A new energy power station cluster coordinated frequency modulation method and related device

Abstract

The application discloses a new energy power station cluster coordinated frequency modulation method and related device, comprising: acquiring multi-source data; predicting the dynamic and safe frequency modulation capability interval of a future specific time window based on the multi-source data; when the power grid frequency deviation exceeds the frequency modulation capability interval at the future specific time window, determining the total frequency modulation power demand required by the cluster; determining the optimal power distribution instruction according to the total frequency modulation power demand required by the cluster, and issuing the optimal power distribution instruction to each station for execution. The method and related device can realize the optimal configuration of the cluster frequency modulation resources, improve the power grid frequency stability, and ensure the safe and economic operation of the new energy power station.

Description

Technical Field

[0001] This invention belongs to the field of frequency regulation technology and relates to a method and related devices for coordinated frequency regulation of new energy power plant clusters. Background Technology

[0002] The penetration rate of new energy sources such as wind power and photovoltaics in the power grid has increased dramatically. However, the inherent intermittency, volatility, and low immunity of new energy power generation pose a severe challenge to the frequency stability of the power grid. Traditional power grids mainly rely on the inertial response and primary frequency regulation function of synchronous units such as thermal power and hydropower to maintain frequency stability.

[0003] Currently, most renewable energy power plants are beginning to be equipped with primary frequency regulation capabilities. This means that by reserving backup capacity, power output can be increased or decreased according to a certain adjustment coefficient when the grid frequency deviates from the rated value. However, existing technologies have the following prominent problems: Single-mode: Each renewable energy power station responds independently to changes in grid frequency, lacking coordination within the cluster. This may lead to multiple power stations simultaneously increasing or decreasing power generation, causing new power imbalances or line congestion in local areas.

[0004] Inaccurate regulation capability assessment: Frequency regulation capability assessment at the field level is usually based on simple real-time power and prediction curves, without fully considering the status of equipment in the field (such as inverter temperature and transformer load rate), micro-meteorology (such as local wind speed and sudden changes in irradiance), and power flow constraints of the grid section, which may cause frequency regulation commands to exceed the actual safe operating range of the field.

[0005] Rigid response strategies: Frequency regulation strategies are typically based on fixed proportional coefficients and lack intelligence. They cannot adaptively optimize allocation based on the severity of frequency events, dynamic grid topology, and the regulation costs of different power plants (such as energy storage SOC status and photovoltaic curtailment costs).

[0006] Therefore, there is an urgent need for a technical solution that can coordinate and optimize the frequency regulation resources of multiple new energy power stations within a region to achieve safe, economical, and efficient collaborative frequency regulation. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related device for coordinated frequency regulation of new energy power plant clusters. This method and related device can optimize the allocation of cluster frequency regulation resources, improve the frequency stability of the power grid, and ensure the safe and economical operation of new energy power plants.

[0008] To achieve the above objectives, this invention discloses a method for coordinated frequency regulation of new energy power plant clusters, comprising: Acquire multi-source data; Based on the multi-source data, predict the dynamic and secure frequency modulation capability range for a specific future time window; When the grid frequency deviation exceeds the frequency regulation capability range within the specified future time window, the total frequency regulation power requirement of the cluster is determined. Based on the total frequency regulation power requirement of the cluster, the optimal power allocation instruction is determined and then sent to each site for execution.

[0009] Furthermore, the multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

[0010] Furthermore, the process of predicting the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data is as follows: The multi-source data is preprocessed and then input into the trained AI estimation model to obtain the dynamic and safe frequency modulation capability range for a specific future time window.

[0011] Furthermore, an objective function is constructed with the goal of minimizing the total regulation cost, while simultaneously satisfying grid security constraints. An optimization model is then built and solved to obtain the optimal power allocation command.

[0012] Furthermore, the site includes at least a wind farm cluster, a photovoltaic power station cluster, and an energy storage power station cluster.

[0013] This invention discloses a collaborative frequency regulation system for a cluster of new energy power plants, characterized in that it includes: The acquisition module is used to acquire data from multiple sources; The prediction module is used to predict the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data. The determination module is used to determine the total frequency regulation power requirement required by the cluster when the grid frequency deviation exceeds the frequency regulation capability range during the specific future time window. The execution module is used to determine the optimal power allocation instruction based on the total frequency regulation power requirement of the cluster, and to send the optimal power allocation instruction to each site for execution.

