Virtual power plant internal wind turbine generator day-ahead stage electric energy report optimization method and device and medium

Abstract

The invention discloses a virtual power plant internal wind turbine generator day-ahead stage electric energy report optimization method and device and a medium, and belongs to the technical field of virtual power plant operation and control. The method comprises the steps of obtaining a next-day wind speed prediction result of a wind turbine generator; setting a next-day wind speed prediction error interval based on the normal probability distribution model, and obtaining an electric energy report feasible interval according to a next-day wind speed prediction result and the next-day wind speed prediction error interval; comprehensively considering the intra-day stage electric energy difference value, the frequency modulation auxiliary service capacity and the carbon emission reduction capacity to obtain a weighted evaluation index of the electric energy report quantity of the wind turbine generator set in the virtual power plant in the day-ahead stage; and determining the optimized electric energy report amount of the day-ahead stage in the feasible interval by taking the optimal mathematical expected value of the weighted evaluation index as a target. According to the method, the feasible interval of the electric energy report is calculated by predicting the wind speed of the next day and setting the error interval, and the electric energy report is determined and optimized by integrating the electric energy difference value in the day, the frequency modulation auxiliary service and the carbon emission reduction capacity and taking the optimal weighted evaluation index as the target.

Description

Technical Field

[0001] The invention relates to a method, a device and a medium for optimizing electric energy reporting of wind turbines in a virtual power plant in the day-ahead phase, and belongs to the technical field of operation and control of virtual power plants. Background Art

[0002] New energy generation technologies such as wind power can provide clean electricity for the power grid, but their output is highly volatile and uncertain. By integrating wind turbines with other flexible and adjustable resources within the power grid into a virtual power plant, as a unified entity participating in the electricity-carbon joint dispatch system, the operating efficiency of wind turbines can be ensured to a certain extent.

[0003] For the wind turbines in the virtual power plant, they can participate in the power dispatching, frequency regulation auxiliary services and carbon emission management in the power-carbon joint system at the same time, and play a role in these activities. Therefore, wind turbines usually need to plan the resource allocation for participating in the power-carbon joint system based on the forecast data of the wind speed for the next day in the day-ahead stage. If the wind turbines in the virtual power plant are fully included in the dispatch according to the day-ahead plan, but the actual wind speed on the next day is lower than the forecast value, it may cause the wind turbines to fail to complete their assigned tasks in the power-carbon joint system as planned. At this time, the wind turbines may need to bear certain performance deviation responsibilities, or cooperate with other entities in the system to make up for the shortcomings, thereby affecting the performance of the wind turbines in the virtual power plant in the power-carbon joint system.

[0004] Therefore, in order to reduce the impact of wind speed forecast errors on the performance of wind turbines in the electricity-carbon integrated system, it is necessary to optimize the day-ahead planning of wind turbines. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device and medium for optimizing the reporting of wind turbines within a virtual power plant participating in an electricity-carbon joint system, aiming to achieve the optimization of the electricity reporting of wind turbines within the virtual power plant in the day-ahead stage through the comprehensive use of technical means such as wind speed prediction, probability distribution models, weighted evaluation indicators and optimization algorithms.

[0006] In order to achieve the above objectives / solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0007] On the one hand, the present invention provides a method for optimizing the power quantity reporting of wind turbines in a virtual power plant in the day-ahead phase, which comprises: Obtain the next day's wind speed forecast result obtained by the wind turbines inside the virtual power plant in the day-ahead stage; Based on the normal probability distribution model of the next day wind speed prediction, the next day wind speed prediction error interval is set, and according to the next day wind speed prediction result and the next day wind speed prediction error interval, the feasible interval of the day-ahead electric energy reported by the wind turbines in the virtual power plant is calculated; Considering the difference in the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase that needs to be supplemented by the external power grid during the day-ahead phase, the frequency regulation auxiliary service capacity that can be provided during the day-ahead phase, and the carbon emission reduction capacity during the day-ahead phase, a weighted evaluation index of the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase is calculated; Taking the optimal mathematical expectation value corresponding to the weighted evaluation index of the wind turbines' internal energy reporting in the day-ahead phase within the virtual power plant as the goal, the optimized energy reporting of the wind turbines in the day-ahead phase within the virtual power plant is determined within the feasible range of the energy reporting.

