Photovoltaic grid-connected electric energy regulation and control method based on photovoltaic power station

Abstract

The invention discloses a photovoltaic grid-connected electric energy regulation and control method based on a photovoltaic power station, relates to the technical field of photovoltaic, and is used for solving the problem that when an existing photovoltaic power station faces emergencies such as tree shadow, new facilities or temporary activities, part of sunlight is shielded by a shielding object from a photovoltaic module, and the power generation efficiency is improved. And the power generation efficiency of the photovoltaic module is influenced. Comprising the following steps: optimizing the installation angle of a photovoltaic module by using a genetic algorithm, evaluating the power generation efficiency of the photovoltaic module shielded by different installation angles by using a fitness function, and iteratively searching the optimal installation angle through selection, intersection, variation and replacement operations; by analyzing the fitness change rate and population diversity of the current generation, the variation probability of the next iteration is adjusted to be increased, decreased or unchanged, the performance of each generation is tracked in real time, the optimal solution, namely the optimal installation angle of the photovoltaic module, is determined step by step, and light energy is utilized to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic technology, and more specifically, to a photovoltaic grid-connected electric energy control method based on a photovoltaic power station. Background Art

[0002] Photovoltaic power station is a facility that uses photovoltaic (PV) technology to convert solar energy into electrical energy. Its main components include photovoltaic modules, inverters and power distribution equipment, etc. Photovoltaic grid-connected power regulation refers to the optimization and management of the power output of photovoltaic power generation systems connected to the power grid through various technologies and measures to ensure the stable operation of the power grid.

[0003] The prior art has the following deficiencies:

[0004] When existing photovoltaic power stations face emergencies such as tree shadows, new facilities or temporary activities, photovoltaic modules will be partially blocked by shielding objects, affecting the power generation efficiency of photovoltaic modules. Therefore, this application proposes a photovoltaic grid-connected power control method based on photovoltaic power stations, aiming to optimize the installation angle of photovoltaic modules in real time through genetic algorithms to maximize the utilization of light energy.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a photovoltaic grid-connected power control method based on a photovoltaic power station to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A photovoltaic grid-connected power control method based on a photovoltaic power station comprises the following steps:

[0009] Step 1: Obtain the geographic coordinates of the photovoltaic power station, and calculate the position of the sun according to the geographic coordinates of the photovoltaic power station and time;

[0010] Step 2: Obtain geometric information of the obstruction and the photovoltaic module, calculate the obstruction ratio of the photovoltaic module according to the position of the sun, the obstruction and the geometric information of the photovoltaic module, and divide the photovoltaic modules into non-obstruction photovoltaic modules and obstruction photovoltaic modules according to the value of the obstruction ratio;

[0011] Step 3: Obtain the power generation efficiency parameters of the shaded photovoltaic components, calculate the regression coefficient of each power generation efficiency parameter through a linear regression model, select the power generation efficiency parameter corresponding to the largest regression coefficient as the selected power generation efficiency parameter, and construct a fitness function with the selected power generation efficiency parameter and the shade ratio;

[0012] Step 4: Use genetic algorithm to optimize the installation angle of photovoltaic modules, use fitness function to evaluate the power generation efficiency of photovoltaic modules blocked by different installation angles, and iterate to find the optimal installation angle through selection, crossover, mutation and replacement operations.

[0013] In a preferred embodiment, in step 1, the precise geographic coordinates of the photovoltaic power station are obtained, including longitude and latitude; the position of the sun at the corresponding time is calculated according to the geographic coordinates of the photovoltaic power station and the time; the time is a combination of the current date and a specific time point; the position of the sun includes the altitude angle and azimuth angle of the sun; the position of the sun at multiple moments in a day is calculated at each unit time interval, the current date is obtained and the day of the year is determined, which is expressed as the dth day; the declination angle of the sun is calculated using the formula: Where d is the day of the year, δ is the declination of the sun; use the formula to calculate the hour angle of the sun, assuming that the standard meridian and the local meridian are the same, then the longitude is corrected to 0 minutes, and the calculation formula is H = (LST-12) × 15; where LST is the current time; use the formula to calculate the solar altitude angle and solar azimuth angle respectively, Where θ j is the solar altitude angle, δ is the solar declination angle, is the latitude of the photovoltaic power station, H is the hour angle; Where δ is the solar declination angle, H is the hour angle, and θ j is the solar altitude angle, θ a is the solar azimuth.

