Active power control distribution method and system for reducing wind abandonment rate during simultaneous power restriction of wind farm groups

Abstract

The invention discloses an active power control allocation method and system for reducing the wind abandonment rate when a group of wind farms are simultaneously limited in power, and relates to the technical field of power dispatching and control of a group of wind farms. The invention aims to solve the problem of how to reasonably allocate the upper limit of the output capacity of each wind farm to minimize the amount of power limitation when the installed capacity of the wind farm group is greater than the capacity of the transmission channel. The invention uses the actual output annual historical data of each wind farm to calculate the distribution probability of the output size, and combines the output probability to theoretically calculate the amount of power limitation under various active power control allocation algorithms, and obtains a situation with the minimum amount of power limitation as the basis of the algorithm. Through example analysis, under the premise of ensuring that the transmission channel is not overloaded, the maximum output of the newly connected wind farm is prioritized, and the output upper limit of the existing wind farm is dynamically adjusted to minimize the amount of power limitation. The wind abandonment rate under the power limitation is reduced by optimizing the algorithm, and the utilization rate of wind power resources and the overall power generation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching and control of wind farm groups, and in particular to an active power control distribution method and system for reducing wind abandonment rate when wind farm groups are simultaneously power-limited. Background Art

[0002] In the field of wind power energy development, with the expansion of wind farm groups and the increase in grid access demand, how wind farms can reduce the wind curtailment rate during power restrictions by optimizing load distribution and power generation calculation has become a crucial technical challenge. Especially when wind farms are connected to the grid system through temporary access and the overall installed capacity is greater than the capacity of the transmission channel, how to reasonably allocate the output capacity upper limit of each wind farm to reduce power restrictions while ensuring that the transmission channel is not overloaded has become a technical problem that needs to be solved urgently.

[0003] At present, the power control and load distribution of wind farms mainly rely on the in-depth analysis and accurate prediction of the output characteristics of wind farms. As a typical intermittent energy source, the output characteristics of wind power generation are affected by many natural factors, such as wind speed, wind direction, air pressure, etc., which makes the actual output of wind farms highly uncertain and volatile. Therefore, when formulating the power control and load distribution strategies of wind farms, the output probability distribution of each wind farm and their mutual influence must be fully considered.

[0004] Specifically for the application scenario of the present invention, wind farm A (500MW) is connected to the power grid system through a temporary transition access method and is connected to the grid for power generation. The access form is to be connected to wind farm B (1300MW) and wind farm C (300MW) through a temporary transmission line, and then the electricity is sent to the power grid through the permanent transmission lines (1600MW) of wind farm B and wind farm C. However, since the permanent lines are already fully loaded, the access of wind farm A poses an overload risk to the transmission channel. Therefore, it is necessary to reduce the output upper limit of wind farms B and C to free up transmission capacity for wind farm A. In this context, how to optimize the load distribution of the three wind farms A, B, and C to reduce the overall power restriction rate has become a key issue to be solved by the present invention.

[0005] In the prior art, the load distribution of wind farm groups is usually based on probability analysis of historical output data to determine the output distribution characteristics of each wind farm. However, in actual applications, due to the large differences in wind resources in the regions where each wind farm is located and different installed capacities, there is a great deal of uncertainty in the output of each wind farm in the same output period, which makes it difficult for traditional load distribution methods to accurately reflect the actual situation and easily leads to an increase in power restrictions. Therefore, a more accurate and efficient load distribution algorithm is needed to achieve the optimal distribution of the output capacity of each wind farm while ensuring that the transmission channel is not overloaded.

