Method and device for predicting power generation of photovoltaic power station based on monte carlo method

Abstract

The application discloses a kind of photovoltaic power station power generation based on the prediction method and device of Monte Carlo method, method includes the following steps: (1), the mismatch attenuation rate S1 caused by the different aging rate of component in simulation year is calculated accumulatively;(2), the aging attenuation rate S2 of photovoltaic module in simulation year is calculated accumulatively;With the average loss calculated in the middle of the year, the aging attenuation rate S2 of photovoltaic module in simulation year is obtained accumulatively;(3), according to the total attenuation rate S=S1+S2 of photovoltaic system accumulatively in simulation year and the power generation of photovoltaic power station in the first year, the power generation of photovoltaic power station can be predicted.The application provides a kind of based on Monte Carlo method, for the attenuation rate of system mismatch caused by the different aging rate of component, provides a calculation method, by data comparison, the mismatch attenuation rate of system accumulatively in simulation year calculated by the application and the mismatch attenuation rate accumulatively in PVsyst have no significant difference.

Description

Technical Field

[0001] This invention discloses a method and apparatus for predicting the power generation of a photovoltaic power plant based on the Monte Carlo method, belonging to the field of photovoltaic technology. Background Technology

[0002] Solar energy is the most direct and convenient clean energy source, and photovoltaic (PV) power generation is a major form of solar energy utilization. In recent years, with the widespread use of PV power generation technology, power generation prediction has become particularly important. It plays a crucial role in the operation and maintenance of PV power plants, improving system power generation capacity, meeting grid connection requirements, and enhancing grid stability. It is also a key indicator for evaluating the return on investment of PV power plants. Currently, there is no model in the PV industry that can accurately and comprehensively predict the power generation of PV power plants. PVsyst is the benchmark for PV design software, and most companies use this simulation software as their standard.

[0003] In recent years, the main methods for predicting the power generation of photovoltaic power plants include physical modeling, statistical analysis, and machine learning. These methods may be affected by factors such as meteorological data and data volume, leading to inaccurate predictions of photovoltaic power plant power generation. Therefore, it is necessary to continuously optimize and improve the prediction models to enhance prediction accuracy.

[0004] Physical modeling method: This method predicts the power generation of photovoltaic power plants based on the solar radiation transfer equation and the photovoltaic module operation equation. However, this method requires detailed geographical information of the photovoltaic power plant and module parameters, and the modeling process is complex.

[0005] Statistical analysis method: This method predicts the power generation of photovoltaic power plants by studying the patterns in a large amount of historical data. It requires less computation and can meet real-time requirements, but it does require a large amount of historical data.

[0006] Machine learning method: also known as the "black box" method. This method uses machine learning to train on sample data, and then uses historical data from photovoltaic power plants to predict their power generation. The predicted power generation can fluctuate significantly with changes in weather conditions, and it requires high-quality historical data. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies in accurately and comprehensively predicting the power generation of photovoltaic power plants. It provides a calculation method for the degradation rate caused by system mismatch due to different component aging rates, so as to achieve a more accurate and comprehensive prediction of the power generation of photovoltaic power plants.

[0008] The implementation idea of ​​this invention is as follows: Based on the Monte Carlo method algorithm, the cumulative mismatch degradation rate of the system caused by the different aging rates of the components within a simulated year is obtained. Normal aging of components occurs at a certain average aging degradation rate, typically 0.004 per year. Under a given simulated year, the average loss calculated mid-year is used to obtain the cumulative aging degradation rate of the photovoltaic modules within the simulated year. The cumulative mismatch degradation rate of the system and the cumulative aging degradation rate of the components are added together to obtain the total cumulative degradation rate of the photovoltaic system within the simulated year. Finally, by combining the power generation of the photovoltaic power plant in its first year of operation with the total cumulative degradation rate of the photovoltaic system within the simulated year, the power generation of the photovoltaic power plant can be predicted more accurately and comprehensively.

[0009] The following are explanations of some key terms in this invention:

[0010] Photovoltaic modules, also known as solar panels, are the core component of a solar power generation system.

[0011] String: In a photovoltaic power generation system, several photovoltaic modules are connected in series to form a circuit unit with a certain DC output, which is called a module string or string.