[0014] In a preferred embodiment, in step 2, the geometric information of the obstruction and the photovoltaic module includes height, width and relative angle; wherein the relative angle is the inclination angle of the obstruction relative to the photovoltaic module; firstly, the width projection is calculated, and W1=w k ×cosθ z ;W1 is the width projection, w k is the actual width of the occluder, θ z It is a relative angle, that is, the inclination angle of the obstruction relative to the photovoltaic module; the height projection is calculated according to the altitude angle of the sun as W2 = h k ×cosθ h ;W2 is the height projection, h k is the actual height of the occluder, θ h is the solar altitude angle;

[0015] Considering the influence of the sun's position, we have W1'=w k ×cos(θ z -θ a ); W2' = h k ×cos(θh -α); where W1' and W2' are the width projection and height projection after considering the sun's position, θ a is the solar azimuth, θ h is the solar altitude angle, θ z is the relative angle between the obstruction and the photovoltaic module, and α is the installation angle of the photovoltaic module; multiply the width projection by the height projection to obtain the projection area of ​​the obstruction on the photovoltaic module; obtain the total area of ​​the photovoltaic module, and take the ratio of the projection area of ​​the obstruction on the photovoltaic module to the total area of ​​the photovoltaic module as the obstruction ratio of the photovoltaic module;

[0016] The blocking ratio ranges from 0 to 1. When the blocking ratio is 0, the obstruction does not block the photovoltaic module, and there is no need to adjust the installation angle, and it is regarded as a non-blocked photovoltaic module. When the blocking ratio is not 0, the obstruction partially blocks the photovoltaic module or completely blocks the photovoltaic module. The installation angle of the photovoltaic module is adjusted and it is regarded as a blocked photovoltaic sub-module.

[0017] In a preferred embodiment, in step 3, the power generation efficiency parameters of the photovoltaic module include solar radiation intensity, ambient temperature and module temperature; the solar radiation intensity, ambient temperature and module temperature are used as independent variables, and the power generation efficiency of the photovoltaic module is used as the dependent variable to construct a linear regression model. The specific formula of the model is E=β0+β1·I s +β2·T a +β3·T b ; Where E is the power generation efficiency of the shading photovoltaic module, I s is the solar radiation intensity, T a is the ambient temperature, T b is the component temperature, β0 is the intercept term, β1, β2, β3 are the regression coefficients of solar radiation intensity, ambient temperature and component temperature respectively;

[0018] The least squares method is used to estimate the coefficients in the model. The estimation formula for the coefficients in the model is β = X T X) -1 X T Y; where X is the independent variable matrix, containing the independent variable values ​​of each sample. The first column of the matrix X is usually a constant column with all 1s, which is used to represent the intercept term; Y is a vector containing the dependent variable values ​​of each sample. T represents the transpose of the matrix X, (X T X) -1 Represents X T The inverse matrix of X, β is a vector containing all the coefficients in the linear model, including the intercept term and the coefficients of the independent variable; the best coefficient estimate is obtained by calculating the formula;

[0019] The calculated coefficient estimates are compared, and the power generation efficiency parameter of the photovoltaic module corresponding to the maximum coefficient value and the shading ratio of the photovoltaic module are selected to construct the fitness function;

[0020] The power generation efficiency parameter of the photovoltaic module corresponding to the maximum value of the coefficient is marked as the selected power generation efficiency parameter as P, and the selected power generation efficiency parameter at the current moment is obtained, and the two are constructed as a fitness function. The specific fitness function is F(α)=P(α)×(1-O(α)); where α is the installation angle of the photovoltaic module, P(α) is the selected power generation efficiency parameter of the photovoltaic module at the installation angle α, and O(α) is the shading ratio of the photovoltaic module at the installation angle α.

[0021] In a preferred embodiment, in step 4, the fitness function is applied to the genetic algorithm to optimize the installation angle of the photovoltaic module, as follows:

[0022] Initialize the population: determine the feasible range of the photovoltaic module installation angle α, usually 0 to 90 degrees, and randomly generate multiple angle values ​​as the initial population, where each individual represents a possible installation angle, and each individual is represented by a numerical value. The fitness function constructed by the selected power generation efficiency parameter and the shading ratio is used as the fitness function of the genetic algorithm, and the fitness value of each individual in the population is calculated through the fitness function;

[0023] Selection operation: Sort the individuals in the population according to the size of the fitness value, with individuals with high fitness values ​​ranked first and individuals with low fitness values ​​ranked last; use the roulette wheel selection algorithm to select the parent individuals; calculate the selection probability of each individual, that is, the ratio of the fitness value to the total fitness value, and mark the selection probability as SP(i); construct the cumulative probability distribution as Where SP(i) is the selection probability of the ith individual, and C(i) is the cumulative probability distribution. For the ith individual, its cumulative probability is the sum of the selection probabilities from the first individual to the ith individual. The parent individuals are selected through the cumulative probability distribution, specifically by generating a random number r between 0 and 1, starting from the first individual, and finding the first individual that satisfies the cumulative probability distribution, i.e., C(i) ≥ r. At this time, the selected individual is the ith individual. Repeatedly generate random numbers and select the first individual that satisfies the probability distribution as the parent until the required number of parent individuals are selected.