[0008] The present invention constructs a weighted evaluation index for the day-ahead electric energy reporting of wind turbines within a virtual power plant by comprehensively considering multiple factors such as wind speed prediction error, electric energy reporting difference, frequency regulation auxiliary service capacity and carbon emission reduction capacity, and performs optimization calculation based on the probability distribution of wind speed prediction error, thereby improving the accuracy and optimization of reporting, and providing strong support for resource scheduling of virtual power plants.

[0009] Optionally, the feasible interval of the electric energy reported by the wind turbines in the virtual power plant in the day-ahead phase is calculated by the following formula: ; In the formula, Indicates the wind speed for the next day The maximum capture power of the wind turbine generator set is used as the upper limit of the power reporting in the feasible range. Indicates the wind speed for the next day The maximum capture power of the wind turbine is taken as the lower limit of the power report in the feasible interval. express In this moment The wind speed forecast result for the next day at the time is: represents the standard deviation of the normal probability distribution, It indicates the optimal wind energy utilization factor of the wind turbine under the maximum power point tracking condition.

[0010] Optionally, the weighted evaluation index of the day-ahead electric energy reported by the wind turbines within the virtual power plant is calculated by the following formula: ; In the formula, represents the weighted evaluation index, Indicates the error range of wind speed forecast for the next day The next day wind speed forecast error is Indicates The value of the wind turbine's day-ahead power consumption at each moment is: , and Represent different weight coefficients, Indicates the difference in electricity that needs to be supplemented by the external power grid during the day. represents the frequency regulation auxiliary service capacity available during the day. Indicates the carbon emission reduction capacity within the day.

[0011] Optionally, the difference in electricity quantity to be supplemented by the external power grid during the intra-day stage is expressed as: ; In the formula, Indicates the duration of each moment.

[0012] Optionally, the frequency regulation auxiliary service capacity that can be provided during the intraday stage is expressed as: ; Optionally, the intra-day carbon emission reduction capacity is expressed as: ; In the formula, Indicates the duration of each moment.

[0013] Optionally, the mathematical expectation value corresponding to the weighted evaluation index of the day-ahead electric energy reported by the wind turbines within the virtual power plant is calculated by the following formula: ; In the formula, Represents the mathematical expectation value corresponding to the weighted evaluation index.

[0014] Optionally, taking the optimal mathematical expectation value corresponding to the weighted evaluation index of the power reported by the wind turbines in the virtual power plant in the day-ahead stage as the goal, the optimized power reported by the wind turbines in the virtual power plant in the day-ahead stage is determined within the feasible interval of the power reported, which is expressed as: ; In the formula, Indicates that the mathematical expectation value corresponding to the weighted evaluation index is maximized.

[0015] In a second aspect, the present invention further provides a device for optimizing the amount of electric energy reported by wind turbines in a virtual power plant in the day-ahead phase, comprising: The next day wind speed forecast result acquisition module is configured to obtain the next day wind speed forecast result obtained by the wind turbines inside the virtual power plant predicting the next day wind speed in the day-ahead stage; The module for determining the feasible interval of electric energy quantity reporting is configured to set the next day wind speed prediction error interval based on the next day wind speed prediction normal probability distribution model, and calculate the feasible interval of electric energy quantity reporting of the wind turbines in the virtual power plant in the day-ahead stage according to the next day wind speed prediction result and the next day wind speed prediction error interval; The weighted evaluation index determination module is configured to calculate the weighted evaluation index of the day-ahead power reported by the wind turbines in the virtual power plant, taking into account the difference in power that needs to be supplemented by the external power grid during the intraday stage, the frequency regulation auxiliary service capacity that can be provided during the intraday stage, and the carbon emission reduction capacity during the intraday stage; In addition, the module for determining the optimization strategy for electric energy reporting is configured to determine the optimized electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase within the feasible range of the electric energy reporting, with the goal of optimizing the mathematical expectation value corresponding to the weighted evaluation index of the electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase.