[0012] Monte Carlo method: also known as statistical simulation or random sampling technique, is a type of random simulation method. Based on probability and statistical theory, it uses random numbers (or more commonly pseudo-random numbers) to solve many computational problems. It links the problem to be solved with a certain probability model and uses a computer to perform statistical simulation or sampling to obtain an approximate solution.

[0013] Photovoltaic module annual degradation rate: The ratio of the difference between the maximum output power of a photovoltaic module after it has been running for a period of time (usually one calendar year) and the maximum output power at the beginning of operation, to the maximum output power at the beginning of operation.

[0014] Photovoltaic system mismatch degradation rate: The coefficient of system mismatch loss caused by the different aging degradation rates of various components in a photovoltaic system.

[0015] The technical solution of the present invention is as follows:

[0016] A method for predicting the power generation of a photovoltaic power plant based on the Monte Carlo method includes the following steps:

[0017] (1) Calculate the cumulative mismatch attenuation rate S1 of the system caused by the different aging rates of components within the simulation year;

[0018] The aging rates of different components in a photovoltaic system vary. If the degradation rate is distributed around the average aging rate, it will lead to additional losses due to system mismatch, which will increase over time. To calculate the cumulative mismatch degradation rate of the system in a simulated year, the cumulative aging degradation rate Sa of the photovoltaic modules and the cumulative average total degradation rate Sd of the system in the simulated year should be calculated. The difference between the latter and the former is the cumulative mismatch degradation rate S1 of the photovoltaic system in the simulated year, which is Sd - Sa.

[0019] (1-1) Calculate the cumulative aging degradation rate Sa of the photovoltaic modules within the simulation year;

[0020] Normal aging of components is mainly caused by the slow degradation of their cells and the performance degradation of the encapsulation materials.

[0021] Considering the normal aging and degradation of the components, the maximum power of the system in each year of the simulation year is obtained by iterative calculation and summed, denoted as P1. Assuming that the photovoltaic modules do not age, the sum of the maximum power of the system in the simulation year is P2 = the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year. Finally, according to the formula for calculating the degradation rate Sa = (P1-P2) / P2, the cumulative aging degradation rate Sa of the photovoltaic modules in the simulation year is obtained.

[0022] The specific calculation steps are as follows:

[0023] (1-1-1) Based on the input component parameters and average annual degradation rate, generate the open-circuit voltage and short-circuit current values ​​of the components for the current year. Then, select the parameter model, either the new five-parameter model or the PVsyst model, to determine the current and voltage of the components. Then, multiply the voltage of the components by the number of components to obtain the voltage of the entire photovoltaic system string. Next, generate a sequence with an initial value of 0 and the last value of the component short-circuit current, with a step size of 0.01, which is the current of the photovoltaic system string. The power of the photovoltaic system for the current year can be calculated and the maximum power for the current year can be found. Repeat the above steps N times, where N is the simulation year. After the loop ends, sum the maximum power of the system found in the N loops, i.e., P1.

[0024] (1-1-2) Assuming that the photovoltaic modules do not age, the sum of the maximum power of the system in the simulation year, P2, is the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year.

[0025] (1-1-3) Considering the normal aging and degradation of the components, the cumulative aging and degradation rate of the photovoltaic modules in the simulated year is Sa = (P1-P2) / P2.

[0026] (1-2) The Monte Carlo method was used to calculate the cumulative average total degradation rate Sd of the photovoltaic system during the simulation year;

[0027] The Monte Carlo method (random large distribution) was used, with the following assumptions:

[0028] The rate of decay of each component is constant over its decay period;

[0029] The distribution selection is limited to the 2σ region (95% hit rate) because high discrepancies would result in high mismatch losses.

[0030] Considering the normal aging and degradation of the components and the mismatch loss caused by the different degradation rates of each component, the maximum power of the system in each year of the simulation year is obtained by iterative calculation and summed, denoted as P3. Assuming that the photovoltaic system does not age, the sum of the maximum power of the system in the simulation year, P4, is equal to the product of the maximum power of the photovoltaic system in the first year of operation and the maximum power in the simulation year. Finally, according to the formula for calculating the degradation rate of photovoltaic components, Sc = (P3-P4) / P4, the cumulative total degradation rate Sc of the photovoltaic system in the simulation year is obtained. The above steps are repeated a times to obtain the total degradation rate s for these a cycles. Then, the cumulative average total degradation rate Sd of the photovoltaic system in the simulation year is Sd = s / a.