[0024] Crossover operation: Randomly select two pairs of parent individuals for operation, select the intersection point within the installation angle range, generate new individuals through crossover operation, and generate two new individuals in one crossover operation;

[0025] Mutation operation: set a mutation probability, usually 10%, and randomly select individuals from the newly generated individuals for mutation according to the mutation probability;

[0026] Replacement operation: Generate new individuals through selection, crossover and mutation operations, and use the newly generated individuals with higher fitness to replace the individuals with lower fitness in the original population;

[0027] The selection operation, crossover operation, mutation operation and replacement operation are executed cyclically until the predetermined number of iterations is reached or the fitness converges;

[0028] After the iteration, the fitness value of each individual is recalculated, and the individual with the highest fitness value is selected as the optimal solution. The installation angle corresponding to the individual is the optimal installation angle of the photovoltaic module at the current moment.

[0029] In a preferred embodiment, an improved variation operation method is also included, which adjusts the installation angle of the photovoltaic module by optimizing the genetic algorithm, as follows:

[0030] For each individual in the population, record its fitness value in different generations; the new population obtained after selection, crossover and mutation operations;

[0031] The average fitness of the population is calculated to replace the overall fitness of the population and is marked as Subtract the absolute value of the population's overall fitness values ​​from those of adjacent generations to obtain the fitness change rate, which is: Where B is the fitness change rate, is the overall fitness value of the population in the tth generation, is the overall fitness value of the population in the t+1th generation;

[0032] Calculate the diversity of the population as Where Q is the diversity of the population, N is the number of individuals in the population, Fi is the fitness value of the i-th individual, is the average fitness of the population;

[0033] The fitness change rate and population diversity of different generations are obtained by calculation and normalized;

[0034] According to the fitness change rate and population diversity, the mutation probability is adjusted. The process is as follows:

[0035] Taking the fitness change rate and population diversity as input variables, they are divided into different fuzzy sets, and taking the adjustment state of mutation probability as output variable, they are divided into different fuzzy sets;

[0036] Formulate fuzzy rules to describe the effects of fitness change rate and population diversity on the mutation probability adjustment state;

[0037] Perform fuzzy reasoning based on fuzzy rules and dynamically adjust the mutation probability.

[0038] The technical effects and advantages of the photovoltaic grid-connected power energy control method based on a photovoltaic power station of the present invention are as follows:

[0039] The present invention adjusts the mutation probability of the next iteration to increase, decrease or remain unchanged by analyzing the fitness change rate and population diversity of the current generation, tracks the performance of each generation in real time, and gradually determines the optimal solution, that is, the optimal installation angle of the photovoltaic components, to maximize the use of light energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a schematic flow chart of a photovoltaic grid-connected electric energy control method based on a photovoltaic power station. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment 1, Figure 1 A photovoltaic grid-connected power control method based on a photovoltaic power station is provided, comprising the following steps:

[0043] Step 1: Obtain the geographic coordinates of the photovoltaic power station, and calculate the position of the sun according to the geographic coordinates of the photovoltaic power station and time.

[0044] Step 2: Obtain the geometric information of the obstruction and the photovoltaic module, and calculate the obstruction ratio of the photovoltaic module according to the position of the sun, the obstruction and the geometric information of the photovoltaic module. And divide the photovoltaic modules into non-obstructed photovoltaic modules and obstructed photovoltaic modules according to the value of the obstruction ratio.

[0045] Step 3: Obtain the power generation efficiency parameters of the shaded PV modules, calculate the regression coefficient of each power generation efficiency parameter through a linear regression model, select the power generation efficiency parameter corresponding to the largest regression coefficient as the selected power generation efficiency parameter, and construct a fitness function with the selected power generation efficiency parameter and the shade ratio.

[0046] Step 4: Use genetic algorithm to optimize the installation angle of photovoltaic modules, use fitness function to evaluate the power generation efficiency of photovoltaic modules blocked by different installation angles, and iterate to find the optimal installation angle through selection, crossover, mutation and replacement operations.

[0047] Specifically, in step 1, use a GPS device or online map service to obtain the precise geographic coordinates of the photovoltaic power station, including longitude and latitude. Calculate the position of the sun at the corresponding time based on the geographic coordinates and time of the photovoltaic power station. The time is a combination of the current date and a specific time point; the position of the sun includes the altitude and azimuth of the sun. Calculate the position of the sun at multiple moments in a day at each unit time interval, specifically obtaining the current date and determining which day of the year it is, and denote it as the dth day. Use the formula to calculate the sun's declination angle as Where d is the day of the year (from 1 to 365), and δ is the declination angle of the sun. Use the formula to calculate the hour angle of the sun. Assuming that the standard meridian and the local meridian are the same, the longitude is corrected to 0 minutes. At this time, the calculation formula is H = (LST-12) × 15; where LST is the current time. Use the formula to calculate the solar altitude angle and solar azimuth angle respectively, Where θ h is the solar altitude angle, δ is the solar declination angle, is the latitude of the photovoltaic power station, H is the hour angle; Where δ is the solar declination angle, H is the hour angle, and θ h is the solar altitude angle, θ a is the solar azimuth.

