Wind farm power dispatching method, control device and new energy station

Abstract

The present disclosure provides a wind farm power dispatching method, a control device and a new energy station. The method comprises: generating power generation power and load models of each wind turbine in a wind farm under a plurality of state spaces according to design information of each wind turbine in the wind farm, wherein the design information comprises wind parameter information and a preset service life of each wind turbine; predicting power generation power and residual life of each wind turbine under a real-time state space according to real-time environmental data, real-time state data, the preset service life and the power generation power and load models under the plurality of state spaces; and adjusting an operation mode of each wind turbine according to the power generation power and the residual life of each wind turbine under the real-time state space and income-related data of the wind farm, so as to achieve a predetermined income target of the wind farm in a life cycle.

Description

Technical Field

[0001] This disclosure relates to the field of wind power generation, specifically to a wind farm power dispatching method, control equipment, and new energy power station. Background Technology

[0002] In recent years, the development of clean energy has been rapid, and the demand for wind power generation is booming. Clean energy may have characteristics such as the inverse distribution of resources and loads. Regions with abundant wind resources often experience wind curtailment and power generation. Therefore, for wind farms with several wind turbines, how to avoid wasting the power generated by the wind farm, and how to combine actual application scenarios to achieve the greatest possible environmental adaptability and maximize the power generation revenue or investment return of the wind farm under the electricity market, have become urgent problems to be solved. Summary of the Invention

[0003] The purpose of this disclosure is to provide a wind farm power dispatching method, control equipment, and new energy power station, which helps to avoid the waste of wind farm power generation, improve the environmental adaptability of wind farms, and increase the power generation revenue or investment returns of wind farms in the electricity market.

[0004] According to embodiments of this disclosure, a wind farm power dispatching method is provided. The method includes: generating a power generation and load model for each wind turbine in a wind farm under multiple state spaces based on the design information of each wind turbine, wherein the design information includes wind parameter information and preset lifespan of each wind turbine; predicting the power generation and remaining lifespan of each wind turbine in the real-time state space based on real-time environmental data, real-time state data, preset lifespan, and the power generation and load model under the multiple state spaces; and adjusting the operating mode of each wind turbine based on the power generation and remaining lifespan of each wind turbine in the real-time state space and the wind farm's revenue-related data to achieve a predetermined revenue target for the wind farm throughout its life cycle.

[0005] According to embodiments of the present disclosure, a computer-readable storage medium storing a computer program is provided, which, when executed by a processor, implements a wind farm power dispatching method according to the present disclosure.

[0006] According to an embodiment of the present disclosure, a control device is provided, the control device comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, a wind farm power dispatching method according to the present disclosure is implemented.

[0007] According to embodiments of this disclosure, a new energy power station is provided, the new energy power station including control equipment according to this disclosure.

[0008] By employing the wind farm power dispatching method, computer-readable storage medium, control equipment, and new energy power station according to embodiments of this disclosure, at least one of the following technical effects can be achieved: adapting to application scenarios of electricity price changes, providing appropriate power generation schemes for different electricity demands, thereby achieving the target economic indicators of the wind farm throughout its entire life cycle; achieving optimal electricity sales revenue or optimal investment returns; evaluating different operating modes based on unit life constraints, selecting appropriate power generation schemes and estimating the maximum achievable power generation based on different wind parameter characteristics and environmental characteristics, fully utilizing dynamically changing electricity price data to output power generation information to the power trading system, optimizing dispatching based on the grid's capacity, and ultimately achieving wind-hydro complementarity, fully obtaining power generation revenue or investment returns from high-price quotas for different periods (e.g., wet season and dry season). Attached Figure Description

[0009] The above and other objects and features of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings.

[0010] Figure 1 This is a flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure;

[0011] Figure 2 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure;

[0012] Figure 3 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure;

[0013] Figure 4 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure;

[0014] Figure 5 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure;

[0015] Figure 6 This is a block diagram of a control device according to an embodiment of the present disclosure. Detailed Implementation

[0016] Renewable energy power generation (such as wind, hydro, and solar power) is greatly affected by factors such as climate, weather, and geographical environment, and is difficult to control by humans. Therefore, controlling each wind turbine in a wind farm requires not only analyzing the control parameters of the wind turbine itself, but also analyzing the impact of related factors such as the wind farm, the power grid, the electricity market, and the time dimension.

