Cluster power regulation and control method and system based on dynamic weight distribution and adjustable granularity calculation

Abstract

The invention relates to the technical field of new energy power generation, in particular to a cluster power regulation and control method and system based on dynamic weight distribution and adjustable granularity calculation, and the method comprises the steps: obtaining basic parameters of each new energy power station, and collecting the real-time operation state data of each power station; constructing an adjustable granularity quantitative model of each power station according to the real-time operation state data, wherein the adjustable granularity quantitative model is a quantitative evaluation model for dynamic adjustment capability of different power stations obtained according to the predicted photovoltaic output and a power grid load prediction curve; a dynamic regulation and control weight model of each power station is obtained according to the adjustable granularity quantification model, the regulation and control quantity distributed by each power station is obtained in combination with the dynamic regulation and control weight model, constraint verification is carried out on the regulation and control matrix according to the actual regulation and control capacity of each power station, and if an excess regulation and control quantity exists, secondary distribution is carried out by adopting an excess redistribution method; according to the efficient and dynamic cluster power regulation and control method provided by the invention, accurate splitting of total target output to each station is realized by quantifying the station regulation capability, dynamically allocating the weight and combining a punishment mechanism, and meanwhile, safe operation of a power grid is ensured.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to a cluster power regulation method and system based on dynamic weight allocation and adjustable granularity calculation. Background Technology

[0002] Driven by carbon neutrality, the energy system is undergoing profound changes. The power system's transition to low-carbon development requires three stages: peak carbon emissions, deep decarbonization, and zero carbon emissions. During this process, the proportion of installed capacity and power generation from new energy sources is gradually increasing, while the proportion of traditional power generation is decreasing. However, new energy power generation clusters suffer from weak synchronization and coordination issues, such as increased grid instability risks, low operating efficiency, and severe wind and solar power curtailment. This makes the formulation and implementation of new energy power plant cluster control strategies particularly important. New energy power plant cluster control strategies refer to a series of measures and methods used to optimize the access and utilization of new energy power plant clusters, coordinating the operation of various power plants and equipment within the cluster through technical, market, and administrative means to achieve safe, efficient, and stable power generation.

[0003] However, traditional cluster power regulation methods rely on fixed weight allocation, failing to consider the real-time status of power plant equipment, resulting in low regulation efficiency. Furthermore, fixed weight allocation cannot be dynamically adjusted according to the actual situation of the power plant, failing to meet the regulation needs of different power plants at different times. Conventional linear normalization methods do not incorporate the response characteristics of the equipment, easily leading to over-adjustment or insufficient regulation, thus failing to achieve precise power regulation. Existing cluster regulation methods have shortcomings in calculating the undetermined regulation quantities, lacking comprehensive consideration of factors such as real-time power generation and installed capacity, which affects the rationality of the regulation strategy. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a cluster power control method based on dynamic weight allocation and adjustable granularity calculation to solve the problem of inaccurate allocation of total target output to each station. This invention also provides a cluster power control system based on dynamic weight allocation and adjustable granularity calculation.

[0005] A first aspect of the present invention provides a cluster power control method based on dynamic weight allocation and adjustable granularity calculation, the method comprising: Acquire basic parameters of each new energy power station and collect real-time operating status data of each power station; Based on the real-time operating status data, an adjustable granularity quantitative model is constructed for each power station. The adjustable granularity quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained from the predicted photovoltaic output and grid load prediction curves. Based on the adjustable granularity quantitative model, a dynamic control weight model for each power station is obtained. Combined with the dynamic control weight model, the control amount allocated to each power station is obtained. Based on the actual control capability of each power station, the control matrix is ​​constrained and verified. If there is an excess control amount, a secondary allocation is performed using the excess redistribution method. Determine whether the conditions for completing the control and allocation have been met. If not, execute the control strategy repeatedly. If so, output the final control strategy.