[0048] Specifically, in step 2, the geometric information of the obstruction and the photovoltaic module, including height, width and relative angle, is obtained through laser rangefinders, GIS systems and other measurement tools, as well as drones equipped with high-precision cameras and lidar sensors. The relative angle is the inclination angle of the obstruction relative to the photovoltaic module. First, the width projection is calculated, and W1 = w k ×cosθ z ;W1 is the width projection, w k is the actual width of the occluder, θ z It is a relative angle, that is, the inclination angle of the shielding object relative to the photovoltaic module. The height projection is calculated according to the altitude angle of the sun as W2 = h k ×cosθ h ;W2 is the height projection, h k is the actual height of the occluder, θ h is the solar altitude angle.

[0049] Further considering the influence of the sun's position, we have W1'=w k ×cos(θ z -θ a ); W2' = h k ×cos(θ h -α); where W1' and W2' are the width projection and height projection after considering the sun's position, θ a is the solar azimuth, θh is the solar altitude angle, θ z is the relative angle between the obstruction and the PV module, and α is the installation angle of the PV module. Multiply the width projection by the height projection to get the projection area of ​​the obstruction on the PV module. The total area of ​​the PV module is obtained from the specifications provided by the manufacturer, and the ratio of the projection area of ​​the obstruction on the PV module to the total area of ​​the PV module is taken as the obstruction ratio of the PV module.

[0050] The blocking ratio ranges from 0 to 1. When the blocking ratio is 0, the obstruction does not block the photovoltaic module. In this case, there is no need to adjust the installation angle and the photovoltaic module is regarded as not blocked. When the blocking ratio is not 0, the obstruction partially blocks the photovoltaic module or completely blocks the photovoltaic module. In this case, the installation angle of the photovoltaic module needs to be adjusted and the photovoltaic module is regarded as blocked.

[0051] Specifically, in step 3, for the obstructed photovoltaic modules, its installation angle needs to be optimized. The power generation efficiency parameters of photovoltaic modules include solar radiation intensity, ambient temperature and module temperature. The specific acquisition method is to use a radiometer to measure the solar radiation intensity reaching the surface of the obstructed photovoltaic module from all directions; for the ambient temperature, install the ambient temperature sensor near the photovoltaic module to record the ambient temperature in real time; for the module temperature, use a thermocouple sensor or an infrared thermometer to measure and record the temperature of the photovoltaic module surface. Collect the solar radiation intensity, ambient temperature, module temperature and corresponding power generation efficiency within a certain period of time in the past. The power generation efficiency is obtained by logging or calculating, which will not be elaborated in detail.

[0052] Obtain the power generation efficiency parameters of the shaded photovoltaic modules, take the solar radiation intensity, ambient temperature and module temperature as independent variables, and the power generation efficiency of the shaded photovoltaic modules as the dependent variable, and construct a linear regression model. The specific formula of the model is E = β0 + β1·I s +β2·T a +β3·T b ; Where E is the power generation efficiency of the shading photovoltaic module, I s is the solar radiation intensity, T a is the ambient temperature, T b is the module temperature, β0 is the intercept term, β1, β2, β3 are the regression coefficients of solar radiation intensity, ambient temperature and module temperature, respectively, indicating the influence of the independent variable on the power generation efficiency of the shaded photovoltaic module. In the linear regression model, the sample refers to the observed data point, each data point represents an observation unit, and a sample contains the solar radiation intensity, ambient temperature and module temperature and the corresponding power generation efficiency of the shaded photovoltaic module.

[0053] The least squares method is used to estimate the coefficients in the model so that the model fits the actual observations as closely as possible. The estimation formula for the coefficients in the model is β = (X TX) -1 X T Y; where X is the independent variable matrix, containing the independent variable values ​​of each sample. The first column of the matrix X is usually a constant column with all 1s, which is used to represent the intercept term. Y is a vector containing the dependent variable value of each sample (the power generation efficiency of the shading photovoltaic module). T represents the transpose of the matrix X, (X T X) -1 Represents X T The inverse matrix of X, β is a vector containing all the coefficients in the linear model, including the intercept term and the coefficients of the independent variable. The best coefficient estimate is obtained by calculating the formula. If the coefficient of the independent variable is greater than zero, the corresponding independent variable has a positive effect on the power generation efficiency. At this time, the larger the coefficient, the greater the positive effect of the corresponding independent variable on the power generation efficiency; if the coefficient of the independent variable is less than zero, the corresponding independent variable has a negative effect on the power generation efficiency. At this time, the smaller the coefficient, the greater the negative effect of the corresponding independent variable on the power generation efficiency.