[0017] This invention proposes a wind farm power dispatching method and equipment, which is beneficial for maximizing the electricity sales revenue, minimizing investment costs, or maximizing the return on investment of wind farms throughout their life cycle. According to embodiments of this disclosure, different power modes are assessed based on turbine life constraints. Appropriate power generation schemes are selected based on different wind parameters and environmental characteristics, and the achievable power generation is estimated. Optimized dispatching is performed based on wind farm revenue-related data (e.g., grid electricity price data, wind farm cost data, etc.) and grid capacity, thereby achieving wind-water complementarity and fully capturing electricity sales revenue or investment returns during different periods of electricity price quotas, such as during wet and dry seasons, peak and off-peak electricity consumption periods, and differences in day and night noise levels.

[0018] The following description, in conjunction with the accompanying drawings, provides specific embodiments to aid the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, upon understanding this disclosure, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be altered as will become clear upon understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0019] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein, which will become clear upon understanding the disclosure of this application.

[0020] As used herein, the term “and / or” includes any one of the associated listed items and any combination of any two or more.

[0021] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, assemblies, regions, layers, or parts, these components, assemblies, regions, layers, or parts should not be limited by these terms. Rather, these terms are used only to distinguish one component, assembly, region, layer, or part from another. Thus, without departing from the teaching of the examples described herein, the first component, first assembly, first region, first layer, or first part referred to as the first component, first assembly, first region, first layer, or first part may also be referred to as the second component, second assembly, second region, second layer, or second part.

[0022] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0023] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0024] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0025] Figure 1 This is a flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure.

[0026] like Figure 1 As shown, in step S11, based on the design information of each wind turbine in the wind farm, a power generation and load model of the corresponding wind turbine under multiple state spaces is generated. In the embodiments of this disclosure, the state space can be a spatial model based on the wind parameter information and operating mode of the wind turbine. However, this disclosure is not limited to this; the state space can encompass more dimensions, such as time dimension, spatial dimension, environmental dimension, etc. For example, wind parameter information may include the maximum wind speed allowed for normal operation, the range of turbulence intensity distribution, the range of vertical wind shear distribution, the range of inflow angle at the turbine location, the range of yaw angle, and any other information related to wind resources. Operating modes may include: over-power mode, normal power mode, power-limited mode, shutdown mode, and any possible operating mode of the wind turbine.

[0027] According to embodiments of this disclosure, the design information for each wind turbine may include wind parameter information and a preset lifespan for each wind turbine. For example, wind parameter information for each wind turbine can be obtained based on the wind resource distribution of the wind farm or the application scope determined by the turbine design. Wind parameter models for each wind turbine can be established based on the obtained wind parameter information, thereby generating power and load models for the corresponding wind turbine in various state spaces based on the wind parameter models of each wind turbine. The preset lifespan may include the overall preset lifespan value of the wind turbine.

[0028] According to embodiments of this disclosure, the design information may also include initialization control parameters and simulation control algorithms. For example, initialization control parameters for one or more wind turbine models can be obtained through the wind farm's control system. As a non-limiting example, the initialization control parameters may include setting parameters for key load points of the corresponding wind turbine (e.g., bending moments at the blade root and tower in various directions in a specific coordinate system), preset lifespan values ​​for key load points, simulation execution cycles, etc.

[0029] The following reference Figure 2 The process of generating power and load models is described, but this disclosure is not limited thereto. Figure 2 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure.

[0030] In step S21, based on the design information of each wind turbine, the operating parameters of the corresponding wind turbine under multiple state spaces are obtained. For example, the design information of each wind turbine can be processed by the wind farm or the operating condition processing module of each wind turbine to obtain the operating parameters of the corresponding wind turbine under multiple state spaces. The obtained operating parameters can be used to obtain the power generation data and load data of the corresponding wind turbine under multiple state spaces.

[0031] In step S22, the power generation data and load data of each wind turbine under the multiple state spaces are obtained from the operating condition parameters. For example, the operating condition parameters can be processed by the operating condition calculation module of the wind farm or each wind turbine to obtain the power generation data and load data of each wind turbine under the multiple state spaces from the operating condition parameters. The power generation data and load data package of each wind turbine under the multiple state spaces can be extracted from the operating condition parameters. The obtained power generation data and load data may include: the power generation of the corresponding wind turbine under the multiple state spaces, equivalent fatigue loads at key load points, and similar data. The power generation data and load data can be used for subsequent model training.

