A power generation amount prediction method suitable for a virtual power plant

Abstract

The application discloses a power generation capacity prediction method suitable for a virtual power plant, and relates to the technical field of virtual power plants.The application preliminarily estimates the power generation capacity of a power generation end by acquiring predicted energy supply data and real-time energy supply data of each power generation end, judges the difference between the actual energy supply data and the predicted energy supply data according to the estimation result, thereby reducing the influence of the energy supply data on the prediction result, and obtains the influence of equipment on the prediction result on the premise of the influence of the energy supply data on the prediction result, thereby obtaining the prediction of the power generation capacity of the virtual power plant which is closer to the real value.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology, specifically a method for predicting power generation in virtual power plants. Background Technology

[0002] With the global energy transition and the intelligent development of power systems, virtual power plants have emerged as an innovative model for integrating distributed energy resources. They aggregate distributed power sources, energy storage systems, and controllable loads to achieve unified management and dispatch; accurate prediction of virtual power plant power generation is therefore crucial.

[0003] In practice, the factors affecting the accuracy of power generation prediction results of virtual power plants are usually energy and equipment. How to reduce the impact of energy supply and equipment factors on the prediction error is a problem we need to solve. To this end, we now provide a power generation prediction method suitable for virtual power plants. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting power generation in virtual power plants.

[0005] The objective of this invention can be achieved through the following technical solution: a method for predicting power generation in a virtual power plant, comprising:

[0006] Obtain the basic equipment parameters of the distributed power source, and construct the corresponding virtual power plant based on the basic equipment parameters of the distributed power source;

[0007] Acquire the predicted and real-time power supply data of distributed power sources, generate the corresponding simulated power generation curve based on the obtained predicted power supply data, and generate the corresponding real-time projected power generation curve based on the real-time power supply data.

[0008] The actual power generation of the distributed power source is obtained, and the actual power generation curve is generated. Based on the generated simulated power generation curve and the real-time predicted power generation curve, the corresponding residual value of power supply impact is obtained. Based on the residual value of power supply impact and the actual power generation curve, the residual value of equipment performance impact is obtained.

[0009] The power generation of the virtual power plant is predicted based on the residual values ​​of the impact of energy supply and the residual values ​​of the impact of equipment performance.

[0010] Based on the predicted power generation from the virtual power plant, the power generation efficiency of each power generation terminal is evaluated, and corresponding early warning information is generated based on the power generation efficiency evaluation results.

[0011] Furthermore, the process of obtaining the basic equipment parameters of distributed power sources and constructing corresponding virtual power plants based on these parameters includes:

[0012] The location of each power generation terminal that makes up the distributed power source and the composition of the power generation equipment are obtained. The equipment parameters of the power generation equipment at the power generation terminal are obtained, including equipment type, equipment specifications and energy conversion rate.

[0013] Construct corresponding virtual power generation nodes for each power generation terminal, associate the virtual power generation nodes with the power generation terminals, and construct a data upload link to link the virtual power generation nodes and the power generation terminals.

[0014] By aggregating all virtual power generation nodes and data upload links, the construction of the virtual power plant is completed.

[0015] Furthermore, the process of acquiring predicted energy supply data for the location of distributed power sources and generating corresponding simulated power generation curves based on the acquired predicted energy supply data includes:

[0016] Based on the equipment type of the power generation equipment at the power generation end, obtain the energy item used for power generation of the corresponding power generation equipment, and obtain the predicted energy supply data of the energy item, which includes the predicted energy supply value and the energy supply time.

[0017] Generate corresponding predicted energy supply curves based on predicted energy supply data;

[0018] The simulated power generation curve is generated based on the energy conversion rate of the power generation equipment.

[0019] Furthermore, the process of acquiring real-time power supply data at the location of distributed power sources and generating corresponding real-time projected power generation curves based on this data includes:

[0020] Obtain the real-time energy supply value of the energy item corresponding to the power generation end, and generate the corresponding real-time energy supply curve;

[0021] Based on the energy conversion rate and real-time power supply curve of the power generation equipment at the power generation end, a corresponding real-time projected power generation curve is generated.

[0022] Furthermore, the process of obtaining the residual value of the energy supply impact corresponding to the power generation end includes:

[0023] Construct a two-dimensional coordinate system, with each two-dimensional coordinate system associated with a power generation terminal;

[0024] The generated predicted power supply curve, simulated power generation curve, real-time power supply curve, and real-time estimated power generation curve are mapped onto a two-dimensional coordinate system.