[0016] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for optimizing the day-ahead electric energy reporting of wind turbines within a virtual power plant as described in the first aspect.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention no longer relies solely on the day-ahead wind speed forecast value to determine the power reporting of the wind turbine generator set, but further considers the influence of the wind speed forecast error. By setting the forecast error interval based on the normal probability distribution model of the next day's wind speed forecast, the uncertainty of the wind speed can be more comprehensively evaluated, thereby formulating a more reasonable power reporting strategy. Secondly, the present invention calculates the feasible interval of the day-ahead power reporting of the wind turbine generator set within the virtual power plant, and comprehensively considers the difference in electricity volume that needs to be supplemented by the external power grid during the intra-day stage, the frequency regulation auxiliary service capacity that can be provided, and the carbon emission reduction capacity, to construct a weighted evaluation index for the day-ahead power reporting of the wind turbine generator set within the virtual power plant, and calculates the mathematical expectation value of the weighted evaluation index for the day-ahead power reporting based on the probability distribution of the wind speed forecast error, thereby realizing a quantitative analysis of the influence of the wind speed forecast error on the day-ahead power reporting of the wind turbine generator set, and then helping the wind turbine generator set to determine the optimal power reporting scheme under the condition of the wind speed forecast error. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of an embodiment of a method for optimizing the amount of electric energy reported by wind turbines in a virtual power plant in the day-ahead stage according to the present invention; Figure 2 It is a flow chart of another embodiment of the method for optimizing the amount of electric energy reported by wind turbines in a virtual power plant in the day-ahead stage according to the present invention; Figure 3 It is a schematic diagram showing the next day wind speed prediction result of the day-ahead stage of the method for optimizing the power reporting amount of wind turbines in the virtual power plant of the present invention; Figure 4 It is a schematic diagram showing the upper and lower limit calculation results of electric energy reporting of wind turbines participating in the electric-carbon joint system in the day-ahead electric energy reporting optimization method of wind turbines in the virtual power plant of the present invention; Figure 5 Shown is a schematic diagram comparing the day-ahead optimized power reporting curve of wind turbines participating in the electricity-carbon joint system in the day-ahead power reporting optimization method for wind turbines within the virtual power plant of the present invention and the day-ahead power reporting curve of the maximum power output based on the day-ahead predicted wind speed. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0020] The term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the associated objects are in an "or" relationship.

[0021] Example 1 refer to Figure 1 This embodiment introduces a method for optimizing the power quantity reporting of wind turbines in a virtual power plant in the day-ahead phase, which includes: Obtain the next day's wind speed forecast result obtained by the wind turbines inside the virtual power plant in the day-ahead stage; Based on the normal probability distribution model of the next day wind speed prediction, the next day wind speed prediction error interval is set, and according to the next day wind speed prediction result and the next day wind speed prediction error interval, the feasible interval of the day-ahead electric energy reported by the wind turbines in the virtual power plant is calculated; Considering the difference in the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase that needs to be supplemented by the external power grid during the day-ahead phase, the frequency regulation auxiliary service capacity that can be provided during the day-ahead phase, and the carbon emission reduction capacity during the day-ahead phase, a weighted evaluation index of the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase is calculated; Taking the optimal mathematical expectation value corresponding to the weighted evaluation index of the wind turbines' internal energy reporting in the day-ahead phase within the virtual power plant as the goal, the optimized energy reporting of the wind turbines in the day-ahead phase within the virtual power plant is determined within the feasible range of the energy reporting.