[0032] In step S23, the power generation and load data of each wind turbine are trained under the various state spaces to obtain the power generation and load models of the corresponding wind turbines under the various state spaces. For example, machine learning algorithms (such as neural networks, data fitting, regression, etc., are used as examples only, and the invention is not limited thereto) can be used to train the model based on the power generation and load data of each wind turbine under the various state spaces to obtain the power generation and load models of the corresponding wind turbines under the various state spaces. For example, wind parameter information and operating modes in the various state spaces can be set as inputs to the power generation and load models, and the power generation and load data corresponding to the various state spaces can be set as outputs to the power generation and load models. In this way, a trained power generation and load model is obtained by training the power generation and load model. The trained power generation and load model can be used to predict the power generation and load data of the corresponding wind turbines under various state spaces based on real-time environmental data, real-time state data, etc., such as power generation under different spatial dimensions, equivalent fatigue loads at key load points, and similar data.

[0033] Refer again Figure 1 In step S12, based on the real-time environmental data, real-time status data, preset lifespan, and power generation and load models of each wind turbine in multiple state spaces, the power generation and remaining lifespan of the corresponding wind turbine in the real-time state space are predicted.

[0034] The following is combined Figure 3 This disclosure describes the process of determining the remaining lifespan of a corresponding wind turbine in the real-time state space, but is not limited thereto. Figure 3 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure.

[0035] In step S31, based on the real-time environmental data of each wind turbine, the equivalent environmental distribution data of the corresponding wind turbine within the simulation execution cycle is obtained. For example, the real-time environmental data of the location of each wind turbine and the real-time status data of each wind turbine can be collected through the wind farm's data collection module. The real-time environmental data may include instantaneous incoming wind speed, instantaneous incoming wind direction, instantaneous turbulence intensity, instantaneous wind shear, instantaneous inflow angle, instantaneous yaw angle, and similar data. The real-time status data may include the wind turbine's operating status data, power limitation status data, turbine operating time, and similar data. In addition, the collected real-time environmental data can be processed by synthesis, filtering, phase adjustment, numerical calculation, etc., to obtain equivalent incoming wind speed, equivalent turbulence intensity, equivalent wind shear, equivalent inflow angle, equivalent yaw angle, and wind condition distribution statistics within a preset period (e.g., the simulation execution cycle).

[0036] In step S32, the equivalent environmental distribution data and real-time state data of each wind turbine during the simulation execution cycle are input into the power generation and load model of the corresponding wind turbine in multiple state spaces, so as to predict the power generation and fatigue load of each wind turbine in the real-time state space.

[0037] For example, the power generation and fatigue load of each wind turbine in the real-time state space can be predicted using a wind farm power prediction system. Data processing and prediction can be performed separately for each wind turbine. For wind turbines that are shut down or in a fault state, prediction is not necessary (but the shutdown process needs to be considered). In the embodiments of this disclosure, equivalent environmental distribution data and real-time state data matching the state space variable names can be substituted into the power generation and load model of the corresponding wind turbine in multiple state spaces to obtain the power generation and fatigue load of the corresponding wind turbine in the real-time state space. In this way, the power generation and fatigue load of any turbine in the current real-time state space can be predicted.

[0038] In step S33, the remaining lifespan of the corresponding wind turbine in the real-time state space is determined based on the equivalent environment distribution data, real-time state data, fatigue load in the real-time state space, and preset lifespan of each wind turbine during the simulation execution cycle.

[0039] For example, the used life of a wind turbine in the real-time state space can be determined based on the equivalent environmental distribution data, real-time state data, and / or fatigue load in the real-time state space for each wind turbine during the simulation execution cycle. In embodiments of this disclosure, the real-time state data may include the current operating duration, current operating status, current operating mode, and similar data of the wind turbine. Based on the used life and preset life of each wind turbine in the real-time state space, the remaining life of the corresponding wind turbine in the real-time state space can be determined. The determined used life can be compared with the preset life requirements (e.g., design service life) of the wind farm or each wind turbine to determine whether the remaining life of the corresponding wind turbine meets the life design requirements.

