Fan group optimization and distribution method, system and equipment and storage medium
By calculating the total active power scheduling target and turbine operation data of the wind farm, and combining the priority ranking of equipment parameters, the power output allocation of wind turbines is optimized, which solves the problem of power fluctuation in the wind farm and achieves efficient wind turbine output allocation and improved grid stability.
Patent Information
- Application Number
- CN202511662457.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-13
- Publication Date
- 2026-02-10
AI Technical Summary
In existing wind farms, due to the data silo problem of the active power integrated control platform, it is difficult to reasonably allocate the output of wind turbines according to the differences in technical parameters of different equipment and the output and stability of wind turbines under different wind conditions, resulting in power fluctuations and affecting the safety and stability of the power grid.
By calculating the total active power scheduling target of the wind farm and the operating data of each turbine group, the available active power and available active power reduction are determined. The allocation order is determined using the active power allocation sequence function. Based on the total active power scheduling target and the real-time active power sum, the active power increase or decrease strategy is executed until the allocation target is reached. Combined with the priority ranking of equipment parameters, the turbine groups with better economic or health conditions are given priority for allocation.
It achieves efficient and optimized allocation of wind turbine output within the wind farm, reduces equipment wear and tear, increases revenue, and reduces real-time computation and information exchange requirements, ensuring the safety and stability of the power grid.
Smart Images
Figure CN121507989A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the field of wind power generation, in particular to a wind turbine group optimization and distribution method, system, device and storage medium. BACKGROUND
[0002] There may be multiple manufacturers and multiple models of wind turbines in the same wind farm. In order to realize active power control of wind turbines, each wind turbine is configured with an active integrated control platform developed by itself. The intermittent, volatile and random characteristics will bring a certain impact on the safety and stability of the power grid. How to actively control the active power output of the wind farm, change the existing load control mode of the wind farm, and optimize the use of wind energy resources are the problems that the power grid and wind farm operators are very concerned about.
[0003] However, due to the poor integration of the existing active integrated control platform, such as data island, it is difficult for the wind farm to reasonably allocate the output of the wind turbine according to the differences in technical parameters of different equipment, output of wind turbines under different wind conditions and stability of equipment. Therefore, how to realize efficient power optimization and distribution in the face of power fluctuations caused by uncertain factors such as wind turbines and loads in the group is also a difficult problem to be solved. SUMMARY
[0004] In order to solve the above technical problems, the present disclosure provides a wind turbine group optimization and distribution method, system, device and storage medium, wherein the method comprises: calculating the increaseable active power, the decreaseable active power of each group and the sum of the real-time active power of each group according to the total active power dispatching target of the wind farm and the operation data of each group in the wind farm; calculating the active power distribution sequence function of each group according to the equipment parameters of each group, and determining the distribution sequence of each group by using the active power distribution sequence function; determining a power distribution strategy based on the total active power dispatching target and the sum of the real-time active power of each group; based on the power distribution strategy and the distribution sequence, performing distribution according to the increaseable active power or the decreaseable active power of each group until the active power of each group reaches the distribution target.
[0005] Further, the determination of the power distribution strategy based on the total active power dispatching target and the sum of the real-time active power of each group comprises: judging the size relationship between the total active power dispatching target and the sum of the real-time active power of each group; when the total active power dispatching target is greater than the sum of the real-time active power of each group, performing an increase active power strategy; when the total active power scheduling target is less than the sum of the real-time active power of each machine group, performing a decrease active power strategy; when the total active power scheduling target is equal to the sum of the real-time active power of each machine group, performing a maintenance operation.
[0006] Further, based on the power allocation strategy and the allocation sequence, the allocation is performed according to the increaseable active power or the decreaseable active power of each machine group until the active power of each machine group reaches the total active power scheduling target of the wind farm, comprising: based on the selected power allocation strategy and the allocation sequence, the allocation is sequentially performed on each machine group according to the increaseable active power or the decreaseable active power of each machine group until the active power of each machine group reaches the total active power scheduling target of the wind farm; or, based on the selected power allocation strategy and the allocation sequence, the allocation is sequentially performed on each machine group according to the increaseable active power or the decreaseable active power of each machine group until the increaseable active power or the decreaseable active power of each machine group is satisfied.