[0006] Furthermore, including: The adjustable particle quantization model includes an up-regulation direction unit, specifically: The adjustable output of the current power station is determined based on the maximum output of the startable inverters and the maximum output of the adjustable inverters of the new energy power station. The maximum output of the startable inverters is the maximum power value of the inverters that are currently off when turned on, and the current sample inverter power is used as the theoretical maximum power. The maximum output of the adjustable inverters is the theoretical maximum power of the adjustable inverters, using the current sample inverter power as the theoretical maximum power. The theoretical maximum output is obtained by calculating the output based on the current state of each inverter in the power station. The upward adjustment direction unit of the adjustable particle size coefficient is determined based on the current adjustable output, theoretical maximum output, and total output of the power station.

[0007] Furthermore, including: The upward adjustment direction unit, which determines the adjustable granularity coefficient based on the current adjustable output, theoretical maximum output, and total output of the power station, is expressed as follows: ; Among them, P i To theoretically increase the output, the following formula is used to calculate the potential increase based on the output of each inverter in the current power station: ; in, This is the maximum output of the inverter that can currently be started. For the maximum output of the adjustable inverter, This represents the current output value of the inverter. For the first i Capacity factor of each power station.

[0008] Furthermore, including: The adjustable particle size quantization model also includes a down-adjustment direction unit, specifically: The minimum output of the shutdown inverter is represented by the output value after the shutdown of the switchable / startable inverter. The minimum output of the adjustable inverter is represented by the minimum output of the adjustable inverter. The current output of the unadjustable inverter is determined based on the template inverter and the uncontrollable inverter, thereby obtaining the minimum output of the power station. The unit represents the downward adjustment direction based on the minimum output and theoretically adjustable output of the power station.

[0009] Furthermore, including: The process of obtaining the dynamic control weight model for each power station based on the adjustable granularity quantification model includes: The capacity weight ratio of each power station is determined based on the capacity adjustment factor and the corresponding installed capacity of each power station. A dynamic control weight model is then established based on the capacity weight ratio and the adjustable granularity representation. The adjustable granularity is the value obtained by inputting the collected real-time operating status data into the adjustable granularity quantification model.

[0010] Furthermore, including: The process of constructing a control matrix by combining the dynamic control weight model to obtain the control amount allocated to each station includes: The controlled output value of each power station is obtained by multiplying the total target output of the cluster control by the normalized control weight ratio of each power station. .

[0011] Furthermore, including: The constraint verification of the control matrix based on the actual control capabilities of each power station includes: Real-time acquisition of control limits for each site With minimum output During the weight allocation phase, after the superimposed constraints are adjusted and regulated, the initial allocation quota for each site is expressed as: ;in, This refers to the upper limit of the power plant's output for regulation. This is the minimum output of the power station. This is for the current power plant's output.

[0012] Furthermore, including: If there is an excess control amount, a secondary allocation will be performed using the excess redistribution method, including: Based on the total adjustable quota for each station and the initial allocated quota for each station. The difference determines the remaining adjustable allowance for each power station; If the current power station's excess quota is greater than the remaining adjustable quota, then the maximum adjustable quota for each station will be allocated to the full amount based on the remaining adjustable quota. Otherwise, if the current power station's excess quota is less than or equal to the remaining adjustable quota, then the remaining adjustable quota will be allocated to each power station with a new proportion. Thus, the adjustment quota for each station is the sum of the initial quota and the newly allocated quota for each station, ensuring that the total amount does not exceed the total remaining adjustable capacity of the system.

[0013] On the other hand, the present invention also provides a cluster power control system based on dynamic weight allocation and adjustable granularity calculation, the system comprising: The data acquisition module is used to obtain the basic parameters of each new energy power station, as well as to collect the real-time operating status data of each power station. The quantitative model construction module is used to construct an adjustable granular quantitative model for each power station based on the real-time operating status data. The adjustable granular quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained based on the predicted photovoltaic output and grid load prediction curves. The secondary allocation module is used to obtain the dynamic control weight model of each power station according to the adjustable granularity quantification model, obtain the control amount allocated to each power station in combination with the dynamic control weight model, and perform constraint verification on the control matrix according to the actual control capacity of each power station. If there is an excess control amount, the excess redistribution method is used for secondary allocation. The judgment module is used to determine whether the conditions for completing the control and allocation have been met. If not, the control strategy is executed repeatedly. If so, the final control strategy is output.

[0014] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the above-described cluster power control method based on dynamic weight allocation and adjustable granularity calculation.