[0031] The specific calculation steps are as follows:

[0032] (1-2-1) Input the relevant parameters and average annual decay rate of the component, and randomly generate several open-circuit voltages and short-circuit currents that are normally distributed during the operating time. The mean of the normal distribution is the open-circuit voltage and short-circuit current of the component in that year, and the standard deviation is the product of the mean, the standard deviation of the distribution and the empirical coefficient. The array size is several.

[0033] From the several open-circuit voltages and short-circuit currents generated each year, m open-circuit voltages and short-circuit currents are randomly selected and sorted in descending order, where m represents the number of modules. Then, the open-circuit voltage and short-circuit current values ​​obtained by each module are made to be between the mean - 2 × standard deviation of the normal distribution and the mean of the normal distribution. Next, a parameter model is selected to determine the current and voltage of the modules. Then, the voltage of the photovoltaic system string is obtained by accumulating the voltage of the modules. Subsequently, a sequence with an initial value of 0 and the last value of the module short-circuit current and a step size of 0.01 is generated, which is the current of the photovoltaic system string. The power of the photovoltaic system in that year can be calculated and the maximum power of that year can be found. The above steps are repeated N times, where N is the simulation year. After the cycle is completed, the maximum power of the system found in each year after the N cycles is summed, which is P3.

[0034] (1-2-2) Assuming the photovoltaic system does not age, the sum of the maximum power of the system in the simulation year, P4, is equal to the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year.

[0035] (1-2-3) Considering the normal aging and degradation of the components and the different degradation rates of each component, the system will suffer mismatch losses. The cumulative total degradation rate of the photovoltaic system in the simulation year is Sc=(P3-P4) / P4.

[0036] (1-2-4) Repeat the above three steps (1-2-1), (1-2-2), and (1-2-3) a times, where a represents the number of attempts. Sum the total attenuation rate of these a times and set it as s. Then, the cumulative average total attenuation rate of the photovoltaic system in the simulation year is Sd = s / a.

[0037] (1-3) Based on the cumulative aging degradation rate Sa of the components and the cumulative average total degradation rate Sd of the system obtained from steps (1-1) and (1-2) above, the cumulative mismatch degradation rate S1 caused by the different aging rates of the components within the simulation year is calculated as Sd = Sd - Sa. For example, if the cumulative mismatch degradation rate of the system within 5 years and 10 years is 0.0014 and 0.0138 respectively, then the cumulative mismatch degradation rate of the system within 7 years caused by the different aging rates of the components is 0.0014 + 2 × (0.0138 - 0.0014) / 5 = 0.00636. The cumulative mismatch degradation rate of the system in other simulation years can be calculated according to the above principle.

[0038] (2) Calculate the cumulative aging degradation rate S2 of the photovoltaic modules within the simulation year;

[0039] The cumulative aging degradation rate S2 of the photovoltaic modules in the simulated year is obtained by using the average loss calculated at mid-year.

[0040] Normal aging of photovoltaic modules is carried out according to a certain average aging degradation rate, generally not exceeding 0.7% per year. The default value in PVsyst is 0.4%. Within a given year, the cumulative aging degradation rate S2 of the photovoltaic modules can be obtained by using the average loss calculated mid-year. For example, the cumulative aging degradation rate over 7 years is 0.004 × 6.5 = 0.026. The cumulative aging degradation rate of modules in other simulation years can be calculated according to the above principle.

[0041] (3) Predicting the future power generation of photovoltaic power plants

[0042] Based on the cumulative total degradation rate of the photovoltaic system over the simulated years, S = S1 + S2, and the power generation of the photovoltaic power station in the first year, the power generation of the photovoltaic power station can be predicted. For example, if the power generation of the photovoltaic power station in the first year is 8743 kWh, and the cumulative total degradation rate of the photovoltaic system over 7 years is S = 0.00636 + 0.026 = 0.03236, then the power generation of the photovoltaic power station in the 7th year is 8743 × (1 - 0.03236) = 8460 kWh. The prediction of the photovoltaic power station's power generation in other simulated years can be calculated according to the above principles.