[0025] Obtain the actual power generation at the power generation end, generate the actual power generation curve, and map the generated actual power generation curve to the corresponding two-dimensional coordinate system;

[0026] The energy conversion rate of the power generation equipment is denoted as K;

[0027] Set a monitoring cycle;

[0028] Each curve within the monitoring period is marked, and the residual value Gc of the power supply impact at the power generation end is obtained based on the marked curve portion.

[0029] Furthermore, the process of obtaining the residual value of equipment performance impact based on the residual value of energy supply impact and the actual power generation curve includes:

[0030] Obtain the real-time power generation corresponding to the current moment on the actual power generation curve, and obtain the residual value of equipment performance impact Sc based on the obtained residual value of energy supply impact and real-time power generation.

[0031] Furthermore, the process of predicting the power generation of the virtual power plant based on the residual values ​​of energy supply impact and equipment performance impact includes:

[0032] Mark the predicted energy supply curve and the simulated power generation curve between the current time t and the end time t2 of the monitoring period;

[0033] Based on the obtained residual values ​​of equipment performance impact, the energy conversion efficiency of the power generation equipment at the power generation end is updated:

[0034] By adjusting the residual value of energy supply impact, the prediction function curve between the current time t0 and the end time t2 of the monitoring period is adjusted. Then, based on the adjusted prediction function curve and the updated energy conversion rate, the simulated power generation curve between the current time t0 and the end time t2 of the monitoring period is updated.

[0035] Based on the updated simulated power generation curves of each power generation terminal, the predicted total power generation Yz of the virtual power plant is obtained.

[0036] Furthermore, the process of evaluating the power generation efficiency of each power generation terminal based on the power generation predicted by the virtual power plant includes:

[0037] Set the dynamic early warning threshold Z0 for total power generation;

[0038] When Yz≥Z0, it means that the total power generation of the distributed power source has reached the expected level; otherwise, it means that the total power generation of the distributed power source has not reached the expected level. If the total power generation of the distributed power source has not reached the expected level, the corresponding power generation deficit ratio of the power generation end is obtained.

[0039] The data are sorted from highest to lowest based on the power generation deficit rate, and corresponding early warning information is generated based on the sorting results.

[0040] Furthermore, the process for setting the dynamic early warning threshold for total power generation is as follows:

[0041] Based on the predicted energy supply value and energy conversion rate corresponding to time t2 of the predicted energy supply curve of each power generation terminal within the monitoring period, the power generation warning threshold corresponding to each power generation terminal is obtained. Then, based on the equipment specifications of the power generation equipment at each power generation terminal, the corresponding dynamic warning threshold for total power generation is obtained.

[0042] This invention relates to the field of industrial big data technology. Compared with existing technologies, the beneficial effects of this invention are:

[0043] By acquiring the predicted and real-time power supply data from each power generation terminal, a preliminary estimate of the power generation at the power generation terminal is made. Based on the estimate, the difference between the actual power supply data and the predicted power supply data is determined, thereby reducing the impact of the power supply data on the prediction results. Based on the premise that the power supply data has an impact on the prediction results, and combined with the difference between the theoretical power generation and the actual power generation at the power generation terminal, the impact of the equipment on the prediction results is obtained, thus obtaining a prediction of the power generation of the virtual power plant that is closer to the true value. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a flowchart of the present invention;

[0046] Figure 2 This is a schematic diagram of the virtual power plant of the present invention;

[0047] Figure 3 This is a flowchart illustrating the power generation efficiency evaluation process at the power generation end according to the present invention. Detailed Implementation

[0048] like Figure 1 As shown, a power generation prediction method suitable for virtual power plants includes:

[0049] Obtain the basic equipment parameters of the distributed power source, and construct the corresponding virtual power plant based on the basic equipment parameters of the distributed power source;

[0050] Acquire the predicted and real-time power supply data of distributed power sources, generate the corresponding simulated power generation curve based on the obtained predicted power supply data, and generate the corresponding real-time projected power generation curve based on the real-time power supply data.

[0051] The actual power generation of the distributed power source is obtained, and the actual power generation curve is generated. Based on the generated simulated power generation curve and the real-time predicted power generation curve, the corresponding residual value of power supply impact is obtained. Based on the residual value of power supply impact and the actual power generation curve, the residual value of equipment performance impact is obtained.