[0015] Advantages of this invention: 1. This invention introduces a dynamic weight allocation algorithm. Each time a strategy is allocated, the adjustable granularity needs to be recalculated based on the current situation, rather than being a fixed value. Combined with the dynamic calculation of the capacity adjustment factor and the adjustable granularity, this enables refined control of each power station. Compared to traditional methods that rely on fixed weight allocation, this approach can more accurately consider the real-time status of equipment, such as inverter adjustability margin and communication anomalies, significantly improving regulation efficiency.

[0016] 2. This invention employs an adjustable granularity quantitative model, which achieves a quantitative assessment of the dynamic adjustment capabilities of different power plants by calculating the adjustable granularity ratio of the power plant in real time. This method can flexibly adjust the control ratio based on real-time data such as the actual power generation and installed capacity of the power plant, effectively solving the problem of unreasonable control caused by fixed weight allocation in traditional methods.

[0017] 3. This invention replaces the constraint and adaptive mechanism of a fixed fine-tuning threshold with real-time dynamic limits, namely the upper limit of regulation P_max and the minimum output P_min. This allows for real-time adjustment of the regulation range based on the actual regulation capability of the power plant, greatly improving the flexibility of regulation. This adaptive mechanism effectively solves the problem of overall deviation caused by single-station limits in traditional methods.

[0018] 4. This invention employs an excess redistribution algorithm, which performs secondary allocation based on the proportion of remaining adjustable capacity at other sites, ensuring the strict achievement of the total target output. This intelligent resource allocation mechanism significantly improves the overall control efficiency of the cluster. Attached Figure Description

[0019] Figure 1 This is a flowchart of the cluster power control method based on dynamic weight allocation and adjustable granularity calculation as described in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the overall process of the cluster control strategy described in this embodiment of the invention. Figure 3 This is a flowchart of the control algorithm described in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, this embodiment provides a cluster power regulation method based on dynamic weight allocation and adjustable granularity calculation. It offers an efficient and dynamic cluster power regulation method that, by quantifying the site's adjustment capability, dynamically allocating weights, and combining a penalty mechanism, achieves precise allocation of the total target output to each site while ensuring the safe operation of the power grid. The method includes the following steps: Step 1: Obtain the basic parameters of each new energy power station and collect real-time operating status data of each power station; Step 2: Construct an adjustable granular quantitative model for each power station based on the real-time operating status data. The adjustable granular quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained from the predicted photovoltaic output and grid load prediction curves.

[0022] In this embodiment, the adjustable particle size quantization model includes an up-adjustment direction unit, specifically: The adjustable output of the current power station is determined based on the maximum output of the startable inverters and the maximum output of the adjustable inverters of the new energy power station. The maximum output of the startable inverters is the maximum power value of the inverters that are currently off when turned on, and the current sample inverter power is used as the theoretical maximum power. The maximum output of the adjustable inverters is the theoretical maximum power of the adjustable inverters, using the current sample inverter power as the theoretical maximum power. The theoretical maximum output is obtained by calculating the output based on the current state of each inverter in the power station. The upward adjustment direction unit of the adjustable particle size coefficient is determined based on the current adjustable output, theoretical maximum output, and total output of the power station.

[0023] The upward adjustment direction unit of the adjustable granularity coefficient is determined based on the current adjustable output, theoretical maximum output, and total output of the power station, and is expressed as follows: ; Among them, P i To theoretically increase the output, the following formula is used to calculate the potential increase based on the output of each inverter in the current power station: ; Among them, P oimax P is the maximum output of the inverter that can currently be started. aimax For the maximum output of the adjustable inverter, P currenti This represents the current output value of the inverter. For the first i Capacity factor of each power station.

[0024] The adjustable particle size quantization model also includes a down-adjustment direction unit, specifically: The minimum output of the shutdown inverter is represented by the output value after the shutdown of the switchable / startable inverter. The minimum output of the adjustable inverter is represented by the minimum output of the adjustable inverter. The current output of the unadjustable inverter is determined based on the template inverter and the uncontrollable inverter, thereby obtaining the minimum output of the power station. The unit represents the downward adjustment direction based on the minimum output and theoretically adjustable output of the power station.