[0043] Using the algorithm of this invention, the prediction of photovoltaic power plant power generation can be processed. According to another aspect of the invention, embodiments of the invention also provide a photovoltaic power plant power generation prediction device based on the Monte Carlo method, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the photovoltaic power generation prediction method based on the Monte Carlo method described above. According to another aspect of the invention, embodiments of the invention also provide a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute the photovoltaic power generation prediction method described in any embodiment of the invention. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0044] The beneficial effects of this invention are:

[0045] This invention proposes a Monte Carlo method to calculate the degradation rate of a system due to mismatch caused by different component degradation rates. Data comparison shows that the cumulative mismatch degradation rate calculated by this invention within the simulated year is not significantly different from the cumulative mismatch degradation rate in PVsyst, thus proving the feasibility of this calculation method. Normal aging degradation of components is calculated based on a certain average aging degradation rate, typically 0.4%. For a given year, the simulation uses the average loss calculated mid-year, allowing the calculation of the cumulative aging degradation rate of photovoltaic modules within the simulated year. The sum of the cumulative system mismatch degradation rate and the cumulative component aging degradation rate gives the total cumulative degradation rate of the photovoltaic system within the simulated year. Comparison with the power generation predicted by PVsyst reveals no significant difference between the simulation results of this invention and PVsyst, thus solving the previous problem in the photovoltaic industry of accurately and comprehensively predicting the future power generation of photovoltaic power plants. Furthermore, this prediction method is simple and the prediction results are highly accurate. In conclusion, this invention is feasible for accurately and comprehensively predicting the future power generation of photovoltaic power plants. Attached Figure Description

[0046] Figure 1 This is a flowchart of the prediction method of the present invention;

[0047] Figure 2 This is the PVsyst simulation result of Example 1;

[0048] Figure 3 These are the PVsyst simulation results for Example 2;

[0049] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3. Detailed Implementation

[0050] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0051] Application scenarios:

[0052] Applying the degradation rate of photovoltaic modules to simulate solar performance can help evaluate the actual performance of photovoltaic modules, accurately and comprehensively predict the power generation of photovoltaic power plants, and compare the performance of different modules, thereby optimizing the design and maintenance of photovoltaic systems.

[0053] Specific scenarios:

[0054] 1. Actual performance of photovoltaic modules: By monitoring the power generation of the modules and using the degradation rate, the actual performance of the modules is calculated to determine whether maintenance or replacement is required.

[0055] 2. Future power generation of the photovoltaic system: The future power generation of the photovoltaic system is calculated by using the degradation rate and the power generation of the photovoltaic power plant in the first year, in order to help predict the long-term performance and benefits of the system.

[0056] 3. Performance of different photovoltaic modules: The performance of different photovoltaic modules is evaluated by comparing their degradation rates, and the most suitable module for a specific application is selected.

[0057] Example 1:

[0058] I. Select basic information such as component model and number of series and parallel connections.

[0059] Basic information:

[0060] The component manufacturer is: Suntech

[0061] The component model is: STP-375-S-24-Vfh

[0062] Number of strings connected in series: 17

[0063] Number of series and parallel connections: 1

[0064] Simulation years: 5, 10, 15, 20, 25

[0065] II. Figure 1 As shown, the cumulative mismatch degradation rate S1 of the system caused by the different aging rates of components within the simulation years is calculated.

[0066] 1. Calculate the cumulative aging degradation rate Sa of the photovoltaic modules within the simulated year.

[0067] Specific procedures:

[0068] (1) Based on the input component parameters and average annual decay rate, generate the open-circuit voltage and short-circuit current values ​​of the components for the current year. Then, select the PVsyst parameter model to determine the current and voltage of the components. Then, multiply the voltage of the components by 17 (number of components) to obtain the voltage of the entire photovoltaic system string. Next, generate a sequence with an initial value of 0, an end value of the component short-circuit current, and a step size of 0.01, which is the current of the photovoltaic system string. Thus, the power of the photovoltaic system for the current year can be obtained and the maximum power of the system can be found. Repeat the above steps N times (N is the simulation year, which is 5, 10, 15, 20, 25 in this invention). After the loop ends, sum the maximum power of the system found in the N loops, i.e., P1.

[0069] (2) Assuming that the photovoltaic modules do not age, the sum of the maximum power of the system in the simulation year, P2, is the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year.

[0070] (3) The normal aging and degradation of the components were taken into account. The cumulative aging and degradation rate of the photovoltaic modules in the simulation year was Sa = (P1-P2) / P2.

[0071] 2. Calculate the cumulative average total degradation rate Sd of the photovoltaic system within the simulation year.

