Wind driven generator cluster power optimization distribution method based on congestion control

Abstract

The invention discloses a wind driven generator cluster power optimization distribution method based on congestion control, and belongs to the technical field of wind driven generator cluster control. According to the method, the power distribution problem of the wind driven generator cluster is solved by using a congestion control thought through the similarity between the power flow and the information flow, so that the optimization solution of the output of each wind driven generator in the wind driven generator cluster is realized. Firstly, a global congestion index reflecting the overall power tracking accuracy of a wind driven generator cluster is constructed, and power tracking of a wind driven generator cluster level is achieved; then, constructing a local congestion index reflecting the operation cost of each wind driven generator, and realizing power optimization distribution of a wind driven generator level, thereby reducing the overall operation cost of a wind driven generator cluster; finally, on the basis of the two congestion indexes, the output power of each wind driven generator is adjusted in a self-adaptive mode, and power optimal distribution is achieved. The method is simple in calculation, low in communication burden, plug-and-play and high in practicability.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine cluster control technology, specifically relating to a method for optimizing power allocation in wind turbine clusters based on congestion control. Background Technology

[0002] Determinism and dispatchability on the generation side are the foundation of power balance in traditional power systems. To achieve grid-friendly integration of wind turbines, power grids in various countries have gradually developed strict operating procedures aimed at smoothing out power fluctuations from wind turbines and making them contributors to power balance. Clearly, improved dispatchability means that wind turbine clusters need to track power commands issued by grid dispatchers. For a wind turbine cluster consisting of dozens or even hundreds of wind turbines, this is a challenging problem.

[0003] Currently, the main power allocation methods for wind turbine clusters can be broadly categorized into centralized and distributed power allocation methods. Centralized power allocation methods, based on global information awareness, generally achieve better optimization results, but the control center typically bears a heavy communication and computational burden. In contrast, distributed power allocation methods reduce the control center's workload by splitting computational tasks; that is, the main power allocation calculations are performed locally by the wind turbines. However, existing distributed power allocation methods struggle to balance communication demands with the optimization characteristics of the results. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention proposes a power optimization allocation method for wind turbine clusters based on congestion control. By leveraging the similarity between power flow and information flow, the method uses congestion control principles to solve the power optimization allocation problem of wind turbine clusters. This allows each wind turbine to solve its power command in a distributed manner, achieving coordinated operation of the wind turbines and avoiding complex optimization processes such as model predictive control, thus improving computational efficiency. Furthermore, since the computational burden is distributed across each wind turbine, the computational efficiency of the method is unaffected by the size of the wind turbine cluster.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for optimizing power allocation in a wind turbine cluster based on congestion control includes the following steps: Step 1: Obtain the power command for the wind turbine cluster from the superior dispatcher. ; Step 2: The control center measures the actual output power of the wind turbine cluster in real time and generates a global congestion index that reflects the deviation between the actual output power of the wind turbine cluster and the power command. This enables power tracking at the wind turbine cluster level. (1) in, This represents the global congestion index. This indicates the actual output power of the wind turbine cluster; when the actual output power of the wind turbine cluster is greater than the power command, then... ;otherwise, ; Step 3: The control center sends the global congestion index to each wind turbine. Step 4: Based on the operating status of each wind turbine, generate the local congestion index of each wind turbine to achieve power optimization allocation at the wind turbine level; for the i-th wind turbine, its local congestion index is calculated using formula (2). (2) in, This represents the local congestion index of the i-th wind turbine. This represents the output power of the i-th wind turbine. This represents the operating cost of the i-th wind turbine. The average operating cost of all wind turbines is calculated as follows: (3) Where n represents the number of wind turbines in the wind turbine cluster; Step 5: Based on the current output power of each wind turbine And calculate the output power command for the next time step using global and local congestion metrics. To achieve optimized power allocation for wind turbine clusters; (4) in, and This only indicates the order of calculation, that is, first measure the current output power of the wind turbine, and then calculate the output power command for the next moment based on the current output power.

[0006] In step 1, the power command issued by the superior dispatcher for the wind turbine cluster is an emergency control power command on a short time scale.

[0007] In step 1, the emergency control power on the short time scale is a primary frequency modulation command.

