 is used to perform the following steps:

[0115] The reciprocal of the ratio of the fitness of each of the M candidate fitnesss to the sum of the fitness of the M candidate fitnesss is taken as the M adjustment coefficients;

[0116] The preset adjustment range is updated using M adjustment coefficients to generate M candidate adjustment ranges;

[0117] The M candidate power dispatch schemes are adjusted multiple times using M candidate adjustment ranges to generate a set of M candidate power dispatch schemes.

[0118] Based on a set of M candidate power dispatch schemes, iterative optimization is performed to generate the target power dispatch scheme.

[0119] Furthermore, the target power dispatch scheme generation module 16 is used to perform the following steps:

[0120] The union of the M candidate power dispatch schemes is used to generate an iterative power dispatch scheme set.

[0121] Without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the first iterative power dispatch scheme, and the first iterative fitness is generated.

[0122] Again, without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the second iterative power dispatch scheme, and the fitness of the second iteration is generated.

[0123] Determine whether the fitness of the first iteration is greater than that of the second iteration. If not, then the second iteration power dispatch scheme is adopted as the stage power dispatch scheme.

[0124] If so, generate a random number and determine whether the random number is greater than the preset value. If so, use the second iteration power dispatch scheme as the stage power dispatch scheme; otherwise, use the first iteration power dispatch scheme as the stage power dispatch scheme.

[0125] After multiple iterations, the stage power dispatch scheme corresponding to the maximum fitness value during the iteration process is taken as the target power dispatch scheme.

[0126] Furthermore, the target power dispatch scheme generation module 16 is used to perform the following steps:

[0127] Multiple historical power station reliability, multiple historical power station cumulative loss, multiple historical target dispatch power, and multiple historical initial power dispatch schemes are obtained as training data.

[0128] The training data is divided into multiple groups, and the network layers constructed by the convolutional neural network are trained in a supervised manner until the output converges, thereby generating the grid-connected power dispatch decision layer that has been trained.

[0129] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1The renewable energy grid-connected power dispatching method and specific examples in Embodiment 1 are also applicable to the renewable energy grid-connected power dispatching system of this embodiment. Through the foregoing detailed description of the renewable energy grid-connected power dispatching method, those skilled in the art can clearly understand the renewable energy grid-connected power dispatching system of this embodiment; therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0130] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0131] Example 3

[0132] Based on the same inventive concept as the new energy grid-connected power dispatching method in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method as described in Embodiment 1.

[0133] Exemplary electronic devices

[0134] The following is for reference. Figure 5 To describe the electronic device of this application,

[0135] Based on the same inventive concept as the new energy grid-connected power dispatching method in the foregoing embodiments, this application also provides a new energy grid-connected power dispatching system, including: a processor coupled to a memory, the memory being used to store a program, and when the program is executed by the processor, the system performs the steps of the method described in Embodiment 1.

[0136] The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, processor 302, and memory 301 can be interconnected via the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0137] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0138] Communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0139] Memory 301 can be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory can exist independently and be connected to the processor via bus architecture 304. Memory can also be integrated with the processor.

[0140] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the new energy grid-connected power dispatching method provided in the above embodiments of this application.

[0141] Those skilled in the art will understand that the various numerical designations, such as "first," "second," etc., used in this application are merely for descriptive convenience and are not intended to limit the scope of this application, nor do they indicate a chronological order. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one" refers to one or more. "At least two" refers to two or more. "At least one," "any one," or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0142] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0143] The various illustrative logic units and circuits described in this application may be implemented or operate the described functions using a general-purpose processor, digital signal processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor may be a microprocessor, and optionally, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented using a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0144] The steps of the methods or algorithms described in this application can be directly embedded in hardware, a software unit executed by a processor, or a combination of both. The software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other storage medium of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and storage medium can be disposed in an ASIC, which can be disposed in a terminal. Optionally, the processor and storage medium can also be disposed in different components within the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of this application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for dispatching grid-connected power from new energy sources, characterized in that, The method includes: Obtain information on new energy power stations in the target area, wherein the information on new energy power stations includes N new energy power stations and the location identifiers of N new energy power stations; Collect the target dispatchable power energy within the preset dispatch window, and take the power dispatchable power energy that meets the target dispatchable power energy as the first constraint condition; Collect operational data from the N new energy power stations within the historical scheduling window to determine the reliability of the N power stations; Based on the location identifiers of N new energy power stations and the location of the load center in the target area, the cumulative losses of the N power stations are determined. Collect the power output value of N new energy power plants within a preset scheduling window, generate N power output values, and use the power dispatched from N new energy power plants during grid-connected power dispatch to meet the N power output values ​​as a second constraint condition; Based on the reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power, the scheduling iterative optimization is performed, and the scheduling iterative optimization process is constrained by the first constraint and the second constraint to generate the target power dispatch scheme. The target power dispatch scheme is used to perform grid-connected power dispatch for N new energy power plants in the target area. The method further includes: Collect the power fluctuation variance of N new energy power plants during historical dispatch windows, and generate N first sub-reliability scores; Analyze the power fault maintenance information of N new energy power stations to generate N second sub-reliability scores; Collect the electricity output value of N new energy power plants per unit time and generate N electricity output values; Each of the N energy output values ​​is divided by the sum of the N energy output values, and N third sub-reliability values ​​are generated based on the calculation results; A comprehensive reliability analysis is performed based on N first sub-reliability, N second sub-reliability, and N third sub-reliability to generate N site reliability values; The method further includes: Construct a second sub-reliability calculation formula, wherein the second sub-reliability calculation formula is: ； in, The second sub-reliability of the i-th renewable energy power station. Let be the total number of power equipment in the i-th renewable energy power station. For the i-th new energy power station, the j-th power equipment generates The losses resulting from the subsequent failure. Let j be the design life of the j-th power equipment in the i-th renewable energy power station. Let j be the service life of the jth power equipment in the i-th new energy power station; Collect the design life and service life of power equipment in N new energy power stations, combine the power fault maintenance information of N new energy power stations, and generate N second sub-reliability values ​​using the second sub-reliability calculation formula.