[0007] Further, the active power allocation sequence function of each machine group is calculated according to the equipment parameters of each machine group, and the allocation sequence of each machine group is determined, comprising: the allocation sequence is sorted from large to small according to the active power allocation sequence function of each machine group to determine the allocation sequence of each machine group; when the active power allocation sequence functions of at least two machine groups are the same, the allocation sequence of each machine group is determined by sorting from large to small according to the increaseable power amount or the decreaseable power amount of the machine group.
[0008] Further, the active power allocation sequence function of each machine group is calculated according to the equipment parameters of each machine group, comprising: a weight is configured for each equipment parameter; the active power allocation sequence function of each machine group is calculated by the equipment parameters of each machine group and the corresponding weight.
[0009] Further, it further comprises: when the total active power scheduling target of the wind farm is greater than the sum of the increaseable active power of each machine group, or is less than the sum of the decreaseable active power of each machine group, an unattainable processing is performed.
[0010] Further, after the active power of each machine group reaches the allocation target, the execution result of the updated active power of each machine group is fed back.
[0011] The present disclosure also provides a wind turbine machine group optimization and allocation system, comprising: The acquisition module is used to calculate the increase in active power, the decrease in active power, and the sum of real-time active power of each turbine group in the wind farm based on the total active power scheduling target of the wind farm and the operating data of each turbine group in the wind farm. The calculation module is used to calculate the active power allocation sequence function of each machine group based on the equipment parameters of each machine group, and to determine the allocation sequence of each machine group using the active power allocation sequence function; The strategy module is used to determine the power allocation strategy based on the total active power scheduling target and the sum of the real-time active power of each generator group; The allocation module is used to perform allocation according to the power allocation strategy and the allocation order, based on the increaseable or decreaseable active power of each generator group, until the active power of each generator group reaches the allocation target.
[0012] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the wind turbine cluster optimization and allocation method.
[0013] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the wind turbine cluster optimization and allocation method.
[0014] The technical solution provided in this disclosure has the following advantages compared with the prior art: By allocating power according to the available or decreasing active power of each turbine cluster, it ensures that every issued command is within the cluster's allowable range, reducing situations where commands cannot be executed or need to be rolled back. Factors such as electricity price and equipment health are incorporated into the priority ranking, and allocation is performed based on this ranking, prioritizing the use of turbine clusters with better economic performance or health, thereby increasing profitability and delaying equipment wear and tear. The process of first summarizing capacity and then prioritizing allocation becomes low-complexity, reducing real-time computation and the need for frequent information exchange. This enables wind farms to rationally allocate wind turbine output based on different equipment technical parameters, wind conditions, and differences in equipment stability, resulting in efficient and optimized power distribution. Attached Figure Description
[0015] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a schematic diagram of the wind turbine group optimization and allocation method described in the embodiments of this disclosure; Figure 2 This is an embodiment of the present disclosure. Figure 1 A schematic diagram of the method for determining the power allocation strategy described herein; Figure 3 This is a schematic diagram illustrating the confidence levels described in the embodiments of this disclosure; Figure 4 This is a schematic diagram comparing the target value of active power and the actual value of active power as described in the embodiments of this disclosure; Figure 5 This is a schematic diagram illustrating the rate of change of active power as described in an embodiment of this disclosure. Detailed Implementation
[0018] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0019] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0020] Figure 1 This is a schematic diagram of the wind turbine cluster optimization and allocation method described in the embodiments of this disclosure, as shown below. Figure 1 As shown, a method for optimizing and allocating wind turbine clusters includes: Step S1: Calculate the potential increase in active power, potential decrease in active power, and the total real-time active power of each turbine group based on the total active power dispatch target of the wind farm and the operating data of each turbine group in the wind farm. In this embodiment, the system automatically reads the current operating data of each turbine group, including real-time active power output and upper and lower limit parameters (i.e., increase and decrease active power), and determines the adjustment range of each turbine group through logical judgment. The entire process is based on data comparison and interval judgment, requiring no complex calculations and can be completed within milliseconds. The system sequentially reads the data of each turbine group, judges the difference between the upper and lower limits, and accumulates the real-time output to obtain the total, then saves the result for subsequent steps. By comprehensively considering the real-time output capacity and adjustable range of each turbine group, the system can instantly determine whether the wind farm has the ability to achieve the scheduling target. Pre-calculating the maximum adjustable range for increase and decrease helps avoid exceeding limits or ineffective adjustments during subsequent allocation, thereby improving control stability. The statistical analysis of the real-time active power sum allows the system to identify whether there are target attainability issues in the initial stage, thereby notifying the upper-level scheduling system in advance or implementing a degradation strategy.