[0022] In the actual application process of this embodiment, the corresponding wind speed prediction model is first used to collect the prediction data of the wind speed for the next day in the day-ahead stage of the wind turbines inside the virtual power plant, wherein the above-mentioned wind speed prediction model can make predictions based on various factors such as historical wind speed data, meteorological conditions, topography, etc.; then, according to the next day's wind speed prediction result, combined with the next day's wind speed prediction normal probability distribution model, a reasonable wind speed prediction error interval is set, and this interval reflects the uncertainty of the wind speed prediction; then, based on this error interval and the wind speed prediction result, the feasible interval of the electric energy reporting of the wind turbines inside the virtual power plant in the day-ahead stage is further calculated, and this interval takes into account the impact of wind speed changes on the power generation of the wind turbines to ensure the reporting The quantity is neither too conservative nor too risky; then, a number of factors are taken into comprehensive consideration to calculate the weighted evaluation index, including: the difference in electricity volume that needs to be supplemented by the external power grid during the intraday stage, which can reflect the impact of the difference between the reported quantity and the actual power generation on the power grid, the frequency regulation auxiliary service capacity that can be provided during the intraday stage, which can reflect the contribution of wind turbines in grid frequency regulation, and the carbon emission reduction capacity during the intraday stage, which reflects the potential of wind turbines in reducing carbon emissions; finally, with the optimal mathematical expectation value corresponding to the weighted evaluation index as the goal, the optimal electricity reported quantity is searched within the feasible range of electricity reported quantity, and the optimal electricity reported quantity will be output to the dispatching system of the virtual power plant, and then used as the electricity reported quantity plan for the day-ahead stage.

[0023] Example 2 refer to Figures 2 to 5 Based on Example 1, this example will comprehensively consider multiple factors such as wind speed prediction error, electric energy reporting difference, frequency regulation auxiliary service capacity and carbon emission reduction capacity, and will introduce in detail the specific implementation process of optimizing electric energy reporting of wind turbines in the virtual power plant during the day-ahead stage.

[0024] S1: Predict the wind speed for the next day in the day-ahead stage, based on the normal probability distribution model of the wind speed prediction error for the next day, select The wind speed prediction error range determines the feasible range of the power consumption reported by the wind turbines in the virtual power plant in the day-ahead phase.

[0025] Specifically, the wind speed for the next day is predicted in the day-ahead stage. If the wind speed forecast for the next day includes Wind speed forecast results at each moment , the wind speed prediction error is described by normal probability distribution, and the standard deviation of normal probability distribution is , will correspond to The actual wind speed change range of the next day is set as , then in At this moment, the upper limit of the power reported by the wind turbines in the virtual power plant in the day-ahead phase is the wind speed Maximum capture power of the downstream wind turbine The lower limit of the power reported by the wind turbines in the virtual power plant is the wind speed Maximum capture power of the downstream wind turbine .in, and Based on formula (1), we can get: ; In the formula, It indicates the optimal wind energy utilization factor of the wind turbine under the maximum power point tracking condition.

[0026] Specifically in this embodiment, the virtual power plant contains 100 wind turbines with a rated power of 1.5 MW. The next day's wind speed forecast results in the day-ahead stage can be found in Figure 3 , the standard deviation of the normal probability distribution of wind speed prediction error is 0.5 m / s, and the optimal wind energy utilization factor of the wind turbine under the maximum power point tracking condition is 0.8763 kW / (m / s) 3 The upper and lower limits of the wind turbine power reported in the virtual power plant in the day-ahead phase calculated based on formula (1) are shown in Figure 4 .

[0027] S2: For the day-ahead power report of wind turbines in the virtual power plant, Within the wind speed prediction error range, the actual deviation of the day-ahead electric energy reporting during the intraday stage is calculated, and the frequency regulation auxiliary service capacity and carbon emission reduction capacity during the intraday stage are taken into account to construct a weighted evaluation index for the day-ahead electric energy reporting of the wind turbines within the virtual power plant.