[0040] Refer again Figure 1 In step S13, based on the power generation and remaining lifespan of each wind turbine in the real-time state space, and the wind farm's revenue-related data, the operating mode of the corresponding wind turbine is adjusted to achieve the wind farm's predetermined revenue target within its life cycle. In embodiments of this disclosure, the wind farm's revenue-related data may include: grid electricity price data, wind farm cost data, and other data related to wind farm revenue.

[0041] The following is combined Figure 4 This disclosure describes the process of adjusting the operating mode of the corresponding wind turbine, but is not limited thereto. Figure 4This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure.

[0042] In step S41, based on the power generation and remaining lifespan of each wind turbine in the real-time state space, a preset operating mode that meets the preset lifespan requirements and allows the maximum power generation is determined for each wind turbine, and the maximum power generation of the corresponding wind turbine in the preset operating mode is determined.

[0043] In embodiments of this disclosure, the preset operating modes may include: a sequentially degraded over-power mode, a normal power mode, a power-limited mode, and a shutdown mode. The remaining lifespan of the wind turbine may be the remaining lifespan of the entire wind turbine or the remaining lifespan of some components (e.g., critical components).

[0044] For each wind turbine, the following operations can be performed. For example, it can be determined whether the remaining lifespan of the wind turbine meets the preset lifespan requirement (e.g., the design lifespan). The preset lifespan requirement can be the overall lifespan requirement of the wind farm or the lifespan requirement for a single wind turbine.

[0045] According to embodiments of this disclosure, in response to the wind turbine's remaining lifespan in the current operating mode (e.g., over-power mode) not meeting the preset lifespan requirement, a search is performed for the next-level preset operating mode (e.g., normal power mode). The next-level preset operating mode can be set as the current operating mode of the corresponding wind turbine, and the power generation and fatigue load of the corresponding wind turbine in the real-time state space can be re-acquired and updated to further determine whether the corresponding remaining lifespan meets the preset lifespan requirement.

[0046] Optionally, in response to the wind turbine's remaining lifespan in the current operating mode (e.g., normal power mode) meeting the preset lifespan requirement, a search is performed on the next higher preset operating mode (e.g., over-power mode). The next higher preset operating mode can be set as the current operating mode of the corresponding wind turbine, and the power generation and fatigue load of the corresponding wind turbine in the real-time state space can be re-acquired and updated to further determine whether the corresponding remaining lifespan meets the preset lifespan requirement.

[0047] In response to the wind turbine's remaining lifespan in the current operating mode meeting the preset lifespan requirement, the current operating mode can be determined as a preset operating mode that meets the preset lifespan requirement and allows the maximum power generation, and the maximum power generation of the wind turbine in the preset operating mode can be output.

[0048] In step S42, based on the maximum power generation of each wind turbine in the preset operating mode and the revenue-related data of the wind farm, the equivalent optimal revenue of the wind farm within the predetermined period is obtained.

[0049] According to embodiments of this disclosure, wind farm revenue-related data, such as grid electricity price data and wind farm cost data, can be obtained through the wind farm's revenue module. In this way, the maximum power generation of each wind turbine under a preset operating mode can be combined with the wind farm's revenue-related data, and in conjunction with electricity market transactions, the equivalent optimal revenue of the wind farm within a predetermined period can be obtained. The equivalent optimal revenue may include: equivalent maximum electricity sales revenue and / or equivalent maximum investment return.

[0050] Furthermore, the equivalent optimal benefit of the wind farm within a predetermined period and / or the maximum power generation of each wind turbine in a preset operating mode can be fed back to the grid, thereby adjusting the operating mode of each wind turbine.

[0051] In step S43, the operating mode of each wind turbine is adjusted according to the equivalent optimal benefit of the wind farm within a predetermined period, so as to achieve the predetermined benefit target of the wind farm within its life cycle, wherein the life cycle includes the predetermined period.