[0021] Specifically, the system is directly integrated into the existing hardware-based Automatic Generation Control (AGC) and Energy Management (EMS) systems of the wind farm. The network structure between the AGC system and the wind turbine energy management system remains unchanged. The wind turbine energy management system independently uploads data and receives commands from the wind turbine active power distributed optimization control system. The AGC system directly forwards the total active power target received from the grid dispatch to the coordination control algorithm server in the wind turbine active power distributed optimization control system. The algorithm server not only receives the dispatch active power target command, but also obtains real-time wind turbine operation information. Based on the number of turbines in the wind farm, the total active power target, the active power upper limit of each turbine, the active power lower limit of each turbine, the real-time active power of each turbine, electricity price information, real-time wind turbine operation information, and control mode data, the turbine operation information is collected and calculated using the turbine operation information in the wind turbine EMS system. The required turbine operation data includes: number of turbines, total active power target value, real-time active power of each turbine, active power upper limit of each turbine, and active power lower limit of each turbine.
[0022] Step S2: Calculate the active power allocation sequence function for each machine group based on the equipment parameters of each machine group, and use the active power allocation sequence function to determine the allocation sequence of each machine group; In this embodiment, by incorporating equipment parameter-related factors, the system can dynamically balance the accumulated load differences between units over a long period, achieving a fairer long-term scheduling strategy. The system reads parameters related to equipment characteristics from the wind farm's EMS (Electric Power Management System), such as power generation revenue, equipment health score, long-term operational stability, or lifespan indicators, and converts them into a sortable priority factor. Operationally, the system standardizes the parameters of each wind farm group, assigns a priority score, and then ranks the wind farm groups according to their scores for subsequent active power increase or decrease allocation. Different wind farm groups differ in electricity price revenue, equipment health status, stability indicators, and power generation capacity. This step allows for intelligent sorting of wind farm groups, making the control strategy more aligned with actual economic efficiency and equipment lifespan management requirements. By prioritizing allocation to wind farm groups more suited to handle power changes, equipment losses can be reduced, unnecessary frequent adjustments can be minimized, and the overall long-term operational efficiency of the wind farm can be improved.
[0023] Step S3: Determine the power allocation strategy based on the total active power scheduling target and the sum of real-time active power of each generator group; In this embodiment, if the target significantly exceeds the overall adjustable range, an anomaly handling or rollback strategy can be initiated at this step to improve system security. The system compares the obtained real-time total active power with the external scheduling target, and determines whether to increase or decrease output based on the sign of the difference. This determination can be completed through comparison, with clear conditions and logical flow, making it suitable for real-time and rapid execution. By directly comparing the target with the current total output, the system can decide whether to increase or decrease the overall output in the shortest possible time, resulting in faster response and more sensitive adjustment. When the target matches the current total output, the system can remain inactive or only execute a stability maintenance strategy to avoid meaningless operations that lead to additional equipment wear.
[0024] Specifically, the number of wind turbine groups that can be divided is n, the total active power target value is P, and the active power upper limit of each group is as follows: , , … The lower limit of active power for each generator group is as follows: , , … The real-time active power of each cluster is as follows: , , … The potential for expansion of each cluster The increased active power are respectively , , … The relocation capability of each aircraft group The active power can be reduced as follows: , , … The cluster has a power allocation sequence function. or Define, judge, and sort according to requirements; the active power allocation order functions for each generator group are as follows: > > >…> or > > >…> .
[0025] Step S4: Based on the power allocation strategy and allocation order, the power allocation is performed according to the increase or decrease of active power of each generator group until the active power of each generator group reaches the allocation target.
[0026] In this embodiment, the system, based on the ranking results, starts with the highest-priority generator group and gradually adjusts its output, ensuring the adjustment does not exceed its allowable increase or decrease range. Once a generator group meets the limit, it automatically switches to the next generator group to continue execution until the total output meets the scheduling command requirements. This process is executed sequentially by the system, with each step involving range checks and recording the adjustment progress to ensure controllable actions and traceable results. Adjusting sequentially by ranking makes the power change process smoother, avoiding system fluctuations caused by large abrupt changes. The system always compares the allocation to the maximum adjustable range of each generator group to avoid exceeding limits and ensure that the equipment operates within the allowable range. Processing by priority group reduces the computational burden, allowing the system to quickly converge to the target state while reducing frequent large-scale readjustments.
