A method, system, storage medium, and electronic device for generating photovoltaic simulated output

Abstract

The present invention discloses a method, system, storage medium and electronic device for generating photovoltaic simulated output. Among them, the method includes the following steps: obtaining the weather historical data and photovoltaic output historical data of a distributed photovoltaic power station; obtaining a weather sequence through the Markov transition matrix; constructing a sunrise and sunset time model; calculating the photovoltaic unshaded output and the photovoltaic unshaded coefficient at each moment under different weather types; establishing a photovoltaic unshaded coefficient mean model and a photovoltaic unshaded coefficient fluctuation value model; and simulating and generating photovoltaic output data. The solution provided by the present invention analyzes the internal relationship between weather types and the volatility of photovoltaic output, obtains the influence of weather randomness on the volatility of photovoltaic output, can simulate the photovoltaic fluctuation conditions under different weathers, is applicable to the volatility modeling of photovoltaic output, and can effectively solve the problem of difficult prediction of photovoltaic power generation output caused by the significant volatility of photovoltaic power generation output.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic fluctuation characteristic quantitative analysis, and particularly relates to a method, a system, a storage medium, and an electronic device for generating photovoltaic simulated output. Background Art

[0002] Photovoltaic power generation, as a clean and renewable energy source, plays a crucial role in the process of building a new power system. With the continuous progress of technology and cost reduction, photovoltaic power generation has gradually become the new favorite in the power market. Especially in the distribution network, a large number of photovoltaics are connected in the form of distributed power sources, which has become an important trend in the development of the current power system. However, the output of photovoltaic power is extremely vulnerable to significant impacts of weather conditions (such as light intensity and cloud cover), resulting in significant volatility and unpredictability of photovoltaic power generation output, which poses a major challenge to the stable operation of the power grid.

[0003] In view of the above, the quantitative analysis of photovoltaic fluctuation characteristics is crucial for the operation analysis of a high-proportion new energy power system and photovoltaic forecasting. Summary of the Invention

[0004] In order to meet the usage requirements for the quantitative analysis of photovoltaic fluctuation characteristics, the purpose of the present invention is to provide a method, a system, a storage medium, and an electronic device for generating photovoltaic simulated output.

[0005] To achieve the purpose of the present invention, the technical solutions provided by the present invention are as follows:

[0006] First Aspect

[0007] The present invention provides a method for generating photovoltaic simulated output, including the following steps:

[0008] Step S1: Obtain the weather historical data and photovoltaic output historical data of the distributed photovoltaic power station;

[0009] Step S2: Classify the weather historical data based on the weather type, construct a weather type transition model based on a multi-dimensional Markov chain according to the classified weather historical data, obtain a Markov transition matrix through the weather type transition model, and obtain a weather sequence through the Markov transition matrix;

[0010] Step S3: Construct a sunrise and sunset time model according to the sunrise time and sunset time of the distributed photovoltaic power station;

[0011] Step S4: Preprocess the photovoltaic output historical data, and calculate the photovoltaic output without shading and the photovoltaic non-shading coefficient at each moment under different weather types based on the preprocessed photovoltaic output historical data and the classified weather historical data;

[0012] Step S5: Obtain the mean value and fluctuation value of the photovoltaic unshaded coefficient under different weather types according to the photovoltaic unshaded coefficient, and respectively establish a photovoltaic unshaded coefficient mean value model and a photovoltaic unshaded coefficient fluctuation value model by combining the mixture Gaussian distribution and the normal distribution;

[0013] Step S6: According to the weather sequence generated by the Markov chain, sequentially select the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence, and respectively use random numbers to generate an unshaded coefficient mean value simulation value sequence and an unshaded coefficient fluctuation simulation value sequence, and the two are superimposed and synthesized into a photovoltaic power generation unshaded coefficient; According to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model and the photovoltaic unshaded day output, simulate and generate photovoltaic output data.

[0014] Second aspect

[0015] The present invention provides a photovoltaic simulated output generation system, including the following units: a data acquisition unit, a weather sequence acquisition unit, a sunrise and sunset time model construction unit, a photovoltaic unshaded day output and photovoltaic unshaded coefficient calculation unit, a model establishment unit, and a photovoltaic output data generation unit;

[0016] The data acquisition unit is used to obtain the weather historical data and photovoltaic output historical data of the distributed photovoltaic power station;

[0017] The weather sequence acquisition unit is used to classify the weather historical data based on the weather type, construct a weather type transition model based on the multi-dimensional Markov chain according to the classified weather historical data, obtain a Markov transition matrix through the weather type transition model, and obtain a weather sequence through the Markov transition matrix;