[0052] In embodiments of this disclosure, the predetermined period may include: a wet season and / or a dry season. However, the invention is not limited to this; the predetermined period may include other types of electricity price change periods, such as periods with different electricity price quotas, such as peak-valley periods or generation differences due to day-night noise variations. Accordingly, the preset operating modes may include: a normal power mode, a limited power mode, and a shutdown mode that are sequentially downgraded for the wet season, and / or an over-generation power mode, a normal power mode, a limited power mode, and a shutdown mode that are sequentially downgraded for the dry season. For example, the limited power mode may include multiple sub-modes, wherein a downgrade switch may be made from a limited power sub-mode with a higher maximum power to a limited power sub-mode with a lower maximum power. The over-generation power mode may include multiple sub-modes, wherein a downgrade switch may be made from an over-generation power sub-mode with a higher maximum power to an over-generation power sub-mode with a lower maximum power. Furthermore, the equivalent optimal benefit may include: equivalent maximum electricity sales revenue and / or equivalent maximum investment return. The predetermined benefit objective may include at least one of the following: maximizing the electricity sales revenue of the wind farm over its life cycle, and maximizing the investment return of the wind farm over its life cycle.

[0053] The following is combined Figure 5 This disclosure describes another process for adjusting the operating mode of the corresponding wind turbine, but is not limited thereto. Figure 5 This is another flowchart of a wind farm power dispatching method according to an embodiment of the present disclosure.

[0054] The grid's power capacity assessment module can determine whether the wind farm's power generation capacity meets the grid requirements based on the maximum power generation of each wind turbine in a preset operating mode.

[0055] In step S51, the maximum power generation of the wind farm within a predetermined period is determined based on the maximum power generation of each wind turbine in a preset operating mode. This allows determination of whether the wind farm's power generation capacity within the predetermined period meets grid requirements. Grid requirements may include the wind farm's maximum power generation within the predetermined period being less than or equal to the grid's maximum capacity within the predetermined period.

[0056] In step S52, in response to the wind farm’s maximum power generation in a predetermined period being greater than the grid’s maximum capacity in a predetermined period, the preset operating mode of at least one wind turbine in the wind farm is downgraded to another preset operating mode in sequence until the wind farm’s maximum power generation in a predetermined period is less than or equal to the grid’s maximum capacity in a predetermined period.

[0057] As a non-restrictive example, taking into account the cycles of wind and hydropower, and given the significant increase in the guiding electricity price within the quota during the dry season and the expansion of the floating electricity price range, an over-generation mode can be adopted to increase wind power generation and achieve a wind-hydropower complementary strategy.

[0058] For example, during the dry season of a predetermined period, the preset operating mode of each wind turbine in the wind farm is set to over-power mode to obtain the maximum power generation of the wind farm within the predetermined period. If the grid cannot fully integrate the power generation, the power generation reported by the wind farm needs to be reduced. In this case, the preset operating mode of at least one wind turbine in the wind farm can be downgraded to normal power mode, and the maximum power generation of the wind farm within the predetermined period can be obtained again to further determine whether it exceeds the maximum capacity of the grid within the predetermined period.

[0059] When the predetermined period coincides with the high-water season, the preset operating mode of each wind turbine in the wind farm can be set to normal power mode to obtain the maximum power generation of the wind farm within the predetermined period. If the grid cannot fully integrate the power generation, the power generation reported by the wind farm needs to be reduced. In this case, the preset operating mode of at least one wind turbine in the wind farm can be downgraded to the limited power mode, and the maximum power generation of the wind farm within the predetermined period can be obtained again to further determine whether it exceeds the maximum capacity of the grid within the predetermined period. In this way, the preset operating mode of at least one wind turbine in the wind farm can be adjusted for different predetermined periods.

[0060] In step S53, the operating mode of at least one wind turbine is set to a downgraded preset operating mode to achieve the predetermined revenue target of the wind farm over its life cycle. For example, the predetermined revenue target includes at least one of the following: maximizing the electricity sales revenue of the wind farm over its life cycle, and maximizing the investment return of the wind farm over its life cycle.

[0061] Furthermore, the equivalent optimal revenue of the wind farm within a predetermined period can be reported to the wind farm's dispatch center, and dispatch can be carried out through the wind farm's Automatic Generation Control (AGC) system according to the adjusted operating mode as described above. In this way, by over-generating and limiting capacity under specific conditions (e.g., wet season, dry season), the optimal electricity sales revenue or investment return can be achieved to meet the life-cycle design requirements of the wind farm.

[0062] According to embodiments of the present disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed, implements a wind farm power dispatching method according to embodiments of the present disclosure.