[0027] This disclosure ensures that every issued instruction is within the allowable range of the generator cluster by allocating power according to the available or decreasing active power of each cluster, reducing situations where instructions cannot be executed or need to be rolled back. Factors such as electricity price and equipment health are incorporated into the priority ranking, and allocation is performed based on this ranking, prioritizing the use of clusters with better economic performance or health, thereby increasing profitability and delaying equipment wear. By first summarizing capacity and then prioritizing allocation, the execution process becomes low-complexity, reducing real-time computation and the need for frequent information exchange, thus facilitating engineering implementation.
[0028] In another embodiment of this disclosure, Figure 2 This is an embodiment of the present disclosure. Figure 1 A schematic diagram of the method for determining the power allocation strategy described in the document; as follows: Figure 2As shown, step S3, based on the total active power scheduling target and the sum of real-time active power of each generator group, determines the power allocation strategy, including: step S31, determining the relationship between the total active power scheduling target and the sum of real-time active power of each generator group; step S32, when the total active power scheduling target is greater than the sum of real-time active power of each generator group, executing the active power increase strategy; step S33, when the total active power scheduling target is less than the sum of real-time active power of each generator group, executing the active power decrease strategy; step S34, when the total active power scheduling target is equal to the sum of real-time active power of each generator group, executing maintenance operations.
[0029] In this embodiment, the total output value of each cluster from the cluster EMS and the field-level total target issued by AGC are read in real time. If the target exceeds the current situation, it is marked as "increase active power"; if it is lower than the current situation, it is marked as "decrease active power"; if they are equal, it is marked as "maintain". After the determination, the scheduling strategy flag is written immediately and the corresponding downstream process is triggered (e.g., increasing active power triggers the sorting and incremental allocation module; decreasing active power triggers the sorting and decrease allocation module; maintaining triggers monitoring and maintenance actions). It can determine the operation direction of the overall output (increase, decrease, or maintain) in the shortest possible time, significantly improving the response speed. The three modes of decision-making reduce the space for fuzzy operation, thereby reducing equipment wear caused by incorrect decisions and frequent adjustments. By determining the strategy in the early stage, the subsequent sorting and allocation steps can be executed more efficiently, improving the overall scheduling efficiency and executability.
[0030] Specifically, based on the collected wind turbine fleet operation data, a scenario assessment is first performed before each power allocation; if P > + + +…+ If so, then execute the active power increase strategy; if P = + + +…+ Then, the active power reduction strategy will be implemented.
[0031] In another embodiment of this disclosure, step S4, based on the power allocation strategy and allocation order, performs allocation according to the increaseable or decreaseable active power of each turbine group until the active power of each turbine group reaches the total active power scheduling target of the wind farm, includes: based on the selected power allocation strategy and allocation order, performing allocation on each turbine group sequentially according to the increaseable or decreaseable active power of each turbine group until the active power of each turbine group reaches the total active power scheduling target of the wind farm; or, based on the selected power allocation strategy and allocation order, performing allocation on each turbine group sequentially according to the increaseable or decreaseable active power of each turbine group until each turbine group meets the increaseable or decreaseable active power.
[0032] In this embodiment, incremental or decremental instructions are calculated and issued starting with the highest-priority machine group, according to the generated priority order (i.e., allocation order). Each time an adjustment is performed on a single machine group, the adjustable space of that group is first read and compared with the difference between the remaining target values. The smaller value is taken as the adjustment amount for this step, and then the instruction is issued and the remaining target is updated. The internal state is updated and logged immediately after each adjustment step. If the remaining target is zero or there is no adjustable space, allocation stops. This ensures that the allocation process terminates with the target, guaranteeing that the final total number of machine groups allocated is as close as possible to the scheduling target. This helps reduce multiple callbacks and redundant calculations, improving the success rate of scheduling on the first attempt. With a clear termination condition, it facilitates rapid interaction and reporting with the superior AGC or scheduling platform in cases of unreachability.
