A new energy cluster abandoned power rate dynamic suppression scheduling method

Abstract

This invention provides a dynamic scheduling method for suppressing the curtailment rate of renewable energy clusters. The method includes acquiring real-time operating data of renewable energy power plants, grid nodes, energy storage units, flexible loads, and transmission channels; calculating the real-time curtailment rate of power plants and the real-time curtailment rate of the cluster; and determining the predicted curtailment rate of power plants and the predicted curtailment rate of the cluster for a preset future period by combining historical curtailment rate data, renewable energy output forecast data, and load forecast data. A scheduling model is solved based on the real-time curtailment rate, the predicted curtailment rate, and the target curtailment rate to generate and execute scheduling instructions. After execution, the scheduling model is continuously corrected based on the deviation between the actual curtailment rate and the corresponding predicted and target curtailment rates, thereby achieving continuous tracking and dynamic suppression of the renewable energy cluster curtailment rate.

Description

Technical Field

[0001] This invention relates to the field of new energy cluster scheduling and control technology, and in particular to a method for dynamically suppressing the curtailment rate of new energy clusters. Background Technology

[0002] With the continuous increase in the installed capacity of new energy sources such as wind power and photovoltaics, the grid-connected operation of multiple new energy power plants in clusters has become an important way to develop and consume new energy. New energy clusters have significant advantages in improving energy development efficiency and expanding the scale of new energy access. However, due to the randomness, volatility, and intermittency of wind and photovoltaic power output, and the influence of factors such as grid peak-shaving capacity, node acceptance capacity, transmission channel capacity, and load timing matching level, new energy clusters are prone to varying degrees of power curtailment during operation. The curtailment rate not only reflects the degree to which new energy output is not effectively consumed, but also directly reflects the matching between the dispatch level of the new energy cluster and the grid's absorption capacity. Therefore, how to effectively control the curtailment rate has become an important issue in the operation and dispatch of new energy clusters. Typical new energy cluster dispatch methods mainly focus on output allocation, energy storage regulation, load response, or channel optimization, emphasizing the improvement of power consumption, optimization of operation economy, or guarantee of system stability. Although such methods can improve the utilization level of new energy to a certain extent, they usually do not take the curtailment rate as a dynamic control quantity in the dispatch process. They lack real-time quantification, predictive analysis, and post-execution correction around the curtailment rate, making it difficult to suppress the curtailment rate in a timely and continuous manner when the output of new energy fluctuates rapidly or network constraints change. To address this, a dynamic suppression and scheduling method for the curtailment rate of new energy clusters is proposed. Summary of the Invention

[0003] In view of this, the present invention provides a dynamic suppression and scheduling method for the curtailment rate of new energy clusters, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0004] The technical solution of this invention is implemented as follows: a dynamic suppression and scheduling method for the curtailment rate of new energy clusters, characterized by comprising the following steps: S1. Obtain real-time operating data of the new energy cluster, including the theoretical maximum output, actual grid-connected output, grid-side node acceptance capacity, energy storage unit operating status, flexible load adjustable capacity, and transmission channel operating status of each new energy power station. S3. Based on the real-time operation data, calculate the real-time power curtailment rate of the power station and the real-time power curtailment rate of the power cluster in the current scheduling cycle; S3. Based on historical curtailment rate data, new energy output forecast data, and load forecast data, determine the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for the preset future time period. S4. Based on the real-time curtailment rate of the power station, the real-time curtailment rate of the cluster, the predicted curtailment rate of the power station, the predicted curtailment rate of the cluster, and the target curtailment rate, solve the curtailment rate dynamic suppression scheduling model to obtain the scheduling instructions for the active power output of the new energy power station, the charging and discharging power of the energy storage unit, the flexible load adjustment amount, and the power allocation of the transmission channel. S5. Collect the actual power curtailment rate after the execution of the scheduling instruction, and perform rolling correction on the power curtailment rate dynamic suppression scheduling model based on the deviation between the actual power curtailment rate and the corresponding predicted power curtailment rate and target power curtailment rate.