[0072] Specific procedures:

[0073] (1) Input the relevant parameters and average annual decay rate of the component, and randomly generate several open-circuit voltages and short-circuit currents that are normally distributed during the operating time (25 years). The mean of the normal distribution is the open-circuit voltage and short-circuit current of the component in that year, and the standard deviation is the product of the mean, the standard deviation of the distribution and the empirical coefficient. The array size is several. From the numerous open-circuit voltages and short-circuit currents generated each year, 17 open-circuit voltages and short-circuit currents (number of modules) are randomly selected and sorted from largest to smallest. Then, the open-circuit voltage and short-circuit current values ​​of each module are made to fall between the mean - 2 × standard deviation of a normal distribution and the mean of a normal distribution. Next, a parameter model is selected to determine the current and voltage of the modules. The module voltages are then summed to obtain the voltage of the photovoltaic system string. A sequence starting at 0, ending at the module short-circuit current, and with a step size of 0.01 is generated, representing the current of the photovoltaic system string. From this, the power of the photovoltaic system for that year can be obtained, and the maximum power of the system can be found. This process is repeated N times (N is the simulation year; in this invention, it is taken as 5, 10, 15, 20, 25). After the cycle ends, the maximum power of the system found in the N cycles is summed, resulting in P3.

[0074] (2) Assuming the photovoltaic system does not age, the sum of the system's maximum power during the simulation years, P4, equals the sum of the photovoltaic system's maximum power during the first year of operation.

[0075] The product of the maximum power in a given year and the simulated year.

[0076] (3) The normal aging and degradation of components and the system mismatch caused by the different degradation rates of each component are taken into account.

[0077] Loss, the cumulative total degradation rate of the photovoltaic system within the simulated year, Sc = (P3 - P4) / P4.

[0078] (4) Repeat the above three steps (1)(2)(3) 100 times (100 attempts), and sum the total attenuation rate of these 100 times as s. Then the cumulative average total attenuation rate of the photovoltaic system in the simulation year is Sd=s / 100.

[0079] 3. Calculate the cumulative mismatch degradation rate S1 of the system caused by the different aging rates of components within the simulation year.

[0080] Based on the cumulative aging degradation rate Sa of the components and the cumulative total degradation rate Sd of the system within the simulation years obtained from steps 1 and 2 above, the cumulative mismatch degradation rate S1 caused by the different aging rates of the components within the simulation years (5, 10, 15, 20, and 25 in this invention) can be obtained as S1 = Sd - Sa. Through the above calculation, the cumulative mismatch degradation rates within 5 years and 10 years in this case are 0.0014 and 0.0138, respectively. Then, using the interpolation method, the cumulative mismatch degradation rate S1 within 7 years is obtained as S1 = 0.0014 + 2 × (0.0138 - 0.0014) / 5.

[0081] III. Calculate the cumulative aging degradation rate S2 of photovoltaic modules within the simulation year.

[0082] Normal aging of photovoltaic modules is carried out according to a certain average aging degradation rate, generally not exceeding 0.7% per year. The default value in PVsyst is 0.4%. Within a given year, the cumulative aging degradation rate S2 of the photovoltaic modules within the simulated year can be obtained by using the average loss calculated at the mid-year. In this case, the cumulative aging degradation rate over 7 years is S2 = 0.004 × 6.5 = 0.026.

[0083] IV. Predicting the future power generation of photovoltaic power plants

[0084] The power generation of a photovoltaic power station can be accurately and comprehensively predicted based on the cumulative total degradation rate of the photovoltaic system over the simulation years, S = S1 + S2, and the power generation of the photovoltaic power station in the first year. In this case, the power generation of the photovoltaic power station in the first year is 8743 kWh. The cumulative total degradation rate of the photovoltaic system over 7 years is S = 0.00636 + 0.026 = 0.03236. Therefore, the power generation of the photovoltaic power station in the 7th year is 8743 × (1 - 0.03236) = 8460 kWh.

[0085] V. Comparison of Simulation Results

[0086] (1) Comparison of cumulative mismatch attenuation rate of the system caused by different component aging rates within the simulation year

[0087] PVsyst simulation results are as follows Figure 2 As shown.

[0088] The results of running this invention are shown in Table 1.

