High-precision simulation method for power generation based on multi-dimensional irradiation data and power station design

Abstract

The present invention discloses a high-precision simulation method for power generation based on multi-dimensional irradiation data and power station design, which belongs to the field of high-precision simulation methods for power generation, and includes the following steps: Step 1: Collect multi-dimensional irradiation data and power station design schemes, and use them to establish a digital simulation model of the power station; Step 2: Run the digital model on a computer to simulate the working state of the power station, and obtain the power generation performance parameters of the power station under different irradiation conditions; Step 3: Collect real-time multi-dimensional irradiation data and the corresponding power station power generation, and use this to evaluate the simulation results, and if the evaluation is qualified, proceed to the next step; Step 4: Simulate the power generation under different design schemes, and optimize and adjust the power station design scheme; Step 5: Simulate the operation of the power station under complex conditions, predict risks and formulate corresponding countermeasures. The present invention can improve the accuracy of the results output by the simulation model, and then more effectively optimize and adjust the power station design scheme.

Description

Technical Field

[0001] The present invention relates to the field of high-precision simulation methods for power generation, and in particular to a high-precision simulation method for power generation based on multi-dimensional irradiation data and power station design. Background Art

[0002] Photovoltaic power stations are photovoltaic power generation systems that use solar energy and are composed of special materials such as crystalline silicon panels and other electronic components. They are connected to the power grid and transmit electricity to the grid. With the advancement and development of science and technology, in order to better manage photovoltaic power stations, people use simulation technology in combination with photovoltaic power stations. The constructed photovoltaic power station simulation model can simulate the impact of different environmental factors such as light and temperature on the power generation of the power station, thereby optimizing the layout and assembly of the power station. The simulation model can also be used to simulate various emergencies in photovoltaic power stations, such as power grid failures, equipment failures, weather changes, etc., which helps to detect and diagnose faults in a timely manner and improve the reliability and stability of the power station.

[0003] The models constructed by existing high-precision power generation simulation methods usually focus on the modeling of various components such as photovoltaic power generation units, inverters, energy storage devices, and electrical parameters, but ignore the impact of multi-dimensional irradiation data and power station design, making the results output by the simulation model inaccurate, and thus unable to more effectively optimize and adjust the power station design plan.

[0004] Therefore, those skilled in the art provide a high-precision power generation simulation method based on multi-dimensional irradiation data and power station design to solve the problems raised in the above background technology. Summary of the invention

[0005] The purpose of the present invention is to provide a high-precision power generation simulation method based on multi-dimensional irradiation data and power plant design, which can improve the accuracy of the results output by the simulation model, and then more effectively optimize and adjust the power plant design scheme to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The high-precision power generation simulation method based on multi-dimensional irradiation data and power station design includes the following steps:

[0008] Step 1: Collect multi-dimensional irradiation data and power plant design plans, and use them to build a digital simulation model of the power plant;

[0009] Step 2: Run the digital model on the computer to simulate the working state of the power station and obtain the power generation performance parameters of the power station under different irradiation conditions;

[0010] Step 3: Collect real-time multi-dimensional irradiation data and the corresponding power generation of the power plant, and use this to evaluate the simulation results. If the evaluation is qualified, proceed to the next step. If the evaluation is unqualified, return to step 1 to re-establish the digital bionic model of the power plant;

[0011] Step 4: Simulate the power generation under different design schemes, and optimize and adjust the power station design scheme;

[0012] Step 5: Simulate the operation of power plants under complex conditions, predict risks and formulate corresponding response measures.

[0013] As a further solution of the present invention: the specific process of evaluating the simulation results is:

[0014] Real-time multi-dimensional irradiation data in ten consecutive time periods are collected and marked as Ai, i = 1···10;

[0015] The actual power generation of the power station corresponding to Ai is collected and marked as Bi, i=1···10;

[0016] Import Ai into the digital bionic model, obtain the corresponding power generation of the power station and mark it as ai, i=1···10;

[0017] Compare Ai with ai one by one. If the absolute value of the difference between all Ai and ai is less than the first preset value, proceed to the next step. If the absolute value of the difference between any group of Ai and ai is greater than or equal to the first preset value, it means that the simulation result evaluation is unqualified.