[0033] Furthermore, based on priority, each generator group is adjusted sequentially to either maximize power generation or minimize output until its adjustable capacity is exhausted (i.e., reaching the upper / lower limit). After each adjustment, the system updates the remaining adjustable capacity of that group and continues to perform the same action on the next group until the adjustable capacity of all groups is exhausted or a preset cycle limit is reached. This method releases as many adjustable resources as possible, facilitating the assessment of the entire site's mobilization capacity and reserve. It is beneficial for capacity verification and grid availability assessment, and facilitates subsequent policy configuration at the operation and maintenance or scheduling levels.
[0034] In another embodiment of this disclosure, step S2, calculating the active power allocation sequence function of each group based on the equipment parameters of each group, and determining the allocation sequence of each group using the active power allocation sequence function, includes: sorting the groups from largest to smallest according to the active power allocation sequence function of each group to determine the allocation sequence of each group; when at least two groups have the same active power allocation sequence function, sorting the groups from largest to smallest according to the amount of power that can be increased or decreased to determine the allocation sequence of each group.
[0035] In this embodiment, indicators such as equipment health, electricity price, and stability are incorporated into the ranking process to achieve a priority allocation that balances economic efficiency and reliability, thereby improving long-term operational benefits and reducing maintenance costs. When priority indicators are the same or similar, the adjustable space is used as a secondary ranking criterion, which avoids deadlock or arbitrariness in the ranking process, thus improving the certainty and executability of the allocation decision.
[0036] Specifically, in the active power increase strategy, the active power allocation sequence function of each generator group is used. Sort and by The active power of each generator group is increased sequentially from largest to smallest. Specifically, this is represented by the generator group active power allocation order function. Sort by > > >…> Then, the active power allocation order is group 1, 2, 3, ..., n. If the active power allocation order function of the groups is the same, that is... Then, when sorting, the increase in active power of the cluster is compared. 可增 ,like 可增 > 可增 Then, the active power increase of cluster i will be sorted before that of cluster j; after sorting, the active power increases will be added sequentially according to the sorting order. , , … When the active power of cluster i reaches the active power limit, that is... Then continue to increase. (i.e., the next group i+1 after group i in the sorting), until P total = + + +…+ ; When implementing an active power allocation strategy, the formula for calculating the active power distribution order function is as follows:
[0037] in, For the on-grid electricity price of the generator cluster; To adjust the parameters, and satisfy ; The average failure rate of the fleet; The average fatigue damage of the aircraft group; , , , , These are the weight parameters.
[0038] Similarly, when implementing a strategy to reduce active power, the active power allocation order function of each generator group is used. Sort and by The active power of each generator group is reduced sequentially from largest to smallest. Specifically, this is represented by the generator group active power allocation order function. Sort by > > >…> Then, the active power allocation is sorted into machine groups 1, 2, 3, ..., n. If the active power allocation order function of the machine groups is the same, that is... Then, when sorting, the scalable active power of the cluster is compared. 可降 ,like 可降 > 可降Then, the reduced active power of cluster i will be sorted before that of cluster j; after sorting, the active power will be reduced sequentially according to the sorting order. , , … When the active power of cluster i reaches the lower limit of active power, that is Then continue to decrease. (i.e., the next group i+1 after group i in the sorting), until... P Total = + + +…+ ; When implementing an active power reduction strategy, the active power allocation order function is calculated as follows:
[0039] in, For the on-grid electricity price of the generator cluster; To adjust the parameters, and satisfy ; The average failure rate of the fleet; The average fatigue damage of the aircraft group; , , , , These are the weight parameters.
[0040] In another embodiment of this disclosure, the active power allocation order function of each generator group is calculated based on the equipment parameters of each generator group, including: Configure weights for each device parameter; The active power allocation sequence function for each generator group is calculated using the equipment parameters and corresponding weights of each generator group.