[0063] In embodiments of this disclosure, the computer-readable storage medium may carry one or more programs, which, when executed, can achieve reference... Figures 1 to 5 The described steps are as follows: Based on the design information of each wind turbine in the wind farm, generate power generation and load models of the corresponding wind turbine in multiple state spaces, wherein the design information includes wind parameter information and preset lifespan of each wind turbine; based on the real-time environmental data, real-time state data, preset lifespan, and power generation and load models of each wind turbine in the multiple state spaces, predict the power generation and remaining lifespan of the corresponding wind turbine in the real-time state space; based on the power generation and remaining lifespan of each wind turbine in the real-time state space and the wind farm's revenue-related data, adjust the operation mode of the corresponding wind turbine to achieve the wind farm's predetermined revenue target within its life cycle.

[0064] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a computer program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof. A computer-readable storage medium can be included in any apparatus; it can also exist independently without being assembled into that apparatus.

[0065] Figure 6This is a block diagram of a control device 6 according to an embodiment of the present disclosure.

[0066] Reference Figure 6 The control device 6 according to an embodiment of the present disclosure may include a memory 61 and a processor 62. A computer program 63 is stored in the memory 61. When the computer program 63 is executed by the processor 62, the wind farm power dispatching method according to an embodiment of the present disclosure is implemented.

[0067] In embodiments of this disclosure, when the computer program 63 is executed by the processor 62, reference can be implemented. Figures 1 to 5 The described wind farm power dispatching method operates as follows: Based on the design information of each wind turbine in the wind farm, a power generation and load model of the corresponding wind turbine under multiple state spaces is generated. The design information includes wind parameter information and preset lifespan for each wind turbine. Based on real-time environmental data, real-time state data, preset lifespan, and the power generation and load model under the multiple state spaces, the power generation and remaining lifespan of the corresponding wind turbine in the real-time state space are predicted. Based on the power generation and remaining lifespan of each wind turbine in the real-time state space and the wind farm's revenue-related data, the operating mode of the corresponding wind turbine is adjusted to achieve the wind farm's predetermined revenue target within its lifecycle.

[0068] Figure 6 The control device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0069] This disclosure also provides a new energy power station, which includes a control device according to an embodiment of this disclosure, capable of executing the wind farm power dispatching method described above.

[0070] The above has been referred to Figures 1 to 6 A wind farm power dispatching method, a computer-readable storage medium, a control device, and a new energy power station according to embodiments of this disclosure are described. However, it should be understood that: Figure 6 The control device shown is not limited to the components shown above, but some components can be added or removed as needed, and the above components can also be combined.

[0071] By employing the wind farm power dispatching method, computer-readable storage medium, control equipment, and new energy power station according to embodiments of this disclosure, at least one of the following technical effects can be achieved: adapting to application scenarios of electricity price changes, providing appropriate power generation schemes for different electricity demands of the power grid, thereby achieving the target economic indicators of the wind farm throughout its entire life cycle; achieving optimal revenue models such as optimal electricity sales revenue or optimal investment return; evaluating different operating modes based on unit life constraints, selecting appropriate power generation schemes and estimating the maximum achievable power generation based on different wind parameter characteristics and environmental characteristics, fully utilizing dynamically changing electricity price data to output power generation and other information to the power trading system, optimizing dispatching based on the power grid's capacity, and ultimately achieving wind-hydro complementarity, fully obtaining power generation revenue or investment returns from high electricity price quotas for different periods (e.g., wet season and dry season).

[0072] Control logic or functions performed by various components or controllers in a control device can be represented by flowcharts or similar diagrams in one or more accompanying figures. These figures provide representative control strategies and / or logic, which can be implemented using one or more processing strategies (such as event-driven, interrupt-driven, multitasking, multithreading, etc.). Therefore, the individual steps or functions shown may be performed in the order shown, in parallel, or in some cases omitted. Although not always explicitly shown, those skilled in the art will recognize that one or more steps or functions shown may be repeatedly performed depending on the specific processing strategy used.

[0073] Although this disclosure has been shown and described with reference to preferred embodiments, those skilled in the art will understand that various modifications and variations may be made to these embodiments without departing from the spirit and scope of this disclosure as defined by the claims.

