Photovoltaic simulation output generation method and system, storage medium and electronic equipment

Abstract

The invention discloses a photovoltaic simulation output generation method and system, a storage medium and electronic equipment. The method comprises the following steps: acquiring weather historical data and photovoltaic output historical data of a distributed photovoltaic power station; obtaining a weather sequence through the Markov transfer matrix; constructing a sunrise and sunset time model; calculating photovoltaic non-shading day output under different weather types and a photovoltaic non-shading coefficient at each moment; establishing a photovoltaic unshielded coefficient mean value model and a photovoltaic unshielded coefficient fluctuation value model; and simulating and generating photovoltaic output data. According to the scheme provided by the invention, the internal relation between the weather type and the photovoltaic output volatility is analyzed to obtain the influence of the weather randomness on the photovoltaic output volatility, the photovoltaic fluctuation conditions under different weathers can be simulated, the method is suitable for photovoltaic output volatility modeling, and the problem that the photovoltaic power generation output has significant volatility, so that the photovoltaic power generation efficiency is greatly improved can be effectively solved. And the photovoltaic power generation output is difficult to predict.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic fluctuation characteristic quantitative analysis, and specifically relates to a photovoltaic simulation output generation method, system, storage medium, and electronic equipment. Background Art

[0002] Photovoltaic power generation, as a clean and renewable energy source, plays a vital role in the process of building a new power system. With the continuous advancement of technology and the reduction of costs, photovoltaic power generation has gradually become the new favorite of the power market, especially in the distribution network. The large-scale access of photovoltaic power in the form of distributed power sources has become an important trend in the development of the current power system. However, the output of photovoltaic power is easily affected by 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 smooth operation of the power grid.

[0003] In view of the above, quantitative analysis of PV fluctuation characteristics is crucial for the operation analysis and PV forecasting of high-proportion renewable energy power systems. Summary of the invention

[0004] In order to meet the use requirements for quantitative analysis of photovoltaic fluctuation characteristics, the purpose of the present invention is to provide a photovoltaic simulation output generation method, system, storage medium, and electronic equipment.

[0005] To achieve the purpose of the present invention, the technical solution provided by the present invention is as follows: First aspect The present invention provides a method for generating photovoltaic simulated output, comprising the following steps: Step S1: Obtaining historical weather data and historical photovoltaic output data of distributed photovoltaic power stations; Step S2: classifying the weather historical data based on weather types, constructing a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtaining a Markov transfer matrix through the weather type transfer model, and obtaining a weather sequence through the Markov transfer matrix; Step S3: constructing a sunrise and sunset time model according to the sunrise and sunset times of the distributed photovoltaic power station; Step S4: preprocessing the photovoltaic output historical data, and calculating the photovoltaic unshaded output under different weather types and the photovoltaic unshaded coefficient at each time based on the preprocessed photovoltaic output historical data and the classified weather historical data; Step S5: according to the photovoltaic unshaded coefficient, the photovoltaic unshaded coefficient mean value and the photovoltaic unshaded coefficient fluctuation value under different weather types are obtained, and the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model are established respectively by combining the mixed Gaussian distribution and the normal distribution; Step S6: 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 each item in the weather sequence in turn, and use random numbers to generate the unshaded coefficient mean simulation value sequence and the unshaded coefficient fluctuation simulation value sequence respectively, 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 output, simulate and generate photovoltaic output data.

[0006] Second aspect The present invention provides a photovoltaic simulation output generation system, comprising 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 weather historical data and photovoltaic output historical data of the distributed photovoltaic power station; The weather sequence acquisition unit is used to classify the weather historical data based on weather types, construct a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtain a Markov transfer matrix through the weather type transfer model, and obtain a weather sequence through the Markov transfer matrix; The sunrise and sunset time model building unit is used to build 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 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; The model building unit is used to obtain the photovoltaic unshading coefficient mean value and the photovoltaic unshading coefficient fluctuation value under different weather types according to the photovoltaic unshading coefficient, and to respectively establish the photovoltaic unshading coefficient mean value model and the photovoltaic unshading coefficient fluctuation value model by combining the mixed Gaussian distribution and the normal distribution; The photovoltaic output data generating unit is used to select the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence in turn according to the weather sequence generated by the Markov chain, and respectively generate the unshaded coefficient mean value simulation value sequence and the unshaded coefficient fluctuation simulation value sequence by using random numbers, 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 output, simulate and generate photovoltaic output data.

[0007] The third aspect The present invention provides a storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the photovoltaic simulation output generation method.

[0008] The fourth aspect The present invention provides an electronic device, which includes a processor and a memory, wherein 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.