[0006] In summary, the present invention aims to provide an active power control allocation method and system for reducing the wind abandonment rate when a group of wind farms are simultaneously limited in power. This method can make full use of the actual output annual historical data of each wind farm, calculate the distribution probability of the output size of each wind farm, and combine the output probability to theoretically calculate the power limit under various active power control allocation methods, and obtain a situation with the minimum power limit as the basis for the active power control allocation method. In this way, the optimal allocation of the output capacity of each wind farm can be achieved under the premise of ensuring that the transmission channel is not overloaded, thereby reducing the overall power limit rate and improving the power generation efficiency and economic benefits of the wind farm group. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an active power control distribution method and system for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, thereby solving the problem of high wind abandonment rate of a group of wind farms when power is limited simultaneously, and at the same time overcoming the limitation of the prior art that waste of wind abandonment resources is caused by the lack of an effective scheduling strategy when a group of wind farms are power limited.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: an active power control and distribution method for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, comprising the following steps: Step 1: Data collection and preprocessing: collect historical output data and weather forecast information of each wind farm and perform preprocessing; Step 2: Output probability distribution prediction: Based on the preprocessed data, the output of each wind farm in the future is predicted and its output probability distribution is calculated; Step 3: Construct an active power control allocation model. Combined with the carrying capacity of the transmission channel, the active power control allocation model is constructed with the goal of minimizing the power limit. Step 4: Solve the optimal output allocation plan, use the optimization algorithm to solve the constructed active power control allocation model, and obtain the optimal output allocation plan of each wind farm under power restriction; Step 5: Real-time control and adjustment: Based on the optimal output allocation solution obtained, the output of each wind farm is controlled and adjusted in real time.

[0009] In a preferred solution, the data preprocessing in Step 1 includes data cleaning, data verification and data normalization.

[0010] In a preferred solution, the output probability distribution prediction in Step 2 adopts a time series prediction model or a machine learning prediction model.

[0011] In a preferred solution, the active power control allocation model in Step 3 also considers the mutual influence and synergy between wind farms, as follows: Theoretical power generation It is expressed as: (1) Actual power generation It is expressed as: (2) Power limit It is expressed as: (3) In the formula, The output size of the wind farm; is the probability corresponding to different treatments; is the number of hours in a natural year, which is a constant; It is the upper limit of wind farm output.

[0012] In a preferred solution, the optimization algorithm in Step 4 adopts a genetic algorithm, a particle swarm algorithm or a combination thereof.

[0013] In a preferred solution, the real-time control and adjustment in Step 5 also includes the monitoring system monitoring the output of each wind farm and the load of the transmission channel in real time, and adjusting the output distribution plan according to the actual situation.

[0014] In a preferred embodiment, the method further includes considering the impact of monsoon activity and seasonal changes in wind speed on wind farm output.

[0015] The active power control system for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited is used to execute any one of the above-mentioned active power control and distribution methods for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, and the control system includes: Data collection module, used to collect historical output data and weather forecast information of each wind farm; A data preprocessing module, used for preprocessing the data collected by the data collection module; The output probability distribution prediction module is used to predict the output of each wind farm in the future and calculate its output probability distribution; Active power control allocation model building module, used to build active power control allocation model based on the carrying capacity of the transmission channel and with the goal of minimizing power limit; The optimal output allocation solution module is used to solve the constructed active power control allocation model using an optimization algorithm to obtain the optimal output allocation solution for each wind farm under power restriction conditions; The real-time control and adjustment module is used to control and adjust the output of each wind farm in real time according to the optimal output allocation plan obtained by solving.

[0016] In a preferred solution, the control system further includes an optimization module for optimizing the active power control allocation method after analyzing the output characteristics of each wind farm.

[0017] In a preferred solution, the control system further includes a monitoring module for continuously monitoring the output of the wind farm group and adjusting the active power control allocation method according to real-time monitoring data.