[0018] The sunrise and sunset time model construction unit is used to construct a sunrise and sunset time model according to the sunrise time and sunset time of the distributed photovoltaic power station;

[0019] The photovoltaic unshaded day output and photovoltaic unshaded coefficient calculation unit is used to preprocess the photovoltaic output historical data, and calculate the photovoltaic unshaded day output and the photovoltaic unshaded coefficient at each moment under different weather types based on the preprocessed photovoltaic output historical data and the classified weather historical data;

[0020] The model establishment unit is used to obtain the mean value and fluctuation value of the photovoltaic unshaded coefficient under different weather types according to the photovoltaic unshaded coefficient, and respectively establish a photovoltaic unshaded coefficient mean value model and a photovoltaic unshaded coefficient fluctuation value model by combining the mixture Gaussian distribution and the normal distribution;

[0021] The photovoltaic output data generation unit is configured to sequentially select the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence generated by the Markov chain, and respectively use random numbers to generate an unshaded coefficient mean simulation value sequence and an unshaded coefficient fluctuation simulation value sequence, and superimpose the two to synthesize the photovoltaic power generation unshaded coefficient; according to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model, and the photovoltaic unshaded day output, simulate and generate photovoltaic output data.

[0022] In a third aspect

[0023] The present invention provides a storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the photovoltaic simulation output generation method described above.

[0024] In a fourth aspect

[0025] The present invention provides an electronic device, which includes a processor and a memory. In the memory, at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the photovoltaic simulation output generation method described above.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] The solution provided by the present invention analyzes the internal relationship between the weather type and the volatility of photovoltaic output, obtains the influence of weather randomness on the volatility of photovoltaic output, can simulate the photovoltaic fluctuation conditions under different weathers, is applicable to the volatility modeling of photovoltaic output, and can effectively solve the problem that it is difficult to predict the photovoltaic power generation output due to the significant volatility of photovoltaic power generation output. Description of the Drawings

[0028] Figure 1 It is a schematic flowchart of the method provided by an embodiment of the present application;

[0029] Figure 2 It is a probability distribution curve graph of the actual photovoltaic output;

[0030] Figure 3 It is a probability distribution curve graph of the simulated photovoltaic output. Detailed Embodiments

[0031] Combined with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Embodiment 1

[0032] As Figure 1 shown, this embodiment provides a method for generating photovoltaic simulated output, including the following steps:

[0033] Step S1: Obtain the weather historical data and photovoltaic output historical data of the distributed photovoltaic power station;

[0034] Step S2: Classify the weather historical data based on the weather type, construct a weather type transition model based on a multi-dimensional Markov chain according to the classified weather historical data, obtain a Markov transition matrix through the weather type transition model, and obtain a weather sequence through the Markov transition matrix.

[0035] In this application, the weather types are mainly divided into five types: sunny (S1), cloudy (S2), overcast (S3), haze (S4), and rain and snow (S5).

[0036] Step S3: Construct a sunrise and sunset time model according to the sunrise time and sunset time of the distributed photovoltaic power station;

[0037] Among them, the Monte Carlo sampling method is used to construct the sunrise and sunset time model. The following is the specific simulation process:

[0038] First, process and analyze the historical data of the sunrise time and sunset time of the distributed photovoltaic power station. Analyze the distribution characteristics of each influencing factor according to the historical data and establish a statistical model to ensure that these distributions can reflect the real changes. Divide by month, count all the sunrise and sunset times, calculate the probability of each time occurrence, and calculate the cumulative probability distribution.

[0039] Use the Monte Carlo sampling method for sampling, set the number of simulation times and randomly generate multiple samples. All samples follow the cumulative probability distribution of the historical data. And sort the sampling results according to the probability size, and select the sunrise and sunset times with the highest probability as the output.

[0040] By using the Monte Carlo sampling method to model the sunrise and sunset time, it is possible to consider factors such as seasons to predict the impact on the sunrise and sunset time, and conduct a large number of simulations based on the probability distribution, and finally obtain the predicted probability distribution. This method is particularly suitable for dealing with systems with uncertainties and complex factors, and can reasonably simulate the sunrise and sunset time.

[0041] Step S4: Preprocess the historical PV output data, and calculate the PV output without shading and the PV shading coefficient at each moment under different weather types based on the preprocessed historical PV output data and the classified historical weather data.

[0042] Specifically, the PV output under ideal unobstructed sunny conditions is regarded as the deterministic benchmark, that is, the "PV output without shading for a clear day". The deviation of the actual PV output from this benchmark is regarded as the uncertain part, which is quantified by calculating the ratio of the actual PV output to the PV output without shading for a clear day. This ratio is defined as the "PV shading coefficient".