[0054] Taking into account that the shading ratio of photovoltaic modules has a negative impact on the power generation efficiency of photovoltaic modules, this application takes the selection of power generation efficiency parameters with positive influence as an example, compares the calculated coefficient estimates, selects the power generation efficiency parameters of the photovoltaic modules corresponding to the maximum coefficient value and the shading ratio of the photovoltaic modules to construct a fitness function, and by selecting the parameters that have the greatest impact on the power generation efficiency of the photovoltaic modules, the power generation performance of the photovoltaic system in actual operation can be more accurately reflected.

[0055] The power generation efficiency parameter of the photovoltaic module corresponding to the maximum value of the coefficient is marked as the selected power generation efficiency parameter as P, and the selected power generation efficiency parameter at the current moment is obtained, and the two are constructed into a fitness function. The specific fitness function is F(α)=P(α)×(1-O(α)); where α is the installation angle of the photovoltaic module, P(α) is the selected power generation efficiency parameter of the photovoltaic module at the installation angle α, and O(α) is the shading ratio of the photovoltaic module at the installation angle α. By constructing the fitness function, the influence of different installation angles on the power generation efficiency can be evaluated.

[0056] Specifically, in step 4, the fitness function is applied to the genetic algorithm to optimize the installation angle of the photovoltaic module, as follows:

[0057] Initialize the population: Determine the feasible range of the photovoltaic module installation angle α, usually 0 to 90 degrees, and randomly generate multiple angle values ​​as the initial population, where each individual (i.e. angle value) represents a possible installation angle, and each individual is represented by a numerical value. The fitness function constructed by the selected power generation efficiency parameter and the shading ratio is used as the fitness function of the genetic algorithm, and the fitness value of each individual in the population is calculated through the fitness function.

[0058] Selection operation: Sort the individuals in the population according to the size of the fitness value. Individuals with high fitness values ​​are ranked first, and individuals with low fitness values ​​are ranked last. Use the roulette selection algorithm to select the parent individual. The principle of the roulette selection algorithm is similar to spinning a roulette wheel. The fitness value of each individual determines the proportion it occupies on the roulette wheel. The higher the fitness value of the individual, the greater the proportion it occupies, and the higher the probability of being selected. Specifically, the fitness values ​​of all individuals are summed up to obtain the total fitness value. Calculate the selection probability of each individual, that is, the ratio of the fitness value to the total fitness value, and mark the selection probability as SP(i). Construct the cumulative probability distribution as Where SP(i) is the selection probability of the ith individual, and C(i) is the cumulative probability distribution. For the ith individual, its cumulative probability is the sum of the selection probabilities from the first individual to the ith individual. The parent individual is selected through the cumulative probability distribution, specifically by generating a random number r between 0 and 1, starting from the first individual, and finding the first individual that satisfies the cumulative probability distribution, that is, C(i)≥r. At this time, the selected individual is the ith individual. Repeatedly generate random numbers and select the first individual that satisfies the probability distribution as the parent until the required number of parent individuals are selected. It should be noted that the required number of parent individuals is preset by the actual situation, and the random number generated each time should be independent to ensure the fairness of the selection process, which will not be elaborated here. The selection probability is allocated according to the individual's fitness value through the roulette selection algorithm, so that individuals with higher fitness have a greater chance of being selected, ensuring that they are more inclined to choose installation angles that can significantly optimize power generation efficiency, thereby improving overall power generation.

[0059] Crossover operation: Randomly select two pairs of parent individuals for operation, select the intersection point within the installation angle range, generate new individuals through crossover operation, and generate two new individuals in one crossover operation. The specific implementation is the same as the existing technology, which will not be described in detail here. The crossover operation generates new individuals by combining the characteristics of the parent individuals, thereby increasing the diversity of the population.

[0060] Mutation operation: Set a mutation probability, usually 10%, and randomly select individuals from the newly generated individuals for mutation according to the mutation probability, that is, select 10% of the individuals for mutation operation, such as increasing or decreasing the installation angle by 1 degree. It should be noted that the installation angle after mutation must be between 0 and 90 degrees. The mutation operation increases the diversity of the solution space by randomly adjusting the installation angle of the individual to avoid falling into the local optimal solution.

[0061] Replacement operation: Generate new individuals through selection, crossover and mutation operations, and use the newly generated individuals with higher fitness to replace the individuals with lower fitness in the original population. For example, sort the individuals newly generated through selection, crossover and mutation operations according to fitness values, and replace the 20 individuals with lower fitness in the original population with the newly generated 20 individuals with higher fitness values. The replacement operation ensures that the installation angle of the photovoltaic module is continuously optimized by replacing the old individuals with lower fitness with new individuals with higher fitness, thereby reducing the impact of obstructions and improving the power generation efficiency of the photovoltaic module.