[0009] Compared with the prior art, the present invention has the following beneficial effects: The solution provided by the present invention analyzes the intrinsic connection between weather types and photovoltaic output volatility, and derives the impact of weather randomness on photovoltaic output volatility. It can simulate photovoltaic fluctuations under different weather conditions and is suitable for photovoltaic output volatility modeling. It can effectively solve the problem of difficult prediction of photovoltaic power generation output due to the significant volatility of photovoltaic power generation output. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a method flow chart provided in an embodiment of the present application; Figure 2 It is the probability distribution curve of the actual photovoltaic output; Figure 3 This is the probability distribution curve of photovoltaic simulation output. DETAILED DESCRIPTION

[0011] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings 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. Example 1

[0012] like Figure 1 As shown, this embodiment provides a photovoltaic simulated output generation method, comprising the following steps: Step S1: Obtaining historical weather data and historical photovoltaic output data of distributed photovoltaic power stations; Step S2: classifying the weather historical data based on weather types, constructing a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtaining a Markov transfer matrix through the weather type transfer model, and obtaining a weather sequence through the Markov transfer matrix; This application mainly divides weather types into five types: sunny day (S1), cloudy day (S2), overcast day (S3), foggy day (S4) and rainy and snowy day (S5).

[0013] Step S3: constructing a sunrise and sunset time model according to the sunrise and sunset times of the distributed photovoltaic power station; Among them, the Monte Carlo sampling method is used to build a sunrise and sunset time model. The following is the specific simulation process: First, the historical data of sunrise and sunset time of distributed photovoltaic power stations are processed and analyzed. The distribution characteristics of various influencing factors are analyzed based on historical data and statistical models are established to ensure that these distributions can reflect the actual changes. All sunrise and sunset times are counted according to the month, the probability of each time is calculated, and the cumulative probability distribution is calculated.

[0014] Monte Carlo sampling method is used for sampling. The number of simulations is set and multiple samples are randomly generated. All samples follow the cumulative probability distribution of historical data. The sampling results are sorted according to the probability size, and the sunrise and sunset time with the highest probability is selected as the output.

[0015] The Monte Carlo sampling method is used to model the sunrise and sunset times. It is possible to consider factors such as seasons to predict the sunrise and sunset times, and to perform a large number of simulations based on probability distribution to ultimately derive 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 times.

[0016] Step S4: preprocessing the photovoltaic output historical data, and calculating the photovoltaic unshaded output under different weather types and the photovoltaic unshaded coefficient at each time based on the preprocessed photovoltaic output historical data and the classified weather historical data; Specifically, the PV output under ideal unobstructed sunny conditions is regarded as a deterministic benchmark, namely "PV unobstructed output". The deviation of the actual PV output from this benchmark is regarded as the uncertainty part, which is quantified by calculating the ratio of the actual PV output to the PV unobstructed output. This ratio is defined as the "PV unobstructed coefficient".

[0017] The photovoltaic unshaded output is calculated using the peak value of each sampling point at that time of the year in the preprocessed photovoltaic output historical data of the whole year, as follows: (1) In formula (1), w j Indicates the condition without occlusion j Photovoltaic power generation at all times without any shade; Pv ij Indicates i Tiandi j The actual photovoltaic output at the moment; m Corresponding to the number of days of historical data, max(.) represents the maximum value function.

[0018] Among them, the time j The actual photovoltaic output is Pv j , and its corresponding photovoltaic unshaded coefficient u j The calculation formula is as follows: (2)

[0019] The photovoltaic unshaded coefficient reflects to a certain extent the impact of weather type, light intensity, etc. on photovoltaic output fluctuations.

[0020] Generally speaking, the unshaded coefficient is higher on sunny days and has low volatility; the unshaded coefficient fluctuates greatly on cloudy days, and the overall photovoltaic output level is lower than that on sunny days; the unshaded coefficient is lower on cloudy days and has low volatility; in foggy weather, particulate matter in the air will scatter and absorb solar radiation, photovoltaic output will decrease, and the corresponding unshaded coefficient will decrease, which can be regarded as an extreme case of cloudy days; on rainy and snowy days, the clouds are thick and often accompanied by precipitation, the photovoltaic output is almost zero, and the corresponding unshaded coefficient is extremely low or zero.

[0021] Step S5: according to the photovoltaic unshaded coefficient, the photovoltaic unshaded coefficient mean value and the photovoltaic unshaded coefficient fluctuation value under different weather types are obtained, and the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model are established respectively by combining the mixed Gaussian distribution and the normal distribution; Among them, the photovoltaic unshaded coefficient fluctuation value The calculation method is as follows: (3) In formula (3), u j (j=1, 2, ..., n ) indicates time j The photovoltaic unshaded coefficient, It indicates the average value of the photovoltaic unshaded coefficient on that day.