[0043] The PV output without shading for a clear day is calculated using the peak value of each sampling point in the preprocessed annual historical PV output data at this moment throughout the year, as follows:

[0044] (1)

[0045] In Equation (1), w j represents the PV output without shading at the j th moment under unobstructed conditions; Pv ij represents the actual PV output at the i th day and the j th moment; m is the number of days corresponding to the historical data, and max(.) represents the maximum value function.

[0046] Among them, the actual PV output at the moment j is Pv j , and its corresponding PV shading coefficient u j The calculation formula is as follows:

[0047] (2)

[0048] The PV shading coefficient reflects to a certain extent the influence of weather types, light intensity, etc. on the fluctuation of PV output.

[0049] Generally, on sunny days, the shading coefficient is relatively high and the volatility is small; on cloudy days, the shading coefficient fluctuates greatly and the overall PV output level is lower than that on sunny days; on overcast days, the shading coefficient is relatively low and the volatility is small; in haze weather, the particulate matter in the air will scatter and absorb solar radiation, resulting in a reduction in PV output and a corresponding decrease in the shading coefficient, which can be regarded as an extreme case of overcast days; on rainy and snowy days, the clouds are thick and often accompanied by precipitation, and the PV output is almost zero, and the corresponding shading coefficient is extremely low or zero.

[0050] Step S5: According to the photovoltaic unshaded coefficient, obtain the mean value and the fluctuation value of the photovoltaic unshaded coefficient under different weather types, and respectively establish a photovoltaic unshaded coefficient mean value model and a photovoltaic unshaded coefficient fluctuation value model by combining the mixture Gaussian distribution and the normal distribution;

[0051] Among them, the fluctuation value of the photovoltaic unshaded coefficient is calculated as follows:

[0052] (3)

[0053] In formula (3), u j (j = 1, 2,..., n ) represents the photovoltaic unshaded coefficient at time j , represents the mean value of the photovoltaic unshaded coefficient on the same day.

[0054] Among them, the distribution characteristics of the fluctuation value of the photovoltaic unshaded coefficient are described by the normal distribution probability density function as follows:

[0055] (4)

[0056] In formula (4), x is a random variable, μ and σ are respectively the overall mean value and the standard deviation of the sample, N () represents following the normal distribution.

[0057] Among them, the distribution characteristics of the mean value of the photovoltaic unshaded coefficient are fitted by the mixture Gaussian distribution probability density function GMM as follows:

[0058] (5)

[0059] In formula (5), m, k are respectively the total number and the index number of the Gaussian distribution functions, λ k is the weight of each Gaussian component, and the number of components m of GMM is determined by using the Akaike information criterion AIC. The calculation method of the AIC is as follows:

[0060] AIC = 2b - 2ln(L) (6)

[0061] In formula (6), b is the number of parameters of the model, and L is the maximum likelihood value.

[0062] Step S6: According to the weather sequence generated by the Markov chain, successively select the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence, and respectively use random numbers to generate an unshaded coefficient mean simulation value sequence and an unshaded coefficient fluctuation simulation value sequence, and the two are superimposed and synthesized into a photovoltaic power generation unshaded coefficient; according to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model, and the photovoltaic unshaded day output, simulate and generate photovoltaic output data.

[0063] The specific steps are as follows:

[0064] According to the weather sequence generated by the Markov chain, select the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model corresponding to the first item in the weather sequence;

[0065] Among them, after determining the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model under a certain weather type, the specific formulas for using random numbers to generate an unshaded coefficient mean simulation value sequence and an unshaded coefficient fluctuation simulation value sequence are as follows:

[0066] (7)

[0067] In formula (7), X t is the generated simulation value sequence, Z t is a random number subject to the standard normal distribution, μ is the overall mean of the sample, and σ is the standard deviation.

[0068] u = X t1 + X t2 (8)

[0069] In formula (8), X t1 is the unshaded coefficient mean simulation value sequence, X t2 is the unshaded coefficient fluctuation value simulation value sequence, u is the synthesized photovoltaic power generation unshaded coefficient.

[0070] Back-calculate the simulated value of the actual photovoltaic output according to formula (2). Among them, the value calculated at this time includes 96 points in a day and includes the situation of night. Therefore, the night situation needs to be removed;

[0071] Combined with the sunrise and sunset time model, set the time points outside the sunrise and sunset time to zero, which is the simulated output data of the photovoltaic under the corresponding weather type.

[0072] Repeat the above operations until all items in the weather sequence are calculated.