[0062] The selection operation, crossover operation, mutation operation and replacement operation are executed cyclically until a predetermined number of iterations is reached or the fitness converges. The number of iterations or the fitness threshold can be set in advance according to specific needs, which will not be described here.

[0063] After the iteration, the fitness value of each individual is recalculated, and the individual with the highest fitness value is selected as the optimal solution. The installation angle corresponding to this individual is the optimal installation angle of the photovoltaic component at the current moment, so that the shading ratio of the photovoltaic component at the current moment is minimized, and the solar energy can be absorbed to the maximum extent, thereby maximizing the power generation.

[0064] Example 2, in the mutation operation of the genetic algorithm in Example 1, the mutation probability is usually a fixed value of 10%, which cannot adapt to the optimization requirements of different iterative processes, thereby affecting the optimization effect of the installation angle of the photovoltaic module. Therefore, this application proposes an improved mutation operation method based on Example 1, aiming to dynamically adjust the mutation probability to make full use of excellent individuals, and further adjust the installation angle of the photovoltaic module by optimizing the genetic algorithm.

[0065] The details are as follows:

[0066] For each individual in the population, record its fitness value in different generations. Different generations refer to the state of the population in each complete iteration. For example, the first generation refers to the state of the population after the first round of selection, crossover, mutation and replacement operations are completed after the genetic algorithm is initialized; the second generation is the new population obtained after selection, crossover and mutation operations based on the first generation.

[0067] The average fitness of the population is calculated to replace the overall fitness of the population and is marked as Subtract the absolute value of the population's overall fitness values ​​from those of adjacent generations to obtain the fitness change rate, which is: Where B is the fitness change rate, is the overall fitness value of the population in the tth generation, is the overall fitness value of the population in the t+1th generation. According to the fitness change rate of adjacent generations, the performance of each generation can be tracked in real time and the mutation probability of the next iteration can be adjusted.

[0068] Calculate the diversity of the population as Where Q is the diversity of the population, N is the number of individuals in the population, Fi is the fitness value of the i-th individual, is the average fitness of the population. High diversity means that there are large differences between individuals, which can reduce the risk of falling into a local optimal solution and help the genetic algorithm explore the best installation angle; low diversity means that the fitness value tends to be concentrated, indicating that the differences between individuals are small, which may lead to convergence to a local optimal solution.

[0069] The fitness change rate and population diversity of different generations are obtained by calculation and normalized. The formula is: Where Normalized_Score is the normalized value.

[0070] According to the fitness change rate and population diversity, the mutation probability is adjusted. The process is as follows:

[0071] The fitness change rate and population diversity are taken as input variables and divided into different fuzzy sets, such as "Low", "Medium", "High" for the fitness change rate, and "Poor", "Average", "Rich" for the population diversity; the adjustment state of the mutation probability is taken as the output variable and divided into different fuzzy sets, such as "Decrease", "Unchanged", "Increa se" for the adjustment state of the mutation probability.

[0072] Formulate fuzzy rules to describe the impact of fitness change rate and population diversity on the adjustment state of mutation probability. The definition of rules can be based on the professional knowledge of the industry or obtained through data analysis and experiments. As above, mark the fitness change rate as B, the population diversity as Q, and the adjustment state of mutation probability as A, then define:

[0073] Rule1:IF(B is Low)AND(Q is Rich)THEN(A is Increase)

[0074] Rule2: IF(B is High)AND(Q is Poor)THEN(A is Decrease) ......

[0076] Perform fuzzy reasoning based on fuzzy rules and dynamically adjust the mutation probability.

[0077] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, this embodiment takes three fuzzy sets as an example. In fact, the adjustment states of fitness change rate, population diversity and mutation probability can be divided into more than three sets, so as to more conveniently adjust the mutation probability.

[0078] Furthermore, for the judgment of whether the fitness change rate is low, medium or high, the fitness change rates of multiple generations can be calculated, sorted from large to small, and the 66th percentile fitness change rate can be selected as the first fitness change rate threshold, and the corresponding generation greater than the first fitness change rate threshold can be marked as "High", and so on. Similarly, the poverty, general and richness of population diversity can be judged, which will not be elaborated here.