[0018] Represent Ai and ai on the same line graph, where the line graph corresponding to Ai is marked as C1, and the line graph corresponding to ai is marked as C2;

[0019] The peaks on the broken line C1 are marked as Di, i=1···n, where n is a positive integer, and the troughs on the broken line C1 are marked as Ei, i=1···n;

[0020] Mark the peaks on the broken line C2 as di, i=1···n, where n is a positive integer, and mark the troughs on the broken line C1 as ei, i=1···n;

[0021] Compare the number of Di and Ei with the number of di and ei respectively. If the number of Di and di is the same, and the number of Ei and ei is the same, proceed to the next step, otherwise it means that the simulation result evaluation is unqualified;

[0022] Match the time period of Di with the time period of di, and the time period of Ei with the time period of ei. If all Di and di time periods are the same, and all Ei and ei time periods are the same, proceed to the next step, otherwise it means that the simulation result evaluation is unqualified;

[0023] Construct a reasoning time analysis sub-model, output the analysis results, and judge whether the simulation results are qualified based on the analysis results.

[0024] As a further solution of the present invention: the specific analysis process of the reasoning time analysis sub-model is:

[0025] The time points at which the ten real-time multi-dimensional irradiation data are imported into the digital bionic model are marked as Ti, i = 1···10;

[0026] The time point when the digital bionic model outputs the simulation results after receiving ten real-time multi-dimensional irradiation data is marked as ti, i=1···10;

[0027] Calculate the inference time of each digital bionic model ti=(ti-Ti),i=1···10;

[0028] Will ti is compared with the second preset value. ti is greater than the second preset value, then ti is planned into the first sequence, if ti is less than or equal to the second preset value, then ti is planned into the second sequence;

[0029] In the first series of statistics The number of ti is marked as F, and the first sequence is screened The maximum value of ti is marked as tmax;

[0030] If F is greater than the third preset value, or If tmax is greater than the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is abnormal. If F is less than or equal to the third preset value, and If tmax is less than or equal to the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is normal;

[0031] If the analysis result shows that the reasoning time of the digital bionic model is normal, it means that the simulation result is qualified.

[0032] As a further solution of the present invention: in step 4, the specific process of optimizing and adjusting the power station design solution is as follows:

[0033] Use digital bionic models to obtain the power plant design scheme with the highest power generation under different irradiation conditions;

[0034] Obtain all radiation conditions of the power station on that day from the Meteorological Bureau, and mark the time period of each radiation condition as Gi according to the timeline, i=1···n, where n is a positive integer;

[0035] The power plant design scheme with the maximum power generation corresponding to the irradiation condition Gi is marked as gi, i = 1···n;

[0036] Collect the number of staff on duty at the current power station and mark it as R;

[0037] Compare the gi of two adjacent Gi, calculate the difference between them, and then calculate the workload of replacing the power station design scheme based on the difference;

[0038] A scheme change analysis sub-model is established to analyze and determine whether the power plant design scheme of the node should be replaced or adjusted.

[0039] As a further solution of the present invention: the specific analysis process of the solution change analysis sub-model is:

[0040] Compare the gi of two adjacent Gi in terms of photovoltaic panel type, installation inclination, and array layout;

[0041] If the gi of two adjacent Gi are different in photovoltaic panel type, a first difference weight value v1=3 is assigned; if the gi of two adjacent Gi are the same in photovoltaic panel type, a first difference weight value v1=0 is assigned;

[0042] If the installation inclination angles of two adjacent Gi are different, a second difference weight value v2=1 is assigned; if the installation inclination angles of two adjacent Gi are the same, a second difference weight value v2=0 is assigned;

[0043] If the gi of two adjacent Gi are different in array layout, a third difference weight value v3=2 is assigned; if the gi of two adjacent Gi are the same in array layout, a third difference weight value v3=0 is assigned;

[0044] Calculate the comprehensive difference value V=(v1+v2+v3) of the gi of two adjacent Gi;

[0045] Calculate the workload of replacing the power station design S = (V* ),in, is the default value;

[0046] Calculate the average working time of each on-duty staff of the current power station: taverage = (S / R);

[0047] The duration corresponding to the Gi with the later timeline among two adjacent Gi is extracted and marked as t later. The t average is compared with t later, and different change plans are adopted according to the comparison results.