[0005] More preferably, the real-time curtailment rate of the power station is calculated according to the following formula: Real-time curtailment rate of power station = (Theoretical maximum output - Actual grid-connected output - Correction value for plant power loss) / Theoretical maximum output × 100%; The real-time curtailment rate of the cluster is the weighted average of the real-time curtailment rates of each new energy power station within the cluster. The weighting coefficient is the proportion of the theoretical maximum output of the corresponding new energy power station to the theoretical maximum total output of the cluster.

[0006] Further preferably, the step of determining the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for a preset future time period includes: inputting the historical curtailment rate sequence, the short-term predicted value of renewable energy output, the predicted value of load, the change value of the grid-side node receiving capacity and the change value of the load rate of the transmission channel into the pre-trained curtailment rate prediction model, and outputting the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for the next 15 minutes to 4 hours.

[0007] Further preferably, the method further includes: matching the real-time curtailment rate of the power station, the real-time curtailment rate of the power cluster, the predicted curtailment rate of the power station and the predicted curtailment rate of the power cluster with preset curtailment rate threshold intervals respectively, so as to determine the curtailment risk level of the corresponding power station and transmission channel; the preset curtailment rate threshold intervals include normal intervals, warning intervals and exceeding intervals.

[0008] More preferably, the dynamic suppression scheduling model for curtailment rate is a two-layer scheduling model. The upper-layer model is used to determine the total output allocation value of the cluster, the total regulation power of energy storage, the total regulation capacity of flexible loads, and the power allocation value of each transmission channel. The lower-layer model is used to decompose the solution results of the upper-layer model into the upper limit of active power output of each new energy power station, the charging and discharging power of each energy storage unit, and the response of each flexible load.

[0009] More preferably, the dispatch instructions are generated in the following order: first, the energy storage units corresponding to the power stations connected to the high-curtailment-risk transmission channels are called; when the available adjustment margin of the energy storage units is insufficient, the flexible loads in the corresponding area are called; when the adjustable capacity of both the energy storage units and the flexible loads is insufficient, the upper limit of the active power output of the corresponding new energy power stations is lowered.

[0010] More preferably, the dynamic curtailment rate suppression scheduling model is rolled over and corrected according to a preset correction cycle, which is 15 minutes. In each correction cycle, real-time operating data of the new energy cluster is collected again, and the dynamic curtailment rate suppression scheduling model is solved again using the latest collected real-time operating data.

[0011] More preferably, the acquisition of real-time operating data of the new energy cluster includes: acquiring corresponding operating data from the site monitoring system, energy management system, energy storage management system and flexible load aggregation management platform respectively.

[0012] More preferably, the new energy cluster includes at least two of the following: wind farms, photovoltaic farms, electrochemical energy storage units, and adjustable flexible loads.

[0013] More preferably, the step of rolling correction of the dynamic curtailment rate suppression scheduling model based on the deviation between the actual curtailment rate and the corresponding predicted curtailment rate and the target curtailment rate includes: calculating a first deviation value between the actual curtailment rate and the corresponding predicted curtailment rate, and a second deviation value between the actual curtailment rate and the target curtailment rate; when at least one of the first deviation value and the second deviation value exceeds a preset allowable range, updating at least one of the constraint boundary parameters, target weight parameters, and resource call priority parameters in the dynamic curtailment rate suppression scheduling model, and entering the rolling solution of the next scheduling cycle.

[0014] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: I. This invention collects real-time operation data of new energy clusters, calculates the real-time curtailment rate of power plants and the real-time curtailment rate of power clusters, and combines historical curtailment rate data, new energy output forecast data, and load forecast data to determine the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for a preset future period. This enables the curtailment rate to be used as a dynamic control quantity in the scheduling process for continuous tracking and early intervention, thereby improving the timeliness and pertinence of curtailment rate management.