[0073] Taking the operation data of a certain photovoltaic power station as an example, the proposed algorithm is verified. From Figure 2 and Figure 3 It can be seen that the probability distribution curves of the photovoltaic simulated output and the actual photovoltaic output are very close. Among them, the α parameters corresponding to the actual output and the simulated output β distribution are 0.732 and 0.593 respectively, and the β parameters are 1.413 and 1.144 respectively. The two distribution curves are very close. Thus, the effectiveness of the proposed modeling method for the volatility of photovoltaic output is verified.

[0074] Embodiment 2

[0075] Corresponding to the above method, this embodiment provides a photovoltaic simulated output generation system, including the following units: a data acquisition unit, a weather sequence acquisition unit, a sunrise and sunset time model construction unit, a photovoltaic unshaded output and photovoltaic unshaded coefficient calculation unit, a model establishment unit, and a photovoltaic output data generation unit;

[0076] The data acquisition unit is used to obtain the weather historical data and the photovoltaic output historical data of the distributed photovoltaic power station;

[0077] The weather sequence acquisition unit is used to classify the weather historical data based on the weather type, construct a weather type transition model based on the multi-dimensional Markov chain according to the classified weather historical data, obtain the Markov transition matrix through the weather type transition model, and obtain the weather sequence through the Markov transition matrix;

[0078] The sunrise and sunset time model construction unit is used to construct a sunrise and sunset time model according to the sunrise time and the sunset time of the distributed photovoltaic power station;

[0079] The photovoltaic unshaded output and photovoltaic unshaded coefficient calculation unit is used to preprocess the photovoltaic output historical data, and calculate the photovoltaic unshaded output and the photovoltaic unshaded coefficient at each moment under different weather types based on the preprocessed photovoltaic output historical data and the classified weather historical data;

[0080] The model establishment unit is used to obtain the mean value of the photovoltaic unshaded coefficient and the fluctuation value of the photovoltaic unshaded coefficient under different weather types according to the photovoltaic unshaded coefficient, and respectively establish a mean value model of the photovoltaic unshaded coefficient and a fluctuation value model of the photovoltaic unshaded coefficient by combining the mixture Gaussian distribution and the normal distribution;

[0081] The photovoltaic output data generation unit is configured to sequentially select the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence generated by the Markov chain, and respectively use random numbers to generate an unshaded coefficient mean simulation value sequence and an unshaded coefficient fluctuation simulation value sequence, and the two are superimposed and synthesized into a photovoltaic power generation unshaded coefficient; according to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model, and the photovoltaic unshaded day output, simulate and generate photovoltaic output data.

[0082] Embodiment 3

[0083] This embodiment provides a storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the photovoltaic simulation output generation method.

[0084] Embodiment 4

[0085] This embodiment provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the photovoltaic simulation output generation method.

[0086] Finally, it should be noted that the above embodiments are only used for exemplifying and illustrating the present invention, and are not intended to limit the present invention to the scope of the described embodiments. In addition, those skilled in the art can understand that the present invention is not limited to the above embodiments, and more variations and modifications can be made according to the teachings of the present invention, and these variations and modifications all fall within the scope claimed by the present invention.

Claims

1. A method for generating photovoltaic simulated output, characterized in that, It includes the following steps: Step S1: Obtain the historical weather data and historical photovoltaic output data of the distributed photovoltaic power station; Step S2: Classify the historical weather data based on weather types, construct a weather type transition model based on a multi-dimensional Markov chain according to the classified historical weather data, obtain a Markov transition matrix through the weather type transition model, and obtain a weather sequence through the Markov transition matrix; Step S3: Construct a sunrise and sunset time model according to the sunrise time and sunset time of the distributed photovoltaic power station; Step S4: Preprocess the historical photovoltaic output data, and calculate the photovoltaic unshaded output and the photovoltaic unshaded coefficient at each moment under different weather types based on the preprocessed historical photovoltaic output data and the classified historical weather data; Step S5: Obtain the mean value and fluctuation value of the photovoltaic unshaded coefficient under different weather types according to the photovoltaic unshaded coefficient, and respectively establish a mean value model and a fluctuation value model of the photovoltaic unshaded coefficient by combining a mixture Gaussian distribution and a normal distribution; Step S6: According to the weather sequence generated by the Markov chain, sequentially select the mean value model and the fluctuation value model of the photovoltaic unshaded coefficient corresponding to each item in the weather sequence, and respectively use random numbers to generate a simulated value sequence of the mean value of the unshaded coefficient and a simulated value sequence of the fluctuation of the unshaded coefficient, and the two are superimposed to synthesize the photovoltaic power generation unshaded coefficient; According to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model, and the photovoltaic unshaded output, simulate and generate photovoltaic output data.