[0052] The power generation of the virtual power plant is predicted based on the residual values ​​of the impact of energy supply and the residual values ​​of the impact of equipment performance.

[0053] Based on the predicted power generation from the virtual power plant, the power generation efficiency of each power generation terminal is evaluated, and corresponding early warning information is generated based on the power generation efficiency evaluation results.

[0054] like Figure 2 As shown, it should be further explained that, in the specific implementation process, the process of obtaining the basic equipment parameters of the distributed power source and constructing the corresponding virtual power plant based on the basic equipment parameters of the distributed power source includes:

[0055] The location of each power generation terminal that makes up the distributed power source and the composition of the power generation equipment are obtained. The equipment parameters of the power generation equipment at the power generation terminal are obtained, including equipment type, equipment specifications and energy conversion rate.

[0056] Based on each power generation terminal, construct corresponding virtual power generation nodes, associate the virtual power generation nodes with the power generation terminals, and construct a data upload link to link the virtual power generation nodes and the power generation terminals; it should be noted that each virtual power generation node is associated with only one power generation terminal.

[0057] By aggregating all virtual power generation nodes and data upload links, the construction of the virtual power plant is completed.

[0058] It should be further explained that, in the specific implementation process, the process of obtaining the predicted energy supply data of the location of the distributed power source and generating the corresponding simulated power generation curve based on the obtained predicted energy supply data includes:

[0059] Based on the equipment type of the power generation equipment at the power generation end, obtain the energy item used for power generation of the corresponding power generation equipment, and obtain the predicted energy supply data of the energy item, which includes the predicted energy supply value and the energy supply time.

[0060] Generate corresponding predicted energy supply curves based on predicted energy supply data;

[0061] A simulated power generation curve is generated based on the energy conversion rate of the power generation equipment.

[0062] Example:

[0063] Taking photovoltaic power generation as an example, the energy item of the corresponding power generation equipment is light energy. The prediction function data of the corresponding energy item is the expected irradiance at each time of the day. The predicted energy supply value of the energy supply item is the expected irradiance, and the energy supply time is each time. The energy conversion rate of the power generation equipment is the light energy conversion rate.

[0064] It should be further explained that, in the specific implementation process, the process of obtaining real-time power supply data at the location of distributed power sources and generating corresponding real-time projected power generation curves based on the real-time power supply data includes:

[0065] Obtain the real-time energy supply value of the energy item corresponding to the power generation end, and generate the corresponding real-time energy supply curve;

[0066] Based on the energy conversion rate and real-time power supply curve of the power generation equipment at the power generation end, a corresponding real-time predicted power generation curve is generated;

[0067] It should be further explained that, in the specific implementation process, the process of obtaining the residual value of the energy supply impact at the power generation end includes:

[0068] Construct a two-dimensional coordinate system, with each two-dimensional coordinate system associated with a power generation terminal;

[0069] The generated predicted power supply curve, simulated power generation curve, real-time power supply curve, and real-time estimated power generation curve are mapped onto a two-dimensional coordinate system.

[0070] Obtain the actual power generation at the power generation end, generate the actual power generation curve, and map the generated actual power generation curve to the corresponding two-dimensional coordinate system;

[0071] The energy conversion efficiency of the power generation equipment is denoted as K;

[0072] Set a monitoring period, and record the start time of the monitoring period as t1 and the end time of the monitoring period as t2;

[0073] Each curve within the monitoring period is marked. Based on the marked curve portions, the residual value of the power supply impact at the generation end is obtained, denoted as Gc, where:

[0074] ;

[0075] Where t represents the current time, This represents the predicted energy supply value corresponding to the predicted energy supply curve at the current moment. This indicates the real-time energy supply value corresponding to the current moment's real-time energy supply curve. This indicates the real-time projected power generation corresponding to the current moment's projected power generation curve.

[0076] It should be further explained that, in the specific implementation process, the process of obtaining the residual value of equipment performance impact based on the residual value of energy supply impact and the actual power generation curve includes:

[0077] The real-time power generation corresponding to the current moment on the actual power generation curve is denoted as... Then, based on the obtained residual value of the energy supply impact and the real-time power generation, the residual value of the equipment performance impact is obtained, denoted as Sc, where:

[0078] .