[0014] Furthermore, the multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

[0015] Furthermore, the process of predicting the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data is as follows: The multi-source data is preprocessed and then input into the trained AI estimation model to obtain the dynamic and safe frequency modulation capability range for a specific future time window.

[0016] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the new energy power plant cluster coordinated frequency regulation method.

[0017] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the new energy power plant cluster coordinated frequency regulation method.

[0018] The present invention has the following beneficial effects: In specific operation, the new energy power plant cluster coordinated frequency regulation method and related device described in this invention predicts the dynamic and safe frequency regulation capability range for a specific future time window based on the multi-source data. When the grid frequency deviation exceeds the frequency regulation capability range, the total frequency regulation power demand required by the cluster is determined. Based on the total frequency regulation power demand required by the cluster, the optimal power allocation command is determined to achieve optimized allocation of cluster frequency regulation resources, improve grid frequency stability, and ensure the safe and economical operation of new energy power plants. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0023] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0025] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0026] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0029] Example 1 The new energy power plant cluster coordinated frequency regulation method of the present invention includes the following steps: 1) Acquire data from multiple sources; The multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

[0030] The multi-source data is cleaned, aligned, and fused to form fused data with a unified time sequence.

[0031] 2) Optimization decisions are made based on multi-source data to obtain the optimal power allocation command; Based on the multi-source data, the trained AI evaluation model is used to calculate in real time the theoretical frequency regulation capability and frequency reduction capability of each station within a specific future time window. The evaluation comprehensively considers predicted power, equipment health, environmental factors and grid constraints, and outputs the dynamic and safe frequency regulation capability range for the specific future time window.

[0032] During this future characteristic time window, when the grid frequency deviation exceeds the frequency regulation capability range, the cluster control center determines the total frequency regulation power demand required by the cluster based on the magnitude and rate of change of the frequency deviation. Then, it constructs an objective function with the goal of minimizing the total regulation cost while satisfying grid security constraints, and builds an optimization model. The optimization model comprehensively considers the dynamic frequency regulation capability of each power station and regulation cost coefficients (such as energy storage cycle life loss cost, wind and solar curtailment loss cost, etc.) to solve for the optimal power allocation command, which is then issued to each power station.

[0033] 3) Execution and closed-loop calibration; Each power station receives and executes the optimal power allocation command, and the cluster control center monitors the execution status of the optimal power allocation command and the frequency recovery status in real time. If a power station is unable to complete the command due to an emergency, or if the frequency recovery does not meet expectations, the command reallocation mechanism is triggered to dynamically allocate the uncompleted power to other power stations that are capable of completing the command. These power stations include at least wind farm clusters, photovoltaic power station clusters, and energy storage power station clusters.

[0034] This invention has the following characteristics: Collaborative optimization: By organizing the dispersed new energy power plants into a virtual frequency regulation resource pool, the negative impact of "each fighting its own battle" is avoided through collaborative optimization, and the overall stability of the regional power grid is improved.

[0035] Accurate assessment: Through multi-source data fusion and AI models, dynamic and accurate assessment of the frequency regulation capability of each station was achieved, ensuring the feasibility of frequency regulation commands and the safety of station operation.

[0036] Economic Intelligence: The concept of adjustment cost is introduced, making the allocation of frequency regulation power more economical, extending the life of key equipment (such as energy storage), and improving the overall benefits of new energy power plants.

[0037] Highly adaptable: It has closed-loop correction and redistribution mechanisms, which can cope with sudden situations in the power grid and power stations, and is more robust.

[0038] Example 2 The new energy power plant cluster coordinated frequency regulation system of the present invention is characterized in that it includes: The acquisition module is used to acquire data from multiple sources; The prediction module is used to predict the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data. The determination module is used to determine the total frequency regulation power requirement required by the cluster when the grid frequency deviation exceeds the frequency regulation capability range during the specific future time window. The execution module is used to determine the optimal power allocation instruction based on the total frequency regulation power requirement of the cluster, and to send the optimal power allocation instruction to each site for execution.