[0018] The active power control and distribution method and system for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited provided by the present invention have the following beneficial effects: 1. The present invention proposes an effective solution to the problem of high wind abandonment rate of wind farm groups under simultaneous power restriction. Through algorithm optimization, the limitation of wind abandonment resource waste caused by lack of effective scheduling strategy when wind farm groups are under power restriction in the prior art is overcome, and the wind abandonment rate is significantly reduced; 2. The present invention innovatively proposes an output probability analysis method based on the actual output annual historical data of each wind farm. By statistically analyzing the probability distribution of each wind farm in different output ranges, reliable data support is provided for subsequent active power control allocation; 3. Based on the above output probability analysis, the present invention designs a new active power control allocation algorithm. The algorithm comprehensively considers the output probability of each wind farm, and reasonably allocates the output of the wind farm group with the goal of minimizing the power limit; 4. By finely calculating the output probability of each wind farm, the present invention realizes fine control of the output of different wind farms. This control strategy can flexibly adjust the output upper limit of each wind farm according to the actual output of the wind farm, thereby improving the utilization rate of the transmission line; 5. In view of the problem of power restriction caused by the overall installed capacity being greater than the capacity of the transmission channel when a group of wind farms are connected to the grid through temporary access, the present invention proposes an effective solution, which ensures that the transmission channel is not overloaded by reasonably allocating the output upper limit of each wind farm and maximizes the use of wind energy resources; 6. The present invention effectively solves this problem by accurately predicting the output probability of each wind farm and allocating active power accordingly; 7. Under the premise of ensuring that the transmission channel is not overloaded, the present invention gives priority to ensuring the maximum output of the newly connected wind farm. By reasonably adjusting the output upper limit of other wind farms, sufficient transmission capacity is freed up for the newly connected wind farm, thereby effectively reducing the overall power limit; 8. The present invention increases the output ratio of newly connected wind farms in the low wind season and improves power generation. Especially in the low wind season, since the wind speed of existing wind farms is obviously low, the probability of low-level output is greater. At this time, by increasing the output ratio of newly connected wind farms, it is expected to effectively increase the overall power generation and further improve the utilization efficiency of wind energy resources. 9. The present invention significantly improves the utilization efficiency of wind energy resources by optimizing the output distribution of wind farm groups. This optimization strategy not only reduces the power limit, but also improves the utilization rate of the transmission line, providing a strong guarantee for the stable operation of the wind farm group; 10. The technical solution of the present invention has the characteristics of dynamic adjustment. It can flexibly adjust the active power control allocation strategy according to the actual output conditions and output probability distribution of each wind farm. This dynamic adjustment strategy can ensure that the wind farm group can maintain the optimal output allocation state under various working conditions, thereby further reducing power restrictions and improving the utilization efficiency of wind energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 A schematic diagram of the flow chart of the allocation method of the present invention; Figure 2 It is a structural schematic diagram of the control system of the present invention; Figure 3 This is a schematic diagram of wind farm output characteristics analysis according to Example A of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments: Example 1 like Figure 1 As shown, in order to solve the above technical problems, the technical solution adopted by the present invention is: an active power control and distribution method for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, comprising the following steps: Step 1: Data collection and preprocessing: collect historical output data and weather forecast information of each wind farm and perform preprocessing; Step 2: Output probability distribution prediction: Based on the preprocessed data, the output of each wind farm in the future is predicted and its output probability distribution is calculated; Step 3: Construct an active power control allocation model. Combined with the carrying capacity of the transmission channel, the active power control allocation model is constructed with the goal of minimizing the power limit. Step 4: Solve the optimal output allocation plan, use the optimization algorithm to solve the constructed active power control allocation model, and obtain the optimal output allocation plan of each wind farm under power restriction; Step 5: Real-time control and adjustment: Based on the optimal output allocation solution obtained, the output of each wind farm is controlled and adjusted in real time.

[0021] In this embodiment, the data preprocessing in Step 1 includes data cleaning, data verification and data normalization.

[0022] Furthermore, the output probability distribution prediction in Step 2 adopts a time series prediction model or a machine learning prediction model.

[0023] Furthermore, the active power control allocation model in Step 3 also considers the mutual influence and synergy between wind farms, as follows: Theoretical power generation It is expressed as: (1) Actual power generation It is expressed as: (2) Power Limitation It is expressed as: (3) In the formula, The output size of the wind farm; is the probability corresponding to different treatments; is the number of hours in a natural year, which is a constant; It is the upper limit of wind farm output.