[0048] As a further solution of the present invention: the modification scheme is specifically as follows:

[0049] If t > (2*t average), the first change plan is adopted, that is, the different photovoltaic panel types, installation inclination angles, and array layouts are adjusted;

[0050] If (2*t average) ≥ t after > t average, the second change plan is adopted, that is, any two items are randomly selected from the different photovoltaic panel types, installation inclination angles, and array layouts for adjustment;

[0051] If tpreset < tafter ≤ taverage, the third change scheme is adopted, that is, any one of the different photovoltaic panel types, installation inclination angles, and array layouts is randomly selected for adjustment, where tpreset is the preset value;

[0052] If t≤tpreset later, the power station design plan will not be replaced or adjusted.

[0053] As a further solution of the present invention: in step 5, the complex situation includes power grid failure and weather changes, and the formulated countermeasures include:

[0054] In response to the risk of power grid failure, during the power grid failure, the power station output is adjusted according to the load conditions to ensure the power supply of key loads while avoiding overload operation;

[0055] In response to the risks of weather changes, a meteorological monitoring system is established to pay real-time attention to weather changes and issue early warning information in a timely manner so that power stations can prepare in advance.

[0056] As a further solution of the present invention: the specific establishment process of the digital simulation model is:

[0057] Collect multi-dimensional radiation data including total solar radiation, direct radiation, scattered radiation, and reflected radiation;

[0058] Obtain information on the geographical location, topography, climate conditions and seasonal changes of the power station location;

[0059] Collect design plans for the power plant, including the type of photovoltaic panels, installation angle, and array layout;

[0060] By using computer technology and virtual reality technology, the power station is abstracted from reality and a digital model of the power station is established.

[0061] As a further solution of the present invention: after the digital simulation model is established, the collected irradiation data is simulated and corrected using the sky scattered radiation anisotropy model and the dual reference meteorological station radiation correction method to more accurately reflect the actual irradiation conditions, and the simulated multi-dimensional irradiation data is integrated into the digital model of the power station to ensure that the digital model can receive and process multi-dimensional irradiation data for simulation calculations.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This application can improve the accuracy of the results output by the simulation model, thereby more effectively optimizing and adjusting the power plant design plan. In addition, in order to ensure the reliability of the simulation model, after the simulation model is constructed, its reasoning time and the difference with the actual results are analyzed to quickly and accurately determine whether the constructed simulation model is qualified.

[0064] 2. The irradiation conditions of photovoltaic power stations are constantly changing every day. Each irradiation condition corresponds to an optimal design plan, but adjusting the power station design plan requires manpower and material resources. This application adopts different change plans for different actual conditions, which has better applicability while increasing power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a high-precision simulation method for power generation based on multi-dimensional irradiation data and power station design;

[0066] Figure 2 Schematic diagram of broken lines C1 and C2 in the high-precision simulation method for power generation based on multi-dimensional irradiation data and power station design. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] As mentioned in the background technology of this application, research has found that the models constructed by existing high-precision power generation simulation methods usually focus on the modeling of various components such as photovoltaic power generation units, inverters, energy storage devices, electrical parameters, etc., but ignore the impact of multi-dimensional irradiation data and power station design, resulting in low accuracy of the results output by the simulation model, and thus unable to more effectively optimize and adjust the power station design plan, which has certain defects.

[0069] In order to solve the above-mentioned defects, the present application discloses a high-precision power generation simulation method based on multi-dimensional irradiation data and power station design, which can improve the accuracy of the results output by the simulation model, and thus more effectively optimize and adjust the power station design scheme.

[0070] The following will describe in detail how the solution of the present application solves the above technical problems in conjunction with the accompanying drawings.