[0079] By analyzing the fitness change rate and population diversity of the current generation, the mutation probability of the next iteration is adjusted to increase, decrease, or remain unchanged, the performance of each generation is tracked in real time, and the optimal solution, that is, the optimal installation angle of the photovoltaic modules, is gradually determined to maximize the use of light energy.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0081] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0082] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0084] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0085] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A photovoltaic grid-connected power control method based on a photovoltaic power station, characterized in that: The method comprises the following steps: Step 1: Obtain the geographic coordinates of the photovoltaic power station, and calculate the position of the sun according to the geographic coordinates of the photovoltaic power station and time; Step 2: Obtain geometric information of the obstruction and the photovoltaic module, calculate the obstruction ratio of the photovoltaic module according to the position of the sun, the obstruction and the geometric information of the photovoltaic module, and divide the photovoltaic modules into non-obstruction photovoltaic modules and obstruction photovoltaic modules according to the value of the obstruction ratio; Step 3: Obtain the power generation efficiency parameters of the shaded photovoltaic components, calculate the regression coefficient of each power generation efficiency parameter through a linear regression model, select the power generation efficiency parameter corresponding to the largest regression coefficient as the selected power generation efficiency parameter, and construct a fitness function with the selected power generation efficiency parameter and the shade ratio; Step 4: Use genetic algorithm to optimize the installation angle of photovoltaic modules, use fitness function to evaluate the power generation efficiency of photovoltaic modules blocked by different installation angles, and iterate to find the optimal installation angle through selection, crossover, mutation and replacement operations.

2. A photovoltaic grid-connected power control method based on a photovoltaic power station according to claim 1, characterized in that: In step 1, the precise geographic coordinates of the photovoltaic power station are obtained, including longitude and latitude; the position of the sun at the corresponding time is calculated according to the geographic coordinates of the photovoltaic power station and the time; the time is a combination of the current date and a specific time point; the position of the sun includes the altitude angle and azimuth angle of the sun; the position of the sun at multiple moments in a day is calculated at each unit time interval, the current date is obtained and the day of the year is determined, which is expressed as the dth day; the declination angle of the sun is calculated using the formula: Where d is the day of the year, δ is the declination of the sun; use the formula to calculate the hour angle of the sun, assuming that the standard meridian and the local meridian are the same, then the longitude is corrected to 0 minutes, and the calculation formula is H = (LST-12) × 15; where LST is the current time; Use the formula to calculate the solar altitude angle and solar azimuth angle respectively: Where θ h is the solar altitude angle, δ is the solar declination angle, is the latitude of the photovoltaic power station, H is the hour angle; Where δ is the solar declination angle, H is the hour angle, and θ h is the solar altitude angle, θ a is the solar azimuth.

3. The photovoltaic grid-connected power control method based on a photovoltaic power station according to claim 1, characterized in that: In step 2, the geometric information of the obstruction and the photovoltaic module includes height, width and relative angle; the relative angle is the inclination angle of the obstruction relative to the photovoltaic module; first calculate the width projection, and W1 = w k ×cosθ z ;W1 is the width projection, w k is the actual width of the occluder, θ z It is a relative angle, that is, the inclination angle of the obstruction relative to the photovoltaic module; the height projection is calculated according to the altitude angle of the sun as W2 = h k ×cosθ h ;W2 is the height projection, h k is the actual height of the occluder, θ h is the solar altitude angle; Considering the influence of the sun's position, we have W1'=w k ×cos(θ z -θ a ); W2' = h k ×cos(θ h -α); where W1' and W2' are the width projection and height projection after considering the sun's position, θ a is the solar azimuth, θ h is the solar altitude angle, θ z is the relative angle between the obstruction and the photovoltaic module, and α is the installation angle of the photovoltaic module; multiply the width projection by the height projection to obtain the projection area of ​​the obstruction on the photovoltaic module; obtain the total area of ​​the photovoltaic module, and take the ratio of the projection area of ​​the obstruction on the photovoltaic module to the total area of ​​the photovoltaic module as the obstruction ratio of the photovoltaic module; The blocking ratio ranges from 0 to 1. When the blocking ratio is 0, the obstruction does not block the photovoltaic module, and there is no need to adjust the installation angle, and it is regarded as a non-blocked photovoltaic module. When the blocking ratio is not 0, the obstruction partially blocks the photovoltaic module or completely blocks the photovoltaic module. The installation angle of the photovoltaic module is adjusted and it is regarded as a blocked photovoltaic sub-module.