[0041] In this embodiment, the equipment parameters involved in priority calculation are clearly defined, such as: grid-connected electricity price of the fleet, average failure rate of the fleet, average fatigue damage of the fleet, and potential increase or decrease in active power. Weight configuration can be set manually, with maintenance / dispatch personnel setting fixed weight combinations in the system based on operational goals; rule-based configuration, pre-setting multiple weight templates based on day / night, season, market price range, or special events; data-driven configuration, periodically optimizing weights using statistical or machine learning methods based on historical operating data and performance evaluation to improve long-term benefits or reliability; and adaptive adjustment, automatically fine-tuning certain weights at the real-time or near-real-time level based on wind speed fluctuations, system deviations, or equipment alarms (e.g., temporarily increasing stability-related weights when wind speed is highly unstable). Reasonable upper and lower limits should be set for weight values, and the reason and time of each change should be recorded to ensure traceability. By assigning weights to multiple equipment parameters of the fleet and synthesizing scores, multiple objectives such as economy, reliability, lifespan, and grid connection stability can be uniformly incorporated into the allocation decision, avoiding biased allocation caused by a single indicator. As a configurable parameter, weights can be flexibly adjusted according to operational strategies (such as prioritizing economy, protecting equipment, or ensuring stability) to meet the needs of different operating periods or strategic objectives. Incorporating lifetime consumption or historical load into the parameters and assigning appropriate weights allows for load distribution over a long-term scale, preventing certain generating units from being overused and prematurely retired. When weight settings prioritize electricity price revenue, priority calculations will encourage the system to utilize generating units with low generation costs or high revenue, thereby improving overall generation revenue.
[0042] In another embodiment of this disclosure, the method further includes: when the total active power scheduling target of the wind farm is greater than the sum of the active power that can be increased by each turbine group, or less than the sum of the active power that can be reduced by each turbine group, an unreachable processing is performed.
[0043] In this embodiment, the system compares the total available incremental and total available decrement quantities obtained by aggregation with the target issued by AGC; if the target is unreachable, it enters the unreachability handling process. The unreachability handling process includes several optional measures; for example, sending an unreachability alarm to AGC and reporting the current available range; triggering a low-priority interruptible load response; enabling or requesting external backup power; or executing orderly wind curtailment within the cluster according to preset rules. This provides a clear handling path in unreachable situations, avoiding blind issuance of dispatch instructions that could lead to equipment malfunctions or system instability. It improves dispatch security and flexibility by providing early warnings and alternative measures, reducing sudden impacts on the power grid. It provides dispatchers or automated systems with decision support information (such as suggested wind curtailment scale, required backup capacity, or target values to be adjusted), facilitating subsequent manual or automated decision-making.
[0044] In another embodiment of this disclosure, once the active power of each cluster reaches the allocation target, the updated execution result of the active power of each cluster is fed back.
[0045] In this embodiment, after each allocation is completed or a milestone is reached, the fleet EMS returns execution confirmation information, including actual output, execution delay, and anomaly flags. This closed-loop feedback confirms that the allocation command has been correctly executed, improving the traceability and reliability of control. It also facilitates the timely detection of execution deviations or equipment anomalies, triggering reallocation or rollback, and reducing the risks associated with long-term error accumulation.
[0046] Figure 4 This is a schematic diagram of the wind turbine cluster optimization and allocation system described in the embodiments of this disclosure, as shown below. Figure 4 As shown, this disclosure also provides a wind turbine fleet optimization and allocation system, including: The acquisition module 401 is used to calculate the increase in active power, decrease in active power, and the sum of real-time active power of each turbine group based on the total active power scheduling target of the wind farm and the operating data of each turbine group in the wind farm. The calculation module 402 is used to calculate the active power allocation sequence function of each group of machines based on the equipment parameters of each group of machines, and to determine the allocation sequence of each group of machines using the active power allocation sequence function. Strategy module 403 is used to determine the power allocation strategy based on the total active power scheduling target and the real-time total active power of each cluster. The allocation module 404 is used to perform allocation based on the power allocation strategy and allocation order, according to the increaseable or decreaseable active power of each generator group, until the active power of each generator group reaches the allocation target.
[0047] In this case, Figure 4 This is a schematic diagram comparing the target value of active power and the actual value of active power as described in the embodiments of this disclosure; Figure 5 This is a schematic diagram illustrating the rate of change of active power as described in an embodiment of this disclosure; as follows: Figure 4 and Figure 5 As shown, before the system is put into operation, a system control command response test is first conducted. A comparison curve of the target active power value and the actual active power value throughout the active power regulation process is obtained to determine the deviation curve between the target and actual active power values. Through two-rise and two-fall active power regulation tests, the rate of change of active power was controlled within 10% (25.05 MW / min) of the total installed capacity, regardless of whether the active power was rising or falling.