[0071] See also Figure 1 In an embodiment of the present invention, a high-precision simulation method for power generation based on multi-dimensional irradiation data and power station design includes the following steps: Step 1: Collect multi-dimensional irradiation data and power station design schemes, and use them to establish a digital simulation model of the power station; Step 2: Run the digital model on a computer to simulate the working state of the power station, and obtain the power generation performance parameters of the power station under different irradiation conditions; Step 3: Collect real-time multi-dimensional irradiation data and the corresponding power station power generation, and use this to evaluate the simulation results. If the evaluation is qualified, proceed to the next step. If the evaluation is unqualified, return to step 1 to re-establish the digital bionic model of the power station; Step 4: Simulate the power generation under different design schemes, and optimize and adjust the power station design scheme; Step 5: Simulate the operation of the power station under complex conditions, predict risks and formulate corresponding countermeasures. The present application can improve the accuracy of the results output by the simulation model, and thus optimize and adjust the power station design scheme more effectively.

[0072] In this embodiment, the specific process of evaluating the simulation results is: collecting real-time multi-dimensional irradiation data in ten consecutive time periods and marking it as Ai, i=1···10; collecting the actual power generation of the power station corresponding to Ai and marking it as Bi, i=1···10; importing Ai into the digital bionic model, obtaining the corresponding power generation of the power station and marking it as ai, i=1···10; comparing Ai with ai one by one, if the absolute value of the difference between all Ai and ai is less than the first preset value, proceed to the next step, if the absolute value of the difference between any group of Ai and ai is greater than or equal to the first preset value, it means that the simulation result evaluation is unqualified; representing Ai and ai on the same line graph, wherein the line graph corresponding to Ai is marked as C1, and the line graph corresponding to ai is marked as C2, such as Figure 2As shown; mark the peak on the broken line C1 as Di, i=1···n, where n is a positive integer, and then mark the trough on the broken line C1 as Ei, i=1···n; mark the peak on the broken line C2 as di, i=1···n, where n is a positive integer, and then mark the trough on the broken line C1 as ei, i=1···n; compare the number of Di and Ei with the number of di and ei respectively, if the number of Di and di is the same, and the number of Ei and ei is the same, then proceed to the next step, otherwise it means that the simulation result evaluation is unqualified; match the time period where Di is located with the time period where di is located, and match the time period where Ei is located with the time period where ei is located. If all the time periods where Di and di are the same, and all the time periods where Ei and ei are the same, then proceed to the next step, otherwise it means that the simulation result evaluation is unqualified; construct an inference time analysis sub-model, output the analysis results, and judge whether the simulation results are qualified according to the analysis results. In order to ensure the reliability of the simulation model, after the simulation model is constructed, its inference time and the difference with the actual results are analyzed to quickly and accurately determine whether the constructed simulation model is qualified. This application is aimed at comparing the simulation results with the actual results, collecting multiple groups of data for individual and overall comparison, and finally constructing a line graph to extract the peak and trough features for further comparison, so as to determine whether the simulation results are qualified.

[0073] In this embodiment, the specific analysis process of the inference time analysis sub-model is as follows: the time point when ten real-time multi-dimensional irradiation data are imported into the digital bionic model is marked as Ti, i=1···10; the time point when the digital bionic model outputs the simulation result after receiving the ten real-time multi-dimensional irradiation data is marked as ti, i=1···10; the inference time of the digital bionic model is calculated each time ti=(ti-Ti),i=1···10; ti is compared with the second preset value. ti is greater than the second preset value, then ti is planned into the first sequence, if ti is less than or equal to the second preset value, then ti is planned into the second sequence; statistics are in the first sequence The number of ti is marked as F, and the first sequence is screened The maximum value of ti is marked as tmax; if F is greater than the third preset value, or If tmax is greater than the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is abnormal. If F is less than or equal to the third preset value, and If tmax is less than or equal to the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is normal; if the analysis result is that the reasoning time of the digital bionic model is normal, it means that the simulation result is qualified. Reasoning time is an important indicator for evaluating model performance. It directly affects the response speed and user experience of the model in practical applications. If the reasoning time is too long, then by the time the simulation result comes out, it is no longer timely.