4. A photovoltaic grid-connected power control method based on a photovoltaic power station according to claim 3, characterized in that ； In step 3, the power generation efficiency parameters of the photovoltaic module include solar radiation intensity, ambient temperature and module temperature; the solar radiation intensity, ambient temperature and module temperature are used as independent variables, and the power generation efficiency of the photovoltaic module is used as the dependent variable to construct a linear regression model. The specific formula of the model is E = β0 + β1·I s +β2·T a +β3·T b ; Where E is the power generation efficiency of the shading photovoltaic module, I s is the solar radiation intensity, T a is the ambient temperature, T b is the component temperature, β0 is the intercept term, β1, β2, β3 are the regression coefficients of solar radiation intensity, ambient temperature and component temperature respectively; The least squares method is used to estimate the coefficients in the model. The estimation formula for the coefficients in the model is β = (X T X) -1 X T Y; where X is the independent variable matrix, containing the independent variable values ​​of each sample. The first column of the matrix X is usually a constant column with all 1s, which is used to represent the intercept term; Y is a vector containing the dependent variable values ​​of each sample. T represents the transpose of the matrix X, (X T X) -1 Represents X T The inverse matrix of X, β is a vector containing all the coefficients in the linear model, including the intercept term and the coefficients of the independent variable; the best coefficient estimate is obtained by calculating the formula; The calculated coefficient estimates are compared, and the power generation efficiency parameter of the photovoltaic module corresponding to the maximum coefficient value and the shading ratio of the photovoltaic module are selected to construct the fitness function; The power generation efficiency parameter of the photovoltaic module corresponding to the maximum value of the coefficient is marked as the selected power generation efficiency parameter as P, and the selected power generation efficiency parameter at the current moment is obtained, and the two are constructed as a fitness function. The specific fitness function is F(α)=P(α)×(1-O(α)); where α is the installation angle of the photovoltaic module, P(α) is the selected power generation efficiency parameter of the photovoltaic module at the installation angle α, and O(α) is the shading ratio of the photovoltaic module at the installation angle α.

5. A photovoltaic grid-connected power control method based on a photovoltaic power station according to claim 4, characterized in that: In step 4, the fitness function is applied to the genetic algorithm to optimize the installation angle of the PV panels as follows: Initialize the population: determine the feasible range of the photovoltaic module installation angle α, usually 0 to 90 degrees, and randomly generate multiple angle values ​​as the initial population, where each individual represents a possible installation angle, and each individual is represented by a numerical value. The fitness function constructed by the selected power generation efficiency parameter and the shading ratio is used as the fitness function of the genetic algorithm, and the fitness value of each individual in the population is calculated through the fitness function; Selection operation: Sort the individuals in the population according to the size of the fitness value, with individuals with high fitness values ​​ranked first and individuals with low fitness values ​​ranked last; use the roulette wheel selection algorithm to select the parent individuals; calculate the selection probability of each individual, that is, the ratio of the fitness value to the total fitness value, and mark the selection probability as SP(i); construct the cumulative probability distribution as Where SP(i) is the selection probability of the ith individual, and C(i) is the cumulative probability distribution. For the ith individual, its cumulative probability is the sum of the selection probabilities from the first individual to the ith individual. The parent individuals are selected through the cumulative probability distribution, specifically by generating a random number r between 0 and 1, starting from the first individual, and finding the first individual that satisfies the cumulative probability distribution, i.e., C(i) ≥ r. At this time, the selected individual is the ith individual. Repeatedly generate random numbers and select the first individual that satisfies the probability distribution as the parent until the required number of parent individuals are selected. Crossover operation: Randomly select two pairs of parent individuals for operation, select the intersection point within the installation angle range, generate new individuals through crossover operation, and generate two new individuals in one crossover operation; Mutation operation: set a mutation probability, usually 10%, and randomly select individuals from the newly generated individuals for mutation according to the mutation probability; Replacement operation: Generate new individuals through selection, crossover and mutation operations, and use the newly generated individuals with higher fitness to replace the individuals with lower fitness in the original population; The selection operation, crossover operation, mutation operation and replacement operation are executed cyclically until the predetermined number of iterations is reached or the fitness converges; After the iteration, the fitness value of each individual is recalculated, and the individual with the highest fitness value is selected as the optimal solution. The installation angle corresponding to the individual is the optimal installation angle of the photovoltaic module at the current moment.

6. A photovoltaic grid-connected power control method based on a photovoltaic power station according to claim 5, characterized in that: The invention also includes an improved mutation operation method, which adjusts the installation angle of the photovoltaic module by optimizing the genetic algorithm, as follows: For each individual in the population, record its fitness value in different generations; the new population obtained after selection, crossover and mutation operations; The average fitness of the population is calculated to replace the overall fitness of the population and is marked as Subtract the absolute value of the population's overall fitness values ​​from those of adjacent generations to obtain the fitness change rate, which is: Where B is the fitness change rate, is the overall fitness value of the population in the tth generation, is the overall fitness value of the population in the t+1th generation; Calculate the diversity of the population as Where Q is the diversity of the population, N is the number of individuals in the population, Fi is the fitness value of the i-th individual, is the average fitness of the population; The fitness change rate and population diversity of different generations are obtained by calculation and normalized; According to the fitness change rate and population diversity, the mutation probability is adjusted. The process is as follows: Taking the fitness change rate and population diversity as input variables, they are divided into different fuzzy sets, and taking the adjustment state of mutation probability as output variable, they are divided into different fuzzy sets; Formulate fuzzy rules to describe the effects of fitness change rate and population diversity on the mutation probability adjustment state; Perform fuzzy reasoning based on fuzzy rules and dynamically adjust the mutation probability.

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