[0048] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of a wind turbine cluster optimization and allocation method.
[0049] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of a wind turbine cluster optimization and allocation method.
[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0051] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. 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 disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for optimizing and allocating wind turbine fleets, characterized in that, include: Based on the total active power scheduling target of the wind farm and the operating data of each turbine group in the wind farm, calculate the possible increase in active power, the possible decrease in active power, and the sum of the real-time active power of each turbine group; The active power allocation sequence function of each machine group is calculated based on the equipment parameters of each machine group, and the allocation sequence of each machine group is determined using the active power allocation sequence function. Based on the total active power scheduling target and the sum of the real-time active power of each generator group, a power allocation strategy is determined; Based on the power allocation strategy and the allocation order, the allocation is performed according to the increase or decrease of active power of each generator group until the active power of each generator group reaches the allocation target.
2. The wind turbine cluster optimization and allocation method according to claim 1, characterized in that, The process of determining a power allocation strategy based on the total active power scheduling target and the sum of the real-time active power of each generator group includes: Determine the relationship between the total active power scheduling target and the sum of the real-time active power of each generator group; When the total active power scheduling target is greater than the sum of the real-time active power of each generator group, the active power increase strategy is executed. When the total active power scheduling target is less than the sum of the real-time active power of each generator group, an active power reduction strategy is executed. When the total active power scheduling target is equal to the sum of the real-time active power of each generator group, a maintenance operation is performed.
3. The wind turbine cluster optimization and allocation method according to claim 1, characterized in that, The allocation based on the power allocation strategy and the allocation order, according to the increaseable or decreaseable active power of each turbine group, until the active power of each turbine group reaches the total active power scheduling target of the wind farm, includes: Based on the selected power allocation strategy and allocation order, the power allocation is performed on each turbine group in sequence according to the increase or decrease of active power of each turbine group until the active power of each turbine group reaches the total active power scheduling target of the wind farm. Alternatively, based on the selected power allocation strategy and the allocation order, the power allocation is performed sequentially on each generator group according to the increaseable or decreaseable active power, until each generator group meets the increaseable or decreaseable active power requirement.
4. The wind turbine cluster optimization and allocation method according to claim 1, characterized in that, The step of calculating the active power allocation sequence function for each generator group based on the equipment parameters of each generator group, and using the active power allocation sequence function to determine the allocation sequence of each generator group, includes: The allocation order is determined by sorting the active power allocation order functions of each generator group from largest to smallest. When at least two of the generator groups have the same active power allocation order function, the generator groups are sorted from largest to smallest according to their available power increase or available power decrease, and the allocation order of each generator group is determined.
5. The wind turbine cluster optimization and allocation method according to claim 1, characterized in that, The step of calculating the active power allocation order function for each generator group based on the equipment parameters of each generator group includes: Assign weights to each of the device parameters; The active power allocation sequence function for each machine group is calculated using the equipment parameters and corresponding weights of each machine group.
6. The wind turbine cluster optimization and allocation method according to any one of claims 1 to 5, characterized in that, Also includes: When the total active power dispatch target of the wind farm is greater than the total active power that can be increased by each group of turbines, or less than the total active power that can be reduced by each group of turbines, an unreachable condition is executed.
7. The wind turbine cluster optimization and allocation method according to any one of claims 1 to 5, characterized in that, Once the active power of each generator group reaches the allocation target, the updated active power execution results for each generator group will be fed back.
8. A wind turbine fleet optimization and allocation system, characterized in that, include: The acquisition module is used to calculate the increase in active power, the decrease in active power, and the sum of real-time active power of each turbine group in the wind farm based on the total active power scheduling target of the wind farm and the operating data of each turbine group in the wind farm. The calculation module is used to calculate the active power allocation sequence function of each machine group based on the equipment parameters of each machine group, and to determine the allocation sequence of each machine group using the active power allocation sequence function; The strategy module is used to determine the power allocation strategy based on the total active power scheduling target and the sum of the real-time active power of each generator group; The allocation module is used to perform allocation according to the power allocation strategy and the allocation order, based on the increaseable or decreaseable active power of each generator group, until the active power of each generator group reaches the allocation target.
9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the wind turbine cluster optimization and allocation method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the wind turbine cluster optimization and allocation method as described in any one of claims 1 to 7.