[0008] In step 3, the operational objective of the wind turbine is to minimize mechanical wear and fatigue. Therefore, the operational cost of the wind turbine is calculated as follows: (5) in, and These represent the mechanical wear weighting factor and the mechanical fatigue weighting factor, respectively. You can choose according to your preference. and These represent the pitch angle and thrust of the wind turbine, respectively. and These represent the sensitivity of the pitch angle to the wind turbine's output power and the sensitivity of the thrust to the wind turbine's output power, respectively.

[0009] Compared with the prior art, the present invention has the following advantages: 1. Low communication requirements: The control center only needs to send the global congestion index to each wind turbine. 2. Low computational load: Power allocation among wind turbines is optimized using congestion metrics, eliminating the need for complex iterative optimization processes and simplifying computation. 3. Distributed implementation and plug-and-play: The calculation of each wind turbine is carried out locally, and since the global congestion index can reflect the output changes of the wind turbine cluster, ensuring that the output power follows the command value, the method of this invention can adapt to the sudden entry and exit of wind turbines. Attached Figure Description

[0010] Figure 1 This is a flowchart of the method of the present invention.

[0011] Figure 2 This is a schematic diagram of a wind turbine cluster structure.

[0012] Figure 3 It refers to the operating wind speed of each wind turbine in the wind turbine cluster.

[0013] Figure 4 It refers to the power command of the wind turbine cluster and its tracking effect.

[0014] Figure 5 It is the effect of optimized power distribution among wind turbine generators. Detailed Implementation

[0015] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0016] like Figure 1 As shown, the present invention provides a method for optimizing power allocation of a wind turbine cluster based on congestion control, comprising the following steps: Step 1: Obtain the power command for the wind turbine cluster from the superior dispatcher. This power command is an emergency control power on a short time scale, such as a primary frequency modulation command.

[0017] Step 2: The control center measures the actual output power of the wind turbine cluster in real time and generates a global congestion index that reflects the deviation between the actual output power of the wind turbine cluster and the power command. ; (1) in, This represents the global congestion index. This indicates the actual output power of the wind turbine cluster; when the actual output power of the wind turbine cluster is greater than the power command, then... ;otherwise, ; This step achieves power tracking at the wind turbine cluster level by constructing a global congestion metric that reflects the overall power tracking accuracy of the wind turbine cluster.

[0018] Step 3: The control center distributes global congestion indicators to each wind turbine. The operational objective of the wind turbine is to minimize mechanical wear and fatigue. Therefore, the operational cost of the wind turbine is calculated as follows: (5) in, and These represent the mechanical wear weighting factor and the mechanical fatigue weighting factor, respectively. You can choose according to your preference. and These represent the pitch angle and thrust of the wind turbine, respectively. and These represent the sensitivity of the pitch angle to the wind turbine's output power and the sensitivity of the thrust to the wind turbine's output power, respectively.

[0019] Step 4: Generate the local congestion index for each wind turbine based on its operating status; for the i-th wind turbine, its local congestion index is calculated using formula (2); (2) in, This represents the local congestion index of the i-th wind turbine. This represents the output power of the i-th wind turbine. This represents the operating cost of the i-th wind turbine. The average operating cost of all wind turbines is calculated as follows: (3) Where n represents the number of wind turbines in the wind turbine cluster; This step achieves optimized power allocation at the wind turbine level by constructing a local congestion index that reflects the operating cost of each wind turbine, thereby reducing the overall operating cost of the wind turbine cluster.

[0020] Step 5: Based on the current output power of each wind turbine And calculate the output power command for the next time step using global and local congestion metrics. To achieve optimized power allocation for wind turbine clusters; (4) in, and This only indicates the order of calculation, that is, first measure the current output power of the wind turbine, and then calculate the output power command for the next moment based on the current output power. Example

[0021] To build a wind turbine cluster consisting of 25 wind turbines, such as Figure 2 As shown. The coefficients of the wind turbine operating cost function are set to [value]. , In addition, the wind speed conditions for each wind turbine are as follows: Figure 3 As shown. To verify the effectiveness of the method of the present invention, a classic centralized power allocation method in the prior art is introduced for comparison, as shown in the following formula: (6) in, Let be the maximum available power of the i-th wind turbine.