[0015] Second, this invention solves the dynamic curtailment rate suppression scheduling model based on the real-time curtailment rate of the power station, the real-time curtailment rate of the power cluster, the predicted curtailment rate of the power station, the predicted curtailment rate of the power cluster, and the target curtailment rate, generates corresponding scheduling instructions, and after execution, performs rolling correction on the scheduling model based on the deviation between the actual curtailment rate and the corresponding predicted curtailment rate and target curtailment rate, thereby improving the adaptability of the new energy cluster to complex operating conditions and the curtailment rate control capability.

[0016] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the dynamic suppression and scheduling method for the curtailment rate of new energy clusters according to the present invention. Detailed Implementation

[0019] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0020] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0021] like Figure 1 As shown, the dynamic suppression and scheduling method for curtailment rate of new energy clusters proposed in this invention includes the following process: S1. Obtain real-time operating data of the new energy cluster. The real-time operating data includes the theoretical maximum output, actual grid-connected output, grid-side node acceptance capacity, energy storage unit operating status, flexible load adjustable capacity, and transmission channel operating status of each new energy power station. S2. Calculate the real-time power curtailment rate of the power station and the real-time power curtailment rate of the power cluster in the current scheduling cycle based on real-time operation data; S3. Based on historical curtailment rate data, new energy output forecast data and load forecast data, determine the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for the preset future time period. S4. Further, based on the real-time curtailment rate of the power plant, the real-time curtailment rate of the power cluster, the predicted curtailment rate of the power plant, the predicted curtailment rate of the power cluster, and the target curtailment rate, solve the curtailment rate dynamic suppression scheduling model to obtain the scheduling instructions for the active power output of the new energy power plant, the charging and discharging power of the energy storage unit, the flexible load adjustment amount, and the power allocation of the transmission channel. S5. After the scheduling instruction is executed, the actual power curtailment rate is collected, and the scheduling model is rolled over based on the deviation between the actual power curtailment rate and the corresponding predicted power curtailment rate and the target power curtailment rate.

[0022] In one implementation, real-time operational data is provided by a power plant monitoring system, an energy management system, an energy storage management system, and a flexible load aggregation management platform. The power plant monitoring system outputs the theoretical maximum output and actual grid-connected output of the new energy power plant; the energy management system outputs the grid-side node capacity and the operating status of the transmission channels; the energy storage management system outputs the state of charge, charging and discharging power, and remaining regulation margin of the energy storage units; and the flexible load aggregation management platform outputs the current adjustable capacity, response time, and sustainable adjustment time of the flexible load. After the above data enters the scheduling master station, it is aligned according to a unified timestamp and used as input data for the current scheduling cycle.

[0023] Based on real-time operational data, this invention calculates the real-time curtailment rate of power plants and the real-time curtailment rate of power clusters for the current scheduling cycle. The real-time curtailment rate of power plants is used to characterize the curtailment level of a single renewable energy power plant within the current scheduling cycle, while the real-time curtailment rate of power clusters is used to characterize the curtailment level of the entire renewable energy cluster within the current scheduling cycle. By simultaneously calculating the curtailment rates at both the power plant and cluster levels, it is possible to balance the identification of local curtailment anomalies with the control of global curtailment levels.

[0024] In one implementation, the real-time curtailment rate of the power station is determined as follows: Real-time curtailment rate of power plants = (Theoretical maximum output - Actual grid-connected output - Correction value for plant power loss) / Theoretical maximum output × 100%; Among them, the power loss correction value of the plant is preset according to the self-consumption characteristics of the corresponding new energy power station, which is used to eliminate the influence of non-curtailment factors on the calculation results. The real-time curtailment rate of the cluster is the weighted average of the real-time curtailment rates of each new energy power station in the cluster. The weighting coefficient is the proportion of the theoretical maximum output of the corresponding new energy power station to the theoretical maximum total output of the cluster. After adopting the above processing method, it is possible to avoid the unreasonable amplification of the overall curtailment rate of the cluster by low-capacity power stations.