[0079] It should be further explained that, in the specific implementation process, the process of predicting the power generation of the virtual power plant based on the residual values ​​of energy supply impact and equipment performance impact includes:

[0080] Mark the predicted energy supply curve and the simulated power generation curve between the current time t and the end time t2 of the monitoring period;

[0081] Based on the obtained residual value of equipment performance impact, the energy conversion efficiency of the power generation equipment at the power generation end is updated, and the updated energy conversion efficiency is denoted as K. g K g =(1-Sc)×K:

[0082] By adjusting the residual value of energy supply impact, the prediction function curve between the current time t0 and the end time t2 of the monitoring period is adjusted. Then, based on the adjusted prediction function curve and the updated energy conversion rate, the simulated power generation curve between the current time t0 and the end time t2 of the monitoring period is updated.

[0083] Based on the updated simulated power generation curves of each power generation terminal, the predicted total power generation of the virtual power plant is obtained.

[0084] Example:

[0085] Each generator terminal is labeled as i, where i = 1, 2, ..., n;

[0086] The simulated power generation of the generator terminal labeled i at time t2 during monitoring period is denoted as Md. i ;

[0087] The predicted total power generation of the virtual power plant is denoted as Yz, where:

[0088] .

[0089] like Figure 3 As shown, it should be further explained that, in the specific implementation process, the process of evaluating the power generation efficiency of each power generation terminal based on the predicted power generation of the virtual power plant includes:

[0090] Set a dynamic early warning threshold for total power generation, denoted as Z0;

[0091] When Yz ≥ Z0, it indicates that the total power generation of the distributed power source has reached the expected level; otherwise, it indicates that the total power generation of the distributed power source has not reached the expected level. If the total power generation of the distributed power source has not reached the expected level, then Md will be... i <Z i The power generation terminals are marked, and the power generation loss ratio is obtained. The power generation loss ratio is (Z). i -Md i ) / Z i ;

[0092] The data are sorted from highest to lowest based on the power generation deficit ratio, and corresponding early warning information is generated based on the sorting results.

[0093] By acquiring the predicted and real-time power supply data from each power generation terminal, a preliminary estimate of the power generation at the power generation terminal is made. Based on the estimate, the difference between the actual power supply data and the predicted power supply data is determined, thereby reducing the impact of the power supply data on the prediction results. Based on the premise that the power supply data has an impact on the prediction results, and combined with the difference between the theoretical power generation and the actual power generation at the power generation terminal, the impact of the equipment on the prediction results is obtained, thus obtaining a prediction of the power generation of the virtual power plant that is closer to the true value.

[0094] It should be further explained that the process for setting the dynamic early warning threshold for total power generation is as follows:

[0095] Based on the predicted energy supply value and energy conversion rate corresponding to time t2 of the predicted energy supply curve of the power generation equipment at each power generation end within the monitoring period, the power generation warning threshold corresponding to each power generation end is obtained. Then, based on the equipment specifications of the power generation equipment at each power generation end, the corresponding dynamic warning threshold for total power generation is obtained.

[0096] Example:

[0097] The predicted power supply value corresponding to the generator terminal labeled i at time t2 of the monitoring period is denoted as Yg. i The power generation warning threshold for the generator terminal labeled i is then denoted as Z. i Z i =Yg i ×K;

[0098] Then obtain the dynamic early warning threshold for the total power generation corresponding to the virtual power plant. ;