[0074] In this embodiment, in step 4, the specific process of optimizing and adjusting the power station design scheme is as follows: obtaining the power station design scheme with the largest power generation under different irradiation conditions through the digital bionic model; obtaining all irradiation conditions of the power station on the day from the meteorological bureau, and marking the time period of each irradiation condition as Gi, i=1···n according to the timeline, where n is a positive integer; marking the power station design scheme with the largest power generation corresponding to the irradiation condition of Gi as gi, i=1···n; collecting the number of on-duty staff of the current power station and marking it as R; comparing the gi of two adjacent Gi, calculating the difference between the two, and then calculating the workload of replacing the power station design scheme according to the difference; establishing a scheme change analysis sub-model to analyze and determine whether to replace and adjust the power station design scheme of the node. The irradiation conditions of photovoltaic power stations are constantly changing every day, and each irradiation condition corresponds to an optimal design scheme, but adjusting the power station design scheme requires manpower and material resources. This application adopts different change schemes for different actual conditions, which has better applicability while increasing power generation.

[0075] In this embodiment, the specific analysis process of the scheme change analysis sub-model is as follows: compare the gi of two adjacent Gi in terms of photovoltaic panel type, installation inclination angle, and array layout; if the gi of two adjacent Gi is different in photovoltaic panel type, a first difference weight value v1=3 is assigned; if the gi of two adjacent Gi is the same in photovoltaic panel type, a first difference weight value v1=0 is assigned; if the gi of two adjacent Gi is different in installation inclination angle, a second difference weight value v2=1 is assigned; if the gi of two adjacent Gi is the same in installation inclination angle, a second difference weight value v2=0 is assigned; if the gi of two adjacent Gi is different in array layout, a third difference weight value v3=2 is assigned; if the gi of two adjacent Gi is the same in array layout, a third difference weight value v3=0 is assigned; calculate the comprehensive difference value V=(v1+v2+v3) of the gi of two adjacent Gi; calculate the workload S=(V* ),in, is the preset value; calculate the average working time of each on-duty staff of the current power station, taverage = (S / R); extract the duration corresponding to the Gi with the later timeline in the two adjacent Gi, mark it as tafter, compare taverage with tafter, and adopt different change plans according to the comparison results. The workload required to replace the type of photovoltaic panels, installation inclination, and array layout is different. Among them, the workload corresponding to replacing the type of photovoltaic panels is the largest, and the workload corresponding to replacing the installation inclination is the smallest. Therefore, this application assigns the largest difference value to the photovoltaic panel type and the smallest difference value to the installation inclination.

[0076] In this embodiment, the change scheme is specifically as follows: if t after> (2*t average), the first change scheme is adopted, that is, the photovoltaic panel type, installation inclination angle, and array layout with differences are all adjusted; if (2*t average) ≥ t after> t average, the second change scheme is adopted, that is, any two items are randomly selected from the photovoltaic panel type, installation inclination angle, and array layout with differences for adjustment; if t preset < t after ≤ t average, the third change scheme is adopted, that is, any item is randomly selected from the photovoltaic panel type, installation inclination angle, and array layout with differences for adjustment, wherein t preset is a preset value; if t after ≤ t preset, the power station design scheme is not replaced or adjusted. When adjusting the power station design scheme, the actual situation needs to be considered. If the time is tight and the task is heavy, the power station design scheme is still adjusted in an all-round way. Not only will the adjustment fail to be completed on time, but it will also increase the burden on the staff and bring adverse effects. To this end, this application makes comprehensive adjustments to the power plant design plan when there is sufficient time, and makes selective adjustments to the power plant design plan when time is not particularly sufficient. When time is very short, no adjustment plan is adopted to avoid wasting work (i.e. entering the next stage before the adjustment is completed).