[0022] Among them, the distribution characteristics of the fluctuation value of the photovoltaic unshaded coefficient are calculated using the normal distribution probability density function To describe: (4) In formula (4),x is a random variable, μ and σ are the population mean and standard deviation of the sample, respectively. N () indicates normal distribution.

[0023] Among them, the distribution characteristics of the mean value of the photovoltaic unshaded coefficient are calculated using the mixed Gaussian distribution probability density function GMM To fit: (5) In formula (5), m, k are the total number and index number of Gaussian distribution functions, λ k The weight of each Gaussian component is determined by Akaike Information Criterion AIC GMM The number of components is m, and the AIC is calculated as follows: AIC=2b-2ln(L) (6) In formula (6), b is the number of parameters of the model and L is the maximum likelihood value.

[0024] Step S6: 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 each item in the weather sequence in turn, and use random numbers to generate the unshaded coefficient mean simulation value sequence and the unshaded coefficient fluctuation simulation value sequence respectively, 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 output, simulate and generate photovoltaic output data.

[0025] The specific steps are as follows: According to the weather sequence generated by the Markov chain, the photovoltaic unshaded coefficient mean model and the photovoltaic unshaded coefficient fluctuation value model corresponding to the weather type in the first item of the weather sequence are selected; Among them, after determining the photovoltaic unshaded coefficient mean value model and photovoltaic unshaded coefficient fluctuation value model under a certain weather type, the specific formula for generating the unshaded coefficient mean value simulation value sequence and the unshaded coefficient fluctuation simulation value sequence using random numbers is as follows: (7) In formula (7), X t is the generated sequence of simulation values, Z t is a random number that follows a standard normal distribution, μ is the population mean of the sample and σ is the standard deviation.

[0026] u=X t1 + X t2 (8) In formula (8), X t1is the simulated value sequence of the mean value of the unobstructed coefficient, X t2 is the simulated value sequence of the fluctuation value of the unshaded coefficient, u is the synthetic photovoltaic power generation unshaded coefficient.

[0027] The simulated value of the actual photovoltaic output is calculated by back-calculating according to formula (2). The calculated value at this time includes 96 points in a day, including the night situation, so the night situation needs to be removed; Combined with the sunrise and sunset time model, the time points other than sunrise and sunset are set to zero, which is the simulated photovoltaic output data under the corresponding weather type.

[0028] Repeat the above steps until all items in the weather series are calculated.

[0029] Taking the operation data of a photovoltaic power station as an example, the proposed algorithm is verified. Figure 2 , Figure 3 It can be seen that the probability distribution curves of the simulated photovoltaic output and the actual photovoltaic output are very close, where the α parameters corresponding to the actual output and the simulated output β distribution are 0.732 and 0.593, and the β parameters are 1.413 and 1.144, respectively. The two distribution curves are very close. This verifies the effectiveness of the proposed photovoltaic output volatility modeling method.

[0030] Example 2 Corresponding to the above method, the present embodiment provides a photovoltaic simulation 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 building unit, and a photovoltaic output data generation unit; The data acquisition unit is used to obtain the weather historical data and photovoltaic output historical data of the distributed photovoltaic power station; The weather sequence acquisition unit is used to classify the weather historical data based on weather types, construct a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtain a Markov transfer matrix through the weather type transfer model, and obtain a weather sequence through the Markov transfer matrix; The sunrise and sunset time model building unit is used to build 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 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; The model building unit is used to obtain the photovoltaic unshading coefficient mean value and the photovoltaic unshading coefficient fluctuation value under different weather types according to the photovoltaic unshading coefficient, and to respectively establish the photovoltaic unshading coefficient mean value model and the photovoltaic unshading coefficient fluctuation value model by combining the mixed Gaussian distribution and the normal distribution; The photovoltaic output data generating unit is used to select the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence in turn according to the weather sequence generated by the Markov chain, and respectively generate the unshaded coefficient mean value simulation value sequence and the unshaded coefficient fluctuation simulation value sequence by using random numbers, 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 output, simulate and generate photovoltaic output data.

[0031] Example 3 This embodiment provides a storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the photovoltaic simulation output generation method.

[0032] Example 4 This embodiment provides an electronic device, which includes a processor and a memory, wherein 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.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate and explain 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 of the protection claimed by the present invention.