[0025] After calculating the real-time curtailment rate, this invention further determines the predicted curtailment rate for power plants and the predicted curtailment rate for power clusters in a preset future time period. The predicted curtailment rate for power plants is used to characterize the curtailment trend of the corresponding power plant in the future time period, while the predicted curtailment rate for power clusters is used to characterize the curtailment change trend of the entire renewable energy cluster in the future time period. By introducing the prediction results, the dispatching system can not only reflect the current curtailment status but also identify potential curtailment risks in the future time period in advance.

[0026] In one implementation, the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters are determined by a pre-trained curtailment rate prediction model. The input data of the curtailment rate prediction model includes historical curtailment rate data, renewable energy output prediction data, load prediction data, grid-side node capacity change data, and transmission channel load rate change data. The model output results include the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters within the next 15 minutes to 4 hours. The historical curtailment rate data is used to characterize the evolution of power cluster curtailment, the renewable energy output prediction data is used to characterize the output change trend of renewable energy power plants in the future period, the load prediction data is used to characterize the impact of system load changes on renewable energy absorption capacity, and the node capacity change data and the transmission channel load rate change data are used together to characterize the future trend of network-side constraint changes. After processing the above-mentioned multiple types of input data together, the model can output a predicted curtailment rate that matches the actual operating conditions.

[0027] In one embodiment, the present invention further matches the real-time curtailment rate of power stations, the real-time curtailment rate of power clusters, the predicted curtailment rate of power stations, and the predicted curtailment rate of power clusters with preset curtailment rate threshold ranges to determine the curtailment risk level of the corresponding power stations and transmission channels. The preset curtailment rate threshold ranges include a normal range, a warning range, and an exceedance range. When the corresponding curtailment rate falls into the normal range, it indicates that the curtailment risk is low under the current operating conditions; when the corresponding curtailment rate falls into the warning range, it indicates that there is a risk of further increase in the future, and it is necessary to prepare control resources in advance; when the corresponding curtailment rate falls into the exceedance range, it indicates that the corresponding power station or the corresponding transmission channel has entered a high-risk state, and the dispatching system needs to immediately initiate coordinated control. By matching real-time curtailment rate with predicted curtailment rate, it is possible to simultaneously identify the current state and predict future risks. After obtaining the real-time curtailment rate, predicted curtailment rate, and risk level, this invention solves the curtailment rate dynamic suppression scheduling model based on the real-time curtailment rate of the power plant, the real-time curtailment rate of the power cluster, the predicted curtailment rate of the power plant, the predicted curtailment rate of the power cluster, and the target curtailment rate. This yields scheduling instructions for the active power output of the new energy power plant, the charging and discharging power of the energy storage unit, the adjustment of flexible loads, and the power allocation of the transmission channel. The target curtailment rate is a pre-set cluster control target of the scheduling master station, used to constrain the curtailment rate control level. The curtailment rate dynamic suppression scheduling model takes the curtailment rate voltage drop as the scheduling core, so that the new energy power plant, energy storage unit, flexible load, and transmission channel are no longer adjusted independently, but are solved in a coordinated manner under a unified target.

[0028] In one implementation, the dynamic suppression scheduling model for curtailment rate is a two-level scheduling model; The upper-level model is designed for the overall operation of the cluster and is used to determine the total output allocation value of the cluster, the total energy storage regulation power, the total regulation capacity of flexible loads, and the power allocation value of each transmission channel. The lower-level model is geared towards the single-site execution layer, and is used to decompose the solution results of the upper-level model into the upper limit of active power output of each new energy site, the charging and discharging power of each energy storage unit, and the response of each flexible load. By adopting a two-layer scheduling model, the overall planning can be completed at the cluster level first, and the execution decomposition can be completed at the station level, thereby ensuring that the scheduling results meet both the global curtailment rate control requirements and the execution constraints of each resource object.