[0077] In this embodiment, in step five, complex situations include power grid failure and weather changes, and the formulated countermeasures include: for the risk of power grid failure, during the power grid failure, adjust the power station output according to the load situation to ensure the power supply of key loads and avoid overload operation; for the risk of weather changes, establish a meteorological monitoring system, pay attention to weather changes in real time, and issue early warning information in a timely manner so that the power station can prepare in advance. Weather change risks include strong winds, rainstorms, heavy rainfall, high temperatures, sandstorms, severe cold and ice and snow weather.

[0078] In this embodiment, the specific process of establishing the digital simulation model is: collecting multi-dimensional irradiation data including total solar radiation, direct radiation, scattered radiation, and reflected radiation; obtaining the geographical location, topography, climate conditions, and seasonal change information of the power station; collecting the design plan of the power station, which includes the type of photovoltaic panels, installation inclination, and array layout; using computer technology and virtual reality technology to abstract the power station from reality and establish a digital model of the power station. The digital model can accurately simulate the operating status of the power station, including power generation efficiency, energy consumption, equipment performance, etc. By inputting different operating parameters and conditions, the model can predict the performance of the power station in different scenarios, providing a scientific basis for the operation and maintenance management of the power station. In the design stage of the power station, the digital model can help engineers evaluate the effects of different design schemes and optimize the layout and equipment configuration of the power station. By simulating the operating data under different schemes, engineers can compare the advantages and disadvantages of different schemes and select the best design scheme. In addition, the digital model can also be used for the upgrading and transformation of the power station. By simulating the operating data before and after the transformation, the transformation effect can be evaluated and the transformation plan can be optimized. The digital model can monitor the operating status of the power station equipment in real time and discover potential fault problems in time. By comparing the model prediction data with the actual operation data, the abnormal behavior of the equipment can be identified and fault diagnosis can be performed. At the same time, the model can also predict future fault trends based on the operating status of the equipment, issue early warning signals in advance, and provide strong support for the operation and maintenance management of the power station. Digital models can also be used for the training and education of power station operation and maintenance personnel. By simulating the actual operating environment of the power station, the operation and maintenance personnel can conduct equipment operation, fault handling and other training in a virtual environment to improve their actual operation and emergency handling capabilities. In addition, digital models can also be used as teaching tools for teaching and scientific research activities in power-related majors. The digital model of the power station provides important decision-making support for power station managers. Through model analysis, managers can understand the operating status, development trends, and potential risks and problems of the power station. This information helps managers make scientific decisions, optimize the operation and maintenance management strategy of the power station, and improve the efficiency and safety of the power station.

[0079] In this embodiment, after the digital simulation model is established, the collected irradiation data is simulated and corrected using the sky scattered radiation anisotropy model and the dual reference meteorological station irradiation correction method to more accurately reflect the actual irradiation situation, and the simulated multi-dimensional irradiation data is integrated into the digital model of the power station to ensure that the digital model can receive and process multi-dimensional irradiation data for simulation calculation. The sky scattered radiation anisotropy model is an important tool for calculating sky scattered radiation. Scattered radiation refers to the radiation generated by the scattering of gas molecules, dust, water droplets, etc. in the atmosphere when the solar radiation passes through the atmosphere. Anisotropy means that the intensity of scattered radiation in different directions is different. Among them, the Perez model is a typical anisotropic scattered radiation model. The model decomposes the solar scattered radiation into three regions: circumstellar scattered radiation, sky scattered radiation, and zenith scattered radiation, and assumes that each region is isotropic. Then, the three regions are integrated and solved separately to obtain the total intensity of sky scattered radiation. The dual reference meteorological station irradiation correction method is a method for improving the accuracy of irradiation measurement. This method is based on measuring the radiation data of the same location at two or more weather stations at the same time, and correcting the measurement error of a single weather station by comparing these data. Specifically, this method requires selecting two weather stations with similar geographical locations and meteorological conditions as reference stations. Then, radiation measurements are performed at the two weather stations at the same time, and the measurement results of the two stations are compared. If there is a difference in the measurement results of the two stations, the measurement error of a single weather station can be corrected by a certain algorithm or model, thereby improving the accuracy of radiation measurement.