[0089] 5 0.14％ 0.14％ 0 10 1.38％ 1.39％ -0.01％ 15 1.63％ 1.62％ 0.01％ 20 1.77％ 1.76％ 0.01％ 25 3.29％ 3.39％ -0.10％

[0090] Table 1

[0091] By comparing the cumulative mismatch attenuation rate of the system in different simulation years, it can be seen that the difference between the cumulative mismatch attenuation rate calculated by this invention and the cumulative mismatch attenuation rate in PVsyst is between -0.1% and 0.01%. Therefore, it is feasible to use this invention to calculate the system mismatch attenuation rate.

[0092] (2) Comparison of predicted power generation

[0093] The PVsyst simulation results are shown in Table 2:

[0094]

[0095] Table 2

[0096] The results of running this invention are shown in Table 3.

[0097]

[0098] Table 3

[0099] By comparing the power generation figures, it can be seen that the relative error between the power generation predicted by this invention and PVsyst is between -0.27% and 0.00%. Therefore, this invention can accurately predict the future power generation of photovoltaic power plants.

[0100] Example 2:

[0101] The verification was performed again according to the procedure in Example 1, with relevant parameters randomly changed:

[0102] Basic information:

[0103] The component manufacturer is: Suntech

[0104] The component model is: STP-375-S-24-Vfh

[0105] Number of strings connected in series: 18

[0106] Number of series and parallel connections: 2

[0107] Mismatch duration: 5, 10, 15, 20, 25 years

[0108] (1) Comparison of cumulative mismatch attenuation rate of the system caused by different component aging rates within the simulation year

[0109] PVsyst simulation results are as follows Figure 3 As shown:

[0110] The results of running this invention are shown in Table 4:

[0111]

[0112] Table 4

[0113] By comparing the cumulative mismatch attenuation rate of the system in different simulation years, it can be seen that the difference between the cumulative mismatch attenuation rate calculated by this invention and the cumulative mismatch attenuation rate in PVsyst is between -0.07% and 0.11%. Therefore, it is feasible to use this invention to calculate the system mismatch attenuation rate.

[0114] (1) Comparison of predicted power generation

[0115] The PVsyst simulation results are shown in Table 5:

[0116]

[0117] Table 5

[0118] The results of running this invention are shown in Table 6:

[0119] 1 12536 0.00％ 0.00％ 2 12456 -0.64％ -0.14％ 3 12401 -1.08％ -0.07％ 4 12346 -1.52％ 0.00％ 5 12291 -1.95％ 0.05％ 6 12202 -2.66％ -0.16％ 7 12117 -3.34％ -0.15％ 8 12032 -4.02％ -0.13％ 9 11948 -4.69％ -0.11％ 10 11863 -5.37％ -0.08％ 11 11800 -5.87％ 0.12％ 12 11738 -6.37％ 0.07％ 13 11676 -6.86％ 0.03％ 14 11613 -7.36％ -0.03％ 15 11551 -7.86％ -0.08％ 16 11496 -8.30％ -0.06％ 17 11441 -8.73％ -0.01％ 18 11386 -9.17％ 0.04％ 19 11331 -9.61％ 0.10％ 20 11276 -10.05％ 0.15％ 21 11169 -10.90％ -0.26％ 22 11061 -11.77％ -0.17％ 23 10953 -12.63％ -0.08％ 24 10846 -13.48％ 0.02％ 25 10738 -14.34％ 0.11％

[0120] Table 6

[0121] By comparing the power generation figures, it can be seen that the relative error between the power generation predicted by this invention and PVsyst is between -0.26% and 0.15%. Therefore, this invention can accurately and comprehensively predict the future power generation of photovoltaic power plants.

[0122] Example 3:

[0123] This invention provides a forecasting device for a photovoltaic power plant based on the Monte Carlo method, which actually uses an electronic device. The electronic device 10 is intended to represent various forms of digital computers. For example... Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11. The processor 11 can perform various processes according to a stored computer program. Various programs and data required for the operation of the electronic device 10 can also be stored in RAM 13. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14. Multiple components in the electronic device 10 are connected to the I / O interface 15; output units 17, such as various types of displays, speakers, microphones, etc.; storage units 18, such as disks, optical disks, etc.; and communication units 19, such as network interface cards, modems, wireless transceivers, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. In some embodiments, a method for predicting the power generation of a photovoltaic power plant based on the Monte Carlo method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of a Monte Carlo method-based photovoltaic power plant prediction method described above may be performed.