Claims

1. A method for generating photovoltaic simulated output, characterized in that: The following steps are involved: Step S1: Obtaining historical weather data and historical photovoltaic output data of distributed photovoltaic power stations; Step S2: classifying the weather historical data based on weather types, constructing a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtaining a Markov transfer matrix through the weather type transfer model, and obtaining a weather sequence through the Markov transfer matrix; Step S3: constructing a sunrise and sunset time model according to the sunrise and sunset times of the distributed photovoltaic power station; Step S4: preprocessing the photovoltaic output historical data, and calculating the photovoltaic unshaded output under different weather types and the photovoltaic unshaded coefficient at each time based on the preprocessed photovoltaic output historical data and the classified weather historical data; Step S5: according to the photovoltaic unshaded coefficient, the photovoltaic unshaded coefficient mean value and the photovoltaic unshaded coefficient fluctuation value under different weather types are obtained, and the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model are established respectively by combining the mixed Gaussian distribution and the normal distribution; Step S6: 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 each item in the weather sequence in turn, and use random numbers to generate the unshaded coefficient mean simulation value sequence and the unshaded coefficient fluctuation simulation value sequence respectively, 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 output, simulate and generate photovoltaic output data.

2. The photovoltaic simulation output generation method according to claim 1, characterized in that: In step S2, the weather types include five types: sunny, cloudy, overcast, foggy and rainy and snowy.

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 at that time throughout the year in the preprocessed photovoltaic output historical data throughout the year, as follows: (1) In formula (1), w j Indicates the condition without occlusion j Photovoltaic power generation at all times without any shade; Pv ij Indicates i Tiandi j The actual photovoltaic output at the moment; m Corresponding to the number of days of historical data, max(.) represents the maximum value function.

4. The photovoltaic simulation output generation method according to claim 3, characterized in that: In step S4, time j The actual photovoltaic output is Pv j , and its corresponding photovoltaic unshaded coefficient u j The calculation formula is as follows: (2) 5. The photovoltaic simulation output generation method according to claim 1, characterized in that: In step S5, the photovoltaic unshaded coefficient fluctuation value The calculation method is as follows: （3） In formula (3), u j (j=1, 2, ..., n ) indicates time j The photovoltaic unshaded coefficient, It indicates the average value of the photovoltaic unshaded coefficient on that day.

6. The photovoltaic simulation output generation method according to claim 1, characterized in that: In step S5, the distribution characteristics of the fluctuation value of the photovoltaic unshaded coefficient are calculated using the normal distribution probability density function To describe: （4） In formula (4), x is a random variable, μ and σ are the population mean and standard deviation of the sample, respectively. N () indicates normal distribution.

7. The photovoltaic simulated output generation method according to claim 6, characterized in that: In step S5, the distribution characteristics of the mean value of the photovoltaic unshaded coefficient are calculated using the mixed Gaussian distribution probability density function GMM To fit: （5） In formula (5), m, k are the total number and index number of Gaussian distribution functions, λ k The weight of each Gaussian component is determined by AIC using the Akaike Information Criterion GMM The number of components is m, and the AIC is calculated as follows: AIC=2b-2ln(L) (6) In formula (6), b is the number of parameters of the model and L is the maximum likelihood value.

8. A photovoltaic simulated power generation system, characterized in that: It includes the following units: data acquisition unit, weather sequence acquisition unit, sunrise and sunset time model construction unit, photovoltaic unshaded output and photovoltaic unshaded coefficient calculation unit, model building unit and photovoltaic output data generation unit; The data acquisition unit is used to obtain the weather historical data and photovoltaic output historical data of the distributed photovoltaic power station; The weather sequence acquisition unit is used to classify the weather historical data based on weather types, construct a weather type transfer model based on a multidimensional Markov chain according to the classified weather historical data, obtain a Markov transfer matrix through the weather type transfer model, and obtain a weather sequence through the Markov transfer matrix; The sunrise and sunset time model building unit is used to build 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 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; The model building unit is used to obtain the photovoltaic unshading coefficient mean value and the photovoltaic unshading coefficient fluctuation value under different weather types according to the photovoltaic unshading coefficient, and to respectively establish the photovoltaic unshading coefficient mean value model and the photovoltaic unshading coefficient fluctuation value model by combining the mixed Gaussian distribution and the normal distribution; The photovoltaic output data generating unit is used to select the photovoltaic unshaded coefficient mean value model and the photovoltaic unshaded coefficient fluctuation value model corresponding to each item in the weather sequence in turn according to the weather sequence generated by the Markov chain, and respectively generate the unshaded coefficient mean value simulation value sequence and the unshaded coefficient fluctuation simulation value sequence by using random numbers, 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 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 the processor to implement the photovoltaic simulation output generation method as described in any one of claims 1-7.

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

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