[0022] First, the power point tracking performance of the allocation method of this invention at the wind turbine cluster level was evaluated. Specifically, the wind turbine cluster initially operates in MPPT mode. Starting from the 10th second, the wind turbine cluster needs to track a specific power curve to achieve power balance in the power system. Considering various possible power dispatch commands, the power reference curve of the wind turbine cluster is designed as follows: Figure 4 The solid black line curve in the diagram consists of four stages. Specifically, these include continuous commands (stage I), ramp commands (stage III), and step commands (stages II and IV); as well as scenarios involving load reduction (stages I and II) and increased output (stages III and IV). The performance of the power allocation method and centralized power allocation method of this invention in tracking the aforementioned power commands is as follows: Figure 4 As shown in the figure. The results demonstrate that the power optimization allocation method of this invention has performance comparable to centralized power allocation methods in power tracking of wind turbine clusters. For various types of power commands, the method of this invention can track quickly and smoothly without requiring frequent two-way communication between the control center and the wind turbines as is required in centralized methods.

[0023] Furthermore, to further verify that the method of the present invention can also achieve optimized power allocation among wind turbines to reduce the cost of power tracking, the following two indicators are introduced to evaluate the performance of the method of the present invention in optimized power allocation. (7) in, Let be the cumulative action of the pitch angle of the i-th wind turbine. This represents the cumulative change in thrust of the i-th wind turbine.

[0024] Figure 5 The optimized results of the method of this invention are presented. Hollow columns and solid black columns represent the cost per wind turbine for power tracking under the average allocation scheme and the method of this invention, respectively. Figure 5 As can be seen in (a), the pitch angle cost index of the method of the present invention is much smaller than that of the average allocation method. This indicates that the method of the present invention significantly reduces the action of the wind turbine pitch control during the power tracking process by optimizing the power distribution among wind turbines, thereby alleviating mechanical wear. Similarly, from Figure 5 As shown in (b), except for a few wind turbines, the thrust cost index of the method of the present invention is lower than that of the centralized power distribution method. This means that mechanical fatigue is less under the same power point tracking task.

Claims

1. A method for optimizing power allocation in a wind turbine cluster based on congestion control, characterized in that, Includes the following steps: Step 1: Obtain the power command for the wind turbine cluster from the superior dispatcher. ; Step 2: The control center measures the actual output power of the wind turbine cluster in real time and generates a global congestion index that reflects the deviation between the actual output power of the wind turbine cluster and the power command. ; （1） in, This represents a global congestion metric. This indicates the actual output power of the wind turbine cluster; when the actual output power of the wind turbine cluster is greater than the power command, then... ;otherwise, ; Step 3: The control center sends the global congestion index to each wind turbine. Step 4: Generate the local congestion index for each wind turbine based on its operating status; for the i-th wind turbine, its local congestion index is calculated using formula (2); （2） in, This represents the local congestion index of the i-th wind turbine. This represents the output power of the i-th wind turbine. This represents the operating cost of the i-th wind turbine. The average operating cost of all wind turbines is calculated as follows: （3） Where n represents the number of wind turbines in the wind turbine cluster; Step 5: Based on the current output power of each wind turbine And calculate the output power command for the next time step using global and local congestion metrics. ; （4） in, and This only indicates the order of calculation, that is, first measure the current output power of the wind turbine, and then calculate the output power command for the next moment based on the current output power.

2. The method for optimizing power allocation of a wind turbine cluster based on congestion control as described in claim 1, characterized in that, In step 1, the power command issued by the superior dispatcher for the wind turbine cluster is an emergency control power command on a short time scale.

3. The method for optimizing power allocation of a wind turbine cluster based on congestion control as described in claim 2, characterized in that, The emergency control power on the short time scale is a primary frequency modulation command.

4. The method for optimizing power allocation of a wind turbine cluster based on congestion control as described in claim 1, characterized in that, In step 3, the operational objective of the wind turbine is to minimize mechanical wear and fatigue. Therefore, the operational cost of the wind turbine is calculated as follows: （5） in, and These represent the mechanical wear weighting factor and the mechanical fatigue weighting factor, respectively. You can choose according to your preference. and These represent the pitch angle and thrust of the wind turbine, respectively. and These represent the sensitivity of the pitch angle to the wind turbine's output power and the sensitivity of the thrust to the wind turbine's output power, respectively.

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