[0124] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting the power generation of a photovoltaic power plant based on the Monte Carlo method, characterized in that... Includes the following steps: (1) Calculate the cumulative mismatch attenuation rate S1 of the system caused by the different aging rates of components within the simulation year; (1-1) Calculate the cumulative aging degradation rate Sa of the photovoltaic modules within the simulation year; The specific calculation steps for step (1-1) are as follows: (1-1-1) Based on the input component parameters and average annual degradation rate, generate the open-circuit voltage and short-circuit current values ​​of the components for the current year. Then, select the parameter model, either the five-parameter model or the PVsyst model, to determine the current and voltage of the components. Then, multiply the voltage of the components by the number of components to obtain the voltage of the entire photovoltaic system string. Next, generate a sequence with an initial value of 0 and the ending value of the component short-circuit current, with a step size of 0.01, which is the current of the photovoltaic system string. Calculate the power of the photovoltaic system for the current year and find the maximum power for the current year. Repeat the above steps N times, where N is the simulation year. After the loop ends, sum the maximum power of the system found in the N loops to obtain P1. (1-1-2) Assuming that the photovoltaic modules do not age, the sum of the maximum power of the system in the simulation year, P2, is the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year. (1-1-3) Considering the normal aging and degradation of the components, the cumulative aging and degradation rate of the photovoltaic modules within the simulated year is Sa = (P1-P2) / P2. (1-2) The Monte Carlo method was used to calculate the cumulative average total degradation rate Sd of the photovoltaic system during the simulation year; (1-2-1) Input the relevant parameters and average annual decay rate of the component, and randomly generate several open-circuit voltages and short-circuit currents that are normally distributed during the operating time. The mean of the normal distribution is the open-circuit voltage and short-circuit current of the component in that year, and the standard deviation is the product of the mean, the standard deviation of the distribution and the empirical coefficient. The array size is several. From the several open-circuit voltages and short-circuit currents generated each year, m open-circuit voltages and short-circuit currents are randomly selected and sorted in descending order, where m represents the number of modules. Then, the open-circuit voltage and short-circuit current values ​​obtained by each module are made to fall between the mean of a normal distribution minus 2 standard deviations and the mean of a normal distribution. Next, a parameter model is selected to determine the current and voltage of the modules. Then, the voltage of the photovoltaic system string is obtained by accumulating the module voltages. Subsequently, a sequence with an initial value of 0 and the last value of the module short-circuit current, with a step size of 0.01, is generated, which is the current of the photovoltaic system string. The power of the photovoltaic system in that year is calculated and the maximum power of that year is found. The above steps are repeated N times, where N is the simulation year. After the cycle is completed, the maximum power of the system found in each year after the N cycles is summed to obtain P3. (1-2-2) Assuming the photovoltaic system does not age, the sum of the maximum power of the system in the simulation year, P4, is the product of the maximum power of the photovoltaic system in the first year of operation and the simulation year. (1-2-3) Considering the normal aging and degradation of the components and the different degradation rates of each component, which leads to mismatch loss in the system, the cumulative total degradation rate of the photovoltaic system in the simulation year is Sc = (P3-P4) / P4. (1-2-4), the above three steps (1-2-1), (1-2-2), (1-2-3) are repeated a times, where a represents the number of attempts. The sum of the total attenuation rate over these a times is set as s. Then the cumulative average total attenuation rate of the photovoltaic system in the simulation year is Sd=s / a. (1-3) Based on the cumulative aging decay rate Sa of the components and the cumulative average total decay rate Sd of the system obtained from the above steps (1-1) and (1-2), the cumulative mismatch decay rate S1 = Sd - Sa caused by the different aging rates of the components in the simulation year is obtained. (2) Calculate the cumulative aging degradation rate S2 of the photovoltaic modules within the simulation year; The cumulative aging degradation rate S2 of the photovoltaic modules in the simulated year is obtained by using the average loss calculated at mid-year. (3) Predict the future power generation of the photovoltaic power station; Based on the cumulative total attenuation rate of the photovoltaic system within the simulation year, S=S1+S2, and the power generation of the photovoltaic power station in the first year, the power generation of the photovoltaic power station is predicted.

2. A device for predicting the power generation of a photovoltaic power plant based on the Monte Carlo method, characterized in that... The device includes: a processor and a memory; The memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory to cause the device to perform the method of claim 1, wherein the device stores instructions that, when executed on the device, cause the device to perform the method of claim 1.

3. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on the device, cause the device to perform the method of claim 1.

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