[0024] Furthermore, the optimization algorithm in Step 4 adopts a genetic algorithm, a particle swarm algorithm or a combination thereof.

[0025] Furthermore, the real-time control and adjustment in Step 5 also includes the monitoring system monitoring the output of each wind farm and the load of the transmission channel in real time, and adjusting the output distribution plan according to the actual situation.

[0026] Furthermore, the method also includes considering the impact of monsoon activity and seasonal changes in wind speed on wind farm output.

[0027] Example 2 In another preferred embodiment, based on Example 1, Figure 2 As shown, the active power control system for reducing the wind abandonment rate when the wind farm group is simultaneously limited is used to execute any one of the above-mentioned active power control allocation methods for reducing the wind abandonment rate when the wind farm group is simultaneously limited, and the control system includes: Data collection module, used to collect historical output data and weather forecast information of each wind farm; A data preprocessing module is used to preprocess the collected data; The output probability distribution prediction module is used to predict the output of each wind farm in the future and calculate its output probability distribution; Active power control allocation model building module, used to build active power control allocation model based on the carrying capacity of the transmission channel and with the goal of minimizing power limit; The optimal output allocation solution module is used to solve the constructed active power control allocation model using an optimization algorithm to obtain the optimal output allocation solution for each wind farm under power restriction conditions; The real-time control and adjustment module is used to control and adjust the output of each wind farm in real time according to the optimal output allocation plan obtained by solving.

[0028] In this embodiment, the control system further includes an optimization module for optimizing the active power control allocation method after analyzing the output characteristics of each wind farm.

[0029] Furthermore, the control system also includes a monitoring module for continuously monitoring the output of the wind farm group and adjusting the active power control allocation method according to real-time monitoring data.

[0030] Example 3 In another preferred embodiment, based on embodiments 1 and 2, a method for controlling and distributing active power of a wind farm group is provided. The method first performs statistical analysis based on the output characteristics of each wind farm to obtain the output distribution probability of each wind farm; then, a mathematical model for controlling and distributing active power of the wind farm group is established in combination with the capacity limitation of the transmission channel; finally, the optimal output distribution plan of each wind farm is obtained by solving the mathematical model to achieve the goal of reducing the wind abandonment rate and improving the utilization rate of wind power resources.

[0031] The key steps of this method include: Collect and organize the historical output data of each wind farm, conduct statistical analysis, and obtain the output distribution probability of each wind farm; According to the capacity limit of the transmission channel and the output distribution probability of the wind farm, a mathematical model for active power control and distribution of the wind farm group is established; The mathematical model is solved by using a suitable optimization algorithm to obtain the optimal output allocation plan for each wind farm; According to the actual operating conditions, the optimization plan is dynamically adjusted to adapt to the changes in the output of the wind farm group.

[0032] Example 4 In another preferred embodiment, based on embodiments 1, 2, and 3, in order to further demonstrate the practical application and practicability of the present invention, the present invention is described in detail below in conjunction with specific embodiments.

[0033] This embodiment relates to an active power control and distribution method when a wind farm is temporarily connected to a power grid system through two other wind farms.

[0034] First, the technical issues are clarified: Wind Farm A (500MW) needs to be connected to the power grid system through temporary transition access for grid-connected power generation. The access form is to be connected to Wind Farm B (1300MW) and Wind Farm C (300MW) through temporary transmission lines, and then the electricity is sent to the grid through the permanent transmission lines (1600MW) of Wind Farm B and Wind Farm C. Since the permanent lines are already fully loaded, there is no transmission space after Wind Farm A is connected. Therefore, it is necessary to reduce the output upper limit of Wind Farm B and Wind Farm C to free up transmission capacity for Wind Farm A. Under the condition of ensuring that the transmission channel is not overloaded, it is necessary to allocate an output capacity upper limit to each wind farm to reduce power restrictions.