[0099] in, For the correction coefficient, and It depends on the specifications of the power generation equipment at the power generation end.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for predicting power generation in a virtual power plant, characterized in that, include: Obtain the basic equipment parameters of the distributed power source, and construct the corresponding virtual power plant based on the basic equipment parameters of the distributed power source; Acquire the predicted and real-time power supply data of distributed power sources, generate the corresponding simulated power generation curve based on the obtained predicted power supply data, and generate the corresponding real-time projected power generation curve based on the real-time power supply data. The actual power generation of the distributed power source is obtained, and the actual power generation curve is generated. Based on the generated simulated power generation curve and the real-time predicted power generation curve, the corresponding residual value of power supply impact is obtained. Based on the residual value of power supply impact and the actual power generation curve, the residual value of equipment performance impact is obtained. The power generation of the virtual power plant is predicted based on the residual values ​​of the impact of energy supply and the residual values ​​of the impact of equipment performance. Based on the predicted power generation from the virtual power plant, the power generation efficiency of each power generation terminal is evaluated, and corresponding early warning information is generated based on the power generation efficiency evaluation results. The process of obtaining the basic equipment parameters of distributed power sources and constructing the corresponding virtual power plant based on these parameters includes: The location of each power generation terminal that makes up the distributed power source and the composition of the power generation equipment are obtained. The equipment parameters of the power generation equipment at the power generation terminal are obtained, including equipment type, equipment specifications and energy conversion rate. Construct corresponding virtual power generation nodes for each power generation terminal, associate the virtual power generation nodes with the power generation terminals, and construct a data upload link to link the virtual power generation nodes and the power generation terminals. By aggregating all virtual power generation nodes and data upload links, the construction of the virtual power plant is completed; The process of obtaining predicted energy supply data for the location of distributed power sources and generating corresponding simulated power generation curves based on the obtained predicted energy supply data includes: Based on the equipment type of the power generation equipment at the power generation end, obtain the energy item used for power generation of the corresponding power generation equipment, and obtain the predicted energy supply data of the energy item, which includes the predicted energy supply value and the energy supply time. Generate corresponding predicted energy supply curves based on predicted energy supply data; A simulated power generation curve is generated based on the energy conversion rate of the power generation equipment. The process of acquiring real-time power supply data at the location of distributed power sources and generating corresponding real-time projected power generation curves based on this data includes: Obtain the real-time energy supply value of the energy item corresponding to the power generation end, and generate the corresponding real-time energy supply curve; Based on the energy conversion rate and real-time power supply curve of the power generation equipment at the power generation end, a corresponding real-time predicted power generation curve is generated; The process of obtaining the residual value of the energy supply impact corresponding to the power generation end includes: Construct a two-dimensional coordinate system, with each two-dimensional coordinate system associated with a power generation terminal; The generated predicted power supply curve, simulated power generation curve, real-time power supply curve, and real-time estimated power generation curve are mapped onto a two-dimensional coordinate system. Obtain the actual power generation at the power generation end, generate the actual power generation curve, and map the generated actual power generation curve to the corresponding two-dimensional coordinate system; The energy conversion efficiency of the power generation equipment is denoted as K; Set a monitoring cycle; Each curve within the monitoring period is marked, and the residual value Gc of the power supply impact at the power generation end is obtained based on the marked curve portion.

2. The power generation prediction method applicable to virtual power plants according to claim 1, characterized in that, The process of obtaining the residual value of equipment performance impact based on the residual value of energy supply impact and the actual power generation curve includes: Obtain the real-time power generation corresponding to the current moment on the actual power generation curve, and obtain the residual value Sc of the equipment performance impact based on the obtained residual value of energy supply impact and real-time power generation.

3. The power generation prediction method applicable to virtual power plants according to claim 2, characterized in that, The process of predicting the power generation of a virtual power plant based on the residual values ​​of energy supply impact and equipment performance impact includes: Mark the predicted energy supply curve and the simulated power generation curve between the current time t and the end time t2 of the monitoring period; Based on the obtained residual values ​​of equipment performance impact, the energy conversion efficiency of the power generation equipment at the power generation end is updated: By adjusting the residual value of energy supply impact, the prediction function curve between the current time t0 and the end time t2 of the monitoring period is adjusted. Then, based on the adjusted prediction function curve and the updated energy conversion rate, the simulated power generation curve between the current time t0 and the end time t2 of the monitoring period is updated. Based on the updated simulated power generation curves of each power generation terminal, the predicted total power generation Yz of the virtual power plant is obtained.

4. The power generation prediction method applicable to virtual power plants according to claim 3, characterized in that, The process of evaluating the power generation efficiency of each power generation terminal based on the power generation predicted by the virtual power plant includes: Set the dynamic early warning threshold Z0 for total power generation; When Yz≥Z0, it means that the total power generation of the distributed power source has reached the expected level; otherwise, it means that the total power generation of the distributed power source has not reached the expected level. If the total power generation of the distributed power source has not reached the expected level, the corresponding power generation deficit ratio of the power generation end is obtained. The data are sorted from highest to lowest based on the power generation deficit rate, and corresponding early warning information is generated based on the sorting results.

5. The power generation prediction method applicable to virtual power plants according to claim 4, characterized in that, The process for setting the dynamic early warning threshold for total power generation is as follows: Based on the predicted energy supply value and energy conversion rate corresponding to time t2 of the predicted energy supply curve of each power generation terminal within the monitoring period, the power generation warning threshold corresponding to each power generation terminal is obtained. Then, based on the equipment specifications of the power generation equipment at each power generation terminal, the corresponding dynamic warning threshold for total power generation is obtained.

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