[0028] Specifically, the wind turbines in the virtual power plant are The day-ahead energy quantity reported at the moment is within its feasible range In the The day-ahead power consumption value of the wind turbine at each moment is expressed as , considering The wind speed prediction error within the interval is The scenario: For the day-ahead power report of wind turbines, Actual wind speed at the moment If it is unable to output enough electricity according to the electricity reported in the previous day, the difference in electricity volume that needs to be supplemented by the external power grid during the day Calculation based on formula (2): ; In the formula, Indicates the duration of each moment.

[0029] For the wind turbines in the virtual power plant, if the wind turbines are The maximum output power under this condition is greater than the power reported by the wind turbine in the day-ahead stage. The excess power can be used as active reserve capacity to provide auxiliary frequency regulation services during the day. Frequency regulation auxiliary service capacity that wind turbines can provide at each moment Calculate based on formula (3): ; The electricity generated by the wind turbines in the virtual power plant is green electricity, which can be used to achieve carbon emission reduction. The actual wind speed of the wind turbines during the day Next, Carbon reduction capacity at a given moment Calculate based on formula (4): ; By comprehensively considering the difference in the day-ahead electric energy reported by wind turbines during the intra-day stage, the frequency regulation auxiliary service capacity that can be provided during the intra-day stage, and the carbon emission reduction capacity during the intra-day stage, a weighted evaluation index for the day-ahead electric energy reported by wind turbines within the virtual power plant is constructed.

[0030] The amount reported on the previous day was , the wind speed prediction error is Under the condition of : ; In the formula, , and They represent different weight coefficients respectively.

[0031] Specifically for this example, the weight coefficient , and They are taken as 0.88, 0.02 and 0.1 respectively. The wind turbines report the day-ahead power quantity based on their maximum power output under the day-ahead predicted wind speed. Table 1 shows the difference in the day-ahead power quantity reported by the wind turbines in the virtual power plant based on equations (2) to (5) under different wind speed prediction error conditions, the frequency regulation auxiliary service capacity that can be provided in the day-ahead stage, the carbon emission reduction capacity in the day-ahead stage, and the weighted evaluation index of the day-ahead power quantity reported.

[0032]

[0033] From the calculation results in Table 1, it can be seen that when the wind speed prediction error is negative, that is, the actual wind speed is lower than the day-ahead prediction value, the wind turbine output cannot reach the day-ahead report based on the maximum power output at the day-ahead predicted wind speed, and needs to rely on external power purchase to make up the difference; while when the wind speed prediction error is positive, that is, the actual wind speed is higher than the day-ahead prediction value, the maximum output of the wind turbine can exceed its day-ahead variable, and its excess power can be used to provide frequency regulation auxiliary service capacity, and as the wind speed prediction error value increases, the frequency regulation auxiliary service capacity also increases. In addition, when the wind speed prediction error is negative, that is, the actual wind speed is lower than the day-ahead prediction value, as the wind speed prediction error value decreases, the amount of power that the wind turbine can generate also decreases accordingly, and its intra-day carbon emission reduction capacity also decreases accordingly. The results in Table 1 show that the wind speed prediction error will affect the evaluation index of the day-ahead power report of the wind turbine in the virtual power plant, so it is necessary to optimize the power report under this condition.

[0034] S3: Based on The probability distribution within the wind speed prediction error interval is used to determine the optimal day-ahead power quantity reported by wind turbines within the virtual power plant, with the mathematical expectation value of the weighted evaluation index of the day-ahead power quantity reported by wind turbines within the virtual power plant as the goal.