[0025] Step 3: Obtain the dynamic control weight model for each power station based on the adjustable granularity quantification model, and obtain the control amount allocated to each power station in combination with the dynamic control weight model. Based on the actual control capacity of each power station, perform constraint verification on the control matrix. If there is an excess control amount, use the excess redistribution method for secondary allocation.

[0026] In this embodiment, obtaining the dynamic control weight model of each power station based on the adjustable granularity quantification model includes: determining the capacity weight ratio of each power station based on the capacity adjustment factor and the corresponding installed capacity of each power station; and representing the dynamic control weight model based on the capacity weight ratio and the adjustable granularity, wherein the adjustable granularity is the value obtained by inputting the collected real-time operating status data into the adjustable granularity quantification model.

[0027] By combining the aforementioned dynamic control weight model, a control matrix is ​​constructed, thereby obtaining the control amounts allocated to each station, including: The controlled output value of each power station is obtained by multiplying the total target output of the cluster control by the normalized control weight ratio of each power station. .

[0028] The constraint verification of the control matrix based on the actual control capabilities of each power station includes: Real-time acquisition of control limits for each site With minimum output During the weight allocation phase, after the superimposed constraints are adjusted and regulated, the initial allocation quota for each site is expressed as: ;in, This refers to the upper limit of the power plant's output for regulation. This is the minimum output of the power station. This is for the current power plant's output.

[0029] If there is an excess control amount, a secondary allocation will be performed using the excess redistribution method, including: Based on the total adjustable quota for each station and the initial allocated quota for each station. The difference determines the remaining adjustable allowance for each power station; If the current power station's excess quota is greater than the remaining adjustable quota, then the maximum adjustable quota for each station will be allocated to the full amount based on the remaining adjustable quota. Otherwise, if the excess amount of the previous power station is less than or equal to the remaining adjustable amount, the remaining adjustable amount will be allocated to each power station according to a new ratio. Thus, the adjustment quota for each station is the sum of the initial quota and the newly allocated quota for each station, ensuring that the total amount does not exceed the total remaining adjustable capacity of the system.

[0030] Step 4: Determine whether the conditions for completing the control and allocation have been met. If not, execute the control strategy repeatedly. If yes, output the final control strategy.

[0031] To demonstrate the effectiveness of this application, the above control methods are explained in more detail, such as... Figure 2 As shown, it includes the following steps: Step 1: Initialize the basic parameters of each power station and collect the operating data of each power station; Step 2: Perform adjustable particle size calculations for each power station; Step 3: Calculate the dynamic control weights for each power station; Step 4: Perform power regulation and allocation according to the strategy; Step 5: Determine whether the conditions for completing the control and allocation have been met. If not, continue with the control and allocation process. If yes, proceed to step 6. Step 6: Output the final power allocation strategy result and issue the control command.

[0032] Step 1 includes: Step 101: Obtain basic parameters such as the theoretical installed capacity, real-time power generation, sample inverter power value, and upper and lower limits of adjustable inverter power for each power station, and initialize the power station capacity adjustment factor. Among them, the capacity factor refers to the fact that the actual maximum output of some power plants cannot reach the theoretical power plant capacity value, and it is necessary to configure a percentage to make the maximum output calculation more reasonable, with a maximum value of 1. Step 102: Collect real-time operating status data of each power station, including the status of inverters that can be started / stopped, communication status, and power station adjustability margin, etc. Step 2 includes: Adjustable granularity quantification model: dynamically calculating the adjustable granularity coefficient of a site The range is 0 to 1, divided into upward and downward adjustment, which quantifies its flexible adjustment capability.

[0033] For example, if the current power output of the power cluster is 60MW, and the master station issues a control target of 50MW to the cluster, then the control direction of the power plants within the entire cluster will be downward, and the adjustment amount will be -10MW. Conversely, if the current value is 40MW and the target value is 60MW, then the overall value should be increased by 20MW to ensure accurate and flexible scheduling. Specific implementation includes: Direction of increase: Parameter: P i To theoretically increase the output, the following formula is used to calculate the potential increase based on the output of each inverter in the current power station:

[0034] In other words, this summation formula is the sum of the values ​​of each adjustable inverter and the current value of each inverter itself.