[0035] Next, we will analyze the output characteristics of the wind farm: Analysis of wind resources and wind speed: The three wind farms A, B and C are located in the southern subtropical marine monsoon climate zone, with obvious monsoon activity and obvious seasonal changes in wind speed, with winter wind speed being greater than summer wind speed; specifically, from July 2023 to June 2024, the average wind speed of wind farm A was 6.99m / s, the average wind speed of wind farm B was 5.86m / s, and the average wind speed of wind farm C was 6.38m / s; in the strong wind season (October to March of the following year), the wind speeds of the three wind farms are slightly different, and it is easy for them to reach a high-speed state at the same time; in the weak wind season (April to September), the wind speeds of wind farms B and C are significantly lower, and the probability of low-level output is higher.

[0036] Output probability statistics: The output of each wind farm from July 2023 to June 2024 is counted to form an output probability table; specifically, the total output of wind farms B and C is below 70% of the installed capacity for more than 90% of the time throughout the year; the output of wind farm A is above 50% of the installed capacity for more than 27.4% of the time throughout the year.

[0037] Based on the above analysis, this embodiment proposes the following solutions: Calculate the output distribution probability of each wind farm: Use the actual output annual historical data of each wind farm to calculate the distribution probability of the output size of each wind farm.

[0038] Theoretical calculation of power limit: Combined with the output probability, the power limit under various active power control allocation algorithms is theoretically calculated; specifically, different output limits of wind farm A are set (such as 100MW, 200MW, 300MW, 400MW, 500MW), and the output limits of wind farms B and C are adjusted accordingly (such as 1500MW, 1400MW, 1300MW, 1200MW, 1100MW), and the power limit under each scheme is calculated.

[0039] Determine the optimal allocation algorithm: compare the power limits under various schemes, and obtain a situation with the minimum power limit as the basis for the active power control allocation algorithm; in this embodiment, it is found through calculation that, under the premise of ensuring that the line grid connection point does not exceed 1600MW, the maximum output of wind farm A (500MW) is given priority. At this time, the total power limit is the lowest (866 million kWh), which is significantly lower than other schemes.

[0040] Implement control allocation: According to the determined optimal allocation algorithm, dynamically adjust the output upper limit of each wind farm in actual operation to achieve the goal of reducing power restrictions and improving the utilization rate of transmission lines.

[0041] In summary, this embodiment deeply explores the output characteristics of each wind farm, combines theoretical calculation and practical verification, and proposes an effective active power control allocation algorithm, which significantly reduces the wind curtailment rate when the wind farm group is simultaneously power-limited.

[0042] Example 5 In another preferred embodiment, based on Embodiment 4 and as a further supplement and verification of Embodiment 4, this embodiment takes into account the changes in wind farm output characteristics in different wind speed seasons and designs a corresponding active power control allocation strategy.

[0043] In the high wind season (October to March of the following year), since the wind speed differences among wind farms A, B, and C are small, they can easily reach high power generation at the same time. At this time, the output upper limit of wind farm A can be appropriately increased to fully utilize the capacity of the transmission channel; at the same time, the output of wind farms B and C should be closely monitored to ensure that the total output does not exceed the capacity limit of the transmission line.

[0044] In the low wind season (April to September), since the wind speeds at wind farms B and C are significantly lower and the probability of low-level output is higher, priority should be given to ensuring the output stability of wind farms B and C to avoid frequent power rationing. At the same time, according to the actual output of wind farm A, its output upper limit should be flexibly adjusted to maximize the overall power generation.

[0045] Through the implementation of the above strategies, this embodiment further verifies the effectiveness and practicability of the active power control allocation method proposed in the present invention, and provides strong technical support for the optimized operation of wind farm groups.

[0046] In the preferred solution, the data preprocessing in Step 1 includes data cleaning, data verification and data normalization; the above settings are intended to ensure the quality and consistency of the input data and lay a solid foundation for subsequent analysis; data cleaning removes invalid and outliers, data verification verifies the accuracy of information, and data normalization adjusts the data scale to improve algorithm efficiency and accuracy.