[0080] The present invention can improve the accuracy of the results output by the simulation model, and thus optimize and adjust the power station design scheme more effectively. In addition, in order to ensure the reliability of the simulation model, after the simulation model is constructed, the reasoning time and the difference with the actual results are analyzed to quickly and accurately determine whether the constructed simulation model is qualified. The irradiation conditions of photovoltaic power stations are constantly changing every day, and each irradiation condition corresponds to an optimal design scheme, but adjusting the power station design scheme requires manpower and material resources. This application adopts different change schemes for different actual conditions, which has better applicability while increasing power generation.

[0081] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0082] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A high-precision power generation simulation method based on multi-dimensional irradiation data and power station design, characterized in that: The following steps are involved: Step 1: Collect multi-dimensional irradiation data and power plant design plans, and use them to build a digital simulation model of the power plant; Step 2: Run the digital model on the computer to simulate the working state of the power station and obtain the power generation performance parameters of the power station under different irradiation conditions; Step 3: Collect real-time multi-dimensional irradiation data and the corresponding power generation of the power plant, and use this to evaluate the simulation results. If the evaluation is qualified, proceed to the next step. If the evaluation is unqualified, return to step 1 to re-establish the digital bionic model of the power plant; Step 4: Simulate the power generation under different design schemes, and optimize and adjust the power station design scheme; Step 5: Simulate the operation of power plants under complex conditions, predict risks and formulate corresponding response measures; The specific process of evaluating the simulation results is as follows: Real-time multi-dimensional irradiation data in ten consecutive time periods are collected and marked as Ai, i = 1···10; The actual power generation of the power station corresponding to Ai is collected and marked as Bi, i=1···10; Import Ai into the digital bionic model, obtain the corresponding power generation of the power station and mark it as ai, i=1···10; Compare Ai with ai one by one. If the absolute value of the difference between all Ai and ai is less than the first preset value, proceed to the next step. If the absolute value of the difference between any group of Ai and ai is greater than or equal to the first preset value, it means that the simulation result evaluation is unqualified. Represent Ai and ai on the same line graph, where the line graph corresponding to Ai is marked as C1, and the line graph corresponding to ai is marked as C2; The peaks on the broken line C1 are marked as Di, i=1···n, where n is a positive integer, and the troughs on the broken line C1 are marked as Ei, i=1···n; Mark the peaks on the broken line C2 as di, i=1···n, where n is a positive integer, and mark the troughs on the broken line C1 as ei, i=1···n; Compare the number of Di and Ei with the number of di and ei respectively. If the number of Di and di is the same, and the number of Ei and ei is the same, proceed to the next step, otherwise it means that the simulation result evaluation is unqualified; Match the time period of Di with the time period of di, and the time period of Ei with the time period of ei. If all Di and di time periods are the same, and all Ei and ei time periods are the same, proceed to the next step, otherwise it means that the simulation result evaluation is unqualified; Construct the reasoning time analysis sub-model, output the analysis results, and judge whether the simulation results are qualified based on the analysis results; The specific analysis process of the reasoning time analysis sub-model is as follows: The time points at which the ten real-time multi-dimensional irradiation data are imported into the digital bionic model are marked as Ti, i = 1···10; The time point when the digital bionic model outputs the simulation results after receiving ten real-time multi-dimensional irradiation data is marked as ti, i=1···10; Calculate the inference time of each digital bionic model ti=(ti-Ti),i=1···10; Will ti is compared with the second preset value. ti is greater than the second preset value, then ti is planned into the first sequence, if ti is less than or equal to the second preset value, then ti is planned into the second sequence; In the first sequence of statistics The number of ti is marked as F, and the first sequence is screened The maximum value of ti is marked as tmax; If F is greater than the third preset value, or If tmax is greater than the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is abnormal. If F is less than or equal to the third preset value, and If tmax is less than or equal to the fourth preset value, the output analysis result is that the reasoning time of the digital bionic model is normal; If the analysis result shows that the reasoning time of the digital bionic model is normal, then the simulation result is qualified; In step 4, the specific process of optimizing and adjusting the power station design scheme is as follows: Use digital bionic models to obtain the power plant design scheme with the highest power generation under different irradiation conditions; Obtain all radiation conditions of the power station on that day from the Meteorological Bureau, and mark the time period of each radiation condition as Gi according to the timeline, i=1···n, where n is a positive integer; The power plant design scheme with the maximum power generation corresponding to the irradiation condition Gi is marked as gi, i = 1···n; Collect the number of staff on duty at the current power station and mark it as R; Compare the gi of two adjacent Gi, calculate the difference between them, and then calculate the workload of replacing the power station design scheme based on the difference; Establish a scheme change analysis sub-model to analyze and determine whether to replace or adjust the power plant design scheme; The specific analysis process of the scheme change analysis sub-model is as follows: Compare the gi of two adjacent Gi in terms of photovoltaic panel type, installation inclination, and array layout; If the gi of two adjacent Gi are different in photovoltaic panel type, a first difference weight value v1=3 is assigned; if the gi of two adjacent Gi are the same in photovoltaic panel type, a first difference weight value v1=0 is assigned; If the installation inclination angles of two adjacent Gi are different, a second difference weight value v2=1 is assigned; if the installation inclination angles of two adjacent Gi are the same, a second difference weight value v2=0 is assigned; If the gi of two adjacent Gi are different in array layout, a third difference weight value v3=2 is assigned; if the gi of two adjacent Gi are the same in array layout, a third difference weight value v3=0 is assigned; Calculate the comprehensive difference value V=(v1+v2+v3) of the gi of two adjacent Gi; Calculate the workload of replacing the power station design S = (V* ),in, is the default value; Calculate the average working time of each on-duty staff of the current power station: taverage = (S / R); The duration corresponding to the Gi with the later timeline among two adjacent Gi is extracted and marked as t later. The t average is compared with t later, and different change plans are adopted according to the comparison results.