Claims

1. A wind farm power dispatching method, characterized in that, The method includes: Based on the design information of each wind turbine in the wind farm, a power generation and load model of the corresponding wind turbine under multiple state spaces is generated. The design information includes the wind parameter information and preset life of each wind turbine. The multiple state spaces include a space model based on the wind parameter information and operating mode of the wind turbine. The operating modes include: over-power mode, normal power mode, power limitation mode, and shutdown mode. Based on the real-time environmental data, real-time status data, preset lifespan, and power generation and load models of each wind turbine in the various state spaces, the power generation and remaining lifespan of the corresponding wind turbine in the real-time state space are predicted. Based on the power generation and remaining lifespan of each wind turbine in the real-time state space, a preset operating mode that meets the preset lifespan requirements and allows the maximum power generation is determined for each wind turbine, and the maximum power generation of the corresponding wind turbine under the preset operating mode is determined. Based on the maximum power generation of each wind turbine in the preset operating mode and the relevant revenue data of the wind farm, the equivalent optimal revenue of the wind farm within the predetermined period is obtained. Based on the equivalent optimal benefit of the wind farm within a predetermined period, the operation mode of each wind turbine is adjusted to achieve the predetermined benefit target of the wind farm within its life cycle. The life cycle includes the predetermined period, which includes the wet season and the dry season. The preset operation modes include: normal power mode, limited power mode, and shutdown mode, which are successively downgraded for the wet season, and over-power mode, normal power mode, limited power mode, and shutdown mode, which are successively downgraded for the dry season.

2. The method according to claim 1, characterized in that, The design information also includes initialization control parameters and simulation control algorithms. The process of generating power generation and load models for each wind turbine in a wind farm under multiple state spaces, based on the design information of each wind turbine, includes: Based on the design information of each wind turbine, obtain the operating parameters of the corresponding wind turbine under the various state spaces; The power generation and load data of each wind turbine under the various state spaces are obtained from the operating parameters. The power generation and load data of each wind turbine are trained under the various state spaces to obtain the power generation and load models of the corresponding wind turbine under the various state spaces.

3. The method according to claim 2, characterized in that, The initialization control parameters include: setting parameters for key load points and simulation execution cycle.

4. The method according to claim 1, characterized in that, The method of predicting the power generation and remaining lifespan of the corresponding wind turbine in the real-time state space based on the real-time environmental data, real-time status data, preset lifespan, and power generation and load models under the multiple state spaces includes: Based on the real-time environmental data of each wind turbine, obtain the equivalent environmental distribution data of the corresponding wind turbine during the simulation execution cycle; The equivalent environmental distribution data and real-time state data of each wind turbine during the simulation execution cycle are input into the power generation and load model of the corresponding wind turbine in the various state spaces to predict the power generation and fatigue load of each wind turbine in the real-time state space. Based on the equivalent environment distribution data, real-time state data, fatigue load in the real-time state space, and preset life of each wind turbine during the simulation execution cycle, the remaining life of the corresponding wind turbine in the real-time state space is determined.

5. The method according to any one of claims 1 to 4, characterized in that, The method of adjusting the operating mode of each wind turbine based on the equivalent optimal return of the wind farm within a predetermined period to achieve the predetermined return target of the wind farm over its life cycle includes: The maximum power generation of the wind farm within a predetermined period is determined based on the maximum power generation of each wind turbine in the preset operating mode. In response to the wind farm’s maximum power generation within a predetermined period being greater than the grid’s maximum capacity within a predetermined period, the preset operating mode of at least one wind turbine in the wind farm is sequentially downgraded to another preset operating mode until the wind farm’s maximum power generation within a predetermined period is less than or equal to the grid’s maximum capacity within a predetermined period. The operating mode of at least one wind turbine is set to a downgraded preset operating mode in order to achieve the predetermined revenue target of the wind farm during its life cycle.

6. The method according to any one of claims 1 to 4, characterized in that, The equivalent optimal return includes: equivalent maximum electricity sales revenue and / or equivalent maximum investment return. The predetermined revenue objective includes at least one of the following: maximizing the electricity sales revenue of the wind farm over its life cycle, and maximizing the investment return of the wind farm over its life cycle.

7. The method according to any one of claims 1 to 4, characterized in that, Data related to the revenue of wind farms includes: electricity price data from the power grid and cost data from the wind farm.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the wind farm power dispatching method as described in any one of claims 1 to 7.

9. A control device, characterized in that, The control device includes: processor; A memory storing a computer program that, when executed by a processor, implements the wind farm power dispatching method as described in any one of claims 1 to 7.

10. A new energy power station, characterized in that, The new energy power station includes the control equipment as described in claim 9.

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