2. The photovoltaic simulation output power generation method according to claim 1, wherein In step S2, the weather types include 5 types: sunny, cloudy, overcast, haze, and rainy and snowy days.

3. The photovoltaic simulation output generation method according to claim 1, characterized in that In step S4, the photovoltaic unshaded output is calculated using the peak value of each sampling point in the annual historical photovoltaic output data after preprocessing at the annual sampling point time, specifically as follows: w j = max(Pv 1j , Pv 2j , …, Pv ij , …, Pv mj )(1) In Equation (1), w j represents the output of the photovoltaic panel without shading at the j-th moment under the condition of no shading; Pv ij represents the actual photovoltaic output at the j-th moment on the i-th day; m corresponds to the number of days of historical data, and max(.) represents the maximum value function.

4. The photovoltaic simulated output generation method according to claim 3, wherein In step S4, the actual photovoltaic output at time j is Pv j , and its corresponding photovoltaic non-shading coefficient u j The calculation formula is as follows:

5. The photovoltaic simulated output power generation method according to claim 1, characterized in that In step S5, the calculation method of the fluctuation value Δu of the photovoltaic unshaded coefficient is specifically as follows: In formula (3), u j (j = 1, 2,..., n) represents the photovoltaic non-shading coefficient at time j, represents the average value of the photovoltaic non-shading coefficient on the current day.

6. The photovoltaic simulated output power generation method according to claim 1, wherein In step S5, the distribution characteristics of the fluctuation value of the photovoltaic unshaded coefficient are described using the normal distribution probability density function ρ(x): In formula (4), x is a random variable, μ and σ are respectively the overall mean and standard deviation of the sample, and N() represents following a normal distribution.

7. The photovoltaic simulation output power generation method according to claim 6, wherein In step S5, the distribution characteristics of the mean value of the photovoltaic unshaded coefficient are fitted using the mixture Gaussian distribution probability density function GMM: In Equation (5), m and k are the total number and the index number of the Gaussian distribution functions, respectively, and λ k is the weight of each Gaussian component. The Akaike Information Criterion (AIC) is used to determine the number of components m of the GMM. The calculation method of the AIC is as follows: AIC = 2b - 2ln(L) (6) In formula (6), b is the number of model parameters, and L is the maximum likelihood value.

8. A photovoltaic simulated output generation system, characterized in that, It includes the following units: a data acquisition unit, a weather sequence acquisition unit, a sunrise and sunset time model construction unit, a photovoltaic unshaded output and photovoltaic unshaded coefficient calculation unit, a model establishment unit, and a photovoltaic output data generation unit; The data acquisition unit is used to obtain the historical weather data and historical photovoltaic output data of the distributed photovoltaic power station; The weather sequence acquisition unit is used to classify the historical weather data based on weather types, construct a weather type transition model based on a multi-dimensional Markov chain according to the classified historical weather data, obtain a Markov transition matrix through the weather type transition model, and obtain a weather sequence through the Markov transition matrix; The sunrise and sunset time model construction unit is used to construct a sunrise and sunset time model according to the sunrise time and sunset time of the distributed photovoltaic power station; The photovoltaic unshaded output and photovoltaic unshaded coefficient calculation unit is used to preprocess the historical photovoltaic output data, and calculate the photovoltaic unshaded output and the photovoltaic unshaded coefficient at each moment under different weather types based on the preprocessed historical photovoltaic output data and the classified historical weather data; The model establishment unit is used to obtain the mean value of the photovoltaic unshaded coefficient and the fluctuation value of the photovoltaic unshaded coefficient under different weather types according to the photovoltaic unshaded coefficient, and respectively establish a mean model of the photovoltaic unshaded coefficient and a fluctuation value model of the photovoltaic unshaded coefficient by combining the mixture Gaussian distribution and the normal distribution; The photovoltaic output data generation unit is used to sequentially select the mean model of the photovoltaic unshaded coefficient and the fluctuation value model of the photovoltaic unshaded coefficient corresponding to each item in the weather sequence according to the weather sequence generated by the Markov chain, and respectively use random numbers to generate a sequence of simulated values of the mean unshaded coefficient and a sequence of simulated values of the unshaded coefficient fluctuation, and the two are superimposed to synthesize the photovoltaic power generation unshaded coefficient; according to the photovoltaic power generation unshaded coefficient, the sunrise and sunset time model and the photovoltaic unshaded output, simulate and generate photovoltaic output data.

9. A storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the photovoltaic simulation output generation method according to any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the photovoltaic simulation output generation method according to any one of claims 1-7.

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