[0029] In one implementation, the generation of scheduling instructions is performed in the following order: Prioritize the use of energy storage units corresponding to power plants connected to transmission lines with high curtailment risk; when the available adjustment margin of energy storage units is insufficient, utilize flexible loads within the corresponding area; when the adjustable capacity of both energy storage units and flexible loads is insufficient, lower the active power output limit of the corresponding renewable energy power plants. Through this sequential setting, the dispatch system first utilizes energy storage to quickly absorb redundant renewable energy output, then enhances local absorption capacity through flexible loads, and finally limits the output of renewable energy power plants, thereby reducing the impact of direct power curtailment on renewable energy utilization levels.

[0030] After the scheduling model is solved, the main scheduling station sends the corresponding scheduling instructions to the new energy power plant control unit, energy storage unit control unit, flexible load response terminal, and transmission channel control unit, respectively. Each execution object responds according to the instructions within the current scheduling cycle. The new energy power plant adjusts its grid-connected power according to the received active power output limit. The energy storage unit performs charging or discharging actions according to the received charging and discharging power instructions. The flexible load adjusts its power supply level up or down according to the received adjustment instructions. The transmission channel adjusts the corresponding transmission ratio according to the received power allocation value.

[0031] After the scheduling command is executed, this invention collects the actual curtailment rate after the scheduling command is executed, and performs rolling correction on the curtailment rate dynamic suppression scheduling model based on the deviation between the actual curtailment rate and the corresponding predicted curtailment rate and the target curtailment rate. This step is used to evaluate the actual control effect of the current scheduling cycle and feed the evaluation results back to the solution process of the next scheduling cycle. Through deviation feedback, the scheduling system can avoid the problem that the model can not adapt to changes in operating conditions because it keeps the parameters fixed for a long time.

[0032] In one implementation, rolling correction includes the following processing: Calculate the first deviation between the actual curtailment rate and the corresponding predicted curtailment rate, and the second deviation between the actual curtailment rate and the target curtailment rate. When at least one of the first deviation value and the second deviation value exceeds the preset allowable range, at least one of the constraint boundary parameters, target weight parameters, and resource allocation priority parameters in the dynamic curtailment rate suppression scheduling model is updated, and the system proceeds to the rolling solution of the next scheduling cycle. The constraint boundary parameters reflect the dynamic changes in node receiving capacity and transmission channel capacity, the target weight parameters reflect the balance between the curtailment rate reduction target and the regulation cost, and the resource allocation priority parameters reflect the order of allocation among energy storage units, flexible loads, and renewable energy power plant output limiting. Through the above parameter updates, the system can make the model for the next scheduling cycle closer to the current actual operating state.

[0033] In one implementation, the rolling correction is performed according to a preset correction cycle, which is 15 minutes. Within each correction cycle, the dispatch master station re-collects real-time operating data from the renewable energy cluster and re-solves the dynamic curtailment rate suppression dispatch model using the latest collected real-time operating data. Adopting a fixed correction cycle ensures that the dispatch system is always updated based on the latest operating conditions, avoiding deviations from actual control requirements due to outdated historical data.

[0034] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamically suppressing and scheduling the curtailment rate of new energy clusters, characterized in that, Includes the following steps: S1. Obtain real-time operating data of the new energy cluster, including the theoretical maximum output, actual grid-connected output, grid-side node acceptance capacity, energy storage unit operating status, flexible load adjustable capacity, and transmission channel operating status of each new energy power station. S3. Based on the real-time operation data, calculate the real-time power curtailment rate of the power station and the real-time power curtailment rate of the power cluster in the current scheduling cycle; S3. Based on historical curtailment rate data, new energy output forecast data, and load forecast data, determine the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for the preset future time period. S4. Based on the real-time curtailment rate of the power station, the real-time curtailment rate of the cluster, the predicted curtailment rate of the power station, the predicted curtailment rate of the cluster, and the target curtailment rate, solve the curtailment rate dynamic suppression scheduling model to obtain the scheduling instructions for the active power output of the new energy power station, the charging and discharging power of the energy storage unit, the flexible load adjustment amount, and the power allocation of the transmission channel. S5. Collect the actual power curtailment rate after the execution of the scheduling instruction, and perform rolling correction on the power curtailment rate dynamic suppression scheduling model based on the deviation between the actual power curtailment rate and the corresponding predicted power curtailment rate and target power curtailment rate.