[0039] In this embodiment, the multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

[0040] In this embodiment, the process of predicting the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data is as follows: The multi-source data is preprocessed and then input into the trained AI estimation model to obtain the dynamic and safe frequency modulation capability range for a specific future time window.

[0041] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0042] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a collaborative frequency regulation method for a new energy power plant cluster. For example, the steps include: acquiring multi-source data; predicting a dynamic and secure frequency regulation capability range for a specific future time window based on the multi-source data; determining the total frequency regulation power demand required by the cluster when the grid frequency deviation exceeds the frequency regulation capability range during the specific future time window; determining an optimal power allocation instruction based on the total frequency regulation power demand required by the cluster, and issuing the optimal power allocation instruction to each power plant for execution. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry standard architecture bus, a peripheral component interconnection standard bus, an extended industry standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0043] Example 4 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the new energy power plant cluster coordinated frequency regulation method. For example, the steps include: acquiring multi-source data; predicting a dynamic and secure frequency regulation capability range for a specific future time window based on the multi-source data; determining the total frequency regulation power demand required by the cluster when the grid frequency deviation exceeds the frequency regulation capability range during the specific future time window; determining an optimal power allocation instruction based on the total frequency regulation power demand required by the cluster, and issuing the optimal power allocation instruction to each power plant for execution. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0044] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0047] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0048] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0049] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0050] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for coordinated frequency regulation of a cluster of new energy power plants, characterized in that, include: Acquire multi-source data; Based on the multi-source data, predict the dynamic and secure frequency modulation capability range for a specific future time window; When the grid frequency deviation exceeds the frequency regulation capability range within the specified future time window, the total frequency regulation power requirement of the cluster is determined. Based on the total frequency regulation power requirement of the cluster, the optimal power allocation instruction is determined and then sent to each site for execution.

2. The method for coordinated frequency regulation of new energy power plant clusters according to claim 1, characterized in that, The multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

3. The method for coordinated frequency regulation of new energy power plant clusters according to claim 1, characterized in that, The process of predicting the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data is as follows: The multi-source data is preprocessed and then input into the trained AI estimation model to obtain the dynamic and safe frequency modulation capability range for a specific future time window.

4. The method for coordinated frequency regulation of new energy power plant clusters according to claim 1, characterized in that, An objective function is constructed with the goal of minimizing the total regulation cost, while also satisfying grid security constraints. An optimization model is then constructed and solved to obtain the optimal power allocation command.

5. The method for coordinated frequency regulation of new energy power plant clusters according to claim 1, characterized in that, The site includes at least a group of wind farms, a group of photovoltaic power stations, and a group of energy storage power stations.

6. A collaborative frequency regulation system for a cluster of new energy power plants, characterized in that, include: The acquisition module is used to acquire data from multiple sources; The prediction module is used to predict the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data. The determination module is used to determine the total frequency regulation power requirement required by the cluster when the grid frequency deviation exceeds the frequency regulation capability range during the specific future time window. The execution module is used to determine the optimal power allocation instruction based on the total frequency regulation power requirement of the cluster, and to send the optimal power allocation instruction to each site for execution.

7. The new energy power plant cluster coordinated frequency regulation system according to claim 6, characterized in that, The multi-source data includes micro-meteorological data, ultra-short-term power prediction data, and real-time frequency data of the power grid. Specifically, micro-meteorological data is acquired through meteorological satellites and radar; ultra-short-term power prediction data of new energy power plants is predicted through a power prediction platform; and real-time frequency data of the power grid is acquired through a wide-area measurement system.

8. The new energy power plant cluster coordinated frequency regulation system according to claim 6, characterized in that, The process of predicting the dynamic and secure frequency modulation capability range for a specific future time window based on the multi-source data is as follows: The multi-source data is preprocessed and then input into the trained AI estimation model to obtain the dynamic and safe frequency modulation capability range for a specific future time window.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the new energy power plant cluster coordinated frequency regulation method as described in any one of claims 1-5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the new energy power plant cluster coordinated frequency regulation method as described in any one of claims 1-5.

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