[0035] Specifically, in The wind speed prediction error range is based on a mean of 0 m / s and a standard deviation of The wind speed prediction error is in normal probability distribution, and the power reported by the wind turbines in the virtual power plant in the day-ahead stage is Under these conditions, the mathematical expectation value of the weighted evaluation index of the day-ahead power consumption of wind turbines in the virtual power plant is calculated based on formula (6). : ; Taking the maximum mathematical expectation value of the weighted evaluation index of the day-ahead power reporting of the wind turbines in the virtual power plant as the goal, the day-ahead power reporting of the wind turbines is optimized. Based on formula (7), the optimization model is constructed and the day-ahead optimized power reporting result of the wind turbines in the virtual power plant is obtained: ; Specifically in this example, based on the optimization model of formula (7), the final day-ahead power optimization quotation curve of the wind turbines in the virtual power plant is compared with the day-ahead power quotation curve based on the maximum power output under the day-ahead predicted wind speed. Figure 5As shown, if the day-ahead phase power consumption curve based on the maximum power output under the day-ahead predicted wind speed is adopted, the calculated mathematical expectation value of the weighted evaluation index of the day-ahead phase power consumption of the wind turbine is 234412; correspondingly, if the day-ahead phase power consumption optimization curve obtained in this embodiment is adopted, the calculated mathematical expectation value of the weighted evaluation index of the day-ahead phase power consumption of the wind turbine is 309498. This shows that this embodiment can obtain an optimized power consumption scheme that maximizes the mathematical expectation value of the weighted evaluation index of the day-ahead phase power consumption of the wind turbine in the virtual power plant under the condition of taking into account the wind speed prediction error.

[0036] Example 3 Based on the same inventive concept as that of Embodiment 1 or 2, this embodiment introduces a device for optimizing the amount of electric energy reported by wind turbines in a virtual power plant in the day-ahead phase, which includes: The next day wind speed forecast result acquisition module is configured to obtain the next day wind speed forecast result obtained by the wind turbines inside the virtual power plant predicting the next day wind speed in the day-ahead stage; The module for determining the feasible interval of electric energy quantity reporting is configured to set the next day wind speed prediction error interval based on the next day wind speed prediction normal probability distribution model, and calculate the feasible interval of electric energy quantity reporting of the wind turbines in the virtual power plant in the day-ahead stage according to the next day wind speed prediction result and the next day wind speed prediction error interval; The weighted evaluation index determination module is configured to calculate the weighted evaluation index of the day-ahead power reported by the wind turbines in the virtual power plant, taking into account the difference in power that needs to be supplemented by the external power grid during the intraday stage, the frequency regulation auxiliary service capacity that can be provided during the intraday stage, and the carbon emission reduction capacity during the intraday stage; In addition, the module for determining the optimization strategy for electric energy reporting is configured to determine the optimized electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase within the feasible range of the electric energy reporting, with the goal of optimizing the mathematical expectation value corresponding to the weighted evaluation index of the electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase.

[0037] refer to Figure 1 and Figure 2 The device for optimizing the day-ahead electric energy reporting of wind turbines within a virtual power plant can execute the method for optimizing the day-ahead electric energy reporting of wind turbines within a virtual power plant introduced in the above-mentioned embodiment 1 or 2, and the specific functional implementation of each functional module will not be repeated here.

[0038] Example 4 This embodiment introduces a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing the day-ahead electric energy reporting of wind turbines within a virtual power plant as described in Embodiment 1 or 2 are implemented.

[0039] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0041] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0042] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0043] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase, characterized in that: include: Obtain the next day's wind speed forecast result obtained by the wind turbines inside the virtual power plant in the day-ahead stage; Based on the normal probability distribution model of the next day wind speed prediction, the next day wind speed prediction error interval is set, and according to the next day wind speed prediction result and the next day wind speed prediction error interval, the feasible interval of the day-ahead electric energy reported by the wind turbines in the virtual power plant is calculated; Considering the difference in the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase that needs to be supplemented by the external power grid during the day-ahead phase, the frequency regulation auxiliary service capacity that can be provided during the day-ahead phase, and the carbon emission reduction capacity during the day-ahead phase, a weighted evaluation index of the amount of electricity reported by the wind turbines in the virtual power plant in the day-ahead phase is calculated; Taking the optimal mathematical expectation value corresponding to the weighted evaluation index of the wind turbines' internal energy reporting in the day-ahead phase within the virtual power plant as the goal, the optimized energy reporting of the wind turbines in the day-ahead phase within the virtual power plant is determined within the feasible range of the energy reporting.