[0035] Among them, power station inverters are divided into the following types:

[0036] Maximum output of currently operable inverters: This refers to the inverters that are currently off. The maximum power value of these inverters when they are turned on is taken as the theoretical maximum power value using the power of the current sample inverter. Maximum output of adjustable inverter: The power of the current prototype inverter is used as the theoretical maximum power of the adjustable inverter.

[0037] P i The theoretical maximum output of the power station is calculated based on the status of each inverter in the power station, as shown in the following formula:

[0038] Maximum output of an inverter that can be turned on / off: This refers to the maximum power value of an inverter that can be turned on / off. The power of the current sample inverter is used as the theoretical maximum power. Unadjustable inverter range: Includes sample inverters and unadjustable inverters (communication abnormality); Maximum output of adjustable inverter: The power of the current prototype inverter is used as the theoretical maximum power of the adjustable inverter.

[0039] Downward adjustment direction: ; parameter: Minimum output P of the power plant j The calculation requires determining the output of the power plant's currently shut-down inverters, the current output of the non-adjustable inverters, and the minimum output of the adjustable inverters, as shown in the following formula:

[0040] in: Uncontrollable inverters include prototype inverters and uncontrollable inverters (communication errors); The minimum output of a stopable inverter refers to the output value after shutdown. Minimum output of adjustable inverter: refers to the minimum output of the adjustable inverter. Once this value is reached, it cannot be adjusted downwards. Theoretically, the output P can be reduced. i The following formula is used to calculate the difference between the current output and the minimum output of the adjustable inverter, plus the current output of the stopable inverter:

[0041] Among them: due to the involvement of adjustable inverter output, the power station capacity factor calculation is increased to reduce control error.

[0042] Step 3 includes: Step 301: Calculate the capacity adjustment factor for each power station based on the parameters obtained in Step 1. ; Step 302: Calculate the adjustable granularity of each power station based on real-time operating status data. ; The calculation of dynamic weight allocation consists of two steps. Step 1: Calculate the capacity weight ratio W for each power station. i The calculation formula is: ; Step 2: Incorporating adjustable granularity G i Calculate the control ratio R allocated to each power station. i : .

[0043] Step 4, as described Figure 3 As shown, it includes: Step 401: Construct an initial control matrix based on step 3, and use the initial control matrix to adjust the control ratio R. i Normalization is performed because the control ratio is calculated by division, which may result in incomplete division and approximation. Without normalization, the sum of the ratio values ​​may be greater than or less than 1. Normalization ensures that the sum of the control ratios of each station is 1.

[0044] After calculating the proportions for each station, normalization verification is required to calculate the control output value for each station. .

[0045] ; in: To contribute to the overall goal of cluster regulation; This is the normalized value of the control weight calculated in the above steps; Step 5 includes: Step 501: Based on the actual control capabilities of each power station, calculate the dynamic limit and perform constraint verification on the control matrix; Dynamic constraint definition: Real-time acquisition of control limits for each site With minimum output .

[0046] Adaptive allocation verification: During the weight allocation phase, constraint correction adjustments are applied. ; The positive value for upward adjustment is the adjustable granularity value obtained above for the upward adjustment direction, and the negative value for downward adjustment is the same as the adjustable granularity value obtained above for the upward adjustment direction.

[0047] Among them, the upper limit of power plant control output value The real-time calculation formula is as follows: ; Among them, minimum output This is a power plant configuration item, with a default setting of 10% of the power plant capacity. (Parameters) This is for the current power plant's output.

[0048] For example, the actual amount of regulation cannot exceed the current regulation capacity of the power plant. For instance, if the current total power of the power plant is 10MW and the theoretical maximum power of the power plant is 11MW, the upward adjustment cannot exceed 1MW. If the allocated upward adjustment amount is 2MW, it needs to be constrained to 1MW, and the remaining amount cannot be adjusted upwards. The downward adjustment is subject to similar constraints.

[0049] Step 502: If there is excess control, then the excess redistribution algorithm is used for secondary allocation; The excess redistribution algorithm includes: This refers to the amount exceeding the limit.