2. The high-precision power generation simulation method based on multi-dimensional irradiation data and power station design according to claim 1 is characterized in that: The specific change plan is: If t > (2*t average), the first change plan is adopted, that is, the different photovoltaic panel types, installation inclination angles, and array layouts are adjusted; If (2*t average) ≥ t after > t average, the second change plan is adopted, that is, any two items are randomly selected from the different photovoltaic panel types, installation inclination angles, and array layouts for adjustment; If tpreset < tafter ≤ taverage, the third change scheme is adopted, that is, any one of the different photovoltaic panel types, installation inclination angles, and array layouts is randomly selected for adjustment, where tpreset is the preset value; If t≤tpreset later, the power station design plan will not be replaced or adjusted.

3. The high-precision power generation simulation method based on multi-dimensional irradiation data and power station design according to claim 2 is characterized in that: In step 5, complex situations include power grid failures and weather changes, and the response measures developed include: In response to the risk of power grid failure, during the power grid failure, the power station output is adjusted according to the load conditions to ensure the power supply of key loads while avoiding overload operation; In response to the risks of weather changes, a meteorological monitoring system is established to pay real-time attention to weather changes and issue early warning information in a timely manner so that power stations can prepare in advance.

4. The high-precision power generation simulation method based on multi-dimensional irradiation data and power station design according to claim 3 is characterized in that: The specific establishment process of the digital simulation model is as follows: Collect multi-dimensional radiation data including total solar radiation, direct radiation, scattered radiation, and reflected radiation; Obtain information on the geographical location, topography, climate conditions and seasonal changes of the power station location; Collect design plans for the power plant, including the type of photovoltaic panels, installation angle, and array layout; By using computer technology and virtual reality technology, the power station is abstracted from reality and a digital model of the power station is established.

5. The high-precision power generation simulation method based on multi-dimensional irradiation data and power station design according to claim 4 is characterized in that: After the digital simulation model is established, the collected irradiation data is simulated and corrected using the sky scattered radiation anisotropy model and the dual reference meteorological station radiation correction method to more accurately reflect the actual irradiation situation, and the simulated multi-dimensional irradiation data is integrated into the digital model of the power station to ensure that the digital model can receive and process multi-dimensional irradiation data for simulation calculations.

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