[0047] In the preferred solution, the output probability distribution prediction in Step 2 adopts a time series prediction model or a machine learning prediction model; the above settings can achieve accurate prediction of future output conditions and improve the reliability and accuracy of the prediction results; at the same time, the solution also takes into account the robustness and adaptability of the model to ensure stable prediction performance under different working conditions.

[0048] In the preferred solution, the active power control allocation model in Step 3 also considers the mutual influence and synergy between wind farms; the above settings further improve the stability and efficiency of wind farms in complex power grid environments, ensuring that each wind farm can be independently adjusted and coordinated as a whole when outputting electricity, thereby achieving optimal allocation of resources.

[0049] In the preferred solution, the optimization algorithm in Step 4 adopts a genetic algorithm, a particle swarm algorithm or a combination thereof; the above setting automatically searches for the optimal solution in the search space by simulating biological evolution or group intelligence behavior, effectively improving the model training efficiency and prediction accuracy, and achieving fast and high-quality solutions to complex problems.

[0050] In the preferred solution, the real-time control and adjustment in Step 5 also includes a monitoring system that monitors the output of each wind farm and the load of the transmission channel in real time, and adjusts the output distribution plan according to the actual situation; the above settings can ensure that the wind farm operates efficiently under different weather and grid demands, reduce energy waste, and ensure grid stability; in addition, the system will also record and analyze data to provide a scientific basis for future wind farm optimization.

[0051] In the preferred scheme, the method also includes considering the impact of monsoon activity and seasonal changes in wind speed on the output of wind farms; the above settings can significantly improve the operating efficiency of wind farms and the accuracy of energy forecasts, ensure effective scheduling during the strong monsoon period and peak wind speed period, and reduce operation and maintenance costs and energy waste in seasons with weaker wind speeds.

[0052] In the preferred solution, the control system also includes an optimization module, which is used to optimize the active power control distribution method after analyzing the output characteristics of each wind farm; the above settings can significantly improve the overall operating efficiency of the wind farm group and reduce energy waste; the optimization module also has adaptive learning capabilities, and can continuously iterate the algorithm according to actual operating data to ensure that the control strategy is always in the optimal state.

[0053] In the preferred solution, the control system also includes a monitoring module for continuously monitoring the output of the wind farm group and adjusting the active power control allocation method according to real-time monitoring data; the above settings can ensure the stable operation of the wind farm group under various meteorological conditions and power grid requirements, optimize energy output, and at the same time improve the overall energy efficiency of the wind farm group and the acceptance capacity of the power grid.

[0054] In summary, the active power control allocation method and system for reducing the wind abandonment rate when a group of wind farms are simultaneously limited in power proposed in the present invention successfully solves the problem of high wind abandonment rate of a group of wind farms when simultaneous power limitation occurs, targeting the power limitation caused by the overall installed capacity being greater than the transmission channel capacity when a wind farm is temporarily connected to the power grid system through other wind farms. Different from the simple average allocation strategy or priority-based allocation strategy in the prior art, the present invention introduces an innovative solution, which uses the actual output annual historical data of each wind farm to calculate the distribution probability of the output size of each wind farm. , and constructed a mathematical model based on the output characteristics of wind farms and the capacity constraints of transmission channels; by solving this model, the output uncertainty of wind farms, seasonal changes in wind speed, wind direction characteristics and capacity constraints of transmission channels can be comprehensively considered to obtain the optimal output allocation plan for each wind farm; this method not only overcomes the limitation of the existing technology that wind farm groups waste wind resources due to the lack of effective scheduling strategies when power is limited in wind farm groups, but also is novel in the field of active power control and allocation of wind farm groups, and provides new ideas for reducing the wind abandonment rate and improving the utilization rate of wind power resources.