[0050] We need to calculate the remaining adjustable quota for each station. ; in: This refers to the adjustable quota for each site. Greater than Then the redistribution of quotas will adopt Allocate the maximum adjustable amount to each station; if Less than Then, the remaining adjustable quota will be allocated to each station according to the new proportion, as follows: ; in: This refers to the remaining adjustable quota for each station; This refers to the amount of each newly added allocation; After applying the above-described excess redistribution algorithm, the previously allocated quota for each station is added to the newly allocated quota to obtain the final control quota for each station; that is, the final control quota is the sum of the initial allocated quota and the newly allocated quota for each station, ensuring that the total amount does not exceed the total remaining adjustable capacity of the system. This method takes into account both the adjustment potential of each station and the overall balance, improving the fairness of allocation and the utilization rate of resources.

[0051] Step 6 includes: Step 601: Output the final control strategy and issue the control command for this operation; Step 602: Collect actual control results; Step 603: Calculate the new round of control parameters and prepare for the next round of control.

[0052] This embodiment provides the following specific examples: 1. Control Process 1) Real-time data acquisition: Obtain the inverter status, output value and communication status of each site.

[0053] 2) Target output breakdown: Allocate the total control amount to each station according to dynamic weights and adjustable granularity.

[0054] 2. Implementation Examples Example 1: Computing an adjustable granularity quantization model: A certain parameter setting:

[0055]

[0056] 1) Verification of the formula for adjusting the direction upwards Calculation steps: Maximum output of adjustable inverter: Based on the current output of the sample inverter of 400 kW, the maximum output of each adjustable inverter is 400 kW.

[0057] Step 1: Molecular Calculations Maximum output of the inverter that can be started: 2 × 400 = 800 kW Adjustable inverter adjustable space: ∑=(400−220)×0.9+(400−250)×0.9+(400−270)×0.9+(400−230)×0.9+(400−260)×0.9=693 kW Total molecule: 800 + 693 = 1493 kW Step 2: Denominator Calculation Theoretical maximum output of the power plant: Start-up capacity = 2 × 400 = 800 kW, Adjustable capacity = 5 × 400 = 2000 kW; Shutdown capacity =340 kW; Uncontrollable = 400 + 300 = 700 kW. Total theoretical maximum output: 800 + 2000 + 340 + 700 = 3840 kW Current total output: 400 + 300 + 0 + (220 + 250 + 270 + 230 + 260) + 340 = 2270 kW Calculate the total denominator: 3840 − 2270 = 1570 kW Step 3: Calculate the particle size results:

[0058] 2) Verification of the formula for adjusting the direction downwards Calculation steps: Step 1: Molecular Calculations Current output of the stoptable inverter: 340 kW Adjustable inverter adjustable space: ∑=(220−10)×0.9+(250−10)×0.9+(270−10)×0.9+(230−10)×0.9+(260−10)×0.9 =1062 kW Total molecule: 340 + 1062 = 1402 kW Step 2: Denominator Calculation: Minimum power output of the power plant: Shutdownable = 0 kW (assuming it can be shut down to 0); Adjustable = 5 × 10 = 50 kW; Uncontrollable = 400 + 300 = 700 kW; Total minimum output: 0 + 50 + 700 = 750 kW Denominator: 2270 − 750 = 1520 kW Step 3: Calculate the adjustable particle size result: ; Example 2: Examples of Control Strategies Assuming there are four sites, the relevant data is as follows: Note: Installed capacity, capacity adjustment factor, penalty coefficient, and minimum output are pre-configured; adjustable granularity, current actual power, current adjustable power, and current adjustable power are calculated in real time.

[0059] Power regulation requirement: Total power reduction ΔPtotal = −30MW (negative value indicates reduction, the target value is compared with the current total power to obtain the secondary value).

[0060] Based on the above data and a fast adjustment and allocation algorithm as an example: Step 1: Set the target for reducing the total power by 30 MW.

[0061] Step 2: Calculate the weight allocation using the formula: The weights of sites A / B / C / D are 0.223 / 0.331 / 0.331 / 0.116 respectively.

[0062] Step 3: Calculate based on the control ratio: After normalization, RA=0.250, RB=0.279, RC=0.325, RD=0.146.