2. The dynamic suppression and scheduling method for the curtailment rate of new energy clusters according to claim 1, characterized in that, The real-time curtailment rate of the power station is calculated according to the following formula: Real-time curtailment rate of power plants = (Theoretical maximum output - Actual grid-connected output - Correction value for plant power loss) / Theoretical maximum output × 100%; The real-time curtailment rate of the cluster is the weighted average of the real-time curtailment rates of each new energy power station within the cluster. The weighting coefficient is the proportion of the theoretical maximum output of the corresponding new energy power station to the theoretical maximum total output of the cluster.

3. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The determination of the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters for a preset future time period includes: The historical curtailment rate sequence, short-term forecast values ​​of renewable energy output, load forecast values, changes in grid-side node capacity and load rate of transmission channels are input into the pre-trained curtailment rate prediction model, which outputs the predicted curtailment rate of power plants and the predicted curtailment rate of power clusters within the next 15 minutes to 4 hours.

4. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The method further includes: matching the real-time curtailment rate of the power station, the real-time curtailment rate of the power cluster, the predicted curtailment rate of the power station and the predicted curtailment rate of the power cluster with preset curtailment rate threshold ranges, respectively, to determine the curtailment risk level of the corresponding power station and transmission channel; The preset curtailment rate threshold range includes a normal range, a warning range, and an exceedance range.

5. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The dynamic suppression scheduling model for curtailment rate is a two-layer scheduling model. The upper-layer model is used to determine the total output allocation value of the cluster, the total regulation power of energy storage, the total regulation capacity of flexible loads, and the power allocation value of each transmission channel. The lower-layer model is used to decompose the solution results of the upper-layer model into the upper limit of active power output of each new energy power station, the charging and discharging power of each energy storage unit, and the response of each flexible load.

6. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 4, characterized in that, The dispatch instructions are generated and executed in the following order: energy storage units corresponding to power plants connected to high-curtailment-risk transmission channels are called first; when the available adjustment margin of the energy storage units is insufficient, flexible loads in the corresponding area are called; when the adjustable capacity of both the energy storage units and the flexible loads is insufficient, the upper limit of the active power output of the corresponding new energy power plants is lowered.

7. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The dynamic suppression and scheduling model for the curtailment rate is rolled over and adjusted according to a preset adjustment period of 15 minutes. In each correction cycle, real-time operating data of the new energy cluster is re-collected, and the dynamic suppression scheduling model of the curtailment rate is re-solved using the latest collected real-time operating data.

8. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The acquisition of real-time operating data of the new energy cluster includes: acquiring corresponding operating data from the site monitoring system, energy management system, energy storage management system, and flexible load aggregation management platform, respectively.

9. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The new energy cluster includes at least two of the following: wind farms, photovoltaic farms, electrochemical energy storage units, and adjustable flexible loads.

10. The dynamic suppression and scheduling method for curtailment rate of new energy clusters according to claim 1, characterized in that, The step of rollingly correcting the dynamic curtailment rate suppression scheduling model based on the deviation between the actual curtailment rate and the corresponding predicted and target curtailment rates includes: Calculate the first deviation between the actual curtailment rate and the corresponding predicted curtailment rate, and the second deviation between the actual curtailment rate and the target curtailment rate. When at least one of the first deviation value and the second deviation value exceeds the preset allowable range, at least one of the constraint boundary parameters, target weight parameters and resource call priority parameters in the dynamic suppression scheduling model of power curtailment rate is updated, and the rolling solution of the next scheduling cycle is entered.

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