[0055] In practical applications, the scheme conducts an in-depth analysis of the output characteristics of wind farms, including the statistics of the output probabilities of the three wind farms A, B, and C, taking into account the changes in wind speed in different seasons and the uncertainty of the total output of wind farms; through solving the mathematical model, the optimal output allocation plan for each wind farm is obtained while ensuring that the transmission channel is not overloaded; the results show that under the premise of ensuring that the output of the line connection point does not exceed the capacity limit of the transmission channel (such as 1600MW), giving priority to ensuring the maximum output of wind farm A can significantly reduce power restrictions and achieve efficient utilization of wind power resources; this scheme has high operability and practicality in practical applications, can provide strong support for the operation and management of wind farm groups, and significantly improve the power generation efficiency and economic benefits of wind farm groups.

Claims

1. An active power control and allocation method for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, characterized in that: The following steps are involved: Step 1: Data collection and preprocessing: collect historical output data and weather forecast information of each wind farm and perform preprocessing; Step 2: Output probability distribution prediction: Based on the preprocessed data, the output of each wind farm in the future is predicted and its output probability distribution is calculated; Step 3: Construct an active power control allocation model. Combined with the carrying capacity of the transmission channel, the active power control allocation model is constructed with the goal of minimizing the power limit. Step 4: Solve the optimal output allocation plan, use the optimization algorithm to solve the constructed active power control allocation model, and obtain the optimal output allocation plan of each wind farm under power restriction; Step 5: Real-time control and adjustment: Based on the optimal output allocation solution obtained, the output of each wind farm is controlled and adjusted in real time.

2. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited in power according to claim 1, characterized in that: The data preprocessing in Step 1 includes data cleaning, data verification and data normalization.

3. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited according to claim 1, characterized in that: The output probability distribution prediction in Step 2 adopts a time series prediction model or a machine learning prediction model.

4. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited according to claim 1, characterized in that: The active power control allocation model in Step 3 also considers the mutual influence and synergy between wind farms, as follows: Theoretical power generation It is expressed as: （1）； Actual power generation It is expressed as: （2）； Power limit It is expressed as: （3）； In the formula, The output size of the wind farm; is the probability corresponding to different treatments; is the number of hours in a natural year, which is a constant; It is the upper limit of wind farm output.

5. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited according to claim 1, characterized in that: The optimization algorithm in Step 4 adopts genetic algorithm, particle swarm algorithm or a combination thereof.

6. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited according to claim 1, characterized in that: The real-time control and adjustment in Step 5 also includes the monitoring system monitoring the output of each wind farm and the load of the transmission channel in real time, and adjusting the output distribution plan according to the actual situation.

7. The active power control and distribution method for reducing wind abandonment rate when wind farms are simultaneously limited according to claim 1, characterized in that: The method also includes considering the impact of monsoon activity and seasonal changes in wind speed on wind farm output.

8. An active power control system for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited, characterized in that: The method for controlling and distributing active power for reducing the wind abandonment rate when a group of wind farms are simultaneously power-limited is used to implement any one of claims 1 to 7, wherein the control system comprises: Data collection module, used to collect historical output data and weather forecast information of each wind farm; A data preprocessing module is used to preprocess the collected data; The output probability distribution prediction module is used to predict the output of each wind farm in the future and calculate its output probability distribution; Active power control allocation model building module, used to build active power control allocation model based on the carrying capacity of the transmission channel and with the goal of minimizing power limit; The optimal output allocation solution module is used to solve the constructed active power control allocation model using an optimization algorithm to obtain the optimal output allocation solution for each wind farm under power restriction conditions; The real-time control and adjustment module is used to control and adjust the output of each wind farm in real time according to the optimal output allocation plan obtained by solving.

9. The active power control system for reducing wind abandonment rate when wind farms are simultaneously limited in power according to claim 8, characterized in that: The control system further comprises an optimization module for optimizing the active power control distribution method after analyzing the output characteristics of each wind farm.

10. The active power control system for reducing wind abandonment rate when wind farms are simultaneously power-limited according to claim 8, characterized in that: The control system also includes a monitoring module for continuously monitoring the output of the wind farm group and adjusting the active power control allocation method according to real-time monitoring data.

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