2. The method as described in claim 1, characterized in that, The method further includes: The reliability of N power stations, the cumulative loss of N power stations, and the target dispatch power are input into the grid-connected power dispatch decision layer for analysis, generating Q initial power dispatch schemes; Traverse the Q initial power dispatch schemes and filter them using the first and second constraints to generate M candidate power dispatch schemes. Perform fitness analysis on M candidate power dispatch schemes to generate M candidate fitnesss.

3. The method as described in claim 2, characterized in that, After generating M candidate fitnesss, the method further includes: The reciprocal of the ratio of the fitness of each of the M candidate fitnesss to the sum of the fitness of the M candidate fitnesss is taken as the M adjustment coefficients; The preset adjustment range is updated using M adjustment coefficients to generate M candidate adjustment ranges; The M candidate power dispatch schemes are adjusted multiple times using M candidate adjustment ranges to generate a set of M candidate power dispatch schemes. Based on a set of M candidate power dispatch schemes, iterative optimization is performed to generate the target power dispatch scheme.

4. The method as described in claim 3, characterized in that, The method further includes: performing iterative optimization of scheduling based on a set of M candidate power dispatch schemes to generate a target power dispatch scheme; and performing the same method on a set of M candidate power dispatch schemes. The union of the M candidate power dispatch schemes is used to generate an iterative power dispatch scheme set. Without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the first iterative power dispatch scheme, and the first iterative fitness is generated. Again, without replacement, a candidate power dispatch scheme is randomly selected from the set of iterative power dispatch schemes as the second iterative power dispatch scheme, and the fitness of the second iteration is generated. Determine whether the fitness of the first iteration is greater than that of the second iteration. If not, then the second iteration power dispatch scheme is adopted as the stage power dispatch scheme. If so, generate a random number and determine whether the random number is greater than the preset value. If so, use the second iteration power dispatch scheme as the stage power dispatch scheme; otherwise, use the first iteration power dispatch scheme as the stage power dispatch scheme. After multiple iterations, the stage power dispatch scheme corresponding to the maximum fitness value during the iteration process is taken as the target power dispatch scheme.

5. The method as described in claim 2, characterized in that, The method further includes: Multiple historical power station reliability, multiple historical power station cumulative loss, multiple historical target dispatch power, and multiple historical initial power dispatch schemes are obtained as training data. The training data is divided into multiple groups, and the network layers constructed by the convolutional neural network are trained in a supervised manner until the output converges, thereby generating the grid-connected power dispatch decision layer that has been trained.

6. A new energy grid-connected power dispatching system, characterized in that, The system includes: The station information acquisition module is used to acquire new energy station information in the target area, wherein the new energy station information includes N new energy stations and N new energy station location identifiers; The first constraint setting module is used to collect the target dispatch power energy within the preset dispatch window, and to set the power energy dispatched by the grid-connected power as the first constraint condition, which requires the power energy to meet the target dispatch power energy. The power station reliability determination module is used to collect the operating data of the N new energy power stations within the historical scheduling window and determine the reliability of the N power stations; The cumulative loss determination module is used to determine the cumulative loss of N power stations based on the location identifiers of N new energy power stations and the load center location of the target area. The second constraint generation module is used to collect the power output value of N new energy power plants within a preset scheduling window, generate N power output values, and use the power dispatched from the N new energy power plants during grid-connected power dispatch to meet the N power output values ​​as the second constraint. The target power dispatch scheme generation module is used to perform iterative optimization of dispatch based on the reliability of N power stations, the cumulative loss of N power stations and the target dispatch power, and to constrain the iterative optimization process using the first constraint condition and the second constraint condition to generate the target power dispatch scheme. The grid-connected power dispatching module is used to perform grid-connected power dispatching on N new energy power plants in the target area using the target power dispatching scheme. Furthermore, the site reliability determination module is used to perform the following steps: Collect the power fluctuation variance of N new energy power plants during historical dispatch windows, and generate N first sub-reliability scores; Analyze the power fault maintenance information of N new energy power stations to generate N second sub-reliability scores; Collect the electricity output value of N new energy power plants per unit time and generate N electricity output values; Each of the N energy output values ​​is divided by the sum of the N energy output values, and N third sub-reliability values ​​are generated based on the calculation results; A comprehensive reliability analysis is performed based on N first sub-reliability, N second sub-reliability, and N third sub-reliability to generate N site reliability values; Furthermore, the site reliability determination module is used to perform the following steps: Construct a second sub-reliability calculation formula, wherein the second sub-reliability calculation formula is: ； in, The second sub-reliability of the i-th renewable energy power station. Let be the total number of power equipment in the i-th renewable energy power station. For the i-th new energy power station, the j-th power equipment generates The losses resulting from the subsequent failure. Let j be the design life of the j-th power equipment in the i-th renewable energy power station. Let j be the service life of the jth power equipment in the i-th new energy power station; Collect the design life and service life of power equipment in N new energy power stations, combine the power fault maintenance information of N new energy power stations, and generate N second sub-reliability values ​​using the second sub-reliability calculation formula.

7. An electronic device, characterized in that, include: A processor coupled to a memory for storing a program that, when executed by the processor, implements the steps of the method according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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