[0063] Step 4: Power needs to be reduced at sites A / B / C / D by -7.5 / -8.37 / -9.75 / -4.38 respectively. Step 5: Determine if the allocated output is below the lower limit. Control measures and constraint judgments for each station: ΔPA = −7.5MW (Maximum reduction of 5MW; any amount exceeding 2.5MW needs to be redistributed) ΔPB = −8.37MW (Maximum reduction of 15MW, meeting the reduction limit) ΔPC = −9.75MW (Maximum reduction of 16MW, meeting the reduction limit) ΔPD = −4.38MW (Maximum reduction of 5MW, meeting the reduction limit) Step Six: Based on calculations, it is determined that Station A has an excess capacity of 2.5 MW and needs to allocate the remaining capacity to Stations B / C / D. Proceed back to Step One and reduce the total power to 2.5 MW. Recalculate the parameters for Stations B / C / D as follows: Remaining capacity at site B: 15 − 8.37 = 6.63 MW; Remaining capacity of site C: 16 − 9.75 = 6.25 MW; Remaining capacity of site D: 5 − 4.38 = 0.62 MW; Allocation rule: The excess 2.5MW will be allocated according to the proportion of remaining capacity.

[0064] Total remaining capacity: 6.63 + 6.25 + 0.62 = 13.5 MW; If the total remaining capacity is greater than 2.5MW, and the excess portion exceeds the total remaining capacity, the maximum value shall be the total remaining capacity.

[0065] Calculate the allocation ratio according to the formula:

[0066] Step 7: Calculate the final allocation and determine whether each station is below the lower limit. ΔPA = −5MW (Maximum reduction of 5MW, meeting the reduction limit) ΔPB = −8.37 - 1.23 = -9.6MW (Maximum reduction of 15MW, meeting the reduction limit) ΔPC = −9.75 - 1.16 = -10.91MW (Maximum reduction of 16MW, meeting the reduction limit) ΔPD = −4.38 - 0.11 = -4.49MW (Maximum reduction of 5MW, meeting the reduction limit) Step 8: Verify whether the sum meets the commissioning requirements; −5−9.6−10.91−4.49=−30 MW.

[0067] This embodiment also provides a cluster power control system based on dynamic weight allocation and adjustable granularity calculation, the system including: The data acquisition module is used to obtain the basic parameters of each new energy power station, as well as to collect the real-time operating status data of each power station. The quantitative model construction module is used to construct an adjustable granular quantitative model for each power station based on the real-time operating status data. The adjustable granular quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained based on the predicted photovoltaic output and grid load prediction curves. The secondary allocation module is used to obtain the dynamic control weight model of each power station according to the adjustable granularity quantification model, obtain the control amount allocated to each power station in combination with the dynamic control weight model, and perform constraint verification on the control matrix according to the actual control capacity of each power station. If there is an excess control amount, the excess redistribution method is used for secondary allocation. The judgment module is used to determine whether the conditions for completing the control and allocation have been met. If not, the control strategy is executed repeatedly. If so, the final control strategy is output.

[0068] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the above-described cluster power control method based on dynamic weight allocation and adjustable granularity calculation.

[0069] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cluster power control method based on dynamic weight allocation and adjustable granularity calculation, characterized in that, The method includes: Acquire basic parameters of each new energy power station and collect real-time operating status data of each power station; Based on the real-time operating status data, an adjustable granular quantitative model is constructed for each power station. The adjustable granular quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained from the predicted photovoltaic output and grid load prediction curves. The dynamic control weight model of each power station is obtained based on the adjustable granularity quantification model. The control amount allocated to each power station is obtained by combining the dynamic control weight model. The control matrix is ​​constrained and verified according to the actual control capacity of each power station. If there is an excess control amount, the excess redistribution method is used for secondary allocation. Determine whether the conditions for completing the control and allocation have been met. If not, execute the control strategy repeatedly. If so, output the final control strategy.

2. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 1, characterized in that, The adjustable particle quantization model includes an up-regulation direction unit, specifically: The adjustable output of the current power station is determined based on the maximum output of the startable inverters and the maximum output of the adjustable inverters of the new energy power station. The maximum output of the startable inverters is the maximum power value of the inverters that are currently in the off state when they are turned on. The power of the current sample inverter is used as the theoretical maximum power. The maximum output of the adjustable inverter is the theoretical maximum power of the adjustable inverter, which is based on the power of the current prototype inverter. The theoretical maximum output is obtained by calculating the output based on the current state of each inverter in the power station. The upward adjustment direction unit of the adjustable particle size coefficient is determined based on the current adjustable output, theoretical maximum output, and total output of the power station.

3. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 2, characterized in that, The upward adjustment direction unit, which determines the adjustable granularity coefficient based on the current adjustable output, theoretical maximum output, and total output of the power station, is expressed as follows: ; Among them, P i To theoretically increase the output, the following formula is used to calculate the potential increase based on the output of each inverter in the current power station: ; Among them, P oimax P is the maximum output of the inverter that can currently be started. aimax For the maximum output of the adjustable inverter, P currenti This represents the current output value of the inverter. For the first i Capacity factor of each power station.

4. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 2, characterized in that, The adjustable particle size quantization model also includes a down-adjustment direction unit, specifically: The minimum output of the shutdown inverter is represented by the output value after the shutdown of the switchable / startable inverter. The minimum output of the adjustable inverter is represented by the minimum output of the adjustable inverter. The current output of the unadjustable inverter is determined based on the template inverter and the uncontrollable inverter, thereby obtaining the minimum output of the power station. The unit represents the downward adjustment direction based on the minimum output and theoretically adjustable output of the power station.

5. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 4, characterized in that, The process of obtaining the dynamic control weight model for each power station based on the adjustable granularity quantification model includes: The capacity weight ratio of each power station is determined based on the capacity adjustment factor and the corresponding installed capacity of each power station. A dynamic control weight model is then established based on the capacity weight ratio and the adjustable granularity representation. The adjustable granularity is the value obtained by inputting the collected real-time operating status data into the adjustable granularity quantification model.

6. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 5, characterized in that, The process of constructing a control matrix by combining the dynamic control weight model to obtain the control amount allocated to each station includes: The controlled output value of each power station is obtained by multiplying the total target output of the cluster control by the normalized control weight ratio of each power station. .

7. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 6, characterized in that, The constraint verification of the control matrix based on the actual control capabilities of each power station includes: Real-time acquisition of control limits for each site With minimum output During the weight allocation phase, after the superimposed constraints are adjusted and regulated, the initial allocation quota for each site is expressed as: ; in, This refers to the upper limit of the power plant's output for regulation. This is the minimum output of the power station. This is for the current power plant's output.

8. The cluster power control method based on dynamic weight allocation and adjustable granularity calculation according to claim 7, characterized in that, If there is an excess control amount, a secondary allocation will be performed using the excess redistribution method, including: Based on the total adjustable quota for each site and the initial allocated quota for each site. The difference determines the remaining adjustable allowance for each power station; If the current power station's excess quota is greater than the remaining adjustable quota, then the maximum adjustable quota for each station will be allocated to the full amount based on the remaining adjustable quota. Otherwise, if the current power station's excess quota is less than or equal to the remaining adjustable quota, then the remaining adjustable quota will be allocated to each power station with a new proportion. Thus, the adjustment quota for each station is the sum of the initial quota and the newly allocated quota for each station, ensuring that the total amount does not exceed the total remaining adjustable capacity of the system.

9. A cluster power control system based on dynamic weight allocation and adjustable granularity calculation, characterized in that, The system includes: The data acquisition module is used to obtain the basic parameters of each new energy power station, as well as to collect the real-time operating status data of each power station. The quantitative model construction module is used to construct an adjustable granular quantitative model for each power station based on the real-time operating status data. The adjustable granular quantitative model is a quantitative evaluation model of the dynamic adjustment capability of different power stations obtained based on the predicted photovoltaic output and grid load prediction curves. The secondary allocation module is used to obtain the dynamic control weight model of each power station according to the adjustable granularity quantification model, obtain the control amount allocated to each power station in combination with the dynamic control weight model, and perform constraint verification on the control matrix according to the actual control capacity of each power station. If there is an excess control amount, the excess redistribution method is used for secondary allocation. The judgment module is used to determine whether the conditions for completing the control and allocation have been met. If not, the control strategy is executed repeatedly. If so, the final control strategy is output.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the cluster power control method based on dynamic weight allocation and adjustable granularity calculation as described in any one of claims 1 to 8.

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