2. According to the method for optimizing the power quantity reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 1, the feasible interval of the power quantity reporting of wind turbines in the virtual power plant in the day-ahead phase is calculated by the following formula: ； In the formula, Indicates the wind speed for the next day The maximum capture power of the wind turbine generator set is used as the upper limit of the power reporting in the feasible range. Indicates the wind speed for the next day The maximum capture power of the wind turbine is taken as the lower limit of the power report in the feasible interval. express In this moment The wind speed forecast result for the next day at the time is: represents the standard deviation of the normal probability distribution, It indicates the optimal wind energy utilization factor of the wind turbine under the maximum power point tracking condition.

3. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 2 is characterized in that: The weighted evaluation index of the day-ahead electric energy reported by the wind turbines in the virtual power plant is calculated by the following formula: ； In the formula, represents the weighted evaluation index, Indicates the error range of wind speed forecast for the next day The next day wind speed forecast error is Indicates The value of the wind turbine's day-ahead power consumption at each moment is: , and Represent different weight coefficients, Indicates the difference in electricity that needs to be supplemented by the external power grid during the day. represents the frequency regulation auxiliary service capacity available during the day. Indicates the carbon emission reduction capacity within the day.

4. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 3 is characterized in that: The difference in electricity that needs to be supplemented by the external power grid during the day is expressed as: ； In the formula, Indicates the duration of each moment.

5. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 3 is characterized in that: The frequency regulation auxiliary service capacity that can be provided during the day is expressed as: 。 6. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 3 is characterized in that: The carbon emission reduction capacity in the daily stage is expressed as: ； In the formula, Indicates the duration of each moment.

7. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 3 is characterized in that: The mathematical expectation value corresponding to the weighted evaluation index of the day-ahead electric energy reported by the wind turbines in the virtual power plant is calculated by the following formula: ； In the formula, Represents the mathematical expectation value corresponding to the weighted evaluation index.

8. The method for optimizing the power reporting of wind turbines in a virtual power plant in the day-ahead phase according to claim 7 is characterized in that: Taking the optimal mathematical expectation value corresponding to the weighted evaluation index of the power quantity reported by the wind turbines in the virtual power plant in the day-ahead stage as the goal, the optimized power quantity reported by the wind turbines in the virtual power plant in the day-ahead stage is determined within the feasible interval of the power quantity reported, which is expressed as: ； In the formula, It means that the mathematical expectation value corresponding to the weighted evaluation index is maximized.

9. A device for optimizing the amount of power reported by wind turbines in a virtual power plant in the day-ahead phase, characterized in that: include: The next day wind speed forecast result acquisition module is configured to obtain the next day wind speed forecast result obtained by the wind turbines inside the virtual power plant predicting the next day wind speed in the day-ahead stage; The module for determining the feasible interval of electric energy quantity reporting is configured to set the next day wind speed prediction error interval based on the next day wind speed prediction normal probability distribution model, and calculate the feasible interval of electric energy quantity reporting of the wind turbines in the virtual power plant in the day-ahead stage according to the next day wind speed prediction result and the next day wind speed prediction error interval; The weighted evaluation index determination module is configured to calculate the weighted evaluation index of the day-ahead power reported by the wind turbines in the virtual power plant, taking into account the difference in power that needs to be supplemented by the external power grid during the intraday stage, the frequency regulation auxiliary service capacity that can be provided during the intraday stage, and the carbon emission reduction capacity during the intraday stage; In addition, the module for determining the optimization strategy for electric energy reporting is configured to determine the optimized electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase within the feasible range of the electric energy reporting, with the goal of optimizing the mathematical expectation value corresponding to the weighted evaluation index of the electric energy reporting of the wind turbines in the virtual power plant in the day-ahead phase.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the day-ahead electric energy reporting of wind turbines within a virtual power plant according to any one of claims